Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 2 (Lecture Notes in Networks and Systems, 236) [1st ed. 2022] 9811623791, 9789811623790

This book gathers selected high-quality research papers presented at the Sixth International Congress on Information and

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English Pages 1059 [1002] Year 2021

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Table of contents :
Preface
Contents
Editors and Contributors
Highly Efficient Stochastic Approaches for Computation of Multiple Integrals for European Options
1 Introduction
2 Problem Settings and Motivation
3 Highly Efficient Stochastic Approaches Based on Lattice Rules
4 Numerical Examples and Results
5 Conclusion
References
Spectrum Sensing Data Falsification Attack Reputation and Q-Out-of-M Rule Security Scheme
1 Introduction
2 Related Work
3 Network Model
4 Results and Analysis
5 Conclusion
References
Lean Manufacturing Tools for Industrial Process: A Literature Review
1 Introduction
2 Research Methodology
3 Literature Review
3.1 Lean Manufacturing
3.2 The Lean Manufacturing Implementation Route
3.3 Discussion and Analysis of Results
4 Conclusions
References
Lambda Computatrix (LC)—Towards a Computational Enhanced Understanding of Production and Management
1 Introduction
1.1 How Artificial Intelligence (AI) Can Support and Regain Human Society Values
1.2 Production and Management
1.3 Theorem Proving (TP)
1.4 Content and Organization of This Paper
1.5 Goal
2 Lambda Computatrix (LC)
3 Simulation in Production and Management Methods
4 Theorem Proving (TP)
4.1 AI-Legal
4.2 Logistics
5 TP in Production and Management, Summary, Conclusions and Outlook
References
Behavioral Analysis of Wireless Channel Under Small-Scale Fading
1 Introduction
2 Small-Scale Fading Causing Factors
3 Wireless Channel Model
4 Results and Discussions
5 Conclusion
References
Towards a Framework to Address Enterprise Resource Planning (ERP) Challenges
1 Introduction
2 Research Question (RQs)
3 Methodology
4 Review of IS/ERP Frameworks
5 Evaluating Emphases and Challenges in the ERP Frameworks
5.1 Linking ERPs with IS Success Models
5.2 Summary of ERP Challenges
5.3 Discussion and Analysis
6 The Proposed Framework
7 Reasons for Incorporating Formal Methods (FMs) in the Proposed ERP Framework
8 Conclusion
References
Potentials of Digital Business Models in the Construction Industry—Empirical Results from German Experts
1 Introduction
2 Digitization in the Construction Industry
3 Background and Research Design
4 Methodology
5 Results
6 Conclusion
References
An Alternative Auction System to Generalized Second-Price for Real-Time Bidding Optimized Using Genetic Algorithms
1 Introduction
2 Our Novel Advertising Exchange System
2.1 Advertising Exchange System
2.2 Development of the Advertisement Exchange System
3 Calculating the Optimal Value of the Weights Using a Genetic Algorithm
3.1 Representation and Initial Population
3.2 Handling Constraints
3.3 Fitness Function
3.4 Genetic Algorithm Parameter Configuration
3.5 Justification for the Chosen Values of the Coefficients, Penalties and Rules
4 Experiments and Results
4.1 Preparation of the Experiments
4.2 Experiment I
4.3 Experiment II
5 Conclusions and Future Work
References
Low-Cost Fuzzy Control for Poultry Heating Systems
1 Introduction
2 Case Study
3 Solution Proposal
3.1 Fuzzy Inference Based on Linear Programming
3.2 LAMP Server
4 Results
5 Conclusions
References
Towards Empowering Business Process Redesign with Sentiment Analysis
1 Introduction
2 Related Work
2.1 Business Process Redesign
2.2 Business Process Modeling
2.3 BPMN
2.4 Data Mining, Sentiment Analysis, and Opinion Mining in Business Processes
3 BPM Lifecycle and SentiProMo Tool
3.1 Using Sentiment Analysis in BPR
4 Sentiment Analysis Module (SAM)
4.1 Evaluation
5 Preliminary Results
6 Conclusion and Outlook
References
An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country
1 Introduction
1.1 Unified Theory of Acceptance and Use of Technology (UTAUT)
1.2 Task-Technology Fit (TTF)
2 Background
2.1 Theoretical Conceptual Framework
3 Methodology
3.1 Stage One: Initiated Requirement Generation of the Framework
3.2 Stage Two: Discovering the Factors of the Framework Through Perspectives
3.3 Stage Three: Validation of a New Proposed Framework
4 Data Analysis
5 Modified Proposed Framework
6 Discussion and Conclusion
References
Research Trends in the Implementation of eModeration Systems: A Systematic Literature Review
1 Introduction
2 Systematic Literature Review
3 Results and Findings
4 Conclusion
References
From E-Government to Digital Transformation: Leadership
1 Introduction
2 Background
2.1 Digital Transformation (DT)
2.2 Digital Maturity
2.3 Leadership
3 Methodology
4 Results
5 Conclusions
References
Application of Machine Learning Methods on IoT Parking Sensors’ Data
1 Introduction
2 Related Work
3 Data of Parking Sensors
4 Application of Machine Learning and Results
4.1 Normalization of Parking Sensor Data
4.2 XGBoost
4.3 Neural Networks Models
4.4 Using Clustering to Improve Results
5 Conclusion
References
A Fast Algorithm for Image Deconvolution Based on a Rank Constrained Inverse Matrix Approximation Problem
1 Introduction
2 RCIMA Method
3 Fast Algorithm to Compute Pseudoinverse and Low-Rank Approximation
3.1 Pseudoinverse and Tensor Product Matrix
3.2 Low-Rank Approximation and Bilateral Random Projection
4 Fast-RCIMA Method
5 Numerical Simulation
5.1 Speedup and Percent Difference
5.2 Numerical Example for Image Deconvolution
6 Conclusions
References
On-Body Microstrip Patch Antenna for Breast Cancer Detection
1 Introduction
2 Structure and Design Method
2.1 Design of Antenna
2.2 Equations Employed in Design of Propounded Antenna
2.3 Human Breast Phantom Model and Tissue Properties
3 Antenna Characteristics Without Cancerous Tumor
3.1 Reflection Coefficient or S11 Parameter
3.2 Far-Field Radiation Pattern
3.3 VSWR—Voltage Standing Wave Ratio
3.4 SAR—Specific Absorption Rate
4 Antenna Characteristics with Cancerous Tumor
4.1 Reflection Coefficient or S11 Parameter
4.2 Other Characteristics
5 Conclusion
References
Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru
1 Introduction
1.1 Context for the Research Study
2 Literature Review
2.1 Machine Learning
2.2 Regression Learner App
3 Materials y Methods
3.1 Methodology
4 Results
4.1 Results of Machine Learning Models in Predicting the Electric Field
4.2 Description of Solar Radiation with the Electric Field
4.3 Description of the UV Level with the Electric Field
5 Conclusions
References
Enhanced Honeyword Generation Method Using Advanced DNA Algorithm
1 Introduction
2 Literature Review
3 Background Theory
3.1 Enhanced Honeyword Generation
3.2 Enhanced DNA Algorithm
4 Flowchart of the Proposed System
5 Testing Results
6 Comparative Study
6.1 Time Complexity
6.2 Flatness
6.3 Typo Safety
6.4 Storage Overhead
7 Conclusion
References
A Review: How Does ICT Affect the Health and Well-Being of Teenagers in Developing Countries
1 Introduction
2 Methodology
2.1 Search Strategy
2.2 Inclusion and Exclusion Criteria
3 Results and Discussions
3.1 Adoption of ICT
3.2 Gender Gaps
3.3 Mobile Health
3.4 Detrimental Impacts of ICT
4 Conclusion and Future Works
References
Multi-image Crowd Counting Using Multi-column Convolutional Neural Network
1 Introduction
2 Methods
2.1 Pre-processing: Region of Interest (ROI) Selection
2.2 Pre-processing: Perspective Normalization
2.3 Feature Extraction: Multi-column Convolutional Neural Network (MCCNN)
2.4 Crowd Counting Using Modified MCNN
3 Experimental Setup
3.1 Dataset, Network Training, and Performance Criteria
4 Results
5 Conclusion
References
Which Features Are Helpful? The Antecedents of User Satisfaction and Net Benefits of a Learning Management System (LMS)
1 Introduction
2 Methodology
3 Results and Discussion
4 Limitations and Recommendations
References
Performance Analysis of a Neuro-Fuzzy Algorithm in Human-Centered and Non-invasive BCI
1 Introduction
2 Theories Involved
2.1 Butterworth Band-Pass Filters
2.2 Adaptive Neuro-Fuzzy Inference System (ANFIS)
3 Materials and Methods
3.1 Generation of EEG Raw Signals
3.2 Retrieving and Transferring of Generated EEG Data
3.3 Pre-processing of Data and Feeding to Algorithm
4 Results and Discussion
4.1 Simulation Results for First EEG Dataset
4.2 Simulation Results for Second EEG Dataset
5 Conclusion and Recommendation
References
A Workflow-Based Support for the Automatic Creation and Selection of Energy-Efficient Task-Schedules on DVFS Processors
1 Introduction
2 Energy-Efficient Task Scheduling
2.1 Scheduling Problem for Independent Tasks
2.2 Bucket-Based Scheduling and Execution
2.3 Frequency Scaling for Energy Efficiency
3 Schedule Creation and Evaluation Framework
3.1 Workflow to Determine a Schedule
3.2 Framework Implementation
4 Experimental Evaluation
4.1 Definition of Set of Tasks and Baseline Data Collection
4.2 T- and E-Schedule Creation
5 Related Work
6 Conclusion
References
Artificial Intelligence Edge Applications in 5G Networks
1 Introduction
2 Materials and Methods
2.1 Goals
2.2 Environment
2.3 Measurements
2.4 Methodology
3 Results
4 Conclusions
References
A Concept for the Use of Chatbots to Provide the Public with Vital Information in Crisis Situations
1 Introduction
2 Background
2.1 Chatbots
2.2 Robotic Process Automation
2.3 Big Data
3 The Proposed Approach
4 Concluding Remarks
References
Fuzzy Reinforcement Learning Multi-agent System for Comfort and Energy Management in Buildings
1 Introduction
2 Reinforcement Learning
2.1 Fuzzy Q-Learning
2.2 Multi-agent System (MAS) and Q-Learning
3 Building Modelling and Description
4 Multi-agent System (MAS)
5 Simulation Results
6 Conclusion
References
Discrete Markov Model Application for Decision-Making in Stock Investments
1 Introduction
2 Discrete Markov Model Definition
2.1 System State Definition
2.2 Dataset Labeling
2.3 Transition Matrices Definition
3 Model Efficiency Testing
4 Conclusion
References
Howling Noise Cancellation in Time–Frequency Domain by Deep Neural Networks
1 Introduction
2 Theoretical Analysis
2.1 System Model
2.2 Adaptive Filtering
3 Howling Noise Cancellation by DNN
3.1 Feature of Complex Coefficients
3.2 DNN Network
4 Experimental Results and Discussions
4.1 Dataset
4.2 Evaluation Method
4.3 Results and Discussions
5 Conclusions
References
Daily Trajectory Prediction Using Temporal Frequent Pattern Tree
1 Introduction
2 Related Work
3 Methods
3.1 Temporal Frequent Pattern Tree (TFT)
3.2 Route Prediction Modeling
4 Experimental Evaluation
4.1 Impact of the Number of Historical Routes
4.2 Outlier Removal
4.3 Global Predictor
4.4 The Role of Variable Movement Patterns in Prediction Performance
5 Conclusion
References
Quick and Dirty Prototyping and Testing for UX Design of Future Robo-Taxi
1 Background
2 Research Methods
2.1 Our Process
2.2 First Iteration
2.3 Second Iteration
2.4 Co-creation Workshop
3 Results
3.1 First Iteration
3.2 Second Iteration
3.3 First User Scenarios
3.4 Co-creation Workshop
4 Conclusion
References
Iterative Generation of Chow Parameters Using Nearest Neighbor Relations in Threshold Network
1 Introduction
2 Nearest Neighbor Relations in Threshold Function
2.1 Discrimination Using Nearest Neighbor Relations
2.2 Boolean Operations for the NNRs
3 Boundary Vertices for Generation of Chow Parameters
3.1 Characterization of Boundary Vertices
3.2 Boundary Vertices in the NNRs
4 Iterative Generation of the Chow Parameters
5 Conclusion
References
Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm
1 Introduction
2 Literature Review
3 Proposed Ensemble Bootstrap Genetic Algorithm (EnBGA)
3.1 Data Preprocessing
3.2 Feature Creation
3.3 Feature Selection
4 Experimental Discussion
5 Conclusion
References
A Generalization of Secure Comparison Protocol with Encrypted Output and Its Efficiency Estimation
1 Introduction
2 Mathematical Preliminaries
3 Proposed Protocol
3.1 Efficiency Estimation
3.2 Security
4 Conclusion
References
Conceptualizing Factors that Influence Learners’ Intention to Adopt ICT for Learning in Rural Schools in Developing Countries
1 Introduction
2 Methodology
2.1 Identification of the Research Articles
2.2 Classification and Coding Framework
3 Analysis and Discussion of Findings
3.1 Context of Research
3.2 Focus of the Research
3.3 Research Methods
3.4 Theories Adopted
3.5 Factors that Affect the Adoption of ICT Technologies by Learners
4 Conclusion
References
The Innovation Strategy for Citrus Crop Prediction Using Rough Set Theory
1 Introduction
2 Materials and Methods
2.1 Trend of World Citrus Production
2.2 Digital Transformation and Predictive Analysis in Agri-Food Chain
2.3 Methodology—The Rough Sets Theory
3 Results and Discussion
4 Conclusion
References
Predicting Traffic Path Recommendation Using Spatiotemporal Graph Convolutional Neural Network
1 Introduction
2 Related Work
3 Representing Road Traffic Network Features Using Graphs
4 Model for Vehicular Path
4.1 Problem Definition
4.2 Link Prediction Model
4.3 Methodology
5 Experiment and Results
5.1 Data Set: Monthly Strategic Road Network (SRN)
5.2 Experimental Settings
5.3 Evaluation Metrics and Baseline
5.4 Results
6 Conclusion
References
Machine Learning and Context-Based Approaches to Get Quality Improved Food Data
1 Introduction
2 State of the Art
2.1 Food Data Sources
2.2 ETL Process
2.3 Data Profiling
3 Machine Learning Approach for Quality Improvement
3.1 Ontologies for Information Retrieval
3.2 Similarity Analysis
4 Context-Based Response from the API
5 Results
6 Conclusion and Discussion
References
Components of a Digital Transformation Strategy: A South African Perspective
1 Introduction
2 Theoretical Background
2.1 Digital Transformation
2.2 Digital Transformation Strategy
3 Methodology
4 Analysis and Discussion of Findings
4.1 Components of a DTS
4.2 Summary
5 Conclusion
References
Evaluation of Face Detection and Recognition Methods in Smart Mirror Implementation
1 Introduction
2 System Design
3 Evaluation
3.1 Pearson Correlation Test of Presumed Related Variables
3.2 T-Test of Result of Haar Cascade and LBP Method Measurements
4 Conclusion
References
Comparative Analysis of Grid and Tree Topologies in Agriculture WSN with RPL Routing
1 Introduction
2 System Design
2.1 Simulator Design
2.2 Simulator Parameters
2.3 Performance Testing Scenario
3 Results and Analysis
3.1 Power Evaluation
3.2 Routing Metric and ETX Evaluation
3.3 Throughput Evaluation
3.4 Delay Evaluation
4 Conclusion
References
Designing a Monitoring and Prediction System of Water Quality Pollution Using Artificial Neural Networks for Freshwater Fish Cultivation in Reservoirs
1 Introduction
2 System Design
2.1 Water Quality
2.2 Workflow
2.3 Artificial Neural Network
3 Results and Analysis
4 Conclusion
References
Sentence-Level Automatic Speech Segmentation for Amharic
1 Introduction
2 Related Works
3 Amharic Language
3.1 The Amharic Consonants and Vowels
4 Corpus Preparation
5 Speech Segmentation System
5.1 Data Preparation
5.2 HMM Model Building
5.3 Forced Alignment
5.4 Speech Segmenter
5.5 Experimental Results
6 Conclusion and Further Work
References
Urban Change Detection from VHR Images via Deep-Features Exploitation
1 Introduction
2 Methods
2.1 Images Pre-processing
2.2 Deep-Features Extraction
2.3 Deep-Features Selection
3 Experimental Results
3.1 Data Set Description
3.2 Deep-Features Computation and Selection
3.3 Change Map Computation
3.4 Discussion
4 Conclusion
References
Region Awareness for Identifying and Extracting Text in the Natural Scene
1 Introduction
2 Related Work
3 Proposed Method
3.1 Text Detection
3.2 Text Recognition
4 Experimental Results
4.1 Text Detection
4.2 Text Detection and Recognition
5 Conclusion and Future Works
References
Analysis of Effectiveness of Selected Classifiers for Recognizing Psychological Patterns
1 Introduction
2 Data Set and Algorithms
3 Examination of Algorithms
3.1 Big Five Questionnaire
3.2 Depression Test Questionnaire
3.3 Loneliness Scale Questionnaire
3.4 Analysis of the Prediction Precision
3.5 Analysis of Prediction Speed and Training Time
3.6 Summing Up
4 Final Remarks
References
A Virtual Reality System for the Simulation of Neurodiversity
1 Introduction
1.1 Autism Spectrum Disorder
1.2 Virtual Reality for Neurodiversity Simulation
2 Neurodiversity Experience
2.1 Stimuli
2.2 Implementation
3 Discussion and Conclusions
3.1 Further Steps
References
An Algorithm Classifying Brain Signals in the Control Problem
1 Introduction
2 The Classification Algorithm
2.1 The Classification Algorithm in a System Working as BCI
2.2 Gower’s Algorithm in Classification Based on the Graph Theory
2.3 The Classifying Program—The User’s Interface
3 The Classification Function as the Main Function of the Control Algorithm
4 Summary
References
Fast Geometric Reconstruction Using Genetic Algorithms from Single or Multiple Images
1 Introduction
2 Related Works
3 Methodology
3.1 Problem Formulation
3.2 Genetic Algorithm’s Use and Impact
4 Our Technical Approach
4.1 Input Geometric Constraints
4.2 Optimization Step-Based GAs
4.3 Selection and Crossover Operators
5 Experimental Results
5.1 Population’s Optimization
5.2 Comparisons
6 Conclusion
References
Metagenomic Analysis: A Pathway Toward Efficiency Using High-Performance Computing
1 Introduction
2 Pipeline
2.1 Quality Control
2.2 Host Removal
2.3 Searching Through Reference Databases
2.4 Dynamic Programming
2.5 Accelerated Alternatives
3 Research
4 Conclusion
References
A Machine Learning Approach to CCPI-Based Inflation Prediction
1 Introduction
2 Methodology
2.1 Determinants of Inflation
2.2 Research Methodology Overview
2.3 Exploration of Existing Machine Learning Models
2.4 Model Design and Implementation
3 Evaluations and Discussion
4 Conclusion
References
On Profiling Space Reduction Efficiency in Vector Space Modeling-Based Natural Language Processing
1 Introduction
1.1 Space Reduction
1.2 Vector Space Modeling and Natural Language Processing
1.3 Related Work
1.4 Motivations and Scope of the Study
2 The Proposed Summarization Protocol
2.1 The Principal Component Analysis-Based Protocol of Text Summarization
2.2 The Isometric Mapping-Based Protocol of Text Summarization
3 Experimental Results
3.1 Dataset
3.2 Results
3.3 Discussion
4 Conclusion
References
Proposal of a Methodology for the Implementation of a Smart Campus
1 Introduction
2 Theoretical Foundation
2.1 Smart City
2.2 Smart City Methodologies
2.3 Smart Campus
2.4 Similarity Between Smart City and Smart Campus
2.5 Related Works
3 Methodology
3.1 Design of the Methodology
4 Evaluation of the Methodology for a Smart Campus
5 Conclusions
References
Emotion Cause Detection with a Hierarchical Network
1 Introduction
2 Related Work
3 The Approach
3.1 The Framework
3.2 Hierarchical Network
3.3 Features
4 Experiments
4.1 Settings
4.2 Experimental Results
5 Conclusion
References
Skills and Human Resource Management for Industry 4.0 of Small and Medium Enterprises
1 Introduction
2 Literature Review
2.1 Skills
2.2 Skills Gap
2.3 Human Resource Management
2.4 Related Studies
3 Methodology
3.1 Data Collection
3.2 Data Analysis
4 Research Results
4.1 Skills
4.2 Skills Gap
5 Conclusion
References
Fully Passive Unassisted Localization System Without Time Synchronization
1 Introduction
2 Unassisted Localization Systems
3 Related Work
4 Proposed Method
5 System Prototype Implementation
6 Experiment Results
7 Conclusions
References
Appropriation Intention of a Farm Management Information System Through Usability Evaluation with PLS-SEM Analysis
1 Introduction
2 Reference Framework
2.1 Farm Management Information Systems (FMIS)
2.2 FMIS Appropriation Intention Factors
2.3 Usability Evaluation in Information Systems
3 Methodology
3.1 itagüe® FMIS
3.2 User Profile
3.3 Usability Evaluation
3.4 PLS Data Analysis
4 Results and Discussion
4.1 Assessment of the Structural Model
4.2 Model Predictive Relevance
4.3 Discussion
5 Conclusions
References
Collaborative Control of Mobile Manipulator Robots Through the Hardware-in-the-Loop Technique
1 Introduction
2 System Structure
3 Virtual Environment
4 Mobile Manipulator Robots
4.1 Kinematic Model
4.2 Dynamic Model
5 Collaborative Control
5.1 Formation Controller
5.2 Control ith Robot
6 Experimental Results
7 Conclusions
References
Application ArcGIS on Modified-WQI Method to Evaluate Water Quality of the Euphrates River, Iraq, Using Physicochemical Parameters
1 Introduction
2 Methodology
2.1 Study Area
2.2 Assessment of Water Quality
2.3 Computing of Modified Water Quality Index
2.4 Prediction Maps
3 Results and Discussion
3.1 Physicochemical Concentrations for Parameters
3.2 Modified Water Quality Index (MWQI)
4 Conclusions
References
Information Retrieval and Analysis of Digital Conflictogenic Zones by Social Media Data
1 Introduction
1.1 Data
1.2 Method
2 Results and Discussion
3 Conclusion
References
Introducing a Test Framework for Quality of Service Mechanisms in the Context of Software-Defined Networking
1 Introduction
2 Related Literature
3 Framework
3.1 Testing Framework Architecture
3.2 Conceptual Framework
3.3 Theoretical Framework
4 Methodology
5 Results and Discussion
5.1 IFSTAT Results
5.2 Apache Bench Results
5.3 VLC Results
5.4 Round Trip Time Results
6 Conclusion
References
Building a Conceptual Model for the Acceptance of Drones in Saudi Arabia
1 Introduction
2 General Aspects
2.1 Structure of a Drone
2.2 Some Characteristics
2.3 Regulatory and Legal Aspects
3 Applications
4 Privacy and Security
4.1 Challenges
4.2 Solutions
5 Models of Technology Acceptance
6 Proposed Model
7 Conclusion
References
A Channel Allocation Algorithm for Cognitive Radio Users Based on Channel State Predictors
1 Introduction
2 CCAA Using Channel State Prediction
3 CR Performance with Using Channel State Prediction
4 Performance Evaluation of the CCAA
5 CR Performance in Spectrum Usage Improvement and in Sensing Energy Reduction
6 Conclusion
References
A Framework for Studying Coordinated Behaviour Applied to the 2019 Philippine Midterm Elections
1 Introduction
2 Finding Coordinated Behaviour in Social Media
3 Hybrid Approach Finding Coordinated Behaviour
3.1 Computational Extraction and Detection of Networks
3.2 Cleaning up the Data
3.3 Looking at the Data Again
4 Conclusions
References
COVID-19 X-ray Image Diagnosis Using Deep Convolutional Neural Networks
1 Introduction
1.1 Background
1.2 X-ray Image Classification
2 Deep Lung X-COVID
3 Experimental Design
3.1 Data Preparation
3.2 Experiment I: Classification with ResNet18 Trained from Scratch
3.3 Experiment II: Classification with ResNet18 and Transfer Learning
3.4 Experiment III: Classification with InceptionResNetV2 Trained from Scratch
3.5 Experiment IV: Classification with Expanded InceptionResNetV2 Models
4 Results
4.1 Analysis of Classification Results
4.2 Analysis of Distinguishing Features
5 Conclusion
5.1 Saliva Test for COVID-19
5.2 Radiation Magnitude Due to X-rays and CT-Scans
References
Jumping Particle Swarm Optimization
1 Introduction
2 Proposed Jumping Strategy
3 Evaluation Results
4 Conclusion
References
Reinforcement Learning for the Problem of Detecting Intrusion in a Computer System
1 Introduction
2 Related Works
3 Work Description
3.1 Datasets
3.2 Reinforcement Learning
4 Experimental Results
5 Conclusions
References
Grey Wolf Optimizer Algorithm for Suspension Insulator Designing
1 Introduction
2 Electric Field Distribution Computation
2.1 Geometry Modelling in Comsol-Multiphysics
2.2 Electric Field and Potential Distributions Results
3 The GWO Optimization Technique
4 Optimization Results
5 Conclusion
References
Green IT Practices in the Business Sector
1 Introduction
2 Literature Review
2.1 Theoretical Framework
3 Materials and Methods
3.1 Methodology
3.2 Hypothesis and Model
3.3 Instrument for Information Gathering
3.4 Procedure
4 Results
4.1 Validity and Reliability of the Measurement Model
4.2 Assessment of the Structural Model
5 Conclusions
References
Study of Official Government Website and Twitter Content Quality in Four Local Governments of Indonesia
1 Introduction
2 Literatur Review
2.1 The Use of Websites in e-Government
2.2 Measuring the Quality of a Website
2.3 Social Media on Government: Official Twitter
3 Method
4 Result
4.1 Website Quality and Official Twitter Content of City Government
5 Conclusion
References
Design and Implementation of an Industrial Multinetwork TCP/IP of a Distributed Control System with Virtual Processes Based on IOT
1 Introduction
2 Methodology
2.1 Protocol and Configuration
2.2 Structure of the Distributed Control System
2.3 Implementation of Virtual Processes
3 Communication Node-RED
4 Results
5 Conclusions
References
Cross-Textual Analysis of COVID-19 Tweets: On Themes and Trends Over Time
1 Introduction
2 Previous Work
3 Experiment Setup
3.1 Data
3.2 Topic Modeling Using Latent Dirichlet Allocation
3.3 Perplexity
4 Results and Discussion
4.1 Themes from COVID-19 Tweets
4.2 Change of Themes Through Time
5 Conclusion
References
A Novel Approach for Smart Contracts Using Blockchain
1 Introduction
2 Related Works
3 Contribution
4 Experimental Results
5 Discussion
6 Conclusion
References
Redundant Bus Systems Using Dual-Mode Radio
1 Introduction
2 Dual-Mode Radio
3 Experimental Setup
4 Results and Discussion
5 Conclusion
References
Practice of Tech Debt Assessment and Management with TETRA™
1 What is Technical Debt in Software
2 How Technical Debt is Described and Managed
3 Methodology and Results Evaluation
4 Advantages of the Approach
5 Limitations of the Approach
6 Applying TETRA™ to e-Learning Platform Assessment
7 Conclusion
References
Low-Cost Health Monitoring System: A Smart Technological Device for Elderly People
1 Introduction
2 Background and Literature Survey
3 Proposed Design of IoT-Based Health Monitoring System
4 Result Analysis and Discussion
4.1 Implementation of Prototype
4.2 ECG Monitoring
4.3 Pulse Rate, Oxygen saturation Level and Temperature Detection
4.4 Server
5 Cost Analysis
6 Future Scope
7 Conclusion
References
An Improved Genetic Algorithm With Initial Population Strategy and Guided Mutation
1 Introduction
2 Static Analysis Technique of GGA
3 The GGA Approach
3.1 Guided Population
3.2 Guided Mutation
4 Empirical Study
4.1 Branch Coverage Effectiveness
5 Conclusion and Future Work
References
Intuitive Searching: An Approach to Search the Decision Policy of a Blackjack Agent
1 Introduction
1.1 Related Work
2 Intuitive Searching
2.1 Notations and Concepts
2.2 The Correctness of Intuitive Searching
2.3 The Optimal Solution of Training Dataset
3 Experiment
4 Extension
5 Limitations and Future Work
5.1 The Limitation of Intuitive Search in the General Case
5.2 The Limitation When Facing General Poker Game
References
Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders
1 Introduction
2 Related Work
3 Proposed Model Architecture
3.1 Variational Auto Encoder
3.2 Proposed Model Architecture
3.3 VAE GAN
4 Dataset
5 Results
6 Evaluation
7 Conclusion
References
Crime Mapping Approach for Crime Pattern Identification: A Prototype for the Province of Cavite
1 Introduction
2 Methodology
2.1 Data Gathering
2.2 Design and Development
2.3 Testing
3 Results and Discussions
3.1 Description of Technology
3.2 System Features
3.3 Test Results
4 Conclusions and Recommendations
References
Hardware in the Loop of an Omnidirectional Vehicle Using Augmented Reality
1 Introduction
2 Development
2.1 Kinematic Model
3 Kinematic Controller
3.1 Controller Design
3.2 Dynamic Compensation
4 Results Obtained
5 Conclusions
References
Re-hub-ILITY: A Personalized Home System and Virtual Coach to Support and Empower Elderly People with Chronic Conditions
1 Introduction
2 Background: Rationale and Challenge
3 The Re-Hub-ILITY Concept
3.1 Objectives
3.2 Architecture, Technologies and Services
4 Methodological Approach
5 Preliminary Results
6 Expected Impacts and Concluding Remarks
References
An Empirical Evaluation of Machine Learning Methods for the Insurance Industry
1 Introduction
2 The Dataset: Labeled Insurance Claims
2.1 Preprocessing the Dataset
3 ML-Algorithms for Insurance Claims
3.1 Naive Bayes
3.2 K-Nearest Neighbors
3.3 Regression Analysis
3.4 Decision Tree
3.5 Support Vector Machine
3.6 Neural Network
4 Evaluation of the Algorithms
4.1 Expert Sending: The Binary Classification Task
4.2 Claims Reserve Prediction: The Regression Task
5 Related Work
6 Conclusion and Future Work
References
Construction and Practice of Task-Driven Learning Model Based on TBL and CBL in Post MOOC Era
1 Introduction
2 Analysis of Advantages and Disadvantages of Single Teaching Mode
2.1 MOOC Teaching Mode
2.2 CBL Teaching Mode
2.3 TBL Teaching Mode
3 The Necessity of Constructing and Practice CBL + TBL Teaching Mode
4 The Necessity of Constructing and Practice CBL + TBL Teaching Mode
4.1 Preparation Phrase
4.2 Implementation Phrase
4.3 Evaluation Phrase
4.4 Feedback Phrase
5 Conclusion
References
Research Progress on Influencing Factors of Sense of Control in the Elderly and Its Effects on Successful Aging
1 Introduction
2 Influencing Factors of Control
2.1 Influencing Factors of Control
2.2 Social Support
2.3 Self-identity
2.4 Self-affirmation
2.5 Socio-demographic Factors
3 The Role of Control
3.1 Meaning in Life
3.2 Reduce the Risk of Dementia and Depression
3.3 Happiness
3.4 Self-efficacy
4 Summary and Research Outlook
References
Indicators of Choosing Internet User’s Responsible Behavior
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Discussion
6 Conclusion
References
E-Governance and Privacy in Pandemic Times
1 Data Collection and Monitoring by Various Devices
1.1 Bifurcation Point of Privacy
1.2 Data Collection and Monitoring
2 General Trends
References
A Next-Generation Telemedicine and Health Advice System
1 Introduction
2 Research Methodology
2.1 Literature Search Strategy
2.2 Requirements Analysis and System Design
3 Results and Discussion
4 Observations of the Initial Prototype
5 Conclusion
References
``Can Mumbai Indians Chase the Target?'': Predict the Win Probability in IPL T20-20
1 Introduction
2 Related Work
3 Ball-by-Ball Cricket Dataset
4 Feature Extraction
5 Experiments
6 Over by Over Win Prediction
6.1 User Case: MI versus CSK
6.2 User Case:DD versus RPS
7 Conclusion
References
Method for Extracting Cases Relevant to Social Issues from Web Articles to Facilitate Public Debates
1 Introduction
2 Related Works
3 Building CRSI Corpus
3.1 Designing CRSI Ontology
3.2 Article Retrieval and Manual Annotation
3.3 Dataset Statistics
4 Tasks and Proposed Models
4.1 Modeling
5 Experiments and Evaluation
5.1 Experimental Setup
5.2 Results
5.3 Discussion
6 Conclusion
References
Comparative Analysis of Cloud Computing Security Frameworks for Financial Sector
1 Introduction
2 Related Work
3 Cloud Computing Security Frameworks
3.1 Cloud Security Reference Model
3.2 Novel Open Security Framework
3.3 Temenos Enterprise Framework Architecture (TEFA)
3.4 Sherwood Applied Business Security Architecture (SABSA)
4 Comparative Parameters
4.1 Deployment Model Structure
4.2 Service Delivery Model
4.3 Security Policies
4.4 Security Standards and Guidelines
5 Comparative Analysis and Discussion
5.1 Cloud Security Reference Model
5.2 Novel Open Security Framework
5.3 Temenos Enterprise Framework Architecture (TEFA)
5.4 Sherwood Applied Business Security Architecture (SABSA)
6 Discussion
7 Conclusion
References
Author Index
Recommend Papers

Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 2 (Lecture Notes in Networks and Systems, 236) [1st ed. 2022]
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Lecture Notes in Networks and Systems 236

Xin-She Yang Simon Sherratt Nilanjan Dey Amit Joshi   Editors

Proceedings of Sixth International Congress on Information and Communication Technology ICICT 2021, London, Volume 2

Lecture Notes in Networks and Systems Volume 236

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/15179

Xin-She Yang · Simon Sherratt · Nilanjan Dey · Amit Joshi Editors

Proceedings of Sixth International Congress on Information and Communication Technology ICICT 2021, London, Volume 2

Editors Xin-She Yang Middlesex University London, UK

Simon Sherratt University of Reading Reading, UK

Nilanjan Dey JIS University Kolkata, India

Amit Joshi Global Knowledge Research Foundation Ahmedabad, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-16-2379-0 ISBN 978-981-16-2380-6 (eBook) https://doi.org/10.1007/978-981-16-2380-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The Sixth International Congress on Information and Communication Technology was held on February 25–26, 2021, digitally on ZOOM and was organized by Global Knowledge Research Foundation. The associated partners were Springer, SPRINGER NATURE, and InterYIT IFIP. The conference provided a useful and wide platform both for display of the latest research and for exchange of research results and thoughts. The participants of the conference were from almost every part of the world (around 85 countries), with background of either academia or industry, allowing a real multinational multicultural exchange of experiences and ideas. A total of 1150 papers were received for this conference from across 83 countries, among which around 350 papers were accepted and were presented on the digital platform. Due to overwhelming response, we had to drop many papers in a hierarchy of the quality. Totally, 51 technical sessions were organized in parallel in 2 days, and talks were given on both the days. The conference involved deep discussion and issues which are intended to be solved at global levels. New technologies were proposed, experiences were shared and future solutions for design infrastructure for ICT were also discussed. The total papers will be published in 4 volumes of proceedings, among which this is one. The conference consisted of several distinguished authors, scholars, and speakers from all over the world. Amit Joshi, organizing Secretary, ICICT 2021, Sean Holmes, Vice Dean International, College of Business, Arts and Social Sciences, Brunel University London, UK, Mike Hinchey, Immd. Past Chair—IEEE UK and Ireland section & Director of Lero and Professor—Software Engineering, University of Limerick, Ireland, Aninda Bose, Sr. Publishing Editor, Springer Nature, Germany, Xin-She Yang, Professor, Middlesex University, Prof. Jyoti Choudri, Professor, University of Hertfordshire and many were a part of the Inaugural Session and the conference. The conference was organized and conceptualized with collective efforts of a large number of individuals. We would like to thank our committee members and the reviewers for their excellent work in reviewing the papers. Grateful acknowledgements are extended to the team of Global Knowledge Research Foundation for their

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Preface

valuable efforts and support. We are also thankful to the sponsors, press, print, and electronic media for their excellent coverage of this conference. London, UK Reading, UK Kolkata, India Ahmedabad, India

Xin-She Yang Simon Sherratt Nilanjan Dey Amit Joshi

Contents

Highly Efficient Stochastic Approaches for Computation of Multiple Integrals for European Options . . . . . . . . . . . . . . . . . . . . . . . . . Venelin Todorov, Ivan Dimov, Stoyan Apostolov, and Stoyan Poryazov

1

Spectrum Sensing Data Falsification Attack Reputation and Q-Out-of-M Rule Security Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Velempini Mthulisi, Ngomane Issah, and Mapunya Sekgoari Semaka

11

Lean Manufacturing Tools for Industrial Process: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gustavo Caiza, Alexandra Salazar-Moya, Carlos A. Garcia, and Marcelo V. Garcia Lambda Computatrix (LC)—Towards a Computational Enhanced Understanding of Production and Management . . . . . . . . . . . Bernhard Heiden, Bianca Tonino-Heiden, Volodymyr Alieksieiev, Erich Hartlieb, and Denise Foro-Szasz

27

37

Behavioral Analysis of Wireless Channel Under Small-Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mridula Korde, Jagdish Kene, and Minal Ghute

47

Towards a Framework to Address Enterprise Resource Planning (ERP) Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephen Kwame Senaya, John Andrew van der Poll, and Marthie Schoeman

57

Potentials of Digital Business Models in the Construction Industry—Empirical Results from German Experts . . . . . . . . . . . . . . . . . Ralf-Christian Härting, Christopher Reichstein, and Tobias Schüle

73

An Alternative Auction System to Generalized Second-Price for Real-Time Bidding Optimized Using Genetic Algorithms . . . . . . . . . Luis Miralles-Pechuán, Fernando Jiménez, and Josá Manuel García

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Low-Cost Fuzzy Control for Poultry Heating Systems . . . . . . . . . . . . . . . . Gustavo Caiza, Cristhian Monta, Paulina Ayala, Javier Caceres, Carlos A. Garcia, and Marcelo V. Garcia Towards Empowering Business Process Redesign with Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selver Softic and Egon Lüftenegger An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country . . . . . . . . . . . . . . . . . . . . . Soliman Aljarboa and Shah J. Miah Research Trends in the Implementation of eModeration Systems: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . Vanitha Rajamany, J. A. van Biljon, and C. J. van Staden From E-Government to Digital Transformation: Leadership . . . . . . . . . . Miguel Cuya and Sussy Bayona-Oré

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139 147

Application of Machine Learning Methods on IoT Parking Sensors’ Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dražen Vuk and Darko Androˇcec

157

A Fast Algorithm for Image Deconvolution Based on a Rank Constrained Inverse Matrix Approximation Problem . . . . . . . . . . . . . . . . Pablo Soto-Quiros, Juan Jose Fallas-Monge, and Jeffry Chavarría-Molina

165

On-Body Microstrip Patch Antenna for Breast Cancer Detection . . . . . Sourav Sinha, Sajidur Rahman, Mahajabin Haque Mili, and Fahim Mahmud Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru . . . . . . . . . . . . Juan J. Soria, Orlando Poma, David A. Sumire, Joel Hugo Fernandez Rojas, and Maycol O. Echevarria

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Enhanced Honeyword Generation Method Using Advanced DNA Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nwe Ni Khin and Khin Su Myat Moe

201

A Review: How Does ICT Affect the Health and Well-Being of Teenagers in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Willone Lim, Bee Theng Lau, Caslon Chua, and Fakir M. Amirul Islam

213

Multi-image Crowd Counting Using Multi-column Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . O˘guzhan Kurnaz and Cemal Hanilçi

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Contents

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Which Features Are Helpful? The Antecedents of User Satisfaction and Net Benefits of a Learning Management System (LMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bernie S. Fabito, Mico C. Magtira, Jessica Nicole Dela Cruz, Ghielyssa D. Intrina, and Shannen Nicole C. Esguerra

233

Performance Analysis of a Neuro-Fuzzy Algorithm in Human-Centered and Non-invasive BCI . . . . . . . . . . . . . . . . . . . . . . . . . . Timothy Scott C. Chu, Alvin Chua, and Emanuele Lindo Secco

241

A Workflow-Based Support for the Automatic Creation and Selection of Energy-Efficient Task-Schedules on DVFS Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ronny Kramer and Gudula Rünger Artificial Intelligence Edge Applications in 5G Networks . . . . . . . . . . . . . Carlota Villasante Marcos A Concept for the Use of Chatbots to Provide the Public with Vital Information in Crisis Situations . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Staegemann, Matthias Volk, Christian Daase, Matthias Pohl, and Klaus Turowski

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Fuzzy Reinforcement Learning Multi-agent System for Comfort and Energy Management in Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Panagiotis Kofinas, Anastasios Dounis, and Panagiotis Korkidis

291

Discrete Markov Model Application for Decision-Making in Stock Investments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oksana Tyvodar and Pylyp Prystavka

311

Howling Noise Cancellation in Time–Frequency Domain by Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaguo Gan, Gaoyong Luo, Yaqing Luo, and Wenbin Luo

319

Daily Trajectory Prediction Using Temporal Frequent Pattern Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingyi Cai, Runze Yan, and Afsaneh Doryab

333

Quick and Dirty Prototyping and Testing for UX Design of Future Robo-Taxi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dokshin Lim and Minhee Lee

345

Iterative Generation of Chow Parameters Using Nearest Neighbor Relations in Threshold Network . . . . . . . . . . . . . . . . . . . . . . . . . . Naohiro Ishii, Kazuya Odagiri, and Tokuro Matsuo

357

Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jayshree Ghorpade-Aher and Balwant Sonkamble

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A Generalization of Secure Comparison Protocol with Encrypted Output and Its Efficiency Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takumi Kobayashi and Keisuke Hakuta Conceptualizing Factors that Influence Learners’ Intention to Adopt ICT for Learning in Rural Schools in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Siphe Mhlana, Baldreck Chipangura, and Hossana Twinomurinzi The Innovation Strategy for Citrus Crop Prediction Using Rough Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alessandro Scuderi, Giuseppe Timpanaro, Giovanni La Via, Biagio Pecorino, and Luisa Sturiale

377

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403

Predicting Traffic Path Recommendation Using Spatiotemporal Graph Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hitendra Shankarrao Khairnar and Balwant Sonkamble

413

Machine Learning and Context-Based Approaches to Get Quality Improved Food Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Muenzberg, Janina Sauer, Andreas Hein, and Norbert Roesch

423

Components of a Digital Transformation Strategy: A South African Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kudzai Mapingire, Hanlie Smuts, and Alta Van der Merwe

437

Evaluation of Face Detection and Recognition Methods in Smart Mirror Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muhammad Bagus Satrio, Aji Gautama Putrada, and Maman Abdurohman

449

Comparative Analysis of Grid and Tree Topologies in Agriculture WSN with RPL Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Febrian Aji Pangestu, Maman Abdurohman, and Aji Gautama Putrada

459

Designing a Monitoring and Prediction System of Water Quality Pollution Using Artificial Neural Networks for Freshwater Fish Cultivation in Reservoirs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R Raden Muhamad Irvan, Maman Abdurohman, and Aji Gautama Putrada Sentence-Level Automatic Speech Segmentation for Amharic . . . . . . . . . Rahel Mekonen Tamiru and Solomon Teferra Abate

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Urban Change Detection from VHR Images via Deep-Features Exploitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Annarita D’Addabbo, Guido Pasquariello, and Angelo Amodio

487

Region Awareness for Identifying and Extracting Text in the Natural Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinh Loc Cu, Xuan Viet Truong, Tien Dao Luu, and Hoang Viet Nguyen

501

Contents

Analysis of Effectiveness of Selected Classifiers for Recognizing Psychological Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Emirsajłow and Łukasz Jele´n

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A Virtual Reality System for the Simulation of Neurodiversity . . . . . . . . Héctor López-Carral, Maria Blancas-Muñoz, Anna Mura, Pedro Omedas, Àdria España-Cumellas, Enrique Martínez-Bueno, Neil Milliken, Paul Moore, Leena Haque, Sean Gilroy, and Paul F. M. J. Verschure

523

An Algorithm Classifying Brain Signals in the Control Problem . . . . . . Urszula Jagodzi´nska-Szyma´nska and Edward S˛edek

533

Fast Geometric Reconstruction Using Genetic Algorithms from Single or Multiple Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Afafe Annich, Imane Laghman, Abdellatif E. L. Abderrahmani, and Khalid Satori Metagenomic Analysis: A Pathway Toward Efficiency Using High-Performance Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gustavo Henrique Cervi, Cecília Dias Flores, and Claudia Elizabeth Thompson

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A Machine Learning Approach to CCPI-Based Inflation Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Maldeni and M. A. Mascrenghe

567

On Profiling Space Reduction Efficiency in Vector Space Modeling-Based Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . Alaidine Ben Ayed, Ismaïl Biskri, and Jean-Guy Meunier

577

Proposal of a Methodology for the Implementation of a Smart Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sonia-Azucena Pupiales-Chuquin, Gladys-Alicia Tenesaca-Luna, and María-Belén Mora-Arciniegas Emotion Cause Detection with a Hierarchical Network . . . . . . . . . . . . . . . Jing Wan and Han Ren Skills and Human Resource Management for Industry 4.0 of Small and Medium Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sukmongkol Lertpiromsuk, Pittawat Ueasangkomsate, and Yuraporn Sudharatna Fully Passive Unassisted Localization System Without Time Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ´ Przemysław Swiercz

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Appropriation Intention of a Farm Management Information System Through Usability Evaluation with PLS-SEM Analysis . . . . . . . Helga Bermeo-Andrade and Dora González-Bañales

633

Collaborative Control of Mobile Manipulator Robots Through the Hardware-in-the-Loop Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luis F. Santo, Richard M. Tandalla, and H. Andaluz

643

Application ArcGIS on Modified-WQI Method to Evaluate Water Quality of the Euphrates River, Iraq, Using Physicochemical Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Chabuk, Hussein A. M. Al-Zubaidi, Aysar Jameel Abdalkadhum, Nadhir Al-Ansari, Salwan Ali Abed, Ali Al-Maliki, Jan Laue, and Salam Ewaid

657

Information Retrieval and Analysis of Digital Conflictogenic Zones by Social Media Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Pilgun and Alexander A. Kharlamov

677

Introducing a Test Framework for Quality of Service Mechanisms in the Context of Software-Defined Networking . . . . . . . . . Josiah Eleazar T. Regencia and William Emmanuel S. Yu

687

Building a Conceptual Model for the Acceptance of Drones in Saudi Arabia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roobaea Alroobaea

701

A Channel Allocation Algorithm for Cognitive Radio Users Based on Channel State Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nakisa Shams, Hadi Amirpour, Christian Timmerer, and Mohammad Ghanbari

711

A Framework for Studying Coordinated Behaviour Applied to the 2019 Philippine Midterm Elections . . . . . . . . . . . . . . . . . . . . . . . . . . . William Emmanuel S. Yu

721

COVID-19 X-ray Image Diagnosis Using Deep Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alisa Kunapinun and Matthew N. Dailey

733

Jumping Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atiq Ur Rehman, Ashhadul Islam, Nabiha Azizi, and Samir Brahim Belhaouari Reinforcement Learning for the Problem of Detecting Intrusion in a Computer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quang-Vinh Dang and Thanh-Hai Vo

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Grey Wolf Optimizer Algorithm for Suspension Insulator Designing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dyhia Doufene, Slimane Bouazabia, Sid A. Bessedik, and Khaled Ouzzir Green IT Practices in the Business Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Mory, Diego Cordero, Silvana Astudillo, and Ana Lucia Serrano

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Study of Official Government Website and Twitter Content Quality in Four Local Governments of Indonesia . . . . . . . . . . . . . . . . . . . . Nita Tri Oktaviani, Achmad Nurmandi, and Salahudin

783

Design and Implementation of an Industrial Multinetwork TCP/IP of a Distributed Control System with Virtual Processes Based on IOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wilson Sánchez Ocaña, Elizabeth Alvarado Rubio, Edwin Torres López, and Alexander Toapanta Casa

797

Cross-Textual Analysis of COVID-19 Tweets: On Themes and Trends Over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph Marvin Imperial, Angelica De La Cruz, Emmanuel Malaay, and Rachel Edita Roxas

813

A Novel Approach for Smart Contracts Using Blockchain . . . . . . . . . . . . Manar Abdelhamid and Khaled Nagaty

823

Redundant Bus Systems Using Dual-Mode Radio . . . . . . . . . . . . . . . . . . . . Felix Huening, Franz-Josef Wache, and David Magiera

835

Practice of Tech Debt Assessment and Management with TETRA™ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boris Kontsevoi, Denis Syraeshko, and Sergei Terekhov

843

Low-Cost Health Monitoring System: A Smart Technological Device for Elderly People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tamanna Shaown, M. Shohrab Hossain, and Tasnim Morium Mukur

851

An Improved Genetic Algorithm With Initial Population Strategy and Guided Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Aburas

861

Intuitive Searching: An Approach to Search the Decision Policy of a Blackjack Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhenyu Pan, Jie Xue, and Tingjian Ge

869

Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmad Moussa and Hiroshi Watanabe

889

Crime Mapping Approach for Crime Pattern Identification: A Prototype for the Province of Cavite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aries M. Gelera and Edgardo S. Dajao

899

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Contents

Hardware in the Loop of an Omnidirectional Vehicle Using Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jonathan A. Romero, Edgar R. Salazar, Edgar I. De la Cruz, Geovanny P. Moreno, and Jéssica D. Mollocana Re-hub-ILITY: A Personalized Home System and Virtual Coach to Support and Empower Elderly People with Chronic Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Claudio Pighini, Ambra Cesareo, Andrea Migliavacca, Matilde Accardo, and Maria Renata Guarneri An Empirical Evaluation of Machine Learning Methods for the Insurance Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Dammann, Nicolai Gnoss, Pamela Kunert, Eike-Christian Ramcke, Tobias Schreier, Ulrike Steffens, and Olaf Zukunft

911

923

933

Construction and Practice of Task-Driven Learning Model Based on TBL and CBL in Post MOOC Era . . . . . . . . . . . . . . . . . . . . . . . . Cuiping Li and Hanbin Wu

943

Research Progress on Influencing Factors of Sense of Control in the Elderly and Its Effects on Successful Aging . . . . . . . . . . . . . . . . . . . . Haiying Qian and Hanbin Wu

953

Indicators of Choosing Internet User’s Responsible Behavior . . . . . . . . . Olga Shipunova, Irina Berezovskaya, Swetlana Kedich, and Nina Popova

961

E-Governance and Privacy in Pandemic Times . . . . . . . . . . . . . . . . . . . . . . Kotov Alexander and Naumov Victor

971

A Next-Generation Telemedicine and Health Advice System . . . . . . . . . . Shah Siddiqui, Adrian Hopgood, Alice Good, Alexander Gegov, Elias Hossain, Wahidur Rahman, Rezowan Ferdous, Murshedul Arifeen, and Zakir Khan

981

“Can Mumbai Indians Chase the Target?”: Predict the Win Probability in IPL T20-20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. G. T. L. Karunathilaka, S. K. Rajakaruna, R. Navarathna, K. Anantharajah, and M. Selvarathnam

991

Method for Extracting Cases Relevant to Social Issues from Web Articles to Facilitate Public Debates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 Akira Kamiya and Shun Shiramatsu Comparative Analysis of Cloud Computing Security Frameworks for Financial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015 Sudhish Mohanan, Nandagopal Sridhar, and Sajal Bhatia Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027

Editors and Contributors

About the Editors Xin-She Yang obtained his D.Phil. in Applied Mathematics from the University of Oxford and subsequently worked at the Cambridge University and the National Physical Laboratory (UK) as Senior Research Scientist. He is currently Reader in Modelling and Optimization at Middlesex University London and Adjunct Professor at Reykjavik University (Iceland). He is also Elected Bye-Fellow at the Cambridge University and IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management. He was included in the “2016 Thomson Reuters Highly Cited Researchers” list. Simon Sherratt was born near Liverpool, England, in 1969. He is currently Professor of Biosensors at the Department of Biomedical Engineering, University of Reading, UK. His main research area is signal processing and personal communications in consumer devices, focusing on wearable devices and health care. Professor Sherratt received the 1st place IEEE Chester Sall Memorial Award in 2006, the 2nd place in 2016 and the 3rd place in 2017. Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, JIS University, Kolkata, India. He has authored/edited more than 75 books with Springer, Elsevier, Wiley and CRC Press and published more than 300 peer-reviewed research papers. Dr. Dey is Editor-in-Chief of the International Journal of Ambient Computing and Intelligence; Series Co-Editor of Springer Tracts in Nature-Inspired Computing (STNIC); and Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier. Amit Joshi is Director of the Global Knowledge Research Foundation and the International Chair of InterYIT at the International Federation of Information Processing (IFIP, Austria). He has edited more than 40 books for Springer, ACM and other reputed publishers. He has also organized more than 50 national and international

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Editors and Contributors

conferences and workshops in association with the ACM, Springer and IEEE in, e.g. India, Thailand and the UK.

Contributors Hussein A. M. Al-Zubaidi University of Babylon, Babylon, Iraq Solomon Teferra Abate Addis Ababa University, Addis Ababa, Ethiopia Aysar Jameel Abdalkadhum Al-Qasim Qasim, Iraq

Green

University-Babylon/Iraq,

Al

Manar Abdelhamid The British University in Egypt, Cairo, Egypt Abdellatif E. L. Abderrahmani Department of Computer Sciences FSDM, LISAC, University Sidi Mohammed Ben Abdellah, Atlas FEZ, Morocco Maman Abdurohman School of Computing, Telkom University, Bandung, Indonesia Ali Aburas SUNY Morrisville, Morrisville, NY, USA Matilde Accardo Info Solution s.p.a, Vimodrone, Italy Nadhir Al-Ansari Lulea University of Technology, Lulea, Sweden Kotov Alexander Dentons Europe LLP, Saint Petersburg, Russia Salwan Ali Abed University of Al-Qadisiyah, Diwaniya, Iraq Volodymyr Alieksieiev National Technical University ‘Kharkiv Polytechnic Institute’, Kharkiv, Ukraine Soliman Aljarboa Department of Management Information System, College of Business and Economics, Qassim University, Buridah, Saudi Arabia; Business School, Victoria University, Footscray, VIC, Australia Ali Al-Maliki Ministry of Science and Technology, Baghdad, Iraq Roobaea Alroobaea Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia Hadi Amirpour Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria Angelo Amodio Planetek Italia S.R.L, Bari, Italy K. Anantharajah Faculty of Engineering, University of Jaffna, Kilinochchi, Sri Lanka H. Andaluz Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador

Editors and Contributors

xvii

Darko Androˇcec Faculty of Organization and Informatics, University of Zagreb, Varaždin, Croatia Afafe Annich Department of Computer Sciences FSDM, LISAC, University Sidi Mohammed Ben Abdellah, Atlas FEZ, Morocco; Higher Institute of Information and Communication, Rabat, Morocco Stoyan Apostolov Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria Murshedul Arifeen Time Research & Innovation (Tri), Southampton, UK; Khilgaon, Dhaka, Bangladesh Silvana Astudillo Universidad de Cuenca, Cuenca, Ecuador Paulina Ayala Universidad Tecnica de Ambato UTA, Ambato, Ecuador Nabiha Azizi Electronic Document Management Laboratory (LabGED), Badji Mokhtar-Annaba University, Annaba, Algeria Sussy Bayona-Oré Universidad Nacional Mayor de San Marcos, Lima, Peru; Universidad Autónoma del Perú, Lima, Peru Samir Brahim Belhaouari ICT Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar Alaidine Ben Ayed Université du Québec à Montréal, Montréal, QC, Canada Irina Berezovskaya Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia; Emperor Alexander I St. Petersburg State Transport University, St. Petersburg, Russia Helga Bermeo-Andrade Universidad de Ibagué, Ibagué, Colombia Sid A. Bessedik Université Amar Telidji de Laghouat, Laghouat, Algeria Sajal Bhatia Sacred Heart University, Fairfield, Connecticut, USA Ismaïl Biskri Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada Maria Blancas-Muñoz Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain Slimane Bouazabia University of Science and Technology Houari Boumediene Bab Ezzouar, Laghouat, Algeria Javier Caceres Universidad Tecnica de Ambato UTA, Ambato, Ecuador Mingyi Cai Carnegie Mellon University, Pittsburgh, PA, USA Gustavo Caiza Universidad Politecnica Salesiana, UPS, Quito, Ecuador

xviii

Editors and Contributors

Alexander Toapanta Casa Department of Electricity and Electronics, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Gustavo Henrique Cervi Federal University of Health Sciences (UFCSPA), Porto Alegre, Brazil Ambra Cesareo LifeCharger s.r.l, Milan, Italy Ali Chabuk University of Babylon, Babylon, Iraq Jeffry Chavarría-Molina Escuela de Matemática, Instituto Tecnológico de Costa Rica, Cartago, Costa Rica Baldreck Chipangura University of South Africa, Johannesburg, South Africa Timothy Scott C. Chu Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK; Mechanical Engineering Department, De La Salle University, Manila, Philippines Alvin Chua Mechanical Engineering Department, De La Salle University, Manila, Philippines Caslon Chua Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, Australia Diego Cordero Universidad Católica de Cuenca, Cuenca, Ecuador Jessica Nicole Dela Cruz National University, Manila, Sampaloc, Philippines Vinh Loc Cu Can Tho University, Can Tho, Vietnam Miguel Cuya Universidad Nacional Mayor de San Marcos, Lima, Peru Annarita D’Addabbo IREA-CNR, Bari, Italy Christian Daase Otto-von-Guericke University Magdeburg, Magdeburg, Germany Matthew N. Dailey Asian Institute of Technology, Pathumthani, Thailand; Information and Communication Technologies, Pathumthani, Thailand Edgardo S. Dajao Graduate School of Engineering, Pamantasan ng Lungsod ng Maynila, Manila, Philippines Michael Dammann Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany Quang-Vinh Dang Industrial University of Ho Chi Minh city, Ho Chi Minh city, Vietnam Angelica De La Cruz National University, Manila, Philippines Edgar I. De la Cruz Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

Editors and Contributors

xix

Ivan Dimov Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Department of Parallel Algorithms, Sofia, Bulgaria Afsaneh Doryab University of Virginia, Charlottesville, VA, USA Dyhia Doufene University of Science and Technology Houari Boumediene Bab Ezzouar, Laghouat, Algeria Anastasios Dounis Department of Biomedical Engineering, University of West Attica, Athens, Greece Maycol O. Echevarria Universidad Peruana Unión, Lima, Peru Marta Emirsajłow Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland Shannen Nicole C. Esguerra National University, Manila, Sampaloc, Philippines Àdria España-Cumellas Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Salam Ewaid Southern Technical University, Basra, Iraq Bernie S. Fabito National University, Manila, Sampaloc, Philippines Rezowan Ferdous Time Research & Innovation (Tri), Southampton, UK; Khilgaon, Dhaka, Bangladesh Cecília Dias Flores Federal University of Health Sciences (UFCSPA), Porto Alegre, Brazil Denise Foro-Szasz Carinthia University of Applied Sciences, Villach, Austria Huaguo Gan School of Physics and Materials Science, Guangzhou University, Guangzhou, China Carlos A. Garcia Universidad Técnica de Ambato,UTA, Ambato, Ecuador Marcelo V. Garcia Universidad Técnica de Ambato,UTA, Ambato, Ecuador; University of Basque Country, UPV/EHU, Bilbao, Spain Josá Manuel García Department of Information and Communication Engineering, University of Murcia, Murcia, Spain Tingjian Ge University of Massachusetts, Lowell, MA, USA Alexander Gegov School of Computing, The University of Portsmouth (UoP), Portsmouth, UK Aries M. Gelera Department of Computer Studies, Cavite State University, Rosario, Cavite, Philippines

xx

Editors and Contributors

Mohammad Ghanbari Institute of Information Technology, Alpen-AdriaUniversität Klagenfurt, Klagenfurt, Austria; University of Essex, Colchester, UK Jayshree Ghorpade-Aher P.I.C.T, MIT World Peace University, Pune, India Minal Ghute Yeshwantrao Chavan College of Engineering, Nagpur, India Sean Gilroy BBC, Manchester, UK Nicolai Gnoss Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany Dora González-Bañales Instituto Tecnológico de Durango/Tecnológico Nacional de México, Durango, Mexico Alice Good School of Computing, The University of Portsmouth (UoP), Portsmouth, UK Maria Renata Guarneri LifeCharger s.r.l, Milan, Italy Keisuke Hakuta Shimane University, Matsue, Shimane, Japan Cemal Hanilçi Electrical and Electronic Engineering, Bursa Technical University, Bursa, Turkey Leena Haque BBC, Manchester, UK Ralf-Christian Härting Aalen University of Applied Sciences, Business Administration, Aalen, Germany Erich Hartlieb Carinthia University of Applied Sciences, Villach, Austria Bernhard Heiden Carinthia University of Applied Sciences, Villach, Austria; University of Graz, Graz, Austria Andreas Hein Carl von Ossietzky University Oldenburg, Oldenburg, Germany Adrian Hopgood School of Computing, The University of Portsmouth (UoP), Portsmouth, UK Elias Hossain Time Research & Innovation (Tri), Southampton, UK; Khilgaon, Dhaka, Bangladesh Felix Huening University of Applied Science Aachen, Aachen, Germany Joseph Marvin Imperial National University, Manila, Philippines Ghielyssa D. Intrina National University, Manila, Sampaloc, Philippines R Raden Muhamad Irvan School of Computing, Telkom University, Bandung, Indonesia Naohiro Ishii Advanced Institute of Industrial Technology, Tokyo, Japan

Editors and Contributors

xxi

Ashhadul Islam ICT Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar Fakir M. Amirul Islam Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia Ngomane Issah Department of Computer Science, University of Limpopo, Menkweng, South Africa Urszula Jagodzinska-Szyma ´ nska ´ PIT-RADWAR S.A., Warsaw, Poland Łukasz Jelen´ Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland Fernando Jiménez Department of Information and Communication Engineering, University of Murcia, Murcia, Spain Juan Jose Fallas-Monge Escuela de Matemática, Instituto Tecnológico de Costa Rica, Cartago, Costa Rica Akira Kamiya Nagoya Institute of Technology, Aichi, Japan D. G. T. L. Karunathilaka Faculty of Engineering, Kilinochchi, Sri Lanka

University of Jaffna,

Swetlana Kedich Emperor Alexander I St. Petersburg State Transport University, St. Petersburg, Russia Jagdish Kene Shri Ramdeobaba College of Engineering and Management, Nagpur, India Hitendra Shankarrao Khairnar Research Scholar PICT, Cummins College of Engineering, Pune, India Zakir Khan Time Research & Innovation (Tri), Southampton, UK; Khilgaon, Dhaka, Bangladesh Alexander A. Kharlamov Institute of Higher Nervous Activity and Neurophysiology, RAS, Moscow, RF, Russia; Moscow State Linguistic University, Moscow, RF, Russia; Higher School of Economics, Moscow, RF, Russia Nwe Ni Khin Yangon Technological University, Computer Engineering and Information Technology, Yangon, Republic of the Union of Myanmar Takumi Kobayashi Shimane University, Matsue, Shimane, Japan Panagiotis Kofinas Department of Biomedical Engineering, University of West Attica, Athens, Greece Boris Kontsevoi Intetics Inc., Naples, FL, USA Mridula Korde Shri Ramdeobaba College of Engineering and Management, Nagpur, India

xxii

Editors and Contributors

Panagiotis Korkidis Department of Biomedical Engineering, University of West Attica, Athens, Greece Ronny Kramer Department of Computer Science, Chemnitz University of Technology, Chemnitz, Germany Alisa Kunapinun Asian Institute of Technology, Pathumthani, Thailand; Industrial Systems Engineering, Pathumthani, Thailand Pamela Kunert Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany O˘guzhan Kurnaz Mechatronics Engineering, Bursa Technical University, Bursa, Turkey Giovanni La Via Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Imane Laghman Department of Computer Sciences FSDM, LISAC, University Sidi Mohammed Ben Abdellah, Atlas FEZ, Morocco Bee Theng Lau Faculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Sarawak, Malaysia Jan Laue Lulea University of Technology, Lulea, Sweden Minhee Lee Samsung Electronics, Suwon, Gyeonggi, South Korea Sukmongkol Lertpiromsuk Regular MBA Program, Kasetsart Business School, Kasetsart University, Bangkok, Thailand Cuiping Li Jiangxi University of Traditional Chinese Medicine, Jiangxi, China Dokshin Lim Department of Mechanical and System Design Engineering, Hongik University, Seoul, South Korea Willone Lim Faculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Sarawak, Malaysia Edwin Torres López Department of Electricity and Electronics, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Héctor López-Carral Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain Egon Lüftenegger CAMPUS 02 University of Applied Sciences, IT & Business Informatics, Graz, Austria Gaoyong Luo School of Physics and Materials Science, Guangzhou University, Guangzhou, China

Editors and Contributors

xxiii

Wenbin Luo School of Physics and Materials Science, Guangzhou University, Guangzhou, China Yaqing Luo Department of Mathematics, London School of Economics and Political Science, London, UK Tien Dao Luu Can Tho University, Can Tho, Vietnam David Magiera University of Applied Science Aachen, Aachen, Germany Mico C. Magtira National University, Manila, Sampaloc, Philippines Fahim Mahmud American International University-Bangladesh (AIUB), Dhaka, Bangladesh Emmanuel Malaay National University, Manila, Philippines R. Maldeni Robert Gordon University, Aberdeen, Scotland Kudzai Mapingire University of Pretoria, Pretoria, South Africa Enrique Martínez-Bueno Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain M. A. Mascrenghe Informatics Institute of Technology, Colombo, Sri Lanka Tokuro Matsuo Advanced Institute of Industrial Technology, Tokyo, Japan Jean-Guy Meunier Université du Québec à Montréal, Montréal, QC, Canada Siphe Mhlana University of South Africa, Johannesburg, South Africa Shah J. Miah Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia Andrea Migliavacca LifeCharger s.r.l, Milan, Italy Mahajabin Haque Mili American International University-Bangladesh (AIUB), Dhaka, Bangladesh Neil Milliken Atos, London, UK Luis Miralles-Pechuán School of Computing, Technological University Dublin, Dublin, Ireland Khin Su Myat Moe Yangon Technological University, Computer Engineering and Information Technology, Yangon, Republic of the Union of Myanmar Sudhish Mohanan Sacred Heart University, Fairfield, Connecticut, USA Jéssica D. Mollocana Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Cristhian Monta Universidad Tecnica de Ambato UTA, Ambato, Ecuador

xxiv

Editors and Contributors

Paul Moore Atos, London, UK María-Belén Mora-Arciniegas Departamento de Ciencias de La Computación y Electrónica, Universidad Técnica Particular de Loja, San Cayetano Alto y Marcelino Champagnat S/N, Loja, Ecuador Geovanny P. Moreno Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Andrea Mory Universidad de Las Islas Baleares, Palma de Mallorca, Spain Ahmad Moussa Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan Velempini Mthulisi Department of Computer Science, University of Limpopo, Menkweng, South Africa Alexander Muenzberg University of Applied Science Zweibrücken, Germany; Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Kaiserslautern,

Tasnim Morium Mukur United International University, Dhaka, Bangladesh Anna Mura Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Khaled Nagaty The British University in Egypt, Cairo, Egypt R. Navarathna OCTAVE, John Keells Group Centre of Excellence for Data and Advanced Analytic, Colombo, Sri Lanka Hoang Viet Nguyen Can Tho University, Can Tho, Vietnam Achmad Nurmandi Master of Government Science, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia Wilson Sánchez Ocaña Department of Electricity and Electronics, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Kazuya Odagiri Sugiyama Jyogakuen University, Nagoya, Japan Nita Tri Oktaviani Master of Government Science, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia Pedro Omedas Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain Khaled Ouzzir University of Science and Technology Houari Boumediene Bab Ezzouar, Laghouat, Algeria Zhenyu Pan University of Massachusetts, Lowell, MA, USA

Editors and Contributors

xxv

Febrian Aji Pangestu School of Computing, Telkom University, Bandung, Indonesia Guido Pasquariello IREA-CNR, Bari, Italy Biagio Pecorino Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Claudio Pighini LifeCharger s.r.l, Milan, Italy; Politecnico di Milano, Milan, Italy Maria Pilgun Institute of Linguistics, RAS, Moscow, RF, Russia; Moscow State Linguistic University, Moscow, RF, Russia; Higher School of Economics, Moscow, RF, Russia Matthias Pohl Otto-von-Guericke University Magdeburg, Magdeburg, Germany Orlando Poma Universidad Peruana Unión, Lima, Peru Nina Popova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia Stoyan Poryazov Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Department of Information Modeling, Sofia, Bulgaria Pylyp Prystavka Department of Applied Mathematics, National Aviation University, Kyiv, Ukraine Sonia-Azucena Pupiales-Chuquin Departamento de Ciencias de La Computación y Electrónica, Universidad Técnica Particular de Loja, San Cayetano Alto y Marcelino Champagnat S/N, Loja, Ecuador Aji Gautama Putrada Advanced and Creative Networks Research Center, Telkom University, Bandung, Indonesia Haiying Qian Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, China Sajidur Rahman Universität Bremen, Bremen, Germany Wahidur Rahman Time Research & Innovation (Tri), Southampton, UK; Khilgaon, Dhaka, Bangladesh S. K. Rajakaruna Faculty of Engineering, University of Jaffna, Kilinochchi, Sri Lanka Vanitha Rajamany School of Computing, UNISA, Pretoria, South Africa Eike-Christian Ramcke Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany Josiah Eleazar T. Regencia Ateneo de Manila University, Quezon City, Philippines

xxvi

Editors and Contributors

Christopher Reichstein Cooperative State University BW, Heidenheim/Brenz, Germany Han Ren Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou, China Norbert Roesch University of Applied Science Kaiserslautern, Zweibrücken, Germany Joel Hugo Fernandez Rojas Universidad Peruana Unión, Lima, Peru Jonathan A. Romero Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Rachel Edita Roxas National University, Manila, Philippines Elizabeth Alvarado Rubio Department of Electricity and Electronics, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Gudula Rünger Department of Computer Science, Chemnitz University of Technology, Chemnitz, Germany Salahudin Government Science, Universitas Muhammadiyah Malang, Malang City, Indonesia Edgar R. Salazar Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador Alexandra Salazar-Moya Universidad Técnica de Ambato,UTA, Ambato, Ecuador Luis F. Santo Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador Khalid Satori Department of Computer Sciences FSDM, LISAC, University Sidi Mohammed Ben Abdellah, Atlas FEZ, Morocco Muhammad Bagus Satrio School of Computing, Telkom University, Bandung, Indonesia Janina Sauer University of Applied Science Kaiserslautern, Zweibrücken, Germany; Carl von Ossietzky University Oldenburg, Oldenburg, Germany Marthie Schoeman School of Computing, University of South Africa, Johannesburg, South Africa Tobias Schreier Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany Tobias Schüle Aalen University of Applied Sciences, Business Administration, Aalen, Germany Alessandro Scuderi Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy

Editors and Contributors

xxvii

Emanuele Lindo Secco Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK Edward S˛edek PIT-RADWAR S.A., Warsaw, Poland; University of Science and Technology (UTP), Bydgoszcz, Poland M. Selvarathnam Faculty of Engineering, University of Jaffna, Kilinochchi, Sri Lanka Mapunya Sekgoari Semaka Department of Computer Science, University of Limpopo, Menkweng, South Africa Stephen Kwame Senaya School of Computing, University of South Africa, Johannesburg, South Africa Ana Lucia Serrano Universidad de Cuenca, Cuenca, Ecuador Nakisa Shams Department of Electrical Engineering, École de technologie supérieure, Montreal, QC, Canada Tamanna Shaown Brac University, Dhaka, Bangladesh Olga Shipunova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia Shun Shiramatsu Nagoya Institute of Technology, Aichi, Japan M. Shohrab Hossain Bangladesh University of Engineering and Technology, Dhaka, Bangladesh Shah Siddiqui School of Computing, The University of Portsmouth (UoP), Portsmouth, UK Sourav Sinha Technische Universität München, Munich, Germany Hanlie Smuts University of Pretoria, Pretoria, South Africa Selver Softic CAMPUS 02 University of Applied Sciences, IT & Business Informatics, Graz, Austria Balwant Sonkamble Pune Institute of Computer Technology, Pune, India Juan J. Soria Universidad Peruana Unión, Lima, Peru Pablo Soto-Quiros Escuela de Matemática, Instituto Tecnológico de Costa Rica, Cartago, Costa Rica Nandagopal Sridhar Sacred Heart University, Fairfield, Connecticut, USA Daniel Staegemann Otto-von-Guericke Germany

University

Magdeburg,

Magdeburg,

Ulrike Steffens Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany

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Editors and Contributors

Luisa Sturiale Department of Civil Engineering and Architecture (DICAR), University of Catania, Catania, Italy Yuraporn Sudharatna Department of Management, Kasetsart Business School, Kasetsart University, Bangkok, Thailand David A. Sumire Universidad Peruana Unión, Lima, Peru ´ Przemysław Swiercz Faculty of Electronics, Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland Denis Syraeshko Intetics Bel Ltd., Minsk, Belarus Rahel Mekonen Tamiru Bahir Dar University, Bahir Dar, Ethiopia Richard M. Tandalla Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador Gladys-Alicia Tenesaca-Luna Departamento de Ciencias de La Computación y Electrónica, Universidad Técnica Particular de Loja, San Cayetano Alto y Marcelino Champagnat S/N, Loja, Ecuador Sergei Terekhov Intetics Bel Ltd., Minsk, Belarus Claudia Elizabeth Thompson Federal University of Health Sciences (UFCSPA), Porto Alegre, Brazil Christian Timmerer Institute of Information Universität Klagenfurt, Klagenfurt, Austria; Bitmovin, Klagenfurt, Austria

Technology,

Alpen-Adria-

Giuseppe Timpanaro Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Venelin Todorov Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Department of Information Modeling, Sofia, Bulgaria; Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Department of Parallel Algorithms, Sofia, Bulgaria Bianca Tonino-Heiden University of Graz, Graz, Austria Xuan Viet Truong Can Tho University, Can Tho, Vietnam Klaus Turowski Otto-von-Guericke University Magdeburg, Magdeburg, Germany Hossana Twinomurinzi University of Johannesburg, Johannesburg, South Africa Oksana Tyvodar Department of Applied Mathematics, National Aviation University, Kyiv, Ukraine Pittawat Ueasangkomsate Department of Management, Kasetsart Business School, Kasetsart University, Bangkok, Thailand

Editors and Contributors

xxix

Atiq Ur Rehman ICT Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar J. A. van Biljon School of Computing, UNISA, Pretoria, South Africa Alta Van der Merwe University of Pretoria, Pretoria, South Africa John Andrew van der Poll Graduate School of Business Leadership (SBL), University of South Africa, Midrand, South Africa C. J. van Staden School of Computing, UNISA, Pretoria, South Africa Paul F. M. J. Verschure Synthetic Perceptive Emotive Cognitive Systems (SPECS) Lab, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain Naumov Victor Institute of State and Law, Russian Academy of Sciences, Saint Petersburg, Russia Carlota Villasante Marcos Ericsson España SA, Madrid, Spain Thanh-Hai Vo Industrial University of Ho Chi Minh city, Ho Chi Minh city, Vietnam Matthias Volk Otto-von-Guericke University Magdeburg, Magdeburg, Germany Dražen Vuk Mobilisis d.o.o, Varaždin, Jalkovec, Croatia Franz-Josef Wache University of Applied Science Aachen, Aachen, Germany Jing Wan Guangdong University of Foreign Studies, Guangzhou, China Hiroshi Watanabe Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan Hanbin Wu Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, China Jie Xue University of California, Santa Barbara, CA, USA Runze Yan University of Virginia, Charlottesville, VA, USA William Emmanuel S. Yu Ateneo de Manila University, Quezon City, Philippines Olaf Zukunft Hamburg University of Applied Sciences, Department of Informatics, Hamburg, Germany

Highly Efficient Stochastic Approaches for Computation of Multiple Integrals for European Options Venelin Todorov, Ivan Dimov, Stoyan Apostolov, and Stoyan Poryazov

Abstract In this work we investigate advanced stochastic methods for solving a specific multidimensional problems related to computation of European style options in computational finance. Recently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance. The advantages and disadvantages of several highly efficient stochastic methods connected to European options evaluation will be analyzed. For the first time multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient lattice rules. Keywords Monte Carlo and quasi-Monte Carlo methods · Multidimensional integrals · Option pricing · High performance computing

1 Introduction Recently Monte Carlo (MC) and quasi-Monte Carlo (QMC) approaches are established as a very attractive and necessary computational tools in finance [11]. The field of computational finance is more complicated with increasing number of applications [2]. The option pricing is a key problem in financial markets [5, 6, 12] and especially V. Todorov · S. Poryazov Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Department of Information Modeling, Acad. Georgi Bonchev Str., Block 8, 1113 Sofia, Bulgaria e-mail: [email protected] V. Todorov (B) · I. Dimov Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Department of Parallel Algorithms, Acad. G. Bonchev Str., Block 25 A, 1113 Sofia, Bulgaria e-mail: [email protected]; [email protected] I. Dimov e-mail: [email protected] S. Apostolov Faculty of Mathematics and Informatics, Sofia University, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_1

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difficult when the dimension of the problem goes higher [1]. MC and QMC methods are appropriate for solving multidimensional problems [7] and are used not only for option pricing [9], but also in other problems in computational finance [4, 13]. The basic definitions that we are using are taken from [11].

2 Problem Settings and Motivation Let’s deal with a European call option [11] whose payoff depends on k > 1 assets with prices Si , i = 1, ..., k.. Following [11] we assume that at expiry time T , and risk-free interest rate r , the payoff is given by h(S1 , . . . , Sk ), S  is the value at expiry of the i-th asset. Then for the option value V : V = e−r (T −t) (2π(T − t))−k/2 (det )−1/2 (σ1 . . . σk )−1 ∞

∞ ...

0

0

h(S1 , . . . , Sk ) S1 . . . Sk

   exp −0.5α   −1 α dS1 . . . dSk , −1    ln(Si /Si ) − (r − σi2 /2)(T − t) . αi = σi (T − t)1/2 According to [11] the most important case in recent models is when the payoff function is the exponent function. We will now give a brief explanation which demonstrates the strength of the MC and QMC approach [7]. According to [7], a time of order 1093 s will be necessary for computation of the integral with the deterministic approach, and 1 year has 31536 × 103 s. According to [7] a time of 10 × 107 × 2 × 10−7 ≈ 20s will be necessary in order to evaluate the multidimensional integral with the same accuracy. We summarize that in the case of 100-dimensional integral it is 5 × 1091 times faster than the deterministic one. That motivates our study on the new highly efficient stochastic approaches for the problem under consideration.

Highly Efficient Stochastic Approaches for Computation …

3

3 Highly Efficient Stochastic Approaches Based on Lattice Rules We will use this rank-1 lattice sequence [14]:  xk =

 k z , k = 1, . . . , N , N

(1)

where N is an integer, N ≥ 2, z = (z 1 , z 2 , . . . z s ) is the generating vector and {z} denotes the fractional part of z. For the definition of the E sα (c) and Pα (z, N ) see [14] and for more details, see also [1]. In 1959 Bahvalov showed that [1] there exists an optimal choice of the generating vector z:     N −1  

  1  (log N )β(s,α) k   z − f f (u)du  ≤ cd(s, α) (2)   N N Nα k=0   [0,1)s for the function f ∈ E sα (c) with α > 1. The generating vector z which satisfies (2) is an optimal generating vector [14]. The main bottleneck lies in the creation of the optimal vectors, especially for very high dimensions [11]. The first generating vector in our study is the generalized Fibonacci numbers, for more details see [14]: z = (1, Fn(s) (2), . . . , Fn(s) (s)).

(3)

If we change the generating vector to be optimal in the way described in [10] we have improved the lattice sequence. This is a 200-dimensional base-2 generating vector of prime numbers for up to 220 = 1048576 points, constructed recently by Dirk Nuyens [10]. The special choice of this optimal generating vector is definitely more efficient than the Fibonacci generating vector, which is only optimal for the two-dimensional case [14]. For this improved lattice rule is satisfied [10]: D ∗N = O



log s N N

.

4 Numerical Examples and Results The numerical study includes high performance computing of the multidimensional integrals:

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 ex p(x1 x2 x3 ) ≈ 1.14649907.

(4)

⎛ ⎞⎞ ⎛ 5 5   exp ⎝ 0.5ai xi2 ⎝2 + sin x j ⎠⎠ ≈ 2.923651,

(5)

[0,1]3



j=1, j =i

i=1

[0,1]5

where ai = (1, 0.5, 0.2, 0.2, 0.2).  exp

 8 

[0,1]20

0.1xi

= 1.496805.

(6)

i=1

[0,1]8





 20   exp xi ≈ 1.00000949634.

(7)

i=1

We also have done high performance computing with our methods for the first time on a 100-dimensional integral: 

 100   exp xi ,

[0,1]100

i=1

I100 =

(8)

using the exponential function in Taylor series and integrating (x1 · · · x100 )n we receive  100    exp xi = [0,1]100

=

∞  n=0

i=1

1 =100 F100 (1, · · · , 1; 2, · · · , 2; 1). (n + 1)100 n!

where p Fq (a1 , · · · , a p ; b1 , · · · , bq ; x) is the generalized hypergeometric function p Fq (a1 , · · ·

, a p ; b1 , · · · , bq ; x) =

∞  (a1 )n · · · (a p )n x n , (b1 )n · · · (bq )n n! n=0

and (c)n = c(c + 1) · · · (c + n − 1) is the Pochhammer symbol. We also include in the experiments the 50-dimensional integral of the same kind:

Highly Efficient Stochastic Approaches for Computation …



 50   exp xi .

[0,1]50

i=1

I50 =

5

(9)

The results are given in the Tables including the relative error (RE) of the MC and QMC method that has been used, the CPU-time (T) in seconds and the samples (#). We will make a high performance computation, including the Optimized lattice rule (OP), the Fibonacci based rule (FI), the Adaptive approach (AD) [8] and the Sobol quasi-random sequence (SO) [3]. For the 3-dimensional integral, for the number of samples Generalized Fibonacci numbers of the corresponding dimensionality, the best relative error is produced by the optimized lattice algorithm OP—see Table 1, but for a preliminary given time in seconds the optimized method OPT and the Fibonacci latice rule FI gives results of the same order—see Table 2. For the 5-dimensional integral again the best approach is OPT method, for N = 440096 it gives relative error of 8.16e − 7—see Table 3, while for 20s again FI method gives results of the same order as the optimized method—see Table 4. For the 8-dimensional integral the Adaptive approach, the Sobol QMC algorithm, and the Fibonacci approach produce relative error of the same order—see Table 5, but for a preliminary given time in seconds, Fibonacci approach is better than both Sobol QMC and Adaptive approach—see Table 6. For the 20-dimensional integral Sobol QMC approach is better than both Fibonacci and Adaptive approach—see Table 7 and Adaptive approach requires very huge amount of time—near one hour for number of samples N = 524888 due to the division of the subareas in the description of the algorithm. Thats why we omit this algorithm for the 50- and 100-dimensional integrals. For 20s for 20-dimensional integral the best result is produced again by the optimized lattice rule—1.23e − 8 in Table 8. For the 50-dimensional integral Fibonacci approach is worse than Sobol approach by at least 1 order—see Table 9, but for a preliminary given time in seconds Sobol QMC and Fibonacci approach give relative errors of the same order—see Table 10. It is worth mentioning that the Sobol approach requires more amount of time due to generation of the sequence, while Fibonacci lattice rules and Optimized approach are more faster and computationally efficient algorithms. For the 100-dimensional integral the best result is produced by the optimized lattice approach—it gives 4.78e − 6 for number of samples N = 220 —see Table 11 and for 100s it produces a relative error of 8.16e − 7 which is very high accuracy and with 3–4 orders better than the other stochastic approaches. So we demonstrate here the advantages of the new lattice method and its capability to achieve very high accuracy for less than a minute on a laptop with a quad-core CPU (Table 12).

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Table 1 Algorithmic comparison of RE for (4) # OP T AD T 19513 35890 66012 121415 223317

1.93e-5 3.18e-6 2.65e-6 9.16e-7 8.01e-7

0.01 0.04 0.07 0.12 0.20

3.21e-4 6.55e-5 5.12e-5 5.11e-5 9.34e-5

2.21 6.41 9.86 15.4 24.2

FI

T

SO

T

4.69e-4 5.46e-6 5.34e-6 5.34e-6 1.73e-6

0.02 0.06 0.11 0.12 0.22

4.98e-5 1.56e-5 8.11e-6 3.08e-6 2.05e-6

0.56 1.45 2.31 3.80 6.13

Table 2 Algorithmic comparison of RE for the (4) T OP AD 0.1 1 2 5 10 20

9.16e-7 6.37e-7 4.22e-7 1.84e-7 6.09e-8 1.57e-8

8.67e-4 2.96e-5 5.45e-4 1.14e-4 6.56e-5 2.04e-5

Table 3 Algorithmic comparison of RE for the (5) # OP T AD T 13624 52656 103519 203513 400096

6.72e-5 1.53e-5 8.48e-6 6.25e-6 8.16e-7

0.02 0.06 0.09 0.15 0.40

1.89e-3 2.31e-3 2.01e-3 3.42e-4 9.12e-4

2.33 6.18 9.94 16.2 45.6

Table 4 Algorithmic comparison of RE for the (5) T OP AD 0.1 1 5 10 20

3.07e-6 1.32e-6 1.13e-6 5.47e-7 3.52e-7

1.34e-2 2.44e-3 4.93e-4 1.88e-3 2.71e-4

FI

SO

1.32e-6 3.22e-7 2.06e-7 1.47e-7 3.89e-7 1.53e-8

3.21e-4 8.21e-5 2.96e-5 5.00e-6 2.71e-6 1.65e-6

FI

T

SO

T

9.59e-4 6.96e-4 8.72e-5 8.04e-5 7.26e-5

0.03 0.06 0.13 0.25 0.50

1.76e-4 5.05e-5 2.70e-5 7.57e-6 2.52e-6

0.56 1.45 2.52 6.07 10.63

FI

SO

7.26e-5 2.28e-5 5.94e-6 3.85e-7 7.49e-7

8.22e-4 2.91e-4 1.71e-5 1.79e-5 4.71e-6

Highly Efficient Stochastic Approaches for Computation … Table 5 Algorithmic comparison of RE for the (6) # OP T AD T 16128 32192 64256 128257 510994

1.79e-6 1.56e-6 8.01e-7 6.22e-7 3.21e-7

0.04 0.05 0.08 0.13 0.34

1.10e-5 3.32e-5 4.65e-5 8.25e-6 7.07e-6

12.6 33.3 54.2 88.3 233.6

FI

T

SO

T

8.08e-4 1.03e-4 5.03e-5 8.13e-6 5.95e-6

0.03 0.07 0.11 0.14 0.57

8.87e-5 5.42e-5 2.34e-5 4.45e-6 3.32e-6

0.13 0.58 2.49 6.36 19.45

Table 6 Algorithmic comparison of RE for the (6) T OP AD 1 2 5 10 20

2.18e-7 1.32e-7 9.03e-8 5.00e-8 2.55e-8

6.34e-4 1.58e-4 1.44e-4 6.61e-5 2.77e-5

Table 7 Algorithmic comparison of RE for the (7) # OP T AD T 2048 16384 65536 131072 524288

2.84e-6 1.04e-6 9.21e-7 6.15e-7 5.33e-8

0.02 0.12 0.91 2.13 8.13

1.14e-2 4.96e-4 9.75e-4 1.25e-5 1.96e-6

8.6 60.3 474.2 888.3 2356

9.14e-7 1.08e-7 5.87e-8 3.56e-8 1.23e-8

210 212 216 220

7.88e-6 1.88e-6 8.44e-8 4.28e-8

0.05 0.17 2.14 17.65

6.23e-4 1.55e-4 9.72e-5 6.08e-5

SO 2.02e-5 2.73e-5 8.88e-6 5.23e-6 2.11e-6

FI

T

SO

T

0.03 0.13 1.17 2.34 8.34

8.44e-4 6.82e-5 8.34e-6 3.77e-6 1.91e-7

0.13 1.68 8.69 14.36 57

1.58e-3 1.028e-3 8.58e-4 4.31e-4 1.27e-4

Table 9 Algorithmic comparison of RE for the (9) # OP T FI

FI 5.34e-6 2.57e-6 1.52e-7 3.45e-6 1.82e-7

8.22e-5 3.12e-5 1.36e-5 8.85e-6 2.15e-6

Table 8 Algorithmic comparison of RE for the (7) T OP AD 1 2 5 10 20

7

FI

SO

1.48e-5 9.17e-6 5.19e-6 1.73e-6 1.38e-7

3.25e-5 3.97e-5 1.45e-5 2.71e-6 1.76e-6

T

SO

T

0.08 0.35 5.21 32.76

8.88e-5 5.21e-5 9.11e-4 4.88e-6

3.5 16 73 276

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Table 10 Algorithmic comparison of RE for the (9) T OP FI 1 2 10 100

9.14e-7 7.51e-7 9.34e-8 1.34e-9

SO

1.58e-3 1.028e-3 3.01e-4 5.23e-5

Table 11 Algorithm comparison of the RE for the (8) # OP T FI T 210 212 216 220

6.83e-3 3.77e-4 3.36e-5 4.78e-6

0.05 0.17 9.1 57.6

4.13e-1 1.15e-1 6.12e-2 3.18e-2

0.06 0.18 9.2 58.7

1.48e-4 9.17e-5 8.73e-5 1.03e-5

SO

T

6.31e-2 1.23e-2 2.31e-3 2.34e-4

18 34 170 861

Table 12 Algorithm comparison of the RE for the 100-dimensional integral (8) T OP FI SO 1 2 10 100

2.67e-3 1.89e-4 3.22e-5 8.16e-7

7.18e-2 6.02e-2 4.12e-2 1.13e-2

9.31e-2 8.66e-2 6.94e-2 3.88e-3

5 Conclusion A comprehensive experimental study of optimized lattice rule, Fibonacci lattice sets, Sobol sequence, and Adaptive approach has been done for the first time on some case test functions related to option pricing. Optimized lattice rule described here is not only one of the best available algorithms for high dimensional integrals but also one of the few possible methods, because in this work we show that the deterministic algorithms need an huge amount of time for the evaluation of the multidimensional integral, as it was discussed in this paper. The numerical tests show that the improved lattice rule is efficient for multidimensional integration and especially for computing multidimensional integrals of a very high dimensions up to 100. The novelty is that the new proposed optimized method gives very high accuracy for less than a minute on laptop even for 100-dimensional integral. It is an important element since this may be crucial in order to achieve a more reliable interpretation of the results in European style options which is foundational in computational finance. Acknowledgements Venelin Todorov is supported by the Bulgarian National Science Fund under Project DN 12/5-2017 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems” and by the National Scientific Program “Information and Communication Technologies for a Single

Highly Efficient Stochastic Approaches for Computation …

9

Digital Market in Science, Education and Security (ICT in SES)”, contract No DO1-205/23.11.2018, financed by the Ministry of Education and Science in Bulgaria. Stoyan Apostolov is supported by the Bulgarian National Science Fund under Young Scientists Project KP-06-M32/2 - 17.12.2019 “Advanced Stochastic and Deterministic Approaches for Large-Scale Problems of Computational Mathematics”.

References 1. Bakhvalov N (2015) On the approximate calculation of multiple integrals. J Complex 31(4):502–516 2. Boyle PP, Lai Y, Tan K (2001) Using lattice rules to value low-dimensional derivative contracts 3. Bratley P, Fox B (1988) Algorithm 659: implementing Sobol’s Quasirandom sequence generator. ACM Trans Math Softw 14(1):88–100 4. Broadie M, Glasserman P (1997) Pricing American-style securities using simulation. J Econ Dyn Control 21(8–9):1323–1352 5. Centeno V, Georgiev IR, Mihova V, Pavlov V (2019) Price forecasting and risk portfolio optimization. In: AIP conference proceedings, vol 2164, no 1, p 060006 6. Chance DM, Brook R (2009) An introduction to derivatives and risk management, 8th edn. South-Western College Pub 7. Dimov I (2008) Monte Carlo methods for applied scientists. World Scientific, London, Singapore, p 291p 8. Dimov I, Georgieva R (2010) Monte Carlo algorithms for evaluating Sobol’ sensitivity indices. Math Comput Simul 81(3):506–514 9. Duffie D (2001) Dynamic asset pricing theory, 3rd edn. Princeton University Press 10. Kuo FY, Nuyens D (2016) Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients—a survey of analysis and implementation. Found Comput Math 16(6):1631–1696 11. Lai Y, Spanier J (1998) Applications of Monte Carlo/Quasi-Monte Carlo methods in finance: option pricing. In: Proceedings of the Claremont Graduate University conference 12. Raeva E, Georgiev I (2018) Fourier approximation for modeling limit of insurance liability. In AIP conference proceedings, vol 2025, no 1, p 030006 13. Tan KS, Boyle PP (2000) Applications of randomized low discrepancy sequences to the valuation of complex securities. J Econ Dyn Control 24:1747–1782 14. Wang Y Hickernell FJ (2000) An historical overview of lattice point sets. In: Monte Carlo and Quasi-Monte Carlo methods. In: Proceedings of a conference held at Hong Kong Baptist University, China

Spectrum Sensing Data Falsification Attack Reputation and Q-Out-of-M Rule Security Scheme Velempini Mthulisi , Ngomane Issah, and Mapunya Sekgoari Semaka

Abstract Cognitive Radio Networks equipped with dynamic spectrum access are envisioned to address spectrum scarcity by allowing secondary users (SU) to utilise vacant spectrum bands opportunistically. The SUs utilise cooperative spectrum sensing (CSS) to make accurate spectrum access decision and to avoid interference. Unfortunately, malicious users can cooperate with SUs and share false observations leading to inaccurate spectrum access decision. The Spectrum Sensing Data Falsification (SSDF) attack is caused by malicious users. In this study, we investigated the SSDF attack and a dynamic defence mechanism called the reputation and q-outof-m rule scheme designed to address the effects of SSDF attack. The scheme was implemented in cognitive radio ad hoc networks. The fusion node was not considered. The success, missed detection, and false alarm probabilities were considered as evaluation metrics and the MATLAB simulations. Keywords Cognitive radio ad hoc networks · Cooperative spectrum sensing · Fusion centre · Primary users · Secondary users · Reputation-based system · Q-out-of-m rule · SSDF attack

1 Introduction The ever-increasing wireless devices lead to the overcrowding of unlicensed spectrum [1–3], and the underutilization of licensed spectrum. The Cognitive Radio Networks (CRN) address the spectrum scarcity by enabling Secondary Users (SUs) to access the vacant licensed spectrum opportunistically [4–10]. This is achieved V. Mthulisi (B) · N. Issah · M. S. Semaka Department of Computer Science, University of Limpopo, Menkweng, South Africa e-mail: [email protected] N. Issah e-mail: [email protected] M. S. Semaka e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_2

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through Cooperative Spectrum Sensing (CSS) where SUs collaborative in sensing, [11–14]. Unfortunately, malicious SUs share false spectrum observations [3, 15, 16], leading to incorrect spectrum access decisions. This may cause Primary Users (PUs) interference or denial of service (DoS) to SUs [17]. The attack is known as Spectrum Sensing Data Falsification (SSDF)/Byzantine Attack [18, 19]. In this paper, an investigation of the SSDF attack in Cognitive Radio Ad Hoc Networks (CRAHN) was conducted. Further, we proposed a scheme, which integrates the reputation and q-out-of-m rule schemes [20]. The reputation-based system evaluates each SU’s past reports to determine its trustworthiness while the q-out-of-m rule in which q sensing reports out of 60% m nodes are randomly polled to make the final decision [21, 22]. MATLAB was used to and simulate the proposed scheme. The scheme can detect and isolate malicious nodes.

2 Related Work The SSDF attack can cause DoS to SUs or interference to PUs. The authors in [23] proposed a scheme to counter the SSDF attack in CRAHN. The scheme implements a q-out-of-m rule with a modified z-test. Chen et al. [24] implemented a scheme that mitigates the SSDF attack in a distributed CRN environment called destiny-based scheme (DBS). This scheme incorporated CSS where SUs share their sensing reports. However, the hit and run attack was not considered. Pongaliur et al. [25] proposed a distributed scheme to counter the SSDF attack known as the multi-fusion-based distributed spectrum sensing (MFDSS). The scheme implemented the modified z-test to combat extreme outliers and the reputation-based system for the final decision-making. The authors in [26] proposed a reputation-based system that clustered the SUs based on their history and initial reputation. However, the study did not consider the unintentionally misbehaving SUs and the hit and run attacks. The work in [27] used the suspicious level to address the SSDF attack. SUs that were deemed suspicious were isolated. The reports from SUs with trustworthy history were included in decision-making. The advantage of this study was that it restored the reports of unintentionally misbehaving SUs. Ye et al. [28] investigated and isolated the SSDF attack by implementing a statistical consensus-based algorithm in CRAHN. The scheme also isolated reports from unintentionally misbehaving SUs.

3 Network Model We considered two types of users, SUs and PUs. The SUs cooperate in both sensing and sharing sensing data. However, cooperative sensing can be compromised by malicious nodes, which report incorrect spectrum observations during CSS. We study three kinds of malicious nodes; the always yes, always no, and the hit and run. The

Spectrum Sensing Data Falsification Attack …

13

always yes report that a vacant spectrum is occupied. The always no report that an occupied spectrum is vacant. The hit and run malicious node alters its reports to avoid detection [29, 30]. We also studied two types of SUs, the legitimate SUs, and the unintentionally misbehaving SUs. The legitimate SUs make use of the cognitive capabilities and the unintentionally misbehaving SUs report incorrect sensing data due to hidden terminal problem, signal fading, and multipath fading [31]. The SUs incorporate energy detection in sensing the vacant channels. They also determine the off periods of the spectrum by detecting the signal strength of the PUs as shown in Eqs. (1–8). H0 : xi (t) = n i (t), i = 1, 2 . . . .

(1)

H1 : xi (t) = ci (t)si (t) + ai (t), i = 1, 2 . . . , N ,

(2)

where H0 denotes that the PU signal is absent and H1 denotes that the PU is present. N being the number of SUs, xi (t) is the ith sample of the received signal, si (t) is the PU signal, and ci (t) is the channel gain, while ai (t) denotes the additive white Gaussian noise (AWGN). The energy E of a given signal xi (t) is: ∞ E=

|xi (t)|∧ 2dt

(3)

−∞

This can be modelled using Perceval’s theorem as ∞

∞ |xi (t) | dt = 2

−∞

|xiπ ( f )|∧ 2dt

(4)

−∞

where xiπ

∞ =

e−2πi dt

(5)

−∞

The received energy observation E o can be modelled as a Normal random variable with mean u and variance σ 2 following the hypotheses H0 and H1 

  H0 : E 0 ∼ N u i , σi2  H1 : E 0 ∼ N u i1 , σi21

(6)

Comparing E 0 with a given TV γ1 , the local binary decision ϕ was obtained as

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  H0 : E 0 < γ1 whereE o ∼ N u i , σi2  H1 : E 0 > γ1 whereE 0 ∼ N u i1 , σi21

(7)

The local binary decision ϕ was based on the following criterion ⎧ ⎨

ϕ > E 0 accept H1 , conclude that PU is present ϕ < E 0 accept H0 , conclude that PU is absent ⎩ E 0 < ϕ < E 0 accept H1 , make another observation

(8)

Letting s to denote the successful PU detection with F the number of successes and f to denote the success probability with δ = 1 −  failure rate, with N SUs N si . Given F and δ, the probability of correctly detecting PUs can be given by i=1 measured by a binomial probability distribution as follows:

n Φ

=

n n! s Φ f n−Φ , Φ = 0, 1 . . . , n and 0 ≤ p ≤ 1. = P(Φ) = Φ!(n − Φ)! Φ (9)

With mean n

n n! Φ n−Φ s Φ f n−Φ = ns. s f E(Φ) = = Φ Φ − Φ)!Φ! (n Φ=1 Φ=1 n

(10)

And variance σ 2 = ns[(n − 1)s + 1 − ns] = ns(1 − s) = ns f.

(11)

After the SUs have computed their binary decisions, they share observations as depicted in Fig. 1. After sensing the spectrum, the SUs report their spectrum observations to their neighbouring nodes. Unfortunately, the malicious nodes MU 1 and MU 2 interfere with the normal operations of the network for either greedy reasons or to monopolise the band or to mislead the SUs and cause DoS or interference. The goal of the SSDF attack can be modelled as follows: ∀iwhere i ∈ {neighbour}, report = 1 when actualobservation = 0 ∃iwhere i ∈ {neighbour}, report = 0 when actual_observation = 1

(12)

Reputation-based system The reputation-based system considered the trustworthiness of the SUs by evaluating the history of their reports. SUs with reputation values that were above the TV of 0.6 were considered as malicious and their reports were isolated otherwise they are included in the final decision. When the SUs with TVs below 0.6 are selected, the q-out-of-m is implemented. In the q-out-of-m rule, 60% of the nodes with good

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Fig. 1 Cooperative spectrum sensing [22]

reputation are selected and the final decision is informed by q, which is either 0 or 1. The reputation-based system and the q-out-of-m rule are shown in algorithms 1 and 2, respectively. Algorithm 1 [30]

where m is the assessor node which performs the data fusion. The variable i denotes the neighbour SU and di(t) is the status of the spectrum band. Si(t) is the value of the neighbour report, and gm(t) is the final decision at device m. and r mi is the current reputation of the device i at device m. Algorithm 2 [31]

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The q-out-of-m rule randomly polls 60% of the nodes with good reputation to be considered in decision-making. If majority report 1, then the final transmission decision is that the band is occupied, otherwise 0, it’s not occupied.

4 Results and Analysis MATLAB R2015a was used to implement and simulate the scheme. The false alarm probability and missed detection probability were set to 0.1 with SNR to 10 dB. Energy detection was used as the sensing technique with a detection TV of 0. Different network scenarios ranging from N = 10, 50,100,150–250 nodes and the percentage of malicious nodes ranged from 10, 20, 40, 50–60% on each network size were considered. The SUs sensed the spectrum band then computed their binary observations and shared their observations. We evaluated our scheme and compared its performance with the MFDSS [25] and the DBS [24] schemes. Figures 2, 3, and 4 depict the reputation-based system results. It shows the nodes IDs in a network with 50 nodes and their reputation values. Figure 3 shows the nodes that were isolated because their reputation values were above the TV value of 0.6. The inputs of these nodes were not considered in the final decision-making. Figure 4 shows the results of the nodes that had reputation values that were below the TV. These nodes were selected and their reputation values were included in the q-out-of-m rule phase. In Fig. 5, the scheme’s success probability was evaluated. We varied the number of nodes from (N = 10 to N = 250) with 10% of the nodes being MUs. We investigated the hit and run (H n R), the always yes (Ay), and the always no attacks (An). We also evaluated the impact of the unintentionally misbehaving nodes (U m). In N = 10,

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Fig. 2 Nodes above threshold value

Fig. 3 Reputation-based system

we had 1 Ay attack. As we increased the nodes to 50, we noticed that the proposed scheme was not affected. In Fig. 6, we had Ay = 1, An = 1, and SSDF attack for N = 10. We observed that there was no effect on the success probability of the schemes because the of H n R attack and MU s where not considered. The proposed scheme performed better because of the q-out-of-m rule scheme implemented which isolated the U m nodes and H n R attack before the final decisions were made.

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Fig. 4 Nodes below threshold value

Fig. 5 Success probability with 10% MUs

Figure 7 present the success probability, in N = 10, we had 4 MU s. We set 1 U M, 1 Ay, 1 An, and 1 H n R attack. The performance of the proposed scheme and MFDSS scheme were slightly affected and both schemes managed to detect 75% of the MUs. Figure 8 exhibited different trends compared to Fig. 7 because of the increase in the number of MU s in Fig. 8. In N = 100, we had 40 MU s. We observed a huge drop in the performance however, the proposed scheme achieved the highest detection accuracy, which is more than 60% compared to the MFDSS and DBS schemes. When the number of hit and run attacks increases, the DBSD scheme’s detection

Spectrum Sensing Data Falsification Attack … Fig. 6 Success probability with 20% Mus

Fig. 7 Success probability with 40% Mus

Fig. 8 Success probability with 50% MUs

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Fig. 9 Success probability with 60% MUs

accuracy reduced drastically. This is because in its current form, the DBSD scheme is not designed to combat the hit and run attack and unintentionally misbehaving SUs. The MFDSS scheme is designed to combat the hit and run attack but is not optimised to combat a large number of attacks. MFDSS scheme was implemented using modified Z-test, which performs better when the byzantine failure rate cannot be estimated. The results show that the Um nodes have an effect on the performance of the MFDSS scheme. We present the success probability of the schemes in detecting the SSDF with MUs = 50% in Fig. 8. In N = 10, MUs = 5, we evaluated the performance of the network under 1 Um, 1 4y, 1 An, and 2 HnR attack in Fig. 8. The proposed scheme managed to detect all the attacks in the network because it is optimised to detect all the types of SSDF attacks. The DBS scheme managed to detect 60% of the MUs in the network while the MFDSS scheme managed to detect only 80% of the MUs in the network. In Fig. 9, the results show that with an increase in the number of SUs and MUs, and where many U m and H n R attacks were considered, the schemes’ success probability was reduced. We assigned 40 MU s in the network in Fig. 9. We noted that the H n R attack and U m nodes were the attacks with the highest negative impact on the network. The H n R attack can contain characteristics of legitimate SU s which reduces the detection probability of the schemes. In Fig. 10, we examined the schemes’ missed detection probabilities in detecting the SSDF attack in the network under different scenarios in each network size. In N = 10, we had only one attack implemented, the Ay attack. We observed that all the schemes were able to detect the Ay attack because the attack probability of the Ay exhibits the attributes of an outlier which can be easily detected by any fusion scheme. All the schemes had low miss detection probabilities in detecting the Ay SSDF attack. Increasing the nodes to 50 with 10% MUs and using the same parameters as in success probability, we set 1 Ayattack, 1 An attack, 2 misbehaving SUs, and 1 HnR attack. Our scheme had the lowest missed detection probability. In N = 100 with 10% of the nodes being malicious where 4 were Um nodes, 2 were the Ay attack, 2 An attack,

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Fig. 10 Missed detection probability with 10% Mus

and 2 HnR attack. Due to the Um nodes, observed an increase in the missed detection probability of the proposed scheme. The number of nodes was increased to N = 150, with a random variation of the attack strategies. The Ay attack was set to 4, the An attack to 4, the HnR attack to 4, and Um nodes to 3. The proposed scheme had positive missed detection probability results. The proposed scheme detected and isolated all the malicious nodes with assistance of the q-out-of-m rule scheme implemented that detects all the MUs and Um nodes in the first fusion phase. The DBSD scheme is susceptible to byzantine attacks and Um nodes. In Fig. 11, the performance of the schemes was investigated under different SSDF attack scenarios. For N = 10, we set two different scenarios, we set 1 Ay attack and 1 An attack. We observed that with the Ay and An attacks, the schemes can detect and isolate them given their estimated attack probabilities. In Fig. 12, we analyse the missed detection probability of the schemes in N = 10, 50, 100, 150 to 250, with MU s = 40. In N = 10, we ha d MU s = 4. We set 1

Fig. 11 Missed detection probability with 20% MUs

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Fig. 12 Missed detection probability with 40% MUs

U m, 1 Ay, 1 An, and 1 H n R attack. In N = 50, we set MU s = 20 where Ay = 5, An = 5, H n R = 5, and U m = 5. The results show that the proposed scheme outperformed the MFDSS and DBS schemes in missed detection probability. In Fig. 13, the number of MUs was the same as the number of SUs, this caused the results to exhibit a different pattern. However, the results show that our scheme performed better than the other schemes and had the lowest missed detection. The DBSD scheme had a missed detection percentage of 40% and the MFDSS had a missed detection percentage of 20%. This was caused by the HnR attack and Um nodes. The MFDSS scheme and DBSD scheme had limitations in detecting the HnR attack and Um nodes due to their design properties discussed in the literature. In a network with N = 50, we had 25 MUs with Um = 5, Ay = 6, An = 8, and HnR = 6. The missed detection probability in N = 100 when MUs were 50% increased. We set Um = 12, Ay = 15, An = 15, and HnR = 8. In N = 150 we set MUs = 75, we Fig. 13 Missed detection probability with 50% MUs

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Fig. 14 Missed detection probability with 60% MUs

randomly set Um = 18, Ay = 25, An = 15, and HnR = 17. With an increase in the number of SUs and MUs where we had a high number of Um and HnR attacks, the schemes’ missed detection probability increased. In Fig. 14, the number of MUs was more than the number of SUs, which caused the missed detection probability to increase. The missed detection probability increased when we increased the H n R attack and the Um nodes. The U m nodes can contain malicious results while having good reputations.

5 Conclusion In this study, we proposed a scheme which integrates the reputation and q-out-of-m rule schemes to address the effects of the SSDF attack. We studied the always yes, always no, and the hit and run attacks. We also investigated the legitimate SUs and the unintentionally misbehaving SUs in order to discriminate them from MUs. The proposed scheme was compared to the MFDSS scheme and the DBS scheme. The results show that the proposed scheme performed better in all the metrics as it had the highest success probability and the lowest missed detection probability. Acknowledgements “This work is based on the research supported in part by the National Research Foundation” of South Africa for the grant, Unique Grant No. “94077”.

References 1. Zhang X, Zhang X, Han L, Ruiqing X (2018) Utilization-oriented spectrum allocation in an underlay cognitive radio network. IEEE Access 6:12905–12912 2. Pang D, Deng Z, Hu G, Chen Y, Xu M (2018) Cost sharing based truthful spectrum auction with collusion-proof. China Commun 2(15):74–87

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23. Ngomane I, Velempini M, Dlamini SV (2018) The detection of the spectrum sensing data falsification attack in cognitive radio Ad Hoc networks. In: 2018 conference on information communications technology and society (ICTAS), Durban, South Africa 24. Chen C, Song M, Xin C (2013) A density based scheme to countermeasure spectrum sensing data falsification attacks in cognitive radio networks. In: 2013 IEEE global communications conference (GLOBECOM). Atlanta, GA, USA 25. Pongaliur K, Xiao L (2014) Multi-fusion based distributed spectrum sensing against data falsification attacks and Byzantine failures in CR MANET. In: 2014 IEEE 22nd international symposium on modelling, analysis & simulation of computer and telecommunication systems. Paris, France 26. Hyder CS, Grebur B, Xiao L, Ellison M (2014) ARC: adaptive reputation-based clustering against spectrum sensing data falsification attacks. IEEE Trans Mob Comput 13(8):1707–1719 27. Wang W, Li H, Sun Y, Han Z (2009) Attack-proof collaborative spectrum sensing in cognitive radio networks. In: 2009 43rd annual conference on information sciences and systems, Baltimore, MD, USA, 2009 28. Yu FR, Tang H, Huang M, Li Z, Mason P (2009) “Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios. In: MILCOM 2009–2009 IEEE military communications conference. Boston, MA, USA 29. Ngomane I, Velempini M, Dlamini SD (2018) Trust-based system to defend against the spectrum sensing data falsification attack in cognitive radio ad hoc network. In: International conference on advances in big data, computing and data communication system, Durban, South Africa 30. Ngomane I, Velempini M, Dlamini SV (2017) Detection and mitigation of the spectrum sensing data falsification attack in cognitive radio ad hoc networks. In: Southern Africa telecommunication networks and applications conference (SATNAC) 2017, Barcelona, Spain 31. Fragkiadakis AG, Tragos EZ, Askoxylakis IG (2013) A survey on security threats and detection techniques in cognitive radio networks. IEEE Commun Surveys Tutorials 15(1):428–445

Lean Manufacturing Tools for Industrial Process: A Literature Review Gustavo Caiza , Alexandra Salazar-Moya , Carlos A. Garcia , and Marcelo V. Garcia

Abstract Any company or industry that wants to go ahead as a competitive company should know, analyze, and project its processes toward a reality of conceptual innovation and technical applications that allow it to delve into a sustained control in the use of its resources. To improve the productivity required to increase profits from production processes, the Japanese Philosophy of Lean Manufacturing is proposed as a strategy to reduce anything that does not add value to processes, i.e., Productive Waste. This article proposes a literary review demonstrating the effectiveness of Lean Manufacturing in different industrial approaches, with different themes, always framed in the Continuous Improvement of the productive industrial environment. Keywords Lean manufacturing · Productive waste · Continuous improvement · Productivity

G. Caiza (B) Universidad Politecnica Salesiana, UPS, 170146 Quito, Ecuador e-mail: [email protected] A. Salazar-Moya · C. A. Garcia · M. V. Garcia Universidad Técnica de Ambato,UTA, 180103 Ambato, Ecuador e-mail: [email protected] C. A. Garcia e-mail: [email protected] M. V. Garcia e-mail: [email protected]; [email protected] M. V. Garcia University of Basque Country, UPV/EHU, 48013 Bilbao, Spain

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_3

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1 Introduction In the Production Management structure, the economic component that a manufacturing company must invest to obtain a final product is transcendental to achieve the operational success of the company [6]. So, developing effective resource schemes for competitive production is a strategy that will allow an industry to achieve consistent development for its present and future plans [3]. Today, in an increasingly competitive market, where work is conceived as a survival strategy, manufacturing companies, as well as other companies, are determined to understand its processes and improve them in a systematically way, through the adoption of management tools and philosophies [14] and, as it will demonstrate, the Lean Manufacturing or Lean Production is one of them [18]. The Toyota Production System is the anteroom subsequently known as Lean Manufacturing, influencing manufacturing techniques around the world and is accepted by countless manufacturers in different disciplines, because, through the application of a series of management techniques, it is oriented to produce only what is necessary at the right time, through the elimination of production waste that cause production costs to rise [21]. Due to it is a tool that supports business productivity and higher revenue management for companies, there is a wide range of research carried out over the years, with topics related to Lean Manufacturing, its theoretical basis, the tools that make it up, and the technological support of which it is currently relying on, to go one step further in the innovation of production processes [10]. The above concludes the need to have a greater applied knowledge of Lean Manufacturing, so the present study aims to establish a bibliographic framework that allows everyone knowing at the theoretical level what is the Lean Manufacturing, the principles that govern it, but above all, the applicability of the different techniques that make up this methodology in different types of industries. This paper has the following sections: Sect. 2 describes the outline that was followed to select the most relative sources of information to sustain the research, Sect. 3 indicating an assessment of the information found, noting the issues that would have been important to find and could not be referenced in the related work, finally Sect. 4 points out the conclusions that had been reached after the conduct of this investigation, in addition to a proposal for further research work based on what the present has ceased to learn and unmet expectations.

2 Research Methodology To select the sources of information that are part of this analysis, I used a previous conceptual base knowledge of Lean Manufacturing. Based on this knowledge, the search is made in specialized digital books loaded in different repositories, the selection of these books has not been limited by their publication date due to the theory

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Search and Selection Scheme.png

Item Search and Selection Scheme.png

(a) Book Search

(b) Scientific Item Search

(c) Databases consulted Fig. 1 Research methodology

was born in the early years of the twentieth century and in essence has not undergone changes over the years, although if it has been reached by the modernization of companies, technological advances and evolution of knowledge management. Besides, this search was both in the English language, which is considered the universal language and in which a greater range of technical information was found, as in the Spanish language, in which information can be obtained closer to Latin American reality. To select the books, the authors known (for their impact or development on the subject) were prioritized and then moved on to the content according to the theme raised in this study. To contrast the theoretical framework of the Lean Manufacturing, information was sought in scientific articles, which in addition to containing the conceptual basis already identified, are a good reference of experiments, applications, and research conducted to implement this philosophy in different companies, of different types, of different scopes and obviously, with different results. The scientific articles were searched in a first instance with an age limit of 5 years, and with this age, the largest number of articles have been used, however, the search was expanded years ago to focus more content. The search was also done with priority in English and later in Spanish. The search schema is illustrated in Fig. 1a, b. As a database for the search for scientific articles, IEEE Explorer was selected in the first instance because it has been validated that it is the one which has the most related information to the research topic, with 50% of the total referenced articles, the remaining 50% comes from other bases such as Google Scholar, Springer Link,

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Scientific.Net, Dialnet, Mdpi, and Semantic Scholar (see Fig. 1c), where articles published in scientific journals and conferences proceedings developed around the world were consulted, using for the respective searches the keywords: Lean Manufacturing, Productive Waste, Continuous Improvement, Productivity.

3 Literature Review 3.1 Lean Manufacturing Lean Manufacturing is a philosophy, and that is well known, so in a first definition, it is said that is a philosophy that talks of doing a hundred of small improvements daily, instead of making a single “boom” once a year, with a focus on improvement focused on detail [20]. This philosophy promotes the scope of objectives and the acquisition of a particular culture of savings, based on quality control and the application of strategies, tactics, and skills [19], so it is understood that not only material resources are spoken of, but also knowledge, communication and information flows, according to the analysis carried out by Gleeson, Goodman et al. in their study [7], where they observed the performance of the staff and their involvement in projects and developments concerning Lean Manufacturing, rescuing the value of the cognitive burden in the fruit of labor actions. Lean Manufacturing is also a model of organization and management of manufacturing systems and the resources implicit within such management: human resource, material resource, machinery resource and the method by which processes are executed to improve the quality of the products and the efficiency achieved through the constant identification and reduction of productive waste [12]. By reducing the waste in value-adding activities, the lead time is reduced, which should not be greater than 10 times the time you add value to the product [15]. In the study [16] carried out by A. Sayid and Nar-E-Alam, Lean Manufacturing is applied in the production processes, validating that a reduction in delivery time is achieved, contrasting the initial reality of the plant with the final reality after the project. Lean Manufacturing is not just a concept, it is also important to know the objective that is pursued with its implementation, and this is, to generate a Culture of Continuous Improvement, based on communication and teamwork, adapting the method to each particular case, intending to find new ways of doing things more agilely, flexibly, economically [8] and in all kinds of industries; as demonstrated by Liu and Yang and Prunhetgrueng in their investigations about the effective application of Lean Manufacturing tools in footwear production [11, 13], also B. Arevalo—Barrera, F. Parreno—Marcos et al. in the block building industry [2], as well as Juárez López, Pérez López et al. in the manufacture of railway transport [9] and Durakovic with Demir et al. in Lean Manufacturing applicability study in a lot of types of industries [5].

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Lean Manufacturing application is feasible throughout the supply chain, so, it is not only applicable just in productive part, this is fully proven by Theagarajan and Manojar [17], who developed research on the improvement of the Supply Chain in a leather footwear company using Lean Management practices, concluding that the performance of the system is improved throughout the supply chain, rescuing the application in terms of the culture in which the industry is involved, since one might think that at birth in Eastern culture, it should go inside into the Japanese culture to make the Lean Manufacturing work because the really important thing is to change the views or the mood with which processes are managed in a company, as Chiarini, Baccarani et al. in their study, in which they compare the Toyota Production System, the philosophy of Lean Manufacturing and Zen Philosophy derived from Japanese Zen Buddhism [4].

3.2 The Lean Manufacturing Implementation Route For the implementation of Lean Manufacturing, it is essential to know that there is no universal method, but this development will be affected by the nature of the industry and its production processes. However, in the first instance, you must run the techniques and tools that allow you to modify the ways of working. It is also important to know that it is preferable to start with an area or pilot process and then extend the lessons learned and hits to more processes. It is also essential the commitment of senior management for the implementation of Continuous Improvement due to there is necessary the opening of minds of workers and sowing a philosophy in them, in addition to the need to make investments in both training and structural modifications [8] (Table 1). It is suggested to start by identifying the waste (matrix for analysis) and the relationship between them, to assess the impact of the waste on the productivity and select the most important ones and reach the root cause of the waste (fishbone analysis) to determine the actions to be taken to reduce waste or, in the best case, eliminate it; in the study conducted by E. Amrina and R. Andryan, two specific types of matrices have been used for the identification of residues and prioritization from a study through the use of statistical quality analysis tools [1].

3.3 Discussion and Analysis of Results When reviewing the literature loaded in e-book format, 22 books were investigated in total, referred to 10, which are considered to have the best information for the purpose pursued in the present work, in these books, over time the approach that the Toyota Production System conveyed into the philosophy of the Lean Manufacturing, the techniques, and tools that this philosophy was shown, in addition to presenting

Waiting

Overproduction

Excessive storage space, Containers or boxes too large, Low stock rotation, High storage costs, Excessive means of handling (forklifts, etc.)

Inventory

Possible causes

Processes with low capacity, Unidentified or out-of-control bottlenecks, Excessively long machine change or setup times, Misper-production forecasts, Overproduction, Rework for product quality defects, Hidden problems and inefficiencies Excessive storage space, Containers or Processes with low capacity, Unidentified boxes too large, Low stock rotation, High or out-of-control bottlenecks, storage costs, Excessive means of Excessively long machine change or handling (forklifts, etc.) setup times, Misper-production forecasts, Overproduction, Rework for product quality defects, Hidden problems and inefficiencies The operator waits for the machine to Non-standardized working methods, Poor finish, Excess of material queues within layout due to accumulation or dispersion the process, Unplanned stops, Time to of processes, Capacity imbalances, Lack perform other indirect tasks, Time to of appropriate machinery, Delayed execute reprocessing, The machine waits operations by the omission of materials for the operator to finish a pending task, or parts, Production in large batches, An operator waits for another operator Low coordination between operators, High machine preparation times or tool changes

Feature

Type of waste

Table 1 Actions against waste in production

(continued)

Balanced production; line balancing, Product-specific layout; Manufacturing in cells in u, Automatization with a human touch (Jidoka), Rapid change of techniques and tools (SMED), Multipurpose training of operators, Supplier delivery system, Improve maintenance according to assembly sequences

Balanced production, Product distribution in a specific section; cell manufacturing, Jit supplier deliveries, Intermediate task monitoring, Changing the mindset in organization and production management

Balanced production, Product distribution in a specific section; cell manufacturing, Jit supplier deliveries, Intermediate task monitoring, Changing the mindset in organization and production management

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Defective, rejected products and reworks

Containers are too large, or heavy and difficult to handle, Excessive movement operations and material handling, Maintenance equipment circulates empty through the plant

Transport /unnecessary movements

Possible causes

Obsolete layout, A large lot of batches, Poor and inflexible processes, Non-uniform production programs, High preparation times, Excessive intermediate warehouses, Low efficiency of operators and machines, Frequent reprocesses Waste of time, material resources and Unnecessary movements, Suppliers or money, Inconsistent planning, processes not able, Operator errors, Questionable quality, Complex process Inadequate operator training or flow, Additional human resources needed experience, Inappropriate techniques or for inspection and reprocessing, Extra tools, Poor or poorly designed production space and techniques for reprocessing, process Unreliable machinery, Low motivation of operators

Feature

Type of waste

Table 1 (continued) Flexible manufacturing cell-based equipment layout, Gradual switch to flow production according to set cycle time, Multipurpose or multifunctional workers, Reordering and readjustment of the layout to facilitate employee movements Autonomatization with a human touch (Jidoka), Standardization of operations, Implementation of warning elements or alarm signals (andon), Anti-error mechanisms or systems (Poka-Yoke), Increased reliability of machines, Implementation preventive maintenance, Quality assurance in position, Production in a continuous flow to eliminate manipulations of workpieces, Visual control: Kanban, 5S, and andon

Lean actions against waste

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different ways of applying the Lean Manufacturing to continue with the conceptualization and application of Continuous Improvement. However, among the contents that would be needed to be found in specialized books would be the differences in the application schemes of the methodology in different industries and eventually the difference that exists in the implementation between a manufacturing company and a service company. That is, it would be important to find a compendium of lived experiences and lessons learned, highlighting the successes and errors that have arisen, to this book may be an example and guide of application. Concerning the scientific articles, 58 research documents were selected at first instance (once duplicates have been removed), the first review was according to what is described in the title and in the abstract, after a reading of the complete document in which the tests that have been carried out, the universe analyzed and the explicit of the conclusions are valued, finally 34 articles are referenced, which, when analyzing its contents, allow to counteract theoretical information with the practical application in different management environments. The authors have not agreed on the number of Tools of Lean Manufacturing, their classification, and possible use schemes, more than 30 tools have been identified to implement this methodology, but in the research that was reviewed cover at most 10 of them, with special emphasis on Value Stream Mapping (VSM) as a tool for current state analysis and projection of a future state. Likewise, the Toyota Production System, in representing these tools in House scheme, proposes that they should be applied structurally from its foundations, but, no material has been identified that speaks of the implementation of the methodology under this scheme of "construction", that is, passing through all levels: Foundations, heart, pillars and finally the roof that is the achievement of a balanced, thin or Lean company.

4 Conclusions This work has demonstrated conceptually the value that Lean Manufacturing has in business and industrial management since as has been stated, it focuses on the disposal of productive waste, which leads to a reduction in product costs and ultimately a greater prospect of profit in the industry. Lean Manufacturing consists of different timely and schematic application tools in different work environments, then, the industries will depend on their current reality, their business approach, and their strategic planning to select which of these tools will be most useful to achieve the objectives that have been set when choosing this philosophy as a strategy to improve their productivity. The realization of this work has allowed to define the theme for future research, in which a first work is proposed on the techniques of measuring the increase in productivity and the decrease in costs in finished products, and a second work on the use of technological applications in the different tools of Lean Manufacturing.

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References 1. Amrina E, Andryan R (2019) Assessing wastes in rubber production using lean manufacturing: a case study. In: 2019 IEEE 6th international conference on industrial engineering and applications (ICIEA), no I, pp 328–332. IEEE 2. Arevalo-Barrera B, Parreno-Marcos FE, Quiroz-Flores JC, Alvarez-Merino JC (2019) Waste reduction using lean manufacturing tools: a case in the manufacturing of bricks. In: 2019 IEEE international conference on industrial engineering and engineering management (IEEM), pp 1285–1289. IEEE. 10.1109/IEEM44572.2019.8978508 3. Boginsky AI, Chursin AA (2019) Optimizing product cost. Russ Eng Res 39(11):940–943 4. Chiarini A, Baccarani C, Mascherpa V (2018) Lean production, Toyota production system and Kaizen philosophy. TQM J 30(4):425–438 5. Durakovic B, Demir R, Abat K, Emek C (2018) Lean manufacturing: trends and implementation issues. Period Eng Nat Sci (PEN) 6(1):130 6. Fawcett SE, Smith SR, Bixby Cooper M (1997): Strategic intent, measurement capability, and operational success: making the connection. Int J Phys Distrib Logist Manage 27(7):410–421 7. Gleeson F, Goodman L, Hargaden V, Coughlan P (2017) Improving worker productivity in advanced manufacturing environments. In: 2017 International conference on engineering, technology and innovation (ICE/ITMC), vol 2018, pp 297–304. IEEE 8. Hernández Matías JC, Vizán Idoipe A (2013) Lean manufacturing, Conceptos. Técnicas e Implantación. Fundación EOI, Madrid 9. Juárez López Y, Pérez Rojas A, Rojas Ramírez J (2012) Diagnóstico de Procesos Previos a la Aplicación de la Manufactura Esbelta. Nexo Revista Científica 25(1):09–17 10. Khalaf Albzeirat M (2018) Literature review: lean manufacturing assessment during the time period (2008–2017). Int J Eng Manage 2(2):29 11. Liu Q, Yang H (2017) Lean implementation through value stream mapping: a case study of a footwear manufacturer. In: 2017 29th Chinese control and decision conference (CCDC), pp 3390–3395. IEEE 12. Madariaga Neto F (2020) Lean manufacturing: Exposición Adaptada a la Fabricación Repetitiva de Familias de Productos Mediante Procesos Discretos. Bubok Publishing 13. Phetrungrueng P (2018) Waste reduction in the shoe sewing factory. In: 2018 5th international conference on industrial engineering and applications (ICIEA), pp 340–344. IEEE 14. Prashar A (2016) A conceptual hybrid framework for industrial process improvement: integrating Taguchi methods, Shainin System and Six Sigma. Product Plan Control 27(16):1389–1404 15. Santos J, Wysk R, Torres JM (2006) Improving production with lean thinking. Wiley, Hoboken 16. Sayid Mia A, Nur-E-Alam (2017) Footwear industry in Bangladesh: reduction of lead time by using lean tools. J Environ Sci Comput Sci Eng Technol 6(3). http://jecet.org/download_ frontend.php?id=192&table=SectionC:EngineeringScience 17. Theagarajan SS, Manohar HL (2015) Lean management practices to improve supply chain performance of leather footwear industry. In: 2015 international conference on industrial engineering and operations management (IEOM), pp 1–5. IEEE 18. Ur Rehman A, Usmani YS, Umer U, Alkahtani M (2020) Lean approach to enhance manufacturing productivity: a case study of Saudi Arabian factory. Arab J Sci Eng 45(3):2263–2280 19. Wilson L (2010) How to implement lean manufacturing. Mc Graw Hill Companies, New York 20. Womak JP, Jones DT (2003) Lean thinking, banish waste and create wealth in your Corporation. Simon & Schuster Inc., New York 21. Wong YC, Wong KY, Ali A (2009) Key practice areas of lean manufacturing. In: 2009 international association of computer science and information technology, Spring conference, pp 267–271. IEEE

Lambda Computatrix (LC)—Towards a Computational Enhanced Understanding of Production and Management Bernhard Heiden, Bianca Tonino-Heiden, Volodymyr Alieksieiev, Erich Hartlieb, and Denise Foro-Szasz

Abstract This paper describes why and how the Artificial Intelligence (AI) discipline will affect decisions and what is the difference to decisions in the past, focusing on the application to industrial production. From this analysis with a global economic systemic towards universal model, there will be given a globally emerging company structures outlook, concerning their decision situation, and way how to decide in future rightly. For this purpose, universal logical tools, that implement the lambda calculus for quantification logic may be useful. We define these as Lambda Computatrix (LC). Examples are Theorem Provers (TP) like Isabelle or Lambda-Prolog. They help to decide precisely to reach company, societal and environmental goals. The key issue of the LC is the universal approach of computation, which is connecting graph-theoretically potentially every node with each other one by a communication edge. LC hence enables increasingly intelligent communication, informationally and materially in terms of logistics and production processes. Keywords Lambda computatrix · Artificial intelligence · Industrial production and management · Theorem prover

B. Heiden · E. Hartlieb · D. Foro-Szasz Carinthia University of Applied Sciences, 9524 Villach, Austria B. Heiden (B) · B. Tonino-Heiden University of Graz, 8010 Graz, Austria e-mail: [email protected] URL: http://www.cuas.at V. Alieksieiev National Technical University ‘Kharkiv Polytechnic Institute’, 61002 Kharkiv, Ukraine

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_4

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1 Introduction 1.1 How Artificial Intelligence (AI) Can Support and Regain Human Society Values “First they sleep, then they creep, afterwards they leap.” In the first year of planting something, nature sleeps, in the second year, it creeps and does really hard to start and maintain and from the third year on it leaps and makes its progress to establish a big plant and tree, so a gardener’s saying states. When people want to establish an institution, this is far more the case. So, how can AI provide for starting and maintaining ongoing management and processes? (1) First of all, AI-devices can systematically read all information, that is available on earth today, about the past and the presence. (2) Secondly, AI may find relations between connected relations as a human with intuition would, for instance in a morphic resonance [1, 2] and the state of the “ether” in the magnetic field (Einstein in [3, 4]). The Internet of the united universe web (uuw), as a projection for possible future “universe wide” technologies and cultures. And (3) thirdly, AI gives an output of what “she” has found and thinks can be a fruitful next step, this is the predictive part. An example for the above categorial analysis could be the following: One entity (e.g. country, region, organization, etc.) wants to build a new university with the aim of making scientific research on most needed subjects (cf. [5]). Another entity thinks about strengthening their inter- and transdisciplinary research. A third entity is experienced in making lectures online available for the whole world (cf. [6]). AI would bring all this information according to (1), (2) together and give various outputs (3), how to handle, manage and improve all these institutions. As a short summary in Table 1, a universal informational applicable processing task is shown, in accordance with the above said. This can then also be regarded as basic cybernetic cycle in the close to human context. With regard to the theory U of Otto Scharmer et al. [7, 8] (1) corresponds to the “seeing” process, (2) to the meditation point, the point of maximum relatedness with the world—which he calls “presencing” and (3) corresponds to the emerging paradigm, which he calls “prototyping”.

Table 1 Categorial cybernetic steps in information processing from humans as well as machines (1) Read Data (2) (3)

Correlate Project

Correlation Extrapolation

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1.2 Production and Management Production and management decisions are nowadays necessarily increasingly fast, because of faster product and innovation cycles, and hence their consequences are far reaching in terms of societal impact as well as of economic one. For this reason these decisions have to be “proved” more diligently, always assuming that the company behind those decisions is governing their decisions according to economic market requirements, which includes a lot of other categories of decisions, and which makes those decisions hence multicriterial or multivariate. Example in an automated production, for mass production, the decisions have to be done fast and at the same time rightly with regard to the current best available knowledge in the production task.

1.3 Theorem Proving (TP) TP and with it the higher order logic goes back to Frege’s “Begriffsschrift” in 1879 (cf. [9]). Later, in 1940, Alonzo Church formalized the now valid form as the Simple Type Theory (STT), which is now the base for a wide range of modern TPs [9, 10]. There are nowadays existing TPs that are an implementation of formal logic on the computer [11]. According to [11, p. 11] this logic can be divided into three categories: (1) propositional logic (fully automated), (2) first-order logic (partly automated), (3) Higher Order Logic (HOL) (interactively automated). Applications of TP are manyfolded. They can be used in technical applications to make a reliability analysis of systems, which are too difficult to calculate and are system critical [12]. TPs are, because they are open with regard to solutions, preferable to be used dialogically. This implies human interaction. But another way has recently been opened up to usage: Machine learning combined with TPs. These can then be further automated tremendously [13]. This can be seen as an AI-self application strategy.

1.4 Content and Organization of This Paper In this work we will first give in Sect. 1.5 the goals of this work. In Sect. 2 we describe our understanding of the Lambda Computatrix (LC). In Sect. 3 an overview, what are characteristic tasks in management and what are some methods typically used out of a wide manifold is given. In Sect. 4 we sketch what is TP with regard to applications and in Sect. 5 we look at TP, production and management. We conclude how TP can be applied to the given characteristic tasks and will summarize important findings and finally give an outlook for future directions of TP applications in production and management.

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1.5 Goal The goal of this work is to answer the following three questions: (1) What is TP especially in the context of production and management? (2) How can the management actively use TP in production processes? (3) Why is TP increasingly important for future production and management?

2 Lambda Computatrix (LC) The development of programming languages began as early as the 19th century, but significant progress was not made until after the rise of computation in the fifties (>1940, 1950) [14]. Thus, about a thousand programming languages have been developed since the development of the first computer. Konrad Zuse developed the very first programming language between 1942 and 1945 and called it “Plankalkül”. Over the last 80 years, “Plankalkül” has given rise to many different forms and variations of programming languages [15]. One of these languages is Prolog, which is related to this work. Developed in the early 1970s by Alain Colmerauer, Prolog is a combination of the terms “Programming” and “Logic” and thus stands for a programming language that is based on formal logic [16]. Prolog is a declarative programming language. In addition to this, it is also divided into procedural (or imperative) programming languages. The difference lies in the approach. In imperative programming, a desired result is achieved by telling the computer exactly which sequence it has to follow. In declarative programming you define what you want to achieve and let the computer find out the “optimal way” to achieve it [17]. We see, that this is a “search” process, which can in general be regarded as a key and core feature of AI, and as an optimisation task. The optimisation is done by the logic of reasoning in natural language. Logic can be regarded itself as an algorithm related closely to humans and their language. Hence, it must “logically” inherit a natural process governed by its evolutionary conditions. λ-Prolog is further more a logical programming language, that extends Prolog by terms of higher functions, λ terms, higher order associations, polymorphic types and mechanisms for creating modules and secure abstract data types. By introducing strong type binding, which is missing in Prolog, it is possible, for example, to distinguish between numbers and sets of numbers or entities and functions. λ-Prolog is mainly used for “meta-programming”, i.e. programmes or programming languages that can be self-manipulated or adapted. Meta-programming is also used for TPs. In summary, λ-Prolog is a language based on the Lambda (λ)—calculus, which can be used to clearly prove what a computable function is. The λ-calculus was used in the 1930s by Alonzo Church and Stephen Cole Kleene to give a negative answer to the problem of decision-making and to find the foundations of a logical system. The λ-calculus has significantly influenced the development of functional (typified) programming languages.

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3 Simulation in Production and Management Methods Simulation programmes show a high benefit when measurements or experiments in reality have limited properties. Example population development could be too slow, autocatalytic behaviour could be too fast or factory planning could be too expensive. With suitable modelling, a simulation can execute even extensive systems and present correlations (compare (2) in Table 1) between individual system variables. This provides a basis for logging, analysing and interpreting of possible processes [18]. In management it is important to decide according to a complex environment. A key decision criterion is to look at cycles, which is important as it systemically can be described with this process how order emerges in self-organization as well as in cybernetic systems [19]. As an example in [20] dynamic backcycling is modelled by Technical Innovations Systems (TIS) cycles, Industry Life-Cycles (ILC) and Technology Life-Cycles (TLC). Markard focusses on the term that a TIS-cycle is coupled to TLC or ILC with regard to the sigmoid growth phases (formative, growth, mature, decline) and has a corresponding structure. So, the management has to consider where TIS is in the development. This can also be understood as a higher order process, as explained in Sect. 5 more explicitly. So, with regard to innovative systems (TIS) it is important to adequately model the TIS, which could be done with TP, as one possibility, to predict (cf. also (3) in Table 1) its behaviour and to make management decisions. Actual management methods in the dawn of Industry 4.0 can be found in [21]. A typical management method is to build innovation cooperation or build on a technology and competence analysis [22]. Other methods are strategic technology management [23] or normative corporate governance [24]. A method to build up new businesses by management is trend anticipating business model innovation [21].

4 Theorem Proving (TP) We sum up here possible TP applications for exemplary application types, related to management and production. The AI-legal and the logistics case.

4.1 AI-Legal According to Benzmüller intelligence can be ordered with regard to five steps [25]: “(1) solve hard problems (2), successfully act in known, unknown and dynamic environment (requires perception, planning, agency, etc.), (3) reason abstractly & rationally, trying to avoid inconsistency and contradiction, (4) reflect upon itself and to adjust its reasoning & behaviour with respect to upper goals and norms, and (5) interact socially with others entities and to align own values and norms with those of a larger society for a greater good.”

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Steps (1, 2) are in his opinion the actual state of affairs in the world. With (3) TPs come into play. By means of this, e.g. number theoretic proofs can be given that otherwise could not be accomplished. In this case mathematics and number theory is the application. But there are far more interdisciplinary applications possible, like in law, production, and management. When we look at the internationalizing law, e.g. Lurger 1999 with regard to unification of European contract law, she states that the universal principle is ’contractual solidarity’ (’Vertragliche Solidarität’) [26, p. 155], [27, p. 17,19,76], which can be regarded in good accordance to number (5) of Benzmüller. We come to the same conclusion when we look at this from an evolutionary point of view, as done in Eccles [28], stating that the human language leads to increasing cooperative or social behaviour, as the aggression centre in human evolution is decreasing in growth rate relatively compared to social resp. language brain parts in humans. The natural language can be understood in this context as the precursor of TP, so TP is the other way round the logical extension of natural language for humans, a suitable automation application. This means that it is one possible way of extending the mind by automation support of TP (cf. also Benzmüller above (3)–(5)). By means of this, not only an otherwise unreachable form of social interaction becomes reality, but also their consistency and logical adequacy. Factual social relations become then true on the base of mind-extending ever complex reasoning. For this human and machine read- and controllability is the key driving force. This will then lead not only to increasingly reasonably laws, but also to larger possible communities, uniting them in complex and differentiated form.

4.2 Logistics Theorem provers in logistics are, e.g. investigated in [29] for Logistics Service Supply Chains (LSCCs) and their base elements like serial and parallel process connections. These processes are modelled in HOL, showing that LSCCs and elementary processes like serial/parallel can be translated into HOL, and hence modelled in this way for logistics, production processes and processes in general. Concerning Logistic Management (LM) & TP in the modern world, the logistic industry has a key role. With increasing complexity of production, retail and market systems as well as with the tendency of modern market to switching over the online sales (especially in time of pandemic), the complexity of logistic systems and supply chains are enlarged. With it, the decision-making process (DMP) is becoming more complicated, has to be executed in faster time and with minimal outlays. The formalization of management decisions in logistics using TP may not only decrease the time for DMP, but also reduce the human mistakes and enable to predict the future logistic systems’ developments. In this section we will try to answer the following questions: (1) what are the main managerial tasks and challenges in logistics; (2) how can these tasks and challenges be solved or optimized and what is the connection to using TP; (3) what is necessary for implementation of TP methods in logistic DMP.

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(1) The successful LM is based on reliable communication, integration and coordination throughout the whole supply chain as well as between customers. In the supply chain, e.g. the decision in one element directly affects all other elements, so that these properties are extremely relevant for managing the system as single entity [30]. According to [30, p. 6] the key issues of LM can be, e.g. emphasized: logistics integration, material handling and order picking, transportation and vehicle routing, inventory and warehouse management, etc. In addition to this, according to [31], three strategic level LM-decisions can be distinguished: “(1) Make-to-Order or Make-to-Stock; (2) Push or Pull inventory deployment logic; (3) inventory centralization or decentralization” and we supplement “(4) Make or Buy” as another strategic LM-decision from the organizational point of view. (2) TP can, e.g. be used to check the reliability of the supply chain. First we look at two methods now used in the management of logistics and then to the proposed TP method: (a) In [29] a method consisting of block diagrams, focussing on parallel and series-parallel configurations, or reliability block diagrams, is used for the formal analysis of the logistic service supply chain. (b) In addition to this, the formalization can be used to solve the transportation and vehicle routing problems. Using and proving the algorithms (see [32], Fulkerson Theorem [33, 34]) such problems help to make an, e.g. time-optimal decision fast and with minimal risks. Now to use TP, these methods have to be formalized in form of (c) set theory, formal logic, logical and informational equations [19] or even in natural language to describe the problem of interest in the LM. So, we have to translate (a-b) into a logical form that can be used by TP. (3) To use TP methods in LM there has to be done an automation of the translation processes in both directions. From the language description of LM into, e.g. methods (a-b) and to TP (c) and back. The bidirectional process then generates the higher order according to orgiton theory and leads to an open process through human-machine interaction (cf. [35–37]). An application, how this can be accomplished, is given in the recent work of Benzmüller [38].

5 TP in Production and Management, Summary, Conclusions and Outlook One of the reasons why it is important for future production and management processes to deal with multicriteria decisions intelligently is that interconnected systems are very sensitive to correction solutions, as there arise intrinsic exponential or growth functions, as those are the drivers for economic and efficiency benefit. To deal decisions rightly in such an environment, all participants have to be very careful, as each member can trigger positive or negative exponential caused effects. This means, managers have to be as intelligent as possible, and this will then only be possible by appropriate tools like TP in this case, for logical correct and hence company reasonable multicriteria decisions. In general, this is an important application field of eXplainable AI (XAI) [39].

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An important factor for production and management decisions in a company is that there are increasingly complex environments. In these complex environment decisions of higher order or complexity have to take place. Order here can be understood as dynamic process, which is subject to self-organizational processes of a complex unity or organisation (company, institution, etc.), far away from the thermodynamic equilibrium state. Order in this sense can also be explained, by back-coupling processes, in which case the number of feedback loops of some kind is referred to as the degree of (potential) order measure. Completely defined “order” needs in addition an ethical decision about what is “good” (compare also Benzmüller in Sect. 4 (4), (5)). The higher order is then here constituted by “intelligent” solutions of the staff, which means that each staff member has to be informed about processes and be trained to correctly interpret these. This is usually done by means of education. In the context of complex environment a self-guided answer is essential. For this reason, tools for coping with complex decisions, like TPs, simulation tools, etc. have to be used increasingly in successful management and production. As a conclusion, TP or LC can and will be used increasingly. Despite more than 100 years of development we seem to be just in the beginning. Humans will have to use intelligent computer tools to improve their rational communication and their sociality, which then is also potentially uniting humanity as well as actively peace forming (cf. [27, p. 80]). For future generations it will be important to use such tools, as if they were natively given, to be a rational and hence justice oriented society. Finally, TP allows for reasoning in the same way, and in conjunction with reasonable humans or how reasonable humans do with quantified logic. This sort of logic has been shown for some cases so powerful, that it would not even be possible, in the up to now elapsed time of the universe, to perform the same task with a more simple logic [9]. The unifying feature of LC, to be capable of more simple logical forms and to be open to even other forms, allows for universalisation and unification, that is necessary for an increasingly connected and interactive world of human and machine of the human-o, the human—machine—product triangle [35], that is to be managed adequately on the edge of current time.

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B. Heiden et al. Universität Berlin. https://mycampus.imp.fu-berlin.de/portal/site/4e732ee4-5f15-4065-8bb1471dafd573e4 Lurger B (1999) Grundfragen der Vereinheitlichung des Vertragsrechts in der Europäischen Union. In: Martiny D, Witzleb N (eds) Auf dem Wege zu einem Europäischen Zivilge- setzbuch. Schriftenreihe der Juristischen Fakultät der Europa-Universität Viadrina Frankfurt (Oder). Springer, Berlin, pp 141–167. https://doi.org/10.1007/978-3-642-60141-5_9 Tonino-Heiden B (2020) Vertragsinterpretation nach dem Ubereinkommen der Vereinten Nationen über Verträge über den Internationalen Warenkauf. Unpublished Work Eccles JC (1989) Evolution of the brain creation of the self. Routledge, London. XV, 282 S. ISBN: 0-415-02600-8 Ahmed W, Hasan O, Tahar S (2016) Towards formal reliability analysis of logistics service supply chains using theorem proving. In: Konev B, Schulz S, Simon L (eds), IWIL—2015, 11th international workshop on the implementation of logics, vol 40. EPiC series in computing. EasyChair, pp 1–14. https://doi.org/10.29007/6l77 Louren HR (2005) Logistics management—an opportunity for metaheuristics. In: Rego C, Alidaee B (eds) Metaheuristic optimization via memory and evolution: Tabu search and scatter search. Springer, pp 329–356. https://doi.org/10.1007/0-387-23667-8_15 Wanke P (2004) Strategic logistics decision making. Int J Phys Distribut Logist Manage 34(6):466–478 Dasgupta S, Papadimitriou C, Vazirani U (2008) Algorithms. The McGraw-Hill Companies, Boston. ISBN: 9870073523408 Dantzig G, Fulkerson D (1955) On the max flow min cut theorem of networks. In: RAND corporation (P-826), pp. 1–13. http://www.dtic.mil/dtic/tr/fulltext/u2/605014.pdf Heiden B et al (2020) Framing artificial intelligence (AI) additive manufacturing (AM) (unpublished) Heiden B, Alieksieiev V, Tonino-Heiden B (2020) Communication in Human—Machine— Product Triangle—Universal properties of the automation chain (unpublished) Heiden B et al (2019) Orgiton theory (unpublished) Heiden B, Tonino-Heiden B (2020) Philosophical studies—special Orgiton theory/Philosophische Untersuchungen - Spezielle Orgitontheorie (En- glish and German Edition) (unpublished) Benzmüller C, Parent X, van der Torre L (2020) Designing normative theories for ethical and legal reasoning: LogiKEy framework, methodology, and tool support. In: Artificial intelligence 287. https://doi.org/10.1016/j.artint.2020.103348 Gunning D (2017) Explainable artificial intelligence (XAI). In: Defense advanced research projects agency (DARPA), nd Web 2

Behavioral Analysis of Wireless Channel Under Small-Scale Fading Mridula Korde, Jagdish Kene, and Minal Ghute

Abstract The demand for wireless communication has grown in recent years due to increase use of mobile services. The wireless communication channel constitutes the basic physical link established in the transmitter and the receiver. It is the challenging situation to model any wireless channel for radio wave propagation over tough geographical terrain like hilly areas, sea surface, mountains, etc. Channel modeling is one of the most fundamental aspects for study of optimization and design of transmitter and receiver. The operating environment factors like fading, multipath propagation, types of geographical areas limit the performance of wireless communication system. In this paper, wireless channel is modeled by randomly time-variant linear systems. The behavioral analysis of the channel model is performed for both the Rayleigh and Rician fading channels in terms of error probability, and it is shown that in small-scale fading, the Rayleigh fading can be preferred over Rician fading due to high SNR. Keywords Small-scale fading · Wireless channel · Rician fading · Rayleigh fading

1 Introduction Due to statistical nature of mobile radio channel, performance of wireless communication systems can be affected severely. The direct line of sight path is rarely available unlike satellite links. In practical situations, line of sight path is obstructed severely by high buildings, hilly regions and woods, etc. Wired channels are stationary and their M. Korde (B) · J. Kene Shri Ramdeobaba College of Engineering and Management, Nagpur 440013, India e-mail: [email protected] J. Kene e-mail: [email protected] M. Ghute Yeshwantrao Chavan College of Engineering, Nagpur 441110, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_5

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behavior can be predictable, but wireless channels are always random in behavior and become difficult to predict as mobile terminal moves in space. The presence of cellular systems is dominant in urban area compared to rural areas where these types of obstructions giving rise to multipath fading are commonly noticed. Therefore, modeling of the medium, i.e., channel becomes absolutely important especially in area where line of sight is obstructed by sea, hills, etc. To obtain the power profile and characteristics of received signal, the simplest model can be given as follows: Let X = Transmitted signal, Y = Received signal, H = Impulse response of wireless channel, Then, the received signal is related with the transmitted signal by convolution operation which can be stated as follows: Y ( f ) = H ( f )X ( f ) + n( f )

(1)

Here H (f) is impulse response of channel in frequency domain and n (f) is the noise response in frequency domain. The major purpose of channel characterization is in the design and planning of communication systems. Performance of wireless communication channels can be described mainly by phenomena, namely, path loss, shadowing, and multipath fading. An accurate estimate of propagation parameters is an essential thing in the design and planning of wireless communication system. Two types of fading are considered in propagation models: large-scale and small-scale fading. •Large-scale fading: When there is very long distance between transmitter and receiver (more than thousands of meter), there is significant attenuation in the average signal-power attenuation. When there is movement occurring at large areas with moderate speed, path loss is present. The attenuation in signal strength and path loss can be because geographical obstructions between the transmitter and receiver. This phenomenon of steady decrease in power is refereed as large-scale fading [1]. •Small-scale fading: When receiver is moved away from transmitter for a fraction of wavelength, the variations occur in the instant received signal power. These variations can extend up to 30–40 dB. These immediate and fast changes in amplitude and phase of received radio signal for a very short duration which lasts for few seconds and for a very small distance of the order of small wavelength is referred as small-scale fading [1]. The focus of channel modeling in this paper is studied based on the physical phenomena like multipath propagation, type of terrains, and Doppler shift due to motion of the mobile. The performance evaluation of mainly Rayleigh and Rician fading is taken into consideration for tapped delay model of wireless channel.

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The paper is organized in this way. Section 2 describes various factors affecting small-scale fading. Section 3 elaborates the concept of wireless channel model considering fading parameters of Rayleigh and Rician distribution. Section 4 illustrates simulation results and discussions. Conclusion of the paper is elaborated in Sect. 5.

2 Small-Scale Fading Causing Factors Many physical factors are influential for small-scale fading in the radio propagation. Some of them are as follows: Multipath propagation: The signal energy is constantly dissipated by rapidly changing environment caused by reflecting objects and scattering in the channel. The energy of signal attenuates in the factors like amplitude, phase and time which gives rise to multiple version of a transmitted signal. These signals are moved in accordance with time and space when arrived at the receiver. These random fluctuations in phase and amplitude of the various multipath components give rise to small-scale fading. Due to intersymbol interference, the phenomenon of small-scale fading and multiple components reaching to receiver increase rapidly. Speed of mobile: Because of Doppler shift effect on every multipath component, a random frequency modulation occurs in radio signal as the speed of mobile station moves away from base station. The speeds of pedestrian mobile station and fast moving mobile station are relatively different. This creates positive Doppler shift if the mobile receiver makes a movement in the direction of the base station and it creates negative Doppler shift if a mobile makes movement in opposite direction from the base station. Surrounding of mobile: The objects present in the vicinity of radio channel play an important role in inducing Doppler shift in electromagnetic waves. When the speed of surrounding objects become greater rate than the mobile station, then this effect needs to be encountered in behavior of wireless channel as it can dominate the effect of the small-scale fading. Otherwise, surrounding objects motion can be ignored. Transmission bandwidth of signal: Bandwidth of signal significantly affects the strength of received signal over the channel. Greater the bandwidth of transmitted radio signal compared to bandwidth of the multipath channel, there will be distortion in the received signal. But the strength of received signal remains only over a limited area. The received signal strength fades very fast if there is drastic difference in between signal bandwidth of transmitted radio and that of multipath channel [2].

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3 Wireless Channel Model A linear filter which has time varying impulse response can be considered for basic model of wireless channel. The time variations can be existing due to receiver motion in space. The impulse response of the mobile radio channel relates the small-scale changes in received radio signal. Let v is constant ground velocity with which the receiver moves along the ground. If distance d is fixed for modeling channel between transmitter and receiver, then it can be assumed that channel can behave like a linear time invariant system. The multipath components can be significantly present for the various spatial positions of the receiver. Therefore, the impulse response of the linear time invariant channel varies as per the movement and physical distance of receiver. Let x (t) represents the transmitted signal, then the received signal Y (d, t) at a position d can be expressed as ∞ x(τ )h(d, t − τ )dτ

Y (d, t) = x(t) ⊗ h(d, t) =

(2)

−∞

For a casual system, h (d, t) = 0 for t < 0. If v is the velocity, the position of the receiver can be expressed asd = vt. As v is constant, y (vt, t) is a function of t, above equation can be written as ∞ Y (t) = x(t) ⊗ h(vt, t) =

x(τ )h(vt, t − τ )dτ

(3)

−∞

In scenario of slow fading channel, transmitted baseband signal changes at a faster rate than the rate of change of channel impulse response. In this scenario, the nature channel can be considered as static over one or many reciprocal bandwidth intervals. This gives rise to Doppler spread in frequency domain. Noise is an important factor in degradation of channel performance [1]. Large-scale fading is dependent largely on the distance of mobile unit from receiver and relative obstructions in the entire signal path. The occurrence of moderate reflective paths in small-scale fading can be well described by a Rayleigh or a Rician probability density function (PDF). Hence small-scale fading is also called as Rayleigh or Rician fading [3]. When direct dominant path like line of sight path (LOS) is absent between transmitter and receiver, the fading can be considered as Rayleigh fading. Statistical time varying nature of received spectrum can be well demonstrated using Rayleigh distribution in a flat fading signal. The Rayleigh distribution has a probability density function given by

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 2 r −r 0≤r ≤∞ p(r ) = 2 exp σ 2σ 2

(4)

where σ is the rms value of the received voltage signal before envelope detection and σ2 is the time-average power of the received signal before envelope detection, r is the received signal voltage level. The corresponding cumulative distributions function for threshold value of R beyond which probability that the envelope of the received signal should not is given by R P(R) = Pr(r ≤ R) =



−R 2 p(r )dr = 1 − exp 2σ 2

 (5)

0

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  2 ) Ar r −(r 2 +A 2σ 2 for A ≥ 0, r ≥ 0 e I o σ2 σ2

(6)

A is called as peak amplitude of the dominant signal and I0 is the modified Bessel function of first kind and zero order. Parameter K is used to describe the Rician distribution. K is defined as the ratio between the deterministic signal power and variance of the multipath. K (d B) = 10log

A2 dB 2σ 2

(7)

In Rayleigh fading, multiple reflective paths are many as there is absence of dominant line-of-sight (LOS) propagation path. In Rician fading, there is also a dominant LOS path. With K factor of K = ∞, the fading channel gives worst performance. In this case, it can be considered as worst case fading channel similar to Gaussian channel. With K factor of K = 0, the fading channel gives best performance. In this case, it can be considered as best case fading channel similar to Rician channel [4].

4 Results and Discussions Small-scale fading is the primary source, which affects the performance of wireless channel. For the wireless channel model with described above, Rician fading is considered as small-scale fading factors. Figure 1 shows cumulative distributive function of Rician channel by exploiting Eq. 7. The component, 2σ2 (K + 1) is referred as mean squared value of Rician distribution. σ2 is the variance of the component Gaussian noise processes in (1) [5, 6]. Figure 1 shows CDF of Rician fading channel

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on a logarithmic probability scale. Figure 2 shows approximated and analytical probability distributive function for Rayleigh distribution exploiting Eq. 4 for the wireless channel model under consideration. The mean value of r mean of the Rayleigh distribution is given by 0.9 approximated PDF analytical PDF

0.8 0.7 0.6

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∞ rmean = E[r ] =

r p(r )dr = σ

π = 1.2533σ 2

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(8)

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  = E r 2 − E 2 [r ] =

∞ r 2 p(r )dr −

σ 2π = 0.4292σ 2 2

(9)

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The median value of r is 1.177σ . The mean and median differ by only 0.55 dB in a Rayleigh fading signal. Assuming that there is no intersymbol interference (flat fading), the small-scale fading can be considered for simulation. Fading level can also be considered as to remain approximately constant for one signaling interval. In AWGN channel model, the fading amplitudes are relatively different than Rayleigh or Rician distributed random variables. This significantly affects the amplitude of the signal as well as power spectrum of the received signal. The modeling of fading behavior can be done by a Rician or a Rayleigh distribution. In general, for a Tapped Delay (TDL) model, two types of fading, Rayleigh and Rician, were considered for different number of taps. From the BER versus SNR plots it was seen that if the channel is modeled with Rayleigh fading, the SNR performance gives better result. Various parameters were taken into consideration, e.g., the number of taps, the Doppler spectrum of each tap, the Rician factors K, and the power distribution of each tap. Signal to Noise ratio increases significantly in case of Rayleigh distribution (Figs. 3 and 4).

5 Conclusion Small-scale fading impacts the time delay and the dynamic fading range of signal levels within a small-scale local area at a receiver antenna. Multipath fading and motion of mobile create degrading of the performances of wireless system. There are essentially three atmospheric phenomena responsible for the multipath propagation, viz., reflection, refraction, and scattering. The factors responsible for these phenomena are interfering objects in the atmosphere like buildings, walls, sharp edges or corners, small objects like lamp posts, etc. In such cases, a deterministic description does not give sufficient information about the radio channel. The statistical methods can be reliable to obtain the exact behavior of the channel. Rayleigh fading can give reliable approximation in a large number of practical scenarios. But in many practical scenarios it becomes invalid. Less number of strong fading is observed in the Rician model. Also comparatively stronger Line-of-Sight (LOS)

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component can be found in the Rician model. In this paper, comparison of Rayleigh channel and Rician channels has been performed on the basis of BER (Bit error rate). MATLAB simulations are used for performance comparative analysis of Rayleigh and Rician fading channel models in terms of BER analysis. On comparing the two channel models, Rayleigh model is observed to be the more accurate model that can be considered for developing multipath fading channel model.

References 1. 2. 3. 4. 5.

Garg VK (2007) Radio propagation and propagation path-loss models. Wireless Commun Netw Kostov N (2003) Mobile radio channeling in matlab. Radio Eng, vol 2 Rappaport. Wireless communication, 2nd edn, pp 105–212 Goldsmith A Wireless communications. Stanford University Yoo DS Channel characterization and system design in wireless communication. Communication and Signal Processing Laboratory, University of Michigan 6. Sklar B (1993) Rayleigh fading channels in mobile digital communication systems Part 1: characterization. IEEE Communication Magazine

Towards a Framework to Address Enterprise Resource Planning (ERP) Challenges Stephen Kwame Senaya , John Andrew van der Poll , and Marthie Schoeman

Abstract This paper considers some prominent Information System (IS) models and their emphases concerning correct IS development. An Enterprise Resource Planning (ERP) system as a complex IS noted, and on the strength of the emphases observed in IS models and ERP challenges elicited in earlier work, a comprehensive synthesis of the said emphases and challenges is presented. Following such synthesis, a framework to address the said ERP challenges is developed. Key to such framework is aspects of User Experience (UX) and a Formal Methods (FMs) component aimed at addressing some Software Development Life Cycle (SDLC) issues like complexity, hidden information, and traceability often present in a modern ERP. The framework is considered a useful aid for the analysis, design, and development of complex ERPs in the world of ISs. Keywords Business processes · Enterprise Resource Planning (ERP) · ERP framework · Formal Methods (FMs) · Information System (IS) models · SDLC · UX

1 Introduction Enterprise resource planning (ERP) systems integrate data from all functional departments of an organisation into a single unit [1] and facilitate data and information sharing across the various departments and business units [2]. Many organisations utilise ERP systems to optimise business processes for creating a strategic and S. K. Senaya (B) · M. Schoeman School of Computing, University of South Africa, Johannesburg, South Africa e-mail: [email protected] M. Schoeman e-mail: [email protected] J. A. van der Poll Graduate School of Business Leadership (SBL), University of South Africa, Midrand, South Africa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_6

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competitive advantage [3, 4]. ERP failures in business have become predominant over the years and the causes of these system failures may often be attributed to errors in the development life cycle of software, especially at requirement and specification stages. These may be attributed to the lack of a framework or model for ERP systems development [5, 6]. Numerous scholars suggested models to address such failure, yet succeeded only partially [7]. An ERP may be viewed as a large Information System (IS), consequently we investigate in this paper well-known IS models and elicit common emphases and challenges of ERPs. These emphases and challenges are subsequently synthesised to develop and propose a framework for correct ERPs. The use of formal methods (FMs) is suggested as an integral and critical part of correct ERP development in the proposed framework. The layout of the paper is: Following the introduction, we present our research questions in Sect. 2, our research methodology in Sect. 3 and a review of selected ERP models in Sect. 4. Common emphases and challenges in ERP frameworks are synthesised in Sect. 5, together with a brief discussion on ERP challenges from previous work by the researchers. Owing to the potential of formalisms in software development, an ERP framework with FMs as central to critical parts is presented in Sect. 6. Reasons for incorporating FMs in the proposed ERP framework are discussed in Sect. 7. Conclusions and future work are presented in Sect. 8.

2 Research Question (RQs) The RQs addressed in this paper are as follows: 1. 2. 3.

What frameworks for evaluating an ERP as an IS are available? What are the challenges of ERP frameworks in the IS milieu? How may the incorporation of FMs into an IS or ERP framework assist with the development of correct ERPs?

3 Methodology We conducted a comprehensive literature review on prominent frameworks in the IS/ERP space to identify emphases and further synthesise challenges, identified by the researchers from previous work. The literature on IS/ERP frameworks collected for this study was selected based on the relevance and impact in the ERP arena. A total of eight (8) well-known frameworks from the 1980s to 2019 were considered and examined to determine those that are most often used. The emphases elicited are presented next. Emphases The common emphases presented by the eight (8) IS frameworks are

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Data/Information Quality Systems Quality Service Quality Systems Reliability Usability Organisation Benefit

Challenges We likewise investigated nine (9) common challenges around these frameworks, and found these to be [11] • • • • • • • • •

Complexity Human Resources Project Management Business Processes SDLC Alignment Traceability Hidden Information Partial ERP Integration Little or No Reliability

Subsequently, four (4) IS/ERP frameworks with the highest rate of compliance to the common aspects were selected for further analyses. Our review focused on aspects that are mostly ignored by these IS/ERP frameworks since ERPs are usually very large systems covering many diverse aspects (Fig. 3) as presented in this paper. The nine challenges indicated with ERPs frameworks above prevent organisations from reaping the full benefit of ERPs, hence the need for a framework for correct ERPs to mitigate these challenges. Following an inductive research approach, we developed a framework to address the ERP challenges. The use of formality in the framework was identified to address critical areas in ERP development.

4 Review of IS/ERP Frameworks According to Shannon and Weaver’s [8] model of communication, the correctness and success of a system depend on technical; semantic; and effective, or influence factors. The technical factors deal with the accuracy of the message that has been transmitted, the semantic factors consider the precision of the message to convey the desired semantics, and its influence reflects the effect of the message transmitted. Mason [9] adapted the model in [8] to include a behavioural dimension in developing information theory, emphasising how changes in user behaviour could impact on the success of an IS. DeLone and McLean [10] further adapted both the [8, 9] models to produce the DeLone and McLean (D&M) IS Success Model with six distinctive

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Fig. 1 D&M IS success model [10]

aspects/dimensions to measure IS success as depicted in Fig. 1. Aspects of quality with respect to System Quality and Information Quality (indicated in Sect. 3) are critical parts of their framework as indicated in Fig. 1. Also, the Use and User Satisfaction are dependent variables of System Quality and Information Quality from which Individual Impact and Organisational Impact are derived. Over time the DeLone and McLean model for IS success became an important tool for measuring and justifying dependent variables in IS research. DeLone and McLean [10] further claimed the following: • An IS scholar has a broad list of individual dependent variables to choose from, and the number of dependent variables should be reduced for effectively comparing results in IS research. – This finding recognises the complexity of later ERP systems. • There are too little research efforts towards the measurement of IS impact on organisational performance in IS research. – This finding agrees with later work [11] that indicated Project Management, Business Processes, and Human Resources as critical components of an ERP. • The multidimensional nature of IS factors should be treated as such. – Again, cognizance is given to the complexity and multi-dimensionality of later ERP systems. Subsequently DeLone and McLean [12] enhanced their 1992 model in 2003 by making changes to Quality and Service to expand the scope of dimensions in the new model. The Quality dimension was expanded to embody three major aspects instead of two as in the 1992 model. They also expanded the Use component to Intention to Use and Use and replaced Individual Impact and Organisational Impact with Net Benefits and incorporated feedback loops to Intention to Use and User Satisfaction. Their updated model is given in Fig. 2.

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Fig. 2 Updated D&M IS success model [13]

Fig. 3 ERP system components linking functions [14]

A modern ERP as an IS comprises of Human Resources (HR); Inventory; Sales and Marketing; Procurement; Finance and Accounting; Customer Relationship Management (CRM); Engineering/Production; and Supply Chain Management (SCM) [14, 15]. These are incorporated through a common database that connects all units and allows for information sharing and decision-making in the organisation as per Fig. 3. Both back- and front-office processes are linked directly to the central database, allowing for synchronisation of data from all functional units. The Suppliers and Customers are (indirectly) linked to the same database through the Manufacturing Application and Sales and Distribution/Service Applications modules, respectively, offering end-to-end capabilities to the system. Owing to the said integration, any system/function error in any component of the system could affect the central database.

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Fig. 4 E-Learning success model (Source Holsapple and Lee-Post [18])

Returning to the conceptual development of an IS, DeLone and McLean [16] used their 2003 model to assess the success of e-commerce systems with particular focus on buyers and sellers as key stakeholders (users). Such an e-commerce system would fit as a Financial ERP Application in Fig. 3. The DeLone and McLean models have been consistently enhanced to fit the requirements of several ISs or ERPs, e.g. [17]. Holsapple and Lee-Post [18] also modified the Delone and McLean 2003 model to develop an E-Learning Success Model, aimed at measuring the success of e-learning courses from IS perspectives. Their model incorporated three software system development phases namely; System design, System delivery, and System outcome (Fig. 4). They concluded that the ELearning Success model could be employed to measure ISs or ERPs specific to the online learning environment. Yu and Qian [19] researched the success of an electronic health records (EHR) system (which could be an ERP Service application in Fig. 3) through a theoretical model and survey. The relational variables examined in their research are training to self-efficacy; self-efficacy to use; system quality; information quality; service quality; and use to user satisfaction, as well as the use and user satisfaction to net benefits of the EHR system. They concluded the EHR system’s success model and measurement scale are valuable for measuring the use and administration of health ISs. Their EHR systems success model is depicted in Fig. 5. Tilahun and Fritz [20] applied the D&M 2003 IS model to measure the success of an Electronic Medical Record (EMR) system in low resource areas (EMR would typically be a service application in Fig. 3). Following a quantitative research choice (Saunders et al.’s Research Onion [21]), they determined that Service quality is

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Fig. 5 EHR systems success model [19]

the strongest determinant for Use and User satisfaction, while User satisfaction emerged as the most important factor in ensuring the perceived net benefit of an EMR. They recommended that to improve Service quality there should be continuous basic computer literacy training for users (i.e., health professionals). Their construct given in Fig. 6 was based on the updated D&M IS Success Model. Tilahun and Fritz [20] validated numerous interrelationships, viz., the relationships among various quality dimensions and computer literacy; and UX (user experience), leading to the perceived benefit for a user or organisation (Fig. 6). Naturally, net benefit is vital to a company wishing to achieve a competitive advantage through their IS success. Mustafa et al. [3] also investigated organisational critical success factors (CSFs) and their effect on ERP implementation from a user’s point of view, culminating in the framework in Fig. 7. They concluded that most ERP researchers focus on top managers when evaluating ERP critical success factors (CSFs). The views of the main

Fig. 6 EMR constructs by Tilahun and Fritz [20]

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Fig. 7 Critical success factors (CSFs) model [3]

implementers or users of the system are usually not considered due to the exclusion by most IS researchers. Focusing on top managers and excluding implementers or users could adversely affect the resultant system. Mustafa et al.’s [3] CSFs address more detailed aspects, e.g., Project management, Business process reengineering, etc. This agrees with the findings of Senaya et al. [11]. The above literature review provides an answer to our RQ1. Next, we turn our attention to specific ERP emphases in the frameworks and associated challenges.

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5 Evaluating Emphases and Challenges in the ERP Frameworks Four (4) prominent IS/ERP frameworks evaluated by the researchers from previous work are • • • •

E-Learning Success Model [18] Electronic Health Record (EHR) Systems Success Model [19] Electronic Medical Record (EMR) Constructs [20] Critical success factors (CSFs) model [3]

The selection of the above four (4) frameworks was on the strength of some emphases they possess in union with lists of emphases for evaluating ERPs success from literature reviewed by the researchers. The frameworks’ features were likewise considered to determine the rate of failure their possible weaknesses pose to the success of such ERPs. Senaya et al. [11] likewise identified some challenges as causes of ERP failure discussed below.

5.1 Linking ERPs with IS Success Models Business Processes (BPs): One of the major reasons for ERP systems failure stems from a system’s inability to correctly align with the business processes of the organisation. Management of organisations’ business processes depends on how these organisations achieve efficiency in what they do through integrating ERP systems to optimise activities, while aligning business processes with the organisations’ strategies and goals [22]. Yaseen [23], Friedrich et al. [24], and Zerbino et al. [25] all attribute ERP failure to incorrect business processes. The BP misalignment coincides with [3] “Business process reengineering” CSF (Fig. 7). Project Management (PM): Inadequate (software) project management practices have also been cited as causes of ERP failure [11, 26, 27], hence the implicit recognition of adhering to best PM practices in some of the IS models above. While challenges surrounding ERPs have increased progressively during the past decade in different sectors worldwide, there are no appropriate frameworks that deal with project management issues [28]. Consequently, any framework for addressing ERP failure should, therefore, pay attention to PM, specifically software project management (SPM) aspects. Complexity: System complexity remains a challenge in large ISs and, therefore, ERPs also. The above IS models all aim to address complexity, often via quality considerations. Selecting an appropriate ERP for an organisation remains a complex undertaking [27].

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Human Resources (HR): All components of an ERP system may be deemed to be equally important, with (traditional) HR importance being right up there with the rest. In our work, however, we view the HR challenge as the lack of skill sets of the employees in the company, specifically, the software developers. In this regard, part of ERP failure could be attributed to a shortage of individuals with the necessary software development skills, specifically in the use of Formal Method (FMs) to facilitate correct software development. The application of FMs allows for the identification and subsequent discharging of proof obligations arising from formal specifications. There remains, however, a lack of skilled personnel to take up this challenge [29], equally so in the development of correct commercial ICTs which usually embed large ERPs [30]. SDLC Non-alignment: Incorrect or challenged software development practices are arguably the most prominent reason for ERP failure. Vast amounts of literature and practitioner case studies have been devoted to this aspect. Suffice it to note that the complexity (refer to a previous item) of modern IS systems and their underlying ERPs are at the heart of SDLC misalignments [31]. Hidden Information: ERP failure may also arise as a result of hidden information, compromising the reliability of the system [14, 32]. Hidden information is related to traceability discussed below. Traceability: Challenges around traceability strongly correlate with aspects around SDLC processes and hidden information. Incorrect linking of IS components may lead to challenges in the ERP modules [33]. Partial ERP Module Integration: Incorrect integration of legacy systems with a new ERP or running mixed standalone systems with a partial ERP may lead to incompatibilities. Many of the CSFs in Fig. 7, e.g., technological infrastructure link with challenges around partial ERP integration. Reliability: Reliability is classified as a non-functional requirement and owing to the above challenges an ERP may exhibit little or no reliability. Reliability may, therefore, be viewed as an overarching requirement, as well as a consequence of any or all of the above challenges.

5.2 Summary of ERP Challenges Table 1 summarises the ERP challenges encountered in this paper regarding the emphasis placed on the foregoing IS models and previous research work by the researchers. The extent to which each aspect is recognised is tallied. Such information is utilised in the construction of a high-level framework to address ERP failure.

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Table 1 Summary of ERP emphases and challenges recognised ERP frameworks

[18]

[19]

[20]

[3]

Survey [11]

Total

Emphases Usability

X

X

X

X



4

Information quality

X

X

X





3

User satisfaction

X

X

X





3

System reliability

X

X

X

X



4

Sub total

4

4

4

2

0

14

Challenges Complexity



X

X

X

X

4

Human resources

X

X

X

X

X

5

Project management

X

X

X



X

4

Business processes

X

X

X



X

4

SDLC alignment

X

X

X

X

X

5

Traceability

X

X

X

X

X

5

Hidden information

X

X

X

X

X

5

Partial ERP integration

X

X

X

X

X

5

Little or no reliability

X

X

X

X

X

5

Sub total

8

9

9

7

9

42

Grand totals

12

13

13

9

9

56

Source Synthesised by researchers

5.3 Discussion and Analysis From Table 1 we notice all of Usability, Information Quality, User Satisfaction, and Reliability to be important regarding the emphasis placed on these in the IS frameworks considered. Also, Usability and User Satisfaction may be combined into UX (User Experience), in line with HCI classifications. While the classification in Senaya et al. [11] did not consider the Table 1 emphases per se, it did score high (a value of 9) together with two other frameworks with respect to ERP challenges identified. The CSF framework of Tilahun and Fritz [20] has an overall lower score of 7—they omitted Business Processes and Project Management, but both these are covered by the other IS frameworks, as well as the survey. The above discussions provide an answer to our RQ2.

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6 The Proposed Framework From Table 1 resulting from the analyses of the IS models and the Senaya et al. [11] survey, we synthesise the high-level framework in Fig. 8 aimed at addressing the ERP challenges and emphases identified before. In line with the IS models in Sect. 4, our framework embodies four grouped dimensions as indicated by the dotted-line blocks: ERP Challenges: Eight challenge areas as identified in Table 1 have been embedded in the framework with the 9th challenge (ensuring reliability) being an overarching, non-functional requirement (see next discussion). Success Factors: These are aspects aimed at addressing the challenges. They are training, the use of formality in SDLC processes to address complexity (formal specifications), eliminating hidden information (complete ERP integration), and enhanced traceability as elicited in Table 1. Training was identified as an important success factor relating to HR (e.g., training developers in the use of formality to acquire a desired set of software development skills). A success factor for reducing partial ERPs is integration to (amongst other things) improve the UX for ERP users. Outcomes: It is anticipated that the results of applying the framework would lead to higher data/information and system quality with improved UX, and system reliability leading to organisational benefit. Competitive Advantage: It is hoped that the application of the conceptual framework in Fig. 8 would create a strategic advantage for organisations through an improved ERP environment.

Fig. 8 Framework to facilitate ERP development (Source Synthesised by researchers)

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Following the development of the Fig. 8 framework and accompanying discussion, we arrive at an answer to RQ3.

7 Reasons for Incorporating Formal Methods (FMs) in the Proposed ERP Framework Formal methods (FMs) employ the use of mathematical notations and logical reasoning to define properties of an IS/ERP system correctly to avoid undue constraining of these properties [34]. Owing to the various challenges of ERPs elicited before, the use of FMs may be central in constructing and resolving issues of such systems. For instance, in describing the properties of the system both at lower and higher software levels and integrating the various ERP modules. Hence, the researchers suggest the incorporation of FMs as a central part of the proposed framework (Fig. 8). With the FMs style of developing systems, the specifier begins with creating a formal specification of the system by defining the desired properties before developing the resultant system [35]. A formal specification also offers a dependable approach for investigating system processes, satisfying requirements and testing results, as well as writing an instructional guide for software systems [34]. Also, a formal concept is the notion of what is understood at a human level to give a clear interpretation of the system at the specification phase [36]. It worth noting the value of FMs to produce correct systems such as ERPs [37]. However, the adoption of FMs techniques in practice amongst software developers is not encouraging [38]. Though FMs usage requires a rigorous effort in mastering the underlying discrete mathematics and logical principles, the researchers postulate it to be no harder than mastering any modern programming language.

8 Conclusion This paper reviewed several IS frameworks developed over the past couple of decades. An ERP architecture was presented and acknowledged to be a large and complex component of an IS. The emphases of the said IS frameworks together with ERP challenges identified from previous work by the authors were synthesised into Table 1. The major prominent emphases and challenges were identified and on the strength of these, an ERP framework to adhere to the emphases and address the challenges was synthesised. The framework has four dimensions, in line with the multi-dimensionality of IS frameworks, for example, the Tilahun and Fritz [20] framework gone before. Of particular importance in our framework is the suggested use of Formal Methods to address aspects of complexity, hidden information, and traceability in conjunction with an SDLC.

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Future work in this area may be pursued along a number of avenues: The Fig. 8 framework is conceptual (similar to the IS frameworks discussed above) and needs to be refined by unpacking the individual entities and components. Once completed, a formal methods approach to reason about the properties of the links and entities indicated can be launched, thereby enhancing the framework. An industry survey among practitioners should also be undertaken, either through qualitative interviews or quantitatively to establish the relationships indicated, similar to the Fig. 6 framework in Tilahun and Fritz [20]. All these are aimed at deriving a formal ERP model that could be adopted by ERP software engineers.

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Potentials of Digital Business Models in the Construction Industry—Empirical Results from German Experts Ralf-Christian Härting, Christopher Reichstein, and Tobias Schüle

Abstract Digitization and new business models in the construction industry gain increasing relevance. Therefore, an empirical study was carried out in Germany, Austria and Switzerland based on a theoretical foundation. The target of this study is to examine to what extent digitization has already changed the construction industry, what will change in the future, and what are possible benefits of new digital business models. The structural equation model (SEM) approach identifies four key constructs (KPI, individualization, efficiency, communication) that could have an impact on the potentials of digital business models (PDBM) and their processes in the construction industry. Of those four hypotheses two (efficiency and communication) have a significant impact. Keywords Potentials · Digitization · Construction industry · Empirical results · Quantitative study · German experts

1 Introduction Digitization enables a lot of new opportunities which can be realized from construction industry companies. Meanwhile, increasing competition forces enterprises to keep up with digitization to maintain the level. It is obvious that digitization is an important part of transformation also for the construction industry [1]. Contrary to other sectors, the German construction industry is at the beginning of digitization. Between 2000 and 2011, productivity in the German construction industry rose by only 4.1%, while overall German productivity grew by 11% over the same period [2]. This represents a below-average production development. Like Industry 4.0, the construction industry needs an intelligent construction site that R.-C. Härting (B) · T. Schüle Aalen University of Applied Sciences, Business Administration, Aalen, Germany e-mail: [email protected] C. Reichstein Cooperative State University BW, Heidenheim/Brenz, Germany © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_7

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enables all buildings and machines to be networked. “Digitization is transforming the construction sector throughout its entire life cycle, from design to operation and maintenance” [3]. The structure of this paper is as shown hereafter: in Chap. 2, the terms “digitization” and “construction” are defined as they are understood in this study. Thereafter, Chap. 3 is dealing with the concept and the design of our empirical study. In Chap. 4, the research methods are described to understand, among other things, how the data was gathered. In Chap. 5, the study results are shown. The paper is completed with a conclusion in Chap. 6.

2 Digitization in the Construction Industry The wording “digitization” can be interpreted differently: the first one is the transformation of analog information into digital form using appropriate electronic equipment. Information becomes to data, which are able to be stored and transferred through digital networks [4]. In the second case digitization stands for activities, which are generated by the implementation of innovative applications based on digital approaches. Therefore, digitization can be classified according to various intensity stages: from the presentation of information (website) to digital business models like augmented reality applications [1]. A widespread understanding of digitization leads to the usage of new and efficient concepts, like Artificial Intelligence, Cloud and Mobile Computing or Big Data [5]. The wording “digitization” [8] denotes the just beginning phase of upheaval, in that machine actions are replacing intelligent actions of men for the first time. As a result of these approaches, “intelligent products” will be networked to a new digital infrastructure, generally known as Internet of Things [6]. Mainly the capability to communicate and gather data is the foundation for multiple new conceptions. Firms can learn in much more details than before about the usage of their services and thus can afterward improve their products and services. On that basis, the development of new processes, functions, and business models is possible by taking advantage of advanced analytical capabilities created by digitization technologies like Big Data [7, 8]. Digitization is becoming increasingly important in all industries. This has an impact on established value chains as well as on the redesign of business models. The following quotation specifies the existing potential of the construction industry: “Digitization transforms the construction sector throughout asset’s lifecycle from design to operation and maintenance [3].”

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3 Background and Research Design A conceptual model was developed considering the status quo of the literature [9], which provides the basis for measuring the digitization potential in the construction sector (Fig. 1). Referring to the design of the study, we identified four constructs (KPI, individualization, efficiency, communication) which influence the potentials of digitization with respect to new digital business models in the construction sector. In the following, the four constructs and its effect on the potentials of digital business models (PDBM) will be described in more detail. In this context, the derivation and formulation of the hypotheses also become clear. Through digitization, many processes can be optimized within firm with respect to effectiveness and cost-efficiency which potentially increase key performance indicators (KPI), i.e., monetary business performances such as sales and profit. For example, it is evident that the new Internet technologies enable faster and better distribution of information due to non-existent geographical or time restrictions [10]. H1: KPIs positively influence the PDBM in the construction industry. Companies are making great efforts to use the Internet in the right way to coordinate their value activities with customers, suppliers, and other business partners, with the goal of improving business performance [11]. A lot of technologies classified under Industry 4.0 have long been in use, now they are being networked nationwide. To link cross-company supply chains, further industry standards are necessary. Germany has the best chances to play an outstanding role in the realization of the Smart Factory [12].

Fig. 1 Results of the structural equation model

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H2: Individualization positively influence the PDBM in the construction industry. One of the key success factors of companies today is important information that is collected, processed, and analyzed [13]. This provides potential for a path to more individual products for the customer. The process of digitization allows firms to become closer to their customers by adapting their offerings to their customers’ needs, which is of major importance because of the speed of the digital transformation, as otherwise competitors will be faster. Changed business processes and new business models resulting from digitization also make it possible to improve services which may have significant impacts on company’s success. Within environments of increasing complexity, companies should generally focus on customers [14]. As a result of Industry 4.0 and advancing digitization, many agile approaches have emerged that companies can use to acquire customers individually and to reduce transaction costs. H3: Efficiency positively influence the PDBM in the construction industry. To increase the competitiveness of a company, the technological achievements of Industry 4.0 can be used to make business processes more agile [15]. The more innovative and agile a company is, the simpler it is to develop new processes as well as business models [16]. In particular, improved business processes can reduce future error rates, since new digitized processes are not only faster but also more precise than analog processes [17]. This ultimately leads to increased efficiency. Furthermore, Big Data can be used to generate data that may provide competitive advantages which is further enhanced by new analytical methods [16]. Besides the volume, the speed, and the variation, complex data are getting especially significant for firms because of the possibility to receive valuable information [15]. H4: Communication positively influence the PDBM in the construction industry. One of the main benefits of new digital business processes is the improvement of general communication. Digital technologies, therefore, provide new ways of collaboration and offer the chance of short decision-making processes in companies [18]. Meetings online can also be used for presentations, simulations, or for simple communication. Independent of time and space, Internet technologies today make it possible to hold very different types of meetings [19]. New ways for businesses, individuals, networked devices, and governments to work, communicate, and collaborate result in easy exchange and interactions as well as a multitude of accessible data [13]. Digitalized business models and improved digital processes also increase collaborations. Considering that data such as customer information is a key factor in today’s business environment for offering individual products and services, it is especially rewarding for organizations to communicate and exchange information with each other [20]. If organizations share all data, structures along the entire value chain can be optimized using Big Data, resulting in new digital business processes and, at best, more digital business models [18, 21].

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4 Methodology The conceptual model provides four hypotheses which are to be tested by means of a quantitative research using the open-source software LimeSurvey [22, 23]. In March 2018, the web-based online study started, and it ended in April 2020. After data cleaning the sample is n = 102 responses from construction experts in Germany, Austria, and Switzerland. 27.5% of the experts work in small companies with 0.10) impact. Thus, KPIs indeed describe a positive effect (+0.975), but it is not strong enough on potentials of digital business models in this case. The usage of digital technologies in this field, which is described in literature, could strengthen new business models and their processes in an effective way. For example, Artificial Intelligence (AI) brings a lot of opportunities for increasing productivity or even lower labor and transaction costs. As a result, digital technologies can have a huge impact on new business models driven by digitization to increase effectiveness [13]. H2 (Individualization has a positive influence on the potentials of digital business models) measures the potential of individualization to digital business models. In fact, it has a positive (+0.607) impact. As the impact is not significant (p > 0.10), this hypothesis has to be rejected. The result shows that individualization has a positive, but not significant effect on digital business models in the construction industry. H3 (Efficiency has a positive influence on the potentials of digital business models) treats with the effect if efficiency can be raised in digital business models in the construction industry. This construct has a positive (+0.079) impact on the endogenous variable and the slightly significance level (p ≤ 0.10) is good enough to confirm an influence. Therefore, this hypothesis can be confirmed in terms of statistical requirements. There are further aspects of efficiency. Especially the combination of digital business models and agility is an innovative way to gain more efficiency in business processes [14]. H4 (Communication has a positive influence on the potentials of digital business models) deals with the issue if communication could be improved in digital business models. The p-value of hypothesis four shows a very strong impact (+0.000). The significance is at a maximum low level with p ≤ 0.01. Therefore, hypothesis four can be confirmed. The construct communication leads to a high positive effect on digital business models in the construction industry. The results described in the last sections will be presented in the following table in a detailed way (Table 1). In case of single item sets, there is a different way to work on in the quantitative research. The quality criteria according to Homburg are only used in modeling with multi-items [29]. The potential of digital business models and their processes in the construction industry is abbreviated as PDBM. Table 1 SEM coefficients Hypotheses

SEM path

Path coefficient

Significance (p-value)

H1

KPI → PDMI

0.032

+0.975

H2

Individualization → PDMI

0.515

+0.607

H3

Efficiency → PDMI

1.758

+0.079

H4

Communication → PDMI

3.727

+0.000

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Fig. 2 Results of descriptive questions

Descriptive Analysis of the Use of RFID and BIM The questionnaire has two additional questions, which were not part of the SEM. There are only 89 responses to these two questions in contrast to 102 responses to the other questions in the study. One question is focusing on the use of Building Information Modeling (BIM), the other one on the use of RFID transponders in the construction industry. BIM creates the possibility to work sustainable, as any failure can already be detected on the computer in the planning and simulation phase [18]. RFID transponders offer a great potential to simplify the maintenance process for buildings and machines [30]. The results in Fig. 2 show that the use of BIM is more popular than the use of RFID transponders. The majority of experts agree or strongly agree with the statements that BIM and RFID change the processes in the construction industry and make it more sustainable.

6 Conclusion The study investigates four general influencing impacts on digital business models and their processes in the construction industry. For this purpose, the authors used a structural equation modeling approach. All four influencing constructs which could be identified are described with five detailed indicators in the hypothesis model. One out of four determinants, the construct communication, has a positive and highly significant influence on the research question which describes a great potential on digital business models and their processes. The determinant efficiency has a positive and slightly significant influence. The other two determinants individualization and KPI have no significant impact on the potential of digital business models.

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Concerning the highly significant influence construct communication, the slightly significant influence factor efficiency and the not significant other hypotheses, the study shows interesting results. Considering the construct of communication, new digital business models offer great opportunities. The internal as well as the external communication can be improved. Regarding this fact, cooperation with external stakeholders can be improved and will lead to an enhanced business model. Considering the factor of efficiency, new digital business models provide a reduction of the error frequency in companies. Furthermore, the efficiency can be increased with the use of new, digital technologies. Nonetheless, these hypotheses are worth exploring, as this survey just included construction experts from German-speaking countries. This paper is limited in terms of some framework conditions. The location, sample size, and time frame bring with it some limitations. The research results can be used as a basic research for further elaborations to expand potentials concerning digital business models using digitization technologies (e.g., Big Data, RFID, BIM) in the construction industry. A lot of topics are still not yet fully explored, such as differences in various countries or deeper insights to existing business models. Furthermore, an empirical qualitative research approach could lead to more detailed findings. The fast-growing developments in topics of digital business models offer opportunities to improve business in the construction industry. In the future, the importance of digitization in the construction industry will continue to grow and become an important part of the company’s success. Acknowledgments This work was supported by Thinh Nguyen and Felix Häfner. We would like to take this opportunity to thank you very much for your great support.

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An Alternative Auction System to Generalized Second-Price for Real-Time Bidding Optimized Using Genetic Algorithms Luis Miralles-Pechuán, Fernando Jiménez, and Josá Manuel García

Abstract Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. This system works like a global auction where advertisers bid to display their impressions in the publishers’ ad slots. The most popular system to select which advertiser wins each auction is the Generalized second-price auction, in which the advertiser that offers the most, wins the bet and is charged with the price of the second largest bet. In this paper, we propose an alternative betting system with a new approach that not only considers the economic aspect, but also other relevant factors for the functioning of the advertising system. The factors that we consider are, among others, the benefit that can be given to each advertiser, the probability of conversion from the advertisement, the probability that the visit is fraudulent, how balanced are the networks participating in RTB and if the advertisers are not paying over the market price. In addition, we propose a methodology based on genetic algorithms to optimize the selection of each advertiser. We also conducted some experiments to compare the performance of the proposed model with the famous Generalized Second-Price method. We think that this new approach, which considers more relevant aspects besides the price, offers greater benefits for RTB networks in the medium and long-term. Keywords Advertising exchange system · Online advertising networks · Genetic algorithms · Real-time bidding · Advertising revenue system calculation · Generalized second-price

L. Miralles-Pechuán (B) School of Computing, Technological University Dublin, Dublin, Ireland e-mail: [email protected] F. Jiménez · J. M. García Department of Information and Communication Engineering, University of Murcia, Murcia, Spain e-mail: [email protected] J. M. García e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_8

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1 Introduction Real-Time Bidding consists of a large global market where publishers auction ad slots each time a user accesses their web pages [1]. In this great market, advertisers make an offer for displaying their adverts on the websites of the publishers. If an advertiser wins the bid, its advert is displayed instantaneously. In the real-time auctions, the whole process of auction, acquisition and ad display, takes place in the time a user loads a website (less than 100 ms) [2, 3]. RTB advertisers participate in the auction and the Generalized Second-Price (GSP) system is usually used to select the advertiser. In the well-known GSP system, the highest bid wins the auction and the bidder pays a price equal to the secondhighest amount bidden [4, 5]. Even though the GSP is not a verifiable auction system, it continues to be one of the most implemented auction mechanisms. The Advert selection process can be seen as a combined optimization problem treated as a stochastic control problem. Policies for online advert allocation have been developed based on placement quality and advertisers’ Cost-per-click (CPC) bids [6]. In this respect, the studies of Balseiro [6] should be emphasized, since he makes a deep analysis of the balance that must exist between both economic performances by selecting the most profitable advert and the quality of service offered to advertisers. Machine Learning (ML) models and deep learning models are frequently applied for optimization in many situations related to online campaigns [7]. For example, some publishers want to charge a fee regardless of whether users click or not on the advert, while some advertisers only want to pay if a click is generated. This problem can be solved using an intermediate role called “arbitrageurs”, and its success depends on how accurate the Click-through rate (CTR) estimations are [8]. The CTR represents the number of clicks an advert gets divided by the total number of impressions. Other studies encourage ANs to apply machine learning techniques to online auctions [9]. These models predict in a precise manner the acceptance of a user given an advert so that the probability of purchase increases considerably. In similar studies, adverts are ranked by the probability of being clicked, in such a way that the top-ranked adverts are likelier to be displayed [4, 10]. It is also possible to improve the performance of the RTB systems by maximizing the publishers’ profits. In this sense, Yuan et al. [11] focus on fixing the reserve price or the floor price, which is the price below which the publisher is not willing to sell. Increasing this price means that, in some cases, the winners, instead of having to pay the price of the second-highest bet, they have to pay the reserve price. In addition, in other cases, it will make some advertisers automatically raise their bets to get impressions. It is important not to raise the price too high, since it could trigger the number of impressions that remain unsold. In this research, we propose to optimize the function to select an advertisement based not only on the economic aspect, but we take into account a set of objectives such as the satisfaction of the publishers and the reduction of fraud for the advertising ecosystem to work properly.

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In this paper, we propose an alternative payment model to GSP which takes into account not only the economic performance in the short term, but also considers many other variables in order to guarantee that all involved parties (advertisers, publishers and especially ANs) will make reasonable profits. The presented work is in line with that of Balseiro [6], in which he considers not only maximizing revenues, but also the ad quality. The achievements of this paper consist of developing an RTB platform that evaluates when ranking an advert, all the indispensable requirements to make possible an adequate advertising ecosystem performance. We consider our work to be of great interest due to the fact that it is the first article in RTB aimed at improving system performance by improving the ad selection system. The betting model presented takes into account many factors that are key to the proper development of the RTB advertising system. The idea presented in this article can be adapted by the RTB networks, adding or removing some of the variables, making this model more beneficial for advertisers, improving their advertising experience, and therefore, attracting new advertisers that increase the volume of business on these platforms. The rest of the paper is organized as follows. Section 2 explains the proposed method in general terms and illustrates the structure and each of the modules that compose the RTB platform, especially the ad selector module (ASM). Then in Sect. 3, the main objectives for the proper functioning of the RTB platform, the rules to prevent online fraud and the penalties to ensure that the common objectives are met, are defined. Lastly, a methodology to optimize the weights of the ad selection function through a GA. In Sect. 4, our experiments are described and a brief analysis of the obtained results are drafted. In Sect. 5, the conclusions from our paper and some possible lines of research for future work are presented.

2 Our Novel Advertising Exchange System The proposed AdX system implements an Advert Selection Function (ASF) that evaluates the necessary objectives for a proper system functioning. The objectives of our system are the advertisers’ impression percentage, spam advertisers, campaigns profitability, advertising network balance, publishers’ click-fraud and income maximization. These objectives are described in detail in Sect. 2.2. It seems of most importance to us to develop a system aimed at the satisfaction of all the roles involved in online advertising rather than a system only focused on the selection of the most cost-effective advert. In order to implement our AdX system, one variable will be used to represent each objective and one weight will be used to model each objective’s importance in the advert selection formula, as expressed in Eq. 3. The weights are optimized through a genetic algorithm (GA) according to the system’s performance. The GA uses the system’s performance, expressed in economic terms, as the fitness value. The fitness value is calculated by subtracting the total penalizations (Pen 1 , ..., Pen 5 ) from the

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Fig. 1 Advertising exchange system structure. The AdX consists basically of the AES and all the ANs that take part in the exchange of adverts

total income derived from the system. Our methodology is able to find the best values for the weights, given any configuration. The best weights are those that maximize the income while minimizing the sum of all the penalties. Penalties are economic sanctions that are applied when a goal is not met. The less an objective is met, the higher the associated penalty will be. The value of these weights can be calculated offline and then the system configuration can be updated periodically. Our methodology is able to find the optimal weights using a GA from the definition of the objectives, the penalties and the rules in order to prevent online fraud. As it is shown in Fig. 1, in our proposed system, all ANs exchange adverts among themselves through the Advertising Exchange System (AES). The most important AES processes are: selecting the best advert from among all the candidates, keeping the fraud detection system updated and managing collections and payments from advertisers and publishers [12, 13].

2.1 Advertising Exchange System In order to develop the AdX, we propose the AES shown in Fig. 2. The designed AdX uses the CPC payment model. It is composed of four interconnected and interdependent modules: the CTR estimation module, the Fraud Detection module, the ASM and the database. Each module is designed for a different purpose and all of them are needed to make the advertising exchange possible.

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Fig. 2 Advertising exchange system structure. The AES is the cornerstone of our system, since it performs all the necessary functions for an appropriate advert exchange

The most important module is the Advert Selector. The other three modules (CTR estimation, Fraud detection and Database module) provide the necessary information, so that the Advert Selector can choose the advert with the highest performance.

2.1.1

Module 1: CTR Estimation

The CTR of an advert is calculated as the ratio between the number of clicks and the number of impressions. But in the case of a single visit, the CTR can be computed as the probability that a user generates a click on the advert displayed on a website. This probability is expressed as a real number lying within the range [0, 1]. Accurately estimating the CTR of an advert is one of the biggest challenges of online advertising [14]. Bearing in mind that we implement the CPC payment method in this system, the ANs need to give priority to the most profitable adverts in order to maximize their income. Machine Learning has been applied with great success in classification problems such as image and sound recognition [15], COVID-19 planning [16, 17] or CTR estimation [18]. In the case of CTR estimation, the dataset for machine learning methods contains data fields with users’ features and websites’ features such as advert size, advert position or category of the page. The output of the model is “1” when the user generates a click and “0” when the user does not generate a click. We should clarify that, rather than predicting the class model, what is predicted is the probability that the output belongs to the class “1” in the [0, 1] range.

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Module 2: Fraud Detection

The Fraud Detection module informs about the probability of an advert being spam and the probability of a click being fraudulent. The Fraud Detection module is designed to measure the probability of an advert being spam and the probability of a click on the advert of a publisher’s website being fraudulent. The probability of fraud in both cases can be expressed as a real number within the range [0, 1], r ∈ R, r ∈ [0, 1]. As we have mentioned previously, calculating the probability of fraud is a highly complex process. Therefore, it becomes very difficult to determine when a person is committing fraud from a single click or from a single advert impression. To assess whether an advertiser or a publisher is cheating, it is necessary to evaluate a large enough set of clicks or advert impressions. Moreover, the models that determine the probability of fraud would have to take into account the historical publishers’ clicks and advertisers’ impressions. In the case of spam adverts, some information regarding advertisers should also be considered, such as the duration of the advertisers’ campaigns, the type of products he/she advertises, the adverts’ CTR or the users’ behaviour when the advert is displayed [19]. In the case of click-fraud, some publisher’s features should be examined [20]. Furthermore, data about the users who visited the page need to be collected. Some important factors involved in detecting click-fraud are IP distribution, most visiting hours, publisher’s CTR, countries and cities with more visits to the page, the type of users who obtain access and users’ behaviour before and after generating a click [21]. The probability P of an advert or a publisher’s click Adi being fraudulent can be expressed as P(Adi | f raud) = α, and P(Adi |not f raud) = 1 − P(Adi | f raud).

2.1.3

Module 3: Database for Algorithm Execution

The database records all the necessary information to carry out all the processes involved in online advertising. The database stores all the required information about advertisers, publishers and ANs to allow the ASM to work optimally. The most important data stored in the database consists of information related to the advertisers’ payments and the publishers’ charges. In addition, information about any fraud committed and information used by the ASF such as the advert CTR and the advert CPC fixed by each advertiser is also stored in the database. In the same way, whenever a user makes a visit to a page, an advert is displayed and the database is updated. The value of the probability that the click is fraudulent and that the advertisement is spam is also updated.

2.1.4

Module 4: Advertiser Selection Module

Whenever a user accesses a publisher’s website, a selection from among all adverts takes place. All adverts belonging to a different category from that of the publisher’s

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website that is being accessed are discarded. Those adverts which are not discarded are called candidates. Then, only one advert from among all the candidates is selected, that is, the one that possesses the maximum Ad Rank value. To select the best advert, we apply the (Adver t) function, which assigns a real value in the range [0, 1] to all candidate adverts. The Ad Rank is explained in detail in Sect. 2.2.4. The (Adver t) function includes weights which are assigned in proportion to the importance of each objective. The Ad Rank is calculated considering all the AdX objectives. As can be seen in Fig. 2, this module takes into account both the CTR and the likelihood of advertisers and publishers being fraudulent. It also consults and updates the database where information about advertisers’ campaigns, AN balance, publishers’ account status and ANs’ performance is stored.

2.2 Development of the Advertisement Exchange System To develop the AdX we followed the following steps. First, we defined the necessary objectives in order to ensure the proper functioning of the publicity ecosystem. To ensure that objectives are met, we defined one economic penalty for each objective, in such a way that the more the objectives remain unmet, the greater the penalties sum will be, as explained in the following points. In addition, we created a set of rules in order to prevent the AdX from fraudulent activities. We established a metric expressed in economic terms in order to measure the AdX performance. Finally, we developed Algorithm 2 for the ASF and we defined a methodology to find the optimal configuration of weights using a GA.

2.2.1

Definition of the Objectives for the AdX

Several objectives should be met in order to have a successful AdX [22], where the optimization of some objectives may lead to the detriment of others. For example, the AdX should generate profits to the publishers as high as possible. But, at the same time, the AdX should not charge advertisers a price so high that their campaigns become unprofitable. The objectives of the algorithm comprising all adverts adi belonging to advertisers advi ∈ Adv, and all publishers pubi ∈ Pub of the ANi , where ANi ∈ Ad X , are: • (O1) Advertisers’ impression percentage: All advertisers need to display a reasonable amount of adverts so that all of them are satisfied. If the algorithm focuses just on maximizing the income of the AdX, then some advertisers may be left with no impressions. Thus, we should guarantee an equitable distribution of the advert impression number where advertisers paying a higher price have the advantage that their adverts are more frequently displayed. • (O2) Spam advertisers: Many advertisers display adverts on the Internet with malicious intent. These adverts are known as spam advertisers and they are very

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detrimental to the online advertising ecosystem [23], so we should calculate the probability that an advert is spam. We expect to reduce as much as possible the instances in which they are displayed. In the case of implementing the system, we should also have a team in charge of verifying if an advertiser is trying to mislead users whenever the system alerts that an advertiser may be cheating. (O3) Campaigns profitability: Some inexperienced advertisers may pay for their campaigns a price above the prevailing market price. It is not advisable to take advantage of this kind of advertisers by charging them a higher price. Our AdX should make profitable campaigns for all kinds of advertisers. Hence, we need to ensure that in our AdX, the advert prices are similar to those in the market, that is Pri zead  Pricemkt . (O4) Advertising network balance: Through collaboration, all ANs should make it possible for other ANs to display adverts in other ANs. If we want all ANs to participate in the AdX, then the number of adverts received by each ANs should be similar to the number of adverts delivered, that is, Advr ec − Advdel  0. (O5) Publishers’ click-fraud: Fraud committed by publishers is known as clickfraud and it can become very harmful to advertising campaigns. These fraudulent clicks are not generated by a genuine user interested in the product.1 Due to click-fraud, advertisers end up paying for clicks that do not bring any benefit. This increases the likelihood that advertisers shift to another ANs offering more profitable campaigns. Thus, we should avoid displaying in the AdX spam adverts. (O6) Income maximization: This is the most important goal, but we place it in the last position because each of the previous objectives has an associated penalty for it except this one. The Advert Selector algorithm should look for the most profitable adverts in order to distribute the highest amount of revenue possible among all publishers. The income value represents the money collected from the advertisers. Publishers should obtain reasonable economic returns so that they are discouraged from moving to other platforms and encouraged to recruit new advertisers.

2.2.2

Economic Penalties for the AdX

To ensure that the objectives are met, we define an economic penalty Pen i and a coefficient X i associated with each penalty, for each of the first five objectives Obji , where i = 1, ..., 5. In such a way that each penalty is applied whenever its corresponding AdX objective is not met. The rationale behind these penalties is that those participants (ANs, advertisers and publishers) who are not satisfied with the AdX usually leave the platform, which translates into economic losses. The X i coefficients allow us to increase or diminish the economic penalization that is applied when a goal is not met.

1

They are performed with the intent of increasing the publishers’ revenue or of harming the online platform. Many publishers may click on their own adverts or tell their friends to do so. There are also clicks made by click-bots which aim to harm the advertising ecosystem [24, 25].

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The five penalties we have defined are: • (P1) Impression advert percentage: We must apply a penalty for each advertiser that fails to display a sufficient number of adverts. P1 can be expressed as “For each advertiser whose average ratio of advert impressions lies below 25%, we will subtract X 1 times the average proceeds of the advertisers in these ANs from the total Income”. • (P2) Spam advertisers: We can define P2 as: “For each click from a spam advertiser, we will deduct X 2 times the money generated by these clicks from the total Incomes”. • (P3) Campaign profitability: We want to avoid any abuse against inexperienced advertisers who may be made to pay a price above the market price. P3 can be expressed as “For each advertiser who pays a price 25% above the market price for his/her campaign, we will deduct X 3 times the money generated by that advertiser from the total Income”. • (P4) Advertising network balance: When an AN is not satisfied, it may stop working with the platform. Therefore, P4 is expressed as “For each AN that receives 25% fewer adverts than the number of adverts it delivers, we will reduce X 4 times the incomes of that AN to the total Incomes”. • (P5) Publishers’ click-fraud: As mentioned previously, click-fraud makes advertisers’ campaigns fail. To avoid this, we created the following penalty P5: “For each fraudulent click from a publishr, we will deduct X 5 times the value of this click from total Income”.

2.2.3

Online Fraud AdX Actions

We should highlight that in our present study, fraud is not just considered as an economic issue, but also as an ethical issue. Therefore, we must define a set of policies and rules oriented towards respecting their interests. AdX Policies: Any publisher who wants to participate in the business must accept several AdX policies aimed at reducing fraud to the greatest extent possible, so that the advertising habitat may be protected. These policies seek to expel publishers before they receive any payment if the system’s expert group determines that fraud was intentionally committed. Additionally, we could consider imposing fines on advertisers who use the platform to deliver spam adverts and to all those publishers who use black-hat techniques in order to increase their income. AdX Rules: In addition to the AdX policies, we defined a set of rules focusing on preventing fraud. These rules set clear-cut criteria for expelling from the AdX those publishers, advertisers or ANs who commit fraud. The difference between the rules and the penalties is that infringement of rules leads to expulsion from the AdX platform, while penalties are used to undermine the performance when objectives have not been met. In order to make the algorithm more efficient, we only check the rules that lead to expulsion for each N visits, where N = 1, 000. The rules that we define are:

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• (R1) Fraudulent advertisers: To dissuade advertisers from trying to display spam adverts, we defined the following rule: “If an advertiser commits fraud on more than 20% of his/her adverts and the number of adverts is greater than 200 then he/she will be expelled” • (R2) Fraudulent publishers: We expel those publishers whose malicious clicks amount to a certain percentage above a predetermined threshold μ. Hence, we defined the following rule: “If a publisher commits fraud on more than 20% of his/her clicks and the number of clicks is higher than a specific threshold, in our case 30, then the publisher will be expelled”. • (R3) Fraudulent ANs: To discourage ANs from allowing their publishers and advertisers to commit fraud so as to win more money, we defined the following rule: “If 20% or more of the members of an AN are fraudulent advertisers or fraudulent publishers, and the number of visits is greater than V, where V = 2, 000, then the AN will be expelled from the platform”.

2.2.4

Advert Selector Module

In order to optimize the performance of the algorithm tasked with selecting an advert, we should define a function to evaluate all the objectives defined above according to the pre-established economic metric. Since the system has six objectives, the ASF also has six variables. Each variable is normalized and can be expressed as a real number within the range [0, 1]. The weights assigned to each variable are represented by θi , in such a way that they satisfy the Eq. 1. These weights do not have to be updated for each visit because this would lead to a very high computational cost. The values of these weights can be recalculated offline every few days. In addition, to ensure that the values of the weights are reliable, they must be calculated over a sufficiently large number of visits, since a small number of visits might not represent well the overall advert network behaviour. The weights’ optimal value for a network may vary depending on multiple factors such as the number of advertisers, the number of publishers, the number of ANs, the average click-fraud and the spam adverts within the AdX. 6 

θi = 1

(1)

i=1

To determine the best advert to be displayed on each user visit we assign to each advert the Ad Rank value. The Ad Rank is recalculated for each candidate advert each time a user visits a publisher’s website applying the (Adver t) function as expressed in Eq. 3. Ad Rank ← (Adver t) (2)

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(Adver t) = (θ1 × AN Satis f action) + (θ2 × Adver tiser Satis f action) +(θ3 × Spam Adver ts) + (θ4 × Campaign Cost) +(θ5 × Fraud Publisher ) + (θ6 × Ad V alue) (3) We now describe each of the variables representing the objectives of the AdX system: 1. AN Satisfaction: It expresses the satisfaction of the members of the network represented by the ratio between adverts received and adverts delivered. We should give priority to the advertisers from the unbalanced networks. The closer the value of this variable is to “1”, the more dissatisfied are the members of the network. Hence, we should try to help those networks that are most dissatisfied. The values of the variables are normalized to the range [0, 1] using Eq. 4 to give priority to unbalanced ANs. AN Satis f action = 1 −

Received V isits (Received V isits + Delivered V isits)

(4)

2. Advertiser Satisfaction: As expressed in Eq. 5, this variable measures the satisfaction of an advertiser according to the number of impressions each advertiser obtains. The closer to “1” the value of the variable is, the more discontent the advertiser will be. Therefore, we must give priority to those advertisers by displaying their adverts. Adver tiser Satis f action =

Potential V isits × Ad V alue (Potential V isits + Received V isits)

(5)

3. Spam Adverts: This variable represents the probability that an advert is of spam type. The likelier an advert is to be spam, the closer to zero the value of this variable will be. Therefore, spam ads are less likely to be shown. 4. Campaign Cost: The price of a campaign must be similar to the general market price. If an advertiser pays a price above the market price, the value of this variable will get closer to zero, as expressed in Eq. 6. Campaign Cost =

Advertiser Price (Advertiser Price + Real Price)

(6)

5. Fraud Publisher: It represents the probability that a click is fraudulent. The likelier the publisher is to be fraudulent, the closer to zero its value will be. 6. Ad Value: It represents the price the advertiser is willing to pay and it is calculated by Eq. 7. The closer to “1”, the greater the price the advertiser is willing to pay. To normalize the value of this variable, we divide the price the advertiser is willing to pay by the maximum value of the category. Ad V alue = C T R ×

C PC Adver tiser Max(Categor y C PC Adver tiser )

(7)

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Measuring the Advertising Exchange System Performance

In order to measure the AdX performance, we have established a metric expressed in economic terms. As expressed in Eq. 8, the AdX performance is given by the difference between all the AdX incomes and the sum of all the penalties. The algorithm tries to maximize the AdX incomes, but at the same time, it tries to achieve all the objectives in order to minimize the AdX penalty value so that the AdX performance value will be as high as possible. Ad X Per f or mance = Ad X I ncomes – Ad X Penalties

(8)

The Ad X I ncomes represents the money collected from all advertisers from displaying their adverts, which is equal to the sum of the value of all clicks, as expressed in Eq. 9. N  Click Price ( j) (9) Ad X I ncomes = j=1

Ad X Penalties is the sum total of all penalties, as expressed in Eq. 10, and it represents the financial penalty derived from not fulfilling the AdX objectives. Ad X Penalties =

5 

Penalt y (i)

(10)

i=1

2.2.6

Mathematical System Description

Let us define a set of ANs as AN s =< AN 1 , AN 2 , ..., AN n >, with n number of ANs where each AN n has a list of advertisers Ad j such that ∃Ad j ∈ AN n , a set of publishers such that ∃Pbk ∈ AN n and a set of visits such that ∃vl ∈ AN n . Each Ad j is defined by a set of adverts Ad j =< a1 , ..., am >, where Ad j ⊆ A / Adm ), and A is the set comprising all the adverts. Finally, V and (ai ∈ Ad j ∧ ai ∈ is the set of visits ∀vi ∈ V ; ∀AN s. The selected advert ai is the advert belonging to the advert set A =< a1 , ..., am >  and  also ai ∈ Ad j which leads to the maximum income I , that is, select A = ai | ai ∈ A ∧ A ⊆ A ∴ ai ∈ A . We must maximize the total Incomes Ik and minimize Pk for all adverts ai from ANk , that is, Max  N the sum of allpenalties  ai ai I k − P k where N is the number of ANs, ANk with k =< 1, ..., N >, k=1 for an advert ai ∈ Ad j and a ANk this system is subject to: • Fraud (ai ) > 0: There is fraud on the part of the advertiser. • Fraud ( pi ) > 0: There is fraud on the part of the publisher, where pi ∈ P and P is the set of publishers.

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• C T R ak i = C T R ak i × ϕ j and ϕ j represents the number of categories of ai with ϕ j ≤ p where p is the number of categories C j and j =< 1, ..., p > ∧ϕ ∈ R. i = ai (x1 , x2 , . . . , xw ) where X = feature ai . • C T R ak 

(x| xaw ) is an aadvertiser ai ai × tc i − (ep i × M ai ) where tc is the • I k = Click × C T R ak i × Price Click total number of clicks on the advert, ep is the income received by the publisher per click, M is the number of samples for the adverts and Pz is the corresponding penalty, and

I ak i =

6 

θi

i=1

3 Calculating the Optimal Value of the Weights Using a Genetic Algorithm Each variable of the ASF represents one criterion and it is multiplied by the weight such that the sum total of all the weights equals “1”, as expressed in Eq. 1. To obtain the optimal value for all weights, we applied optimization techniques based on GAs. Each time a visit occurs on a publisher’s site within the AdX, the ASM selects only one advert among the candidates. Algorithm 2 is in charge of taking into account all the objectives and updating the variables used by the ASF. The optimal weight configuration is the combination that generates the highest AdX performance according to the established metric. Algorithm 2 returns the AdX performance for a given weight configuration. We can think of Fig. 4 as a small module that returns the performance of the system (fitness of a GA function) according to the weights that are introduced as inputs. In order to find out the best weight configuration, we apply a GA with the following components.

3.1 Representation and Initial Population As genotype, we use a binary fixed-length representation. As it can be seen in Fig. 3, we used a length of 48 bits to represent each weight. Therefore, each weight can be represented with a value between 0 and 248 − 1, which is very high precision. Each individual I of the population is formed by the six weights and it is represented by a string of (6 × 48 bits = 288 bits) binary digits. The initial population is obtained at random with a uniform distribution. The size of the population is 100 in order to obtain diversity and an appropriate time of convergence [26]. The number of generations is 100 (Number of iterations in the stop criteria). Therefore, the number of evaluations for the function goal

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Fig. 3 Weight codification using individuals of a GA

is 10,000 (100 individuals × 100 generations = 100, 000 evaluations). In some experiments, this number of assessments has been appropriate for the stabilization of the algorithm [26].

3.2 Handling Constraints The genotype used to represent solutions does not satisfy the constraint that all weights add up to “1”. However, an individual genotype I G is a string of random binary digits can be converted in six integer numbers, which is called individual phenotype I P, where each integer is in the range [0, 248 − 1]. Once the individual I G has been decoded into the individual I P, it can be easily transformed into a new array, called repaired individual I R, that satisfies the constraint (all numbers are in the range [0, 1] and add up to one) applying Algorithm 1. The repaired individual I R should be calculated as a prior step to the evaluation of the individual. In this way, the constraint is always satisfied without the need to design specialized operators for solution initialization, crossover or mutation. Algorithm 1 Repair algorithm. Require: Individual IP Ensure: Repaired individual IR Sum ← 0 for i = 1 to 6 do sum = sum + I P[i]; end for for i = 1 to 6 do I R[i] = I P[i]/sum; end for return I R

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3.3 Fitness Function To calculate the fitness of each individual of the population I , the following steps are performed: • Obtaining the repaired individual I R (array of 6 real numbers in [0, 1] that satisfies the constraint) of the individual I P. • Calculating the fitness value using Eq. 11. Fitness(I R) =

N 5   Click Price I R ( j) − Penalt y j=1

IR

(i)

(11)

i=1

3.4 Genetic Algorithm Parameter Configuration We use “Double-point” for the crossover operator, that is, we select two points among which the genes of the individuals are interchanged. The parameter “Elitism percentage” is set to 5%. The parent selection method used is the “roulette wheel” (proportional selection and stochastic sampling with replacement). The replacement used method is “Generational replacement” in which a completely new population is generated with new children from existing parents via crossover and mutation. We applied similar parameters to the simple design GA proposed in Goldberg et al. [26]. The main reason is that our GA entails a high selective pressure (in comparison with other techniques of selection and generation replacement are a binary tournament or replacing steady-state) that takes a reasonable convergence time for our available computing capacity [27]. Since we used a binary simple representation and the constraint management does not require specialized operators, we consider to be appropriate the crossing and the uniform mutation operators proposed in Goldberg et al. [26]. To find the best combination values, the mutation probability, and the crossover probability are tested in the first configuration, which uses 10 ANs, with values from 0.1 to 1 with increments of 0.1. Therefore, we try 100 different combinations as expressed in Table 2. To calculate the best combination, we chose the best average configuration after executing the algorithm 10 times. Once the best combination is selected, we run the algorithm 30 times and then we calculate the average of the fitness function. The time required for each execution to take place is of approximately 14 minutes and 25 s (Fig. 4).

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Algorithm 2 Advertising exchange system algorithm.

6 Require: ( i=1 θi = 1 : values), Data: Advertisers, publishers, ANs and users Ensure: Fitness 1: for all visi ∈ V is do

For all visits 2: for all advi ∈ Adv do

For all advertisers 3: if (Categor y (V isit) = Categor y (Adver t)) then Advert value calculation Function 4: Ad V alue ← F((θ1 × AN Satis) + (θ2 × Adv. Satis) + (θ3 × Spam Adver ts) + (θ4 × Camp Cost) + (θ5 × Fraud Publisher ) + (θ6 × Ad V alue)) end if if (Ad V alue > Max) then

Selects the best advertiser among all possibles Max ← Ad V alue Selected Ad ← Ad j end if end for if (N um (V isits) mod 1000 = 0) then

For each 1000 visits update parameters pen i ∈ Pen, advi ∈ Adv, an i ∈ AN s ← UpdateParameters()

Updates all roles parameters 13: Apply Rule 1( pubi ∈ Pub, Adv j ) It checks if there are cheats publishers and ejects them 14: Apply Rule 2(advi ∈ Adv, Adv j ) It checks if there are cheats advertisers and ejects them 15: Apply Rule 3(ANi ∈ AN s, Adv j ) It checks if there are cheats ANs and ejects them 16: end if 17: end for 18: Calculate the value of the variables: Incomes, Pen 1 , Pen 2 , Pen 3 , Pen 4 , Pen 5 19: Fitness ← I ncomes − (Pen 1 + Pen 2 + Pen 3 + Pen 4 + Pen 5 ) 20: Return Fitness 5: 6: 7: 8: 9: 10: 11: 12:

Fig. 4 Advert exchange weight optimization algorithm using genetic algorithms

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3.5 Justification for the Chosen Values of the Coefficients, Penalties and Rules Click-fraud, spam adverts, and unsatisfied advertisers are factors that hurt the advertising ecosystem. However, determining the exact value of their negative impact on the AdX is a very complex task. Even these values were calculated, we still cannot ensure that they will be optimal for a long time because the scenario could change quickly. Therefore, finding the optimal configuration for all thresholds is out of the scope of our work and, for this reason, these values have been configured manually. However, we can briefly explain why we have configured the followings variables: 1) the coefficients (X 1 , ..., X 5 ) associated with each penalty, 2) the thresholds above which penalties are applied and 3) the conditions of each rule to expel a role from the platform. The thresholds of the penalties Pen 1 , Pen 3 and Pen 4 , representing the satisfaction degree, are configured to approximately 0.25%. Penalties Pen 2 and Pen 5 refer to click-fraud and spam adverts, respectively. In penalties Pen 2 and Pen 5 , 1/2 times the revenue obtained by the fraudulent clicks and the spam adverts is subtracted to the total income. With regards to the thresholds of the rules, we decided to expel from the AdX system all those ANs, publishers or advertisers committing more than 20% of fraud. In order to decide if a party involved in the system has committed fraud, it is necessary to analyze a large enough set of clicks or adverts. In order to do this, we define the followings conditions. For publishers, the number of fraudulent clicks must be greater than 30. For advertisers, the number of adverts must be greater than 200. For ANs, the number of visits must be greater than 2,000. If instead of analyzing 150,000 visits, we analyze 10 million, the threshold values will have to be higher.

4 Experiments and Results To prove that our system is valuable, we compared in experiment I the performance of the GA system with the extended GSP method. After applying the GSP method, we applied the penalties defined in our system. Finally, the aim of experiment II is to demonstrate that our GA is capable of adjusting the values of its weights to the new system configuration.

4.1 Preparation of the Experiments Our system takes into account many parameters to select an advert such as spam adverts, CTR, fraudulent publishers, the bid price and so on. There are some data

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sets covering one of the considered aspects, but they are far from what we need. For this reason, to perform the experiments, both the visits and the configuration of each of the advertisers of all ANs have been simulated. In this work, we launched an experiment that would help us to understand the importance of each variable when the value of the penalty remains constant. To find the optimum values of the weights, we applied a GA. The GA is implemented in the environment Visual Studio C# version 12.0.31101.00 Update 4, on a computer with the following features: Intel®Core i5-2400 [email protected] GHz with 16 Gb RAM, with the operating system Windows 7 Pro, Service Pack 1, 64 bit. We have used the Genetic Algorithm Framework (GAF) package for C#2 to implement the GA. The GAF package was designed to be the simplest way to implement a GA in C#. It includes good documentation and a variety of functions for crossover, mutation and selection operators. In addition, it allows the customization of the operator functions by the developer. For achieving a deep evaluation of our proposed GA, we run the experiments I and II. We developed an environment of AdX with the following configurations. The percentage of an advert of being spam is randomly set within the range from 13 to 16%. The percentage of the publisher being fraudulent is randomly set with values in the range from 17% to 20%. The price the advertiser is willing to pay and the advert’s real value are randomly set with values between 0.2 and 1.2 dollars. In the same way, the CTR value of an advert is randomly set in the range [0, 1]. We used in experiments I the following number of ANs: 10, 20, 30, 40 and 50. Therefore, five different configurations are tested where each AN has 10 advertisers, 100 publishers and 150,000 user visits. Finally, each publisher’s page may belong to one of the 20 different categories and an advert can only be displayed in the pages with the same category. In the first experiment, we compared the system performance both for the cases when ANs collaborate with each other and when they operate independently, by applying the famous GSP auction method [29, 30]. We conducted five configurations for the collaborative system and five for the independent system. The GSP selects the advert with a higher price and the advertiser is charged with the value of the second priciest advert. Our system is focused on a collaborative AdX, so it would make no sense to apply the penalties when ANs operate independently. Therefore, we will not use the GA since there are no weights to be optimized in the ASF. In this experiment, we have compared the profits obtained in the independent and in the collaborative AdXs using the GSP methods. The average values of 30 executions are shown in Table 1. The sum total of all income when ANs operate independently is 375,886.80$ and 810,454.93$ when they collaborate with each other. This is an increase of a 215.61%. When ANs work independently, the AdX displays only those adverts that belong to the AN which the user is visiting. However, when ANs collaborate with each other, adverts from any AN can be displayed.

2

The GAF is a .net/Mono assembly, freely available via NuGet, that allows implementing GA in the environment of programming C# using only a few lines of code [28].

An Alternative Auction System to Generalized … Table 1 Results of the GA and the GSP systems No of ANs 10 20 30 Independent Collaborative

25,149.36 55,811.83

50,039.76 110,588.53

75,402.54 164,773.42

101

40

50

100,097.97 216,562.86

125,197.18 262,718.30

Fig. 5 Experiment I: Obtained profits by the Independent and the Collaborative systems using five configurations

If the AdX can choose an advertiser out of several networks instead of only one, the results will be much better. As can be seen in Fig. 5, the obtained profit when ANs collaborate is much higher than when they do not.

4.2 Experiment I In the second experiment, we configured the GA with the following settings. We set the coefficient value associated with each penalty as follows x1 = x2 = x3 = x4 = x5 = 0.5. Assigning to all weights the same value allows us to see more clearly the relative importance of each objective. These values are calculated by using the average value of ten different experiments for each probability combination. As shown in Table 2, the best probability combination consists of a crossover probability of 0.7 and a mutation probability of 0.2. Once we calculated the best combination, we executed the algorithm 30 times and we calculated the average. The results are shown in Table 3. The optimal values of the weights in the first configuration, which uses 10 ANs, for the best fitness function are shown in Fig. 7. We have ordered the variables in descending order according to their importance. As shown in Fig. 6 and in Table 3, the performance of the GSP system is worse than the performance of our GA system.

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Table 2 Fitness value for crossover and mutation probability for all possible crossover and mutation probability value combinations with 0.1 increments ranging from 0.1 to 1. These values are the average value of 10 executions Mutation prob.

Crossover prob. 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9

1

0.1

9,920.4

9,971.5

9,711.7

9,997.3

9,783.6

9,763.8

10,018.1 9,893.0

0.8

9,750.1

9,898.5

0.2

5,016.2

9,538.9

9,761.9

9,753.2

9,737.0

10,012.7 10,032.3 9,532.8

9,775.0

9,785.7

0.3

9,509.8

9,757.2

9,810.6

9,630.8

9,804.0

9,808.1

9,493.8

9,803.0

9,693.1

9,606.2

0.4

9,761.3

9,819.3

9,756.6

9,920.3

9,687.9

9,547.6

9,844.0

9,443.6

9,549.6

9,755.2

0.5

9,828.0

9,561.0

9,625.4

9,454.0

9,633.1

9,710.0

9,743.5

9,873.1

9,365.4

9,629.7

0.6

9,717.2

9,813.5

9,310.7

9,730.9

9,430.4

9,929.8

9,761.7

9,525.6

9,436.9

9,671.4

0.7

9,507.1

9,604.4

9,569.9

9,691.2

9,565.6

9,490.1

9,532.3

9,878.3

9,297.7

9,255.0

0.8

9,932.8

9,776.1

9,212.0

9,417.7

9,513.3

9,724.2

9,738.0

9,312.8

9,410.1

9,825.9

0.9

9,681.5

9,383.4

9,490.5

9,732.4

9,708.5

9,691.3

9,755.8

9,454.7

9,534.1

9,532.3

1

9,609.7

9,479.9

9,788.1

9,716.4

9,630.7

9,609.4

9,977.5

9,383.0

9,893.3

9,947.2

Table 3 Average of the GA and the GSP systems for the five configurations No of ANs 10 20 30 40 GA GSP

10,146.59 –26,727.29

19,188.95 –41,331.81

30,861.78 –63,645.60

41,587.55 –100,379.85

Fig. 6 Experiment I: comparison between GSP system and our GA system

50 50,167.97 –124,853.94

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Fig. 7 Experiment I: best weight configuration using 10 ANs. Each weight has a value lying within the range [0, 1] and indicating the importance of each of the objectives. We have also represented the average of all the variables with a dashed red line

This is because the GSP system does not take into account any objective defined for the AdX, but only the economic performance, and therefore, the penalizations are very high. This makes us think that our system is interesting for those networks that want all their involved parties to be satisfied and want an ecosystem with little fraud. As can be observed, weights θ4 and θ1 are the most important. We have to keep in mind that the metric used in the fitness function is defined in economic terms. The weight θ4 is associated with Campaign Cost and it indicates if an advertiser’s campaign was priced above the market price. If those advertisers who are willing to pay more money for an advert were to leave the platform, the income would fall dramatically. On the other hand, θ1 regulates the N etwor k Satis f action variable which describes the network satisfaction with respect to the number of visits received and delivered. If a network leaves the AdX, all publishers and all advertisers who belong to this network will be lost, and so the costs would be very large. θ6 represents the weight associated with the variable Ad V alue, which represents the advertisement value. It is logical that it should have a high value because when more profitable adverts are selected, the ANs’ income increases. The weights θ3 , θ5 and θ2 reflect the values associated with fraud. θ3 is associated with the Spam Adver ts variable, which indicates the probability that an advertisement is of the spamming type. θ5 is associated with Fraud Publisher which indicates if an advertisement is fraudulent. Displaying spam adverts and receiving fraudulent clicks have a negative impact on the AdX, this is why the values of these two weights are similar. If we were to increase the value of these weights, we would have to increase the coefficient θ2 associated with this penalty value 2 or 3 times the amount of money obtained through fraud, instead of just 0.5 times. Finally, weight θ2 is associated with Adver tiser Satis f action, which indicates the satisfaction of an advertiser with respect to the number of adverts displayed. This weight usually has a value close to zero and it leads us to think that it is almost of no importance, since it is already automatically defined with weight θ4 . This means that, if the ANs are balanced, it is likely that the number of adverts posted by the publishers will lie also within the objective set.

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Fig. 8 Experiment II: Best weight configuration changing the coefficients of the penalties

Table 4 Values of the genetic algorithm in experiment II Max value Avg value Min value 5,968.59

5,756.76

5,304.92

Std. dev. 153.32

4.3 Experiment II If we recall the results of Experiment I, we realize that θ2 was the least weight on the optimization of the weights of each objective. In the following experiment, we are going to increase the weight associated with objective 3 to verify that the GA is able to adapt to these changes. We also made another experiment with the same configuration as in this experiment, except for the value of the penalties’ coefficients, in order to see how weight values are readjusted. To achieve this purpose, we create an experiment in which we only change the coefficients of the penalties in the following way: x1 = x3 = x4 = x5 = 0.5, while x2 = 3, which represents the value associated with the variable θ2 . The rest of the parameters remains the same, as in the configuration of experiment I. The average value of the 30 executions are 5,756.76. The results of the experiment can be seen in Table 4. In this system, we have used the same configuration as in the previous system and we have also shown the calculated values. Figure 8 shows the results of the best weight configuration with the highest fitness value. As it is shown, the most important value is θ2 , which represents the advertiser’s satisfaction. We can observe how the values of θ1 , θ3 , θ4 and θ5 continue to maintain the same order that they had in Fig. 1, in terms of their weight. This is obvious since all we have done is to change the value of just one variable. The conclusion is simple. We have shown that if we change the coefficients of the penalties, then the values of the weights also change, so that the advert selection formula is again optimized.

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5 Conclusions and Future Work Our work addresses a problem in the literature which, although not much studied, is of no less importance. To our knowledge, there is no other publication that focuses on creating a system for small networks to exchange adverts among themselves in order to improve their performance. We must bear in mind that the majority of ANs do not reveal their algorithms and methods since that would mean giving away part of their competitive advantage, which may have taken them many years of research. In this article, we have seen how to select an advert in an AdX system. We have seen how the selection of an advert is not a trivial task but a complex task that must take into account multiple objectives, often with conflicting interests, and each goal is associated with a weight to be optimized. One of the main achievements of this work is having provided a starting point from which an AdX system can be built and which takes into account the main threats and problems of online advertising. In addition, a methodology was developed to find the appropriate weights for a function that considers all the necessary objectives that create a proper AdX ecosystem. Our goal was not to develop a methodology to improve CTR prediction or fraud detection but to develop a methodology that helps in obtaining the best advert selection function after assuming that the CTR and the Fraud detection modules were correctly developed. Obviously, the more reliable and precise the modules, that provide data, the greater the system’s performance will be. We have seen that the optimum weights for the advert selection module vary depending on the goals, penalties, the number of advertisers and campaigns, as well as the settings of everything that composes the AdX. Therefore, there is no optimal configuration that can be extended to all systems. Studying the optimal value for each optimization would be an interesting line for future works. These values could be found by constructing complex simulated systems and testing them in a real scenario. As a future line of research, we might also attempt to include both the CPM payment model and the CPA payment model to the AdX. Furthermore, we may be able to develop new modules that enable ANs to cooperate among themselves with the aim of improving fraud detection. For this purpose, they could interchange information such as the CTR of the page, the CTR of the adverts or the behavioural patterns of the users. This could be done by collecting samples of behaviour for later analysis using models of machine learning. The more the samples, the greater their quality, so that more accurate models can be built. Further research could also involve developing a scalable system, i.e., instead of building a system of 10 networks with 10 advertisers and 100 publishers in each network, we could develop a system with 1,000 networks comprising 10,000 advertisers and 100,000 publishers. However, this would require better hardware and more computers working in parallel. Furthermore, to carry out this system, we could consider replicating the advert exchange system by using a distributed rather than a centralized architecture. These modules should be synchronized with the ongoing exchange of information within the networks, so that the variables are updated and they can optimize their response

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time for each user. In order to do this, a communication protocol between the different Advert Exchange Systems will be required. This protocol will transfer the necessary information within the system in order to optimize the economic profits of the system, avoid fraud, and finally maintain the level of satisfaction of all the parties involved in the system.

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Low-Cost Fuzzy Control for Poultry Heating Systems Gustavo Caiza , Cristhian Monta, Paulina Ayala , Javier Caceres, Carlos A. Garcia , and Marcelo V. Garcia

Abstract The evolution of technologies advances by leaps and bounds, that is why this article describes the implementation of a prototype of fuzzy monitoring and control with low-cost restrictions to control the heating of poultry farms through the use of under floor heating and solar energy. The monitoring system is based on free distribution LAMP servers. Fuzzy control is implemented with restricted membership functions to keep heating in an optimal state. The benefits provided by the sun and Ecuador’s geographical location, make this resource an important source of renewable energy that was used for the heating process of close environment, thus creating an ideal environment for the process of poultry breeding. Keywords Low-cost automation · Fuzzy control · Solar energy · LAMP server

G. Caiza (B) Universidad Politecnica Salesiana UPS, 170146 Quito, Ecuador e-mail: [email protected] C. Monta · P. Ayala · J. Caceres · C. A. Garcia · M. V. Garcia Universidad Tecnica de Ambato UTA, 180103 Ambato, Ecuador e-mail: [email protected] P. Ayala e-mail: [email protected] J. Caceres e-mail: [email protected] C. A. Garcia e-mail: [email protected] M. V. Garcia e-mail: [email protected]; [email protected] M. V. Garcia University of Basque Country UPV/EHU, 48013 Bilbao, Spain

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_9

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1 Introduction Poultry farming is a fundamental field for sustenance and socioeconomic development of the country, such as agriculture, made up of a large production chain providing supply for poultry feeding, being corn, soybeans, and balanced products in general; it also covers the transport sector that provides its service for the transfer of the product to different places. The labour that is used in this field is also directly and indirectly involved, since the quality and lifestyle of the inhabitants surrounding the production sector is enhanced. For breeding chickens in poultry farms, it is necessary to have a properly monitored and controlled environment that allows an adequate development of the fowl and can increase production at the lowest possible cost, the main factors to control are temperature and humidity that affect the assimilation of food and the energy consumption of the fowl. In recent years, several studies have been presented on the automation and monitoring of humidity, temperature, and other physical poultry variables. Most of these investigations have limited themselves to implementing an automation system which controls temperature and humidity, but with little emphasis on the control model [4], due to this, in the results, a time of 400 s is required to establish the values to be controlled and can be observed in comparison to the set point. Other studies apply the Sliding Mode Controller which allows to develop a non-linear dynamic mathematical model considering the final balance and the mass of the system [6], where the temperature overshoot with the set point is greater compared to humidity. Fuzzy logic has been used to develop optimization models for poultry systems because they allow the correct combination of energy resources to be selected more effectively, taking into account the various conflicting criteria such as costs, availability of supply, type of energy, etc. Ahamed et al. [1]. Models based on fuzzy logic have achieved more realistic solutions from renewable energy sources. It also makes it feasible to conceptualize the uncertainty of the system into a neat quantifiable parameter. Therefore, models based on fuzzy logic can be adopted so that planning and heating control in poultry reaches practical solutions [5]. This project proposes the use of Arduino which is an open source electronics platform and the Raspbian operating system of the Linux distribution, as well as the use of sensors and actuators for process control and the use of the sun as renewable energy to drive heat through the poultry system. Later, the data storage process is carried out in the database mounted on LAMP servers by using the Raspberry Pi3 microcomputer, as the last stage, once the data has been obtained, these were displayed via wireless connection to users registered in the system. The content of the article is structured as follows: Sect. 2; which presents the case study used for the research method. In Sect. 3, the idea is shaped with the implementation of fuzzy control with restrictions in low-cost systems; the results obtained are presented in Sect. 4; finally, conclusions and possible future work are presented in Sect. 5.

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2 Case Study In a poultry farm, it is very important to be able to control its environmental parameters because they directly influence the growth and development of the fowl. For this reason, depending on the fowl’s age, a temperature is recommended within a range of 30–25 °C and 50% humidity, which guarantees an ideal environment for the fowl and thus a final product of very good quality, also complying with the established norms and standards [5]. In this study, a system that allows the control and monitoring of various physical parameters of a poultry farm using fuzzy logic with restrictions together with the Arduino open source development platform and the Raspbian operating system are presented, in addition to the use of sensors and actuators for the control of the process and the use of the sun as renewable energy to drive heat through the system. The monitoring is done thanks to the development of a LAMP server. Several achievements were made by integrating all the devices that are presented in the three sections of Fig. 1, First of all, obtaining real temperature and humidity data through the use of the DS18B20 and DHT11 sensor. Once these data were obtained, they are sent to the microcontroller which performs two processes: first, the control process that uses fuzzy logic through the use of the Mandani model with restrictions to adapt the ideal temperature and humidity signals for the correct growth and development of the fowls in the sheds, and second, the process of storing the data in the database mounted on LAMP servers by using the Raspberry Pi3, as the last stage. Once the data has been obtained, they are displayed via wireless connection to the users registered in the system.

Fig. 1 Design of the prototype of the control and monitoring system

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3 Solution Proposal For the development of this study, it is first necessary to determine the appropriate values for each of the variables to be controlled, such as temperature, humidity, and ventilation. The temperature at the time of vatting the bird should be 37.6 ◦ C, during the first two weeks of life, the fowl cannot control its body temperature; this phenomenon is called “poikilothermia”. After this time, fowls have already developed the ability to regulate their temperature, calling this process “homeothermy”, and also while it continues to grow, fowls need lower temperature values. Regarding humidity, which is the saturation of water with respect to the air at any given temperature, expressed as a percentage, ranges of humidity directly affect the fowl’s ability to cool down through panting, directly influencing ammonia production inside hatchery environments, which, in turn, affects their growth (see Table 1). Ventilation is another variable that is controlled based on systems that operate by means of depression, so called because, inside the poultry farm, pressure is lower than outside, trying to create emptiness inside the poultry farm. To reach this point, it is necessary to precisely control both the air intake, as well as extraction in the windows, thus achieving the adjustment between the air intake and the air outlet. As in the previous variables, the fowls need a certain amount of flow or net air (m 3 / h) as shown in Table 2.

Table 1 Temperature and humidity values by age of the fowl Fowl’s age Temperature ◦ C 1st–2nd day 3rd–7th days 2nd week 3rd week 4th week 5th week onwards.

30–32 29–30 27–29 25–27 23–25 21–23

Table 2 Ventilation by depression in poultry sheds Age (days) Weight (kg) 7 42

0.189 2.2

Relative Humidity (%) 35–40 35–45 40–45 45–50 50–55 55–60

Airflow (m3 /h/fowl) 0.22 9.8

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3.1 Fuzzy Inference Based on Linear Programming Fuzzy linear programming proposes that the fuzzy model is equivalent to a classical linear programming maximization. It can be presented as: cT x ≥∼ z, Ax ≤∼ b and x ≥ 0. Where ≥∼ is the fuzzy version of ≥ which means “essentially greater than or equal to; and ≤∼ which means” “essentially less than or equal to”. The fuzzy constraints of the model are shown below in Eqs. (1)–(4) λ pi + ti ≤ pi ; i = 1, . . . , m + 1

(1)

Bi x − ti ≤ di

(2)

ti ≤ pi

(3)

x, t ≥ 0

(4)

where λ is a new variable introduced that can take values between 0 and 1, pi is the tolerance intervals of each row i; ti is a variable that measures the degree of violation of constraint i; B is equivalent to the combinatorial of −c and A of the objective function and the constraints, Bi is the element of row i of B; d is the combinatorial of −z and b of the objective function and the constraints; di is the element of row i of d [3]. To implement the control with restrictions, the Mamdani-type inference system was chosen because it supports working with two or more outputs at the same time. This type of fuzzy inference was applied to the control of temperature and humidity using the MATLAB software and its “Fuzzy Logic Tool Box”, which is used to develop fuzzy systems using the graphical user interface (GUI) [2]. For the analysis of the fuzzy inference system, the FLT Fuzzy Logic Toolbox used five graphical tools for modelling, the editing section, and finally the observation. The fuzzy inference system Fuzzy Inference System allows determining the inference method used. For this study, the Mandani type inference method was used, and also the following input variables entered: temperature and humidity, and the output variables: motor time and fan bits Restrictions that will be explained in the following sections were used. For the membership functions, the minimum and maximum values of both input variables and output variables, were analyzed, as shown in Table 3, and then five membership functions were written for each variable. In the membership functions, a trapezoidal function was used in each of the extremes since some parameters acquire non-finite values, thus centering the mean value at 0 and to improve precision, triangular functions are used since they facilitate calculations performed by the controller because they are linear functions. Based on reviewed information, a fuzzy linear programming (PLEM) proposal has been developed, by applying uncertainty in the temperature and humidity demand

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Table 3 Maximum and minimum values of variables Variable Maximum Minimum Mean value Maximum value value reference value Temperature Humidity Motor time Fan bits

32.69 ◦ C 60% 25 s 1023 bits

20.5 ◦ C 35% 0s 500 bits

26.595 ◦ C 47.5% 12.5 s 761.5 bits

6.095 ◦ C 12.5% 12.5 s 261.5 bits

Minimum reference value

Mean reference value

–6.095 ◦ C –12.5% –12.5 s –261.5 bits

0 ◦C 0% 0s 0 bits

Fig. 2 Membership functions for the temperature variable

restrictions. The following fuzzy parameters are used to represent the uncertainties of demands for temperature and humidity in the PLEM model. Fuzzy parameters: – – – –

λ1 Degree of temperature satisfaction. λ2 Degree of humidity satisfaction. T˜ Maximum tolerance for temperature demand. H˜ Maximum tolerance for humidity demand

Other parameters based on actuators energy consumption of the poultry house are: – – – – – –

f e p d Energy flow [Wh/day] between points p and d; p = 1, P; d ∈ Qp. f p p d Power Flow [W] between points p and d; p = 1, P; d ∈ Qp. E Ss Energy generated [Wh/day] by a panel type s; s = 1, S. E D p Energy demand [Wh/day] of point p; p = 1, P. P D p Power demand [W] of point p; p = 1, P. P Ii Maximum Power [W] of an inverter type i; i = 1, I.

A strategy to manage the uncertainty in parameters such as: energy consumption, temperature, and humidity set points. These values are represented, through belonging or membership functions, and shown in Fig. 2. Figure 2 shows the memberships of each of the parameters that are transformed into fuzzy, temperature, humidity, energy demand, and power consumption of the actuators. The right side of the figure shows mathematically each section of the function. It is established that the fuzzy parameters will be integrated into the demand

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restrictions for temperature, humidity, energy, and power. The new fuzzy restrictions for energy (5) and power (6) are shown below. p 

f eq p +

S 

˜ 1 − 1) + E D p + E Ss · xs ps  E(λ



q=1| p∈Q q

s=1

d∈Q p

p 

I 



f e pd ; p = 1, ...., P (5)

q=1| p∈Q q

f pq p +

˜ 2 − 1) + P D p + P Ii · xi pi  P(λ

i=1

f p pd ; p = 1, ...., P

d∈Q p

(6) The data used for the temperature variable were, as the highest value (32 ◦ C) and the lowest value (20 ◦ C) as indicated in Table 3, which are the temperature boundary data to control. Taking the EFP function as an example, the lower value p = –4, the upper value d = 0 and the modal value m = 2. For the membership functions of the Motor Time variable, the time it takes to achieve the temperature variation from the lowest value (20 ◦ C) to the highest value (32 ◦ C), which was 25 s from the start of the engine that allows the circulation of the water flow. Therefore, as in the previous case, the mean value was centred at 0. Taking the TFP function as an example, the lower value p = –8.34, the upper value d = 0 and the modal value m = 4.17 For the case of the humidity input variable, the difference between the highest value (60%) and the lowest value (35%) provides us with the range of humidity percentage to be controlled. Taking the HNP function as an example, the lower value p = –8.34, the upper d = 0, and the modal value m = 4.17. The data that controls the fan variable for the membership functions are given in bits. For this case, the microcontroller’s operating resolution is taken into account, taking its lowest value equal to 0 and the highest one equal to 255 bits. Taking the BNP function as an example, the lower value p = –174.33, the upper value d = 0, and the modal value m = 87.16.

3.2 LAMP Server The developed system has the ability to monitor in real time the variables to be controlled, for which a LAMP server was developed, the server makes use of the HTTP protocol (Hypertext Transfer Protocol), which allows us to interact with the application layer within the TCP/IP model. To sum up, it allows the communication among web pages from a web server to the user’s browser (see Fig. 3). A MySql server is also used, which allows managing related open source databases. They are fundamental within the project, thus being able to store the data of the variables to control and organize them in table forms. Therefore, it is able to relate each table in the same way required by the user. The creation of these charts is performed using the specific language SQL (Structured Query Language), managing to give direct instructions to the server

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Fig. 3 Website developed to monitor process

4 Results To carry out system tests, different values of temperature and humidity recommended by the fowl’s age were taken. The graphics charts show the response of the system, each one representing a different temperature and humidity value within the established values. Data was taken every hour for 24 h for 21 days with different setpoints and were stored in the database. For recommended values from the third week of age of the fowl onwards, regarding temperature which ranges from 21 ◦ C to 27 ◦ C, it presents a deviation of ± 0.06 ◦ C to the reference value, and for humidity that ranges from 45 to 60%, it shows a deviation of ± 0.5% to ± 1.5% at different times of the day. See Figs. 4 and 5 The results show that the system works correctly because the temperature and humidity of the poultry is maintained during the 24 h, although the reference values vary, deviations are relatively low since they fluctuate between 0.06 ◦ C and 0.09 ◦ C regarding temperature, and humidity fluctuates between 0.5 and 1.5%.

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Fig. 4 SP versus temperature

Fig. 5 SP versus humidity

5 Conclusions This work presents the implementation of a fuzzy control system with restrictions using the Mamdani inference method, which allowed controlling two input variables and two output variables. For this, it was necessary to know the behaviour of the plant, and thus establish the functions of memberships and fuzzy control rules, where the

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outcome of the set of fuzzy rules was a single value in defuzzification using the centroid method. The use of fuzzy control allows the system to be efficient, versatile, and simple, as the results show. The system also allows real-time monitoring of the processes carried out in the plant from anywhere in the world, using free hardware and software with a LAMP server (Linux, Apache, MySql, PHP) that allows safe browsing, a low maintenance, considerably reducing their costs, making it a low-cost automation solution. The under floor heating system avoids the emission of greenhouse gases, which improves the quality of life of operators and poultry.

References 1. Ahamed NU, Taha ZB, Khairuddin IBM, Rabbi M, Rahaman SM, Sundaraj K (2016) Fuzzy logic controller design for intelligent air-conditioning system. In: 2016 2nd international conference on control science and systems engineering (ICCSSE), pp 232–236. IEEE, Singapore. 10.1109/CCSSE.2016.7784388, https://ieeexplore.ieee.org/document/7784388/ 2. Fannakh M, Elhafyani ML, Zouggar S (2019) Hardware implementation of the fuzzy logic MPPT in an Arduino card using a Simulink support package for PV application. IET Renew Power Gener 13(3):510–518 3. Kumar D, Singh J, Singh OP (2013) Seema: a fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Math Comput Model 58(11– 12):1679–1695 4. Nuyya O, Sergeeva E, Khomenko A (2018) Modeling, simulation and implementation of $a$ low- scale poultry farm control system. In: 2018 10th international congress on ultra modern telecommunications and control systems and workshops (ICUMT), pp 1–5. IEEE, Moscow, Russia. https://doi.org/10.1109/ICUMT.2018.8631253, https://ieeexplore.ieee.org/document/ 8631253/ 5. Padilha A, Farret F, Popov V (2001) Neurofuzzy controller in automated climatization for poultry houses. In: IECON’01. 27th annual conference of the ieee industrial electronics society (Cat. No.37243), vol 1, pp 70–75. IEEE, Denver, CO, USA. https://doi.org/10.1109/IECON.2001. 976456, http://ieeexplore.ieee.org/document/976456/ 6. Upachaban T, Boonma A, Radpukdee T (2016) Climate control system of a poultry house using sliding mode control. In: 2016 international symposium on flexible automation (ISFA), pp 53– 58. IEEE, Cleveland, OH, USA. https://doi.org/10.1109/ISFA.2016.7790135, http://ieeexplore. ieee.org/document/7790135/

Towards Empowering Business Process Redesign with Sentiment Analysis Selver Softic and Egon Lüftenegger

Abstract In this paper, we propose a novel approach of empowering the Business Process Redesign (BPR) by using sentiment analysis on comments collected during the redesign phase of business processes. For this purpose, we trained and tested our Sentiment Analysis Module (SAM) to prioritize and classify the stakeholder comments as a part of software tool for BPMN based modeling and annotation tool. The preliminary result with evaluation test cases seem to be promising regarding effective ranking and classifying the improvement proposals on BPMN design. However, the findings are also leaving space for improvements in training data segment and in extending the tool with social BPMN functionality. Keywords Business process redesign · Business process management · Sentiment analysis · Decision support

1 Introduction The quantity and complexity of business processes that needed to be integrated led to the creation of Business Process Management (BPM). BPM represents a structured, consistent, and coherent approach for understanding, modeling, enacting, analyzing, documenting, and changing business processes for contributing business performance [1, 10, 13]. BPM provides concepts, methods, techniques, and tools that cover all aspects of managing a process—plan, organize, monitor, control—as well as its actual execution [1]. Traditional BPM methodologies often follow a top-down decomposition approach resulting in a long running process improvement process, that requires intensive S. Softic (B) · E. Lüftenegger CAMPUS 02 University of Applied Sciences, IT & Business Informatics, Graz, Austria e-mail: [email protected] E. Lüftenegger e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_10

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negotiations for achieving change within the BPM lifecycle. This traditional process improvement approach can become problematic for companies due to the unpredicted market conditions. Changing preferences in the customer’s needs require fast changes in a business process model. Hence, there is a need for an agile approach for reacting to the changing business landscape. One of the possible empowerment could be using the advanced technologies like artificial intelligence and machine learning and methods such as sentiment analysis to analyze the opinions and insights from different stakeholder in BPM process in a fast and efficient way. In this paper, we consider such case by involving a sentiment analysis module into a conventional design process scenario and using it as empowering assistant for prioritization of redesign suggestions and comments on the process.

2 Related Work 2.1 Business Process Redesign Business Process Redesign (BPR) or Business Process Re-engineering aims at improvement of vital aspects of business processes aiming at achieving some special goal, e.g., reducing costs. The importance of BPR was initially outlined by the work of Davenport and Short [4] in early 90s. However, this wave of enthusiasm flattened out by the end of decade. As main reasons for this, reported in literature were named the concept misuse (false labeling of the projects as BPR project), immaturity of necessary tools, and too intensive approach regarding the phase of application. Revival of the BPR concept according to [5] happened in relation to BPM, where several studies appeared showing that organizations which are more process oriented performed better then those which did not follow this paradigm. Studies that followed confirmed these findings. This established the new credibility to the process thinking. The BPR has been seen in this case as set of tools that can be used within BPM.

2.2 Business Process Modeling The overall goal of Business Process Modeling is to establish a common perspective and understanding for a business process within an enterprise between the relevant stakeholders involved. Hereby, the graphical representation such as flowchart or similar serves as base to show the process steps and workflows. This approach is widely used to recognize and prevent potential weaknesses and implement improvements in companies processes as well as to offer a good base for comprehensive understanding of a process in general.

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2.3 BPMN The BPMN 2.0 (Object Management Group, 2011) is a new standard for business process specification developed by a variety of Business Process Modeling (BPM) tool vendors. This standard is one of the most important forms of representing business process models, offering clear and simple semantics to describe the business process of a business [2, 12]. This language was developed with the intention of modeling typical business modeling activities [9, 10]. This is another important reason for choosing this notation because our software-based methodology is oriented towards business alignment. The goal of the approach presented here is to provide entrepreneurs with a simple BPMN 2.0 tool without the complexity and cost of enterprise software currently offered at the market.

2.4 Data Mining, Sentiment Analysis, and Opinion Mining in Business Processes Data mining is being used in the field of BPM for process mining. The process mining is focused on processes at run-time, more precisely for re-creating a business process from systems logs. Opinion mining is a sub-discipline of data mining and computational linguistics for extracting, classifying, understanding, and assessing opinions. Sentiment analysis is often used in opinion mining for extracting opinions expressed in text. However, current research is focused on e-business and e-commerce like social media and social networks like Twitter and Flickr rather than BPM and BPR [3].

3 BPM Lifecycle and SentiProMo Tool BPM lifecycle described in [5] represents different phases of the process beginning by analysis and ending by process monitoring and controlling and process discovery. Our usage scenario in this lifecycle is placed between the process analysis and process redesign phases. During the design phase of the BPM lifecycle, social software adequately integrates the needs of all stakeholders [11]. We use our SentiPromo Tool [7] for this purpose to empower the (re)-design through integration of stakeholder’s needs expressed as opinions.

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Fig. 1 Commenting workflow and the role of sentiment analysis

3.1 Using Sentiment Analysis in BPR SentiProMo Tool1 was developed in our department in order to provide a possibility of a role-based social intervention within the business process (re)-design. The roles supported in this tool are leaned on prior research on business process knowledge management framework [6]: Activity Performer (AP), Process Owner (PO), Process Designer (PD), Superior Decision Maker (SDM), and Customer (C). According to [11], BPM tools that follow the social BPM paradigm provide a mechanism to handle priorities within a business process [11]. This also applies to SentiProMo Tool (Fig. 1). The architecture relies basically on three layers: user interface, modules, and data base layer. The user interface layer basically offers views on results from underlying layers. The data base layer provides database for storing the comments on processes and handles the BPMN models repository. For our observation, we focus on modules layer. Beside process modeler and business process repository module, the tool has the task commenting module which allows adding task-wise comments to process from the perspective of different roles. As empowerment of commenting process in background runs the Semantic Annotation Module (SAM), which classifies the comments and assign them to a positive or negative sentiment using a real score. The implementation of SAM is described in detail in the next section. The data base layer provides database for storing the comments on processes and handles the BPMN models repository.

1

https://sites.google.com/view/sentipromo.

Towards Empowering Business Process Redesign with Sentiment Analysis Table 1 Training data sets for SAM module Source Amazon IMDB Yelp Twitter Total

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# prelabeled instances 1000 1000 1000 648579 651579

Table 2 Top 5 models explored for SAM module Rank Trainer

Accuracy AUC

AUPRC

F1-score

Duration

1.

AveragedPerceptronBinary

0,8244

0,8549

0,7678

46,3

2.

SdcaLogisticRegressionBinary

0,8186

0,8907

0,8485

0,7585

39,8

3.

LightGbmBinary

0,8082

0,8810

0,8352

0,7373

273,1

4.

SymbolicSgdLogisticRegressionBinary

0,8045

0,8754

0,8276

0,7321

38,9

5.

LinearSvmBinary

0,7930

0,8600

0,8106

0,6997

37,6

0,8963

4 Sentiment Analysis Module (SAM) Sentiment Analysis Module (SAM) was implemented using the ML .NET for classifying comments in English language. The SAM module uses supervised learning as base for comment classification. Table 1 shows an overview over data sets that were used for training. The training data originates from Sentiment Labeled Sentence Data Set from UCI Machine Learning Repository2 and from Sentiment140 data set from Stanford.3 The training was preformed with different number of iteration on different algorithms. Averaged perceptron binary classification model turned to be the best choice in this case. As we can see in Table 2, this model shows best AUC (Area Under The Curve) and other relevant measures [8].

4.1 Evaluation We evaluated the SAM module with additional external data sets (Twitter US Airline Sentiment4 ) containing review tweets on US airplane companies in order to estimate the results obtained through trainings. After data cleaning the evaluation data

2

https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentence. http://help.sentiment140.com/for-students/. 4 https://inclass.kaggle.com/crowdflower/twitter-airline-sentiment. 3

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Table 3 Confusion matrix for test data set True positive Predicted positive Predicted negative

TP = 1782 FN = 581

Table 4 Calculated measures on test data set Measure Value Sensitivity Specificity Accuracy F1 Score

0,7541 0.8427 0.8246 0.6376

True negative FP = 1445 TN = 7741

Derivations TPR =TP/(TP+FN) SPC = TN / (FP + TN) ACC = (TP + TN) / (P + N) F1 = 2TP / (2TP + FP + FN)

set contained 11549 tweets. The result after classifying the data set with SAM are shown in confusion matrix in Tables 3 and 4 through common information retrieval measures [8].

5 Preliminary Results Each time we use the task commenting module to comment a single task from a stakeholders perspective as shown in Fig. 2 SAM module calculates on the fly the sentiment score for the given comment. In Fig. 3, we present the view that shows the processed sentiment analysis of the stakeholders’ comments over all commented tasks within the SentiProMo tool. Each processed comment is presented as a row. For each row, we have the following elements presented as columns from the leftmost to the rightmost as follows: the task identifier, the task name, the stakeholders’ category (from the identified stakeholders we mentioned before), the comment made by a specific stakeholders, the calculated sentiment score as positive or negative number, and a timestamp that registers the time of the comment insertion by the corresponding stakeholder. Figure 4, shows an overview score as positive or negative number performed by SentiProMo of the sentiment of the whole business process as negative sentiment and positive sentiment. The software calculates the resulting number by adding all negative and positives sentiments of each task.

Towards Empowering Business Process Redesign with Sentiment Analysis

Fig. 2 Adding and classifying task-wise comments in SentiProMo Tool

Fig. 3 Sentiment analysis module (SAM) applied to comment analysis Fig. 4 Overall sentiment score in a business process

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6 Conclusion and Outlook Our contribution introduces shows how we use opinion mining, particularly sentiment analysis as empowerment, in the context of BPR and social BPM. Sentiment analysis is a perfect fit for the field of BPR and social BPM because we can analyze the user’s opinions with it and engage immediate changes in the process redesign. Currently, our Sentiment Analysis Module (SAM) in SentiProMo tool is limited to an accuracy of around 80%. In preliminary evaluation, we also obtained encouraging results for accuracy, sensitivity, specificity, and F1-score. In future, we will provide more training data to improve the performance of classification module. We will also further extend our software tool with a social web feature for capturing stakeholders’ feedback on a more massive scale. As possible improvement for ranking would be adding the configurable weighting of scores based on creators profile.

References 1. van der Aalst WMP (2003) Business process management demystified: a tutorial on models, systems and standards for workflow management. In: Desel J, Reisig W, Rozenberg G (eds) Lectures on concurrency and petri nets. Lecture notes in computer science, vol 3098, pp 1–65. Springer 2. Allweyer T (2009) BPMN 2.0: introduction to the standard for business process modeling. Books on Demand 3. Chen H, Zimbra D (2010) Ai and opinion mining. IEEE Intel Syst 25(3):74–76 4. Davenport TH, Short JE (1990) The new industrial engineering: information technology and business process redesign. Sloan Manage Rev 31(4):11–27. http://sloanreview.mit.edu/smr/ issue/1990/summer/1/ 5. Dumas M, Rosa ML, Mendling J, Reijers HA (2018) Fundamentals of business process management. Springer, Berlin Heidelberg 6. Hrastnik J, Cardoso J, Kappe F (2007) The business process knowledge framework, pp 517–520 7. Lüftenegger E, Softic S (2020) Sentipromo: a sentiment analysis-enabled social business process modeling tool. Business process management workshops. BPM 2020. Lecture Notes in business information processing. Springer, Cham 8. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press 9. Muehlen MZ, Recker J (2008) How much language is enough? Theoretical and practical use of the business process modeling notation. In: Bellahsène Z, Léonard M (eds) Advanced information systems engineering, pp 465–479. Springer, Berlin Heidelberg 10. Recker J, Indulska M, Rosemann M, Green P (2006) How good is bpmn really? Insights from theory and practice. In: Proceedings of the 14th European conference on information systems, ECIS 2006 11. Schmidt R, Nurcan S (2009) Bpm and social software. In: Ardagna D, Mecella M, Yang J (eds) Business process management workshops. Springer, Berlin Heidelberg, pp 649–658 12. Zor S, Schumm D, Leymann F (2011) A proposal of bpmn extensions for the manufacturing domain. In: Proceedings of the 44th CIRP international conference on manufacturing systems (2011) 13. Zott C, Amit R, Massa L (2011) The business model: recent developments and future research. J Manage 37(4):1019–1042

An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country Soliman Aljarboa and Shah J. Miah Abstract This paper is to create a basis of theoretical contribution for a new Ph.D. thesis in the area of Clinical Decision Support Systems (CDSS) acceptance. Over the past three years, we conducted the qualitative research into three distinctive phases to develop an extended Task-Technology Fit (TTF) Framework. These phases are for initiating requirement generation of the framework, discovering the factors of the framework through perspectives and evaluating the new proposed framework. The new condition is related to developing country in which various sectors such as healthcare is mostly under attention. We conduct a new inspection for assisting decisions support technology and its usefulness in this sector to integrate with other frameworks for assisting the value, use and how can be better accepted in context of healthcare professionals. Keywords CDSS · Healthcare · Developing countries · Technology acceptance · UTAUT · And TTF

1 Introduction CDSS is one type of Health Information Systems (HIS) that is used in diagnoses, dispensing appropriate medications, making recommendations and providing relevant information that all contribute to medical practitioners’ decision-making [1]. CDSS help medical practitioners to make their decisions and produce good advice based on up-to-date scientific proof [2]. CDSS is a system that needs more research S. Aljarboa (B) Department of Management Information System, College of Business and Economics, Qassim University, Buridah, Saudi Arabia e-mail: [email protected] Business School, Victoria University, Footscray, VIC, Australia S. J. Miah Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_11

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for new knowledge generation to reconcile and increase the interaction between the physician and CDSS in order to assist and support the physician to use the system successfully. It is necessary to investigate the determinants of CDSS acceptance for medical applications. According to Sambasivan et al. [3], developing countries face more difficulties than developed countries in implementing HIS. They also argued that improving the quality of healthcare performance can only be achieved by the acceptance of HIS by doctors. Several frameworks have been studied to determine the factors that affect the acceptance of HIS. However, there is still a lack of research regarding the factors that affect physicians’ acceptance of CDSS in developing countries [4]. Understanding and identifying the factors that influence the acceptance of information technology help designers and suppliers to reach a better understanding regarding the requirements of the end users. This leads to providing information systems which are more appropriate to the characteristics and conditions of the user and the work environment.

1.1 Unified Theory of Acceptance and Use of Technology (UTAUT) The UTAUT model was established and examined by Venkatesh et al. [5], where they investigated and analyzed eight different models and theories in order to discover and identify the factors that influencing user acceptance of technology. These models include the following: the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behaviour, a model combining the technology acceptance model and the theory of planned behaviour; the model of PC utilization, the innovation diffusion theory and the social cognitive theory. This contributed to providing and providing a model capable of interpretation and gaining a greater understanding of user acceptance of technology. In addition to that, the factors most influencing the user acceptance were also identified, and this led to many studies in several fields to use the UTAUT model. The UTAUT model includes four variables: gender, age, experience and voluntariness of use. In addition, UTAUT model comprises four major determinants which are: performance expectancy, effort expectancy, social influence and facilitating conditions [5].

1.2 Task-Technology Fit (TTF) TTF Model indicates that information technology has a positive and effective role on user performance in the event that the features and characteristics of the technology

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are appropriate and fit with the business mission [6]. The TTF model has been adopted and applied in both different technologies and also in HIT [7, 8]. TTF examines both of the two factors, the technology characteristics and the task characteristics to order understanding appropriate the requirements of the task to improve the performance of the user [6]. TTF includes two constructs: task characteristics and technology characteristics, which influence the utilization and task performance [6]. TTF proves that if the technology used provides features that fit the demands, then satisfactory performance will be achieved [9]. The task characteristics and technical characteristics affect TTF, resulting in an impact on system usage and also on user performance. The paper organised as follows. The next section describes the background details of the proposed research. The section after that presents research methodology followed by the data analysis. Section 4 describes m modified proposed framework following by the discussion and conclusion of the paper.

2 Background The study of CDSS acceptance contributes significantly to revealing many of the barriers and advantages in adopting the system and provides a significant opportunity for the success of the implementation of CDSS. The investigation and discovery of the factors that influence the acceptance of CDSS by the end user are crucial to its successful implementation [10]. Several previous studies have indicated the need to conduct high-quality studies to determine the factors that influence the acceptance of CDSS by physicians. In a study by Arts et al. [11], regarding the acceptance and obstacles concerning using CDSS in general practice, their results indicated a need to conduct more research on this issue to have a much better understanding of CDSS features required by GPs and to direct suppliers and designers to produce more effective systems based on the demands and requirements of the end user. Understanding the aspects which contribute to technology acceptance by physicians in the healthcare industry is significant for ensuring a simple application of new technologies [12]. IS acceptance’s motivation is connected directly to the concept that systems are capable of completing their daily activities [1]. Acceptance of the CDSS is crucial in order to provide better health care services, since if the user does not accept the technology, the non-acceptance may affect negatively the health care and well-being of patients [1].

2.1 Theoretical Conceptual Framework On the basis of the study and revision of different acceptance models, this research proposes to integrate TTF with UTAUT as Fig. 1 shows. This seems to be an appropriate conceptual framework to provide a contributed and effective model, which

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Fig. 1 Conceptual Model of Integration of UTAUT and TTF

is able to identify the determinants that affect CDSS as well as distinguishing the determinants that influence the new technology in the domain of HIS (Fig. 1). Several studies have combined TTF with TAM [13, 14]. Usoro et al. [15] asserted that the combined both TAM and TTF together will help provide significant explanatory. In addition, several studies have combined TTF with UTAUT to investigate the technology acceptance [16, 17] The integration of UTAUT and TTF frameworks will contribute considerably in identifying and discovering important factors which contribute to the understanding and investigation of user acceptance of technology. UTAUT and TTF have various advantages that help one to learn the factors that affect technology, so their combination contributes to achieving the most comprehensive advantages and benefits. For understanding user acceptance in the technology of healthcare, we must comprehend not only the facts which affect acceptance, but also how these factors are fit as well. Even though various researches have explained the matter of ‘fit’, it is insufficient since its significance within the organization must be explored in detail, combining the technology with the user, to understand the issues which are concerned with the implementation of healthcare technology. There is actually a strong need to gain, address and understand the empirical support for the factor of fit when determining the acceptance of healthcare technologies by users [18]. Researchers must examine the factors that affect user acceptance when it comes to the evaluation of the issue of user acceptance along with the factor the ‘fit’ among the technology and the users [18]. Khairat et al. [1] indicated that combining the models and frameworks would develop and enhance user acceptance to promote and assist the successful adoption of CDSS. They stated that if the user did not accept the system, there would be a

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lack of use of the technological system and, moreover, may threaten the healthcare and well-being of patients.

3 Methodology This research employed a qualitative approach to collect data by conducting semistructured interviews. Fifty-four interviews have been conducted with GPs in three stages to obtain their perspectives on and attitudes to the factors that influence the acceptance of CDSS. The procedure and implementation of three different stages in the qualitative approach contributes to increase the level of validity and certainty of the data collected. The first stage initiates the factors’ generation of the model through convergent interviews, where researchers interviewed 12 health professionals. The second stage discovered and identified the factors of the model, by interviewing 42 GPs. These interviews helped the researchers to recognize the factors that influence the acceptance of CDSS through collecting perspectives, beliefs and attitudes of GPs towards CDSS. The third stage involved a review of the new proposed framework; researchers sought to increase the validation of the final framework by discussing it with three GPs and the extent of their agreement and views about it. Several studies had collected data based on the first and second stages in order to provide more accurate and detailed results for the phenomenon or issues studied [19, 20]. In this research, a third stage was added to have further investigation results of the proposed new framework.

3.1 Stage One: Initiated Requirement Generation of the Framework In Stage One, 12 exploratory interviews of GPs were conducted, using a convergent interviewing technique to gather insights and reasons for the factors behind the usage of CDSS. In this stage, the UTAUT and TTF factors were reviewed and their appropriateness was also reviewed to clarify and explore the appropriateness for the integration framework. Convergent interviewing is a qualitative approach. It aims to collect, describe and understand individual preferences, attitudes and beliefs or to identify his or her significant interests [21]. The initial interviews in this approach help to make the questions more structured for the subsequent interviews. This enhances the credibility of the research. [22]. The convergent interview technique helped to recognize and identify the themes more easily and accurately [23]. This stage contributed to obtaining and discovering new factors by using convergent interviewing and devising questions based on previous interviews. The convergent technique was very relevant

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and valuable as it enabled the researcher to swiftly find the necessary issues and to establish the questions for the next stage [24].

3.2 Stage Two: Discovering the Factors of the Framework Through Perspectives This stage provided significant data through 42 interviews with GPs. The questions in this stage related to the issues and factors mentioned and raised in the interviews of Stage One. We used the case study approach to gather more data from 42 participants to explore and identify the factors that influence the acceptance of CDSS by GPs. This approach has been widely applied in several different fields of research, due to its distinctiveness and its contribution to obtaining valuable results [25, 26]. A case study collects data which greatly contributes focusing on the research and identifies the issues [27]. Moreover, Gioia et al. [28] pointed out that such an approach provides opportunities for gaining insights into emerging concepts. This approach contributed to the exploration of new factors and the development of a proposal framework that explained the factors that influence the acceptance of CDSS by GPs. In-depth interviews led to the investigation of factors that influence the acceptance of CDSS. This also helps obtaining a broader understanding of perspectives and attitudes of the GPs towards the adoption of CDSS. The results of the in-depth interviews showed that all factors of both UTAUT and TTF influence the acceptance of CDSS by GPs, except social influence factor, and the new discovered factors included Accessibility, Patient satisfaction, Communicability (With physicians) and Perceived Risk.

3.3 Stage Three: Validation of a New Proposed Framework The third stage refers to reviewing and evaluating of the final framework with three physicians in order to obtain views and a final impression on the influencing factors that have been identified. This stage increased along with the second stage of validity, the results and helps to gain a more comprehensive understanding of the final framework by the end users of CDSS. The participants in this stage were among those 12 GPs who were interviewed in Stage One. This stage was added to obtain more views of the influencing factors from the physicians because there new factors had been identified that had not been asked of them. Herm et al. [29] indicated that reviewing the framework through interviews improves the validity of the framework. Maunder et al. [30] developed a framework for assessing the e-health readiness of dietitians. They conducted their study in three stages: a literature review, identification of topics related to the study and interviews with 10 healthcare experts to verify and confirm the validity.

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4 Data Analysis Thematic analysis was employed to analyze the data collected from the participants to understand and discover more about their experiences, perspectives and attitudes regarding the factors that influence the acceptance of CDSS. The thematic analysis technique is widely applied in HIS studies [31]. Following this approach, contributed to the formation of theories and models through a set of steps that assist to generate factors [32]. NVivo software has been used to analyse the data through applying six-step stages of thematic analysis established by Braun, Clarke [33]. This study followed those same phases to analyze the qualitative data which included: (1) Familiarising data, (2) Generating initial codes, (3) Searching for themes, (4) Reviewing themes, (5) Defining and naming themes and (6) Producing the report. In Phase One (Familiarising data), the recording was reviewed more than once for analyzing each interview’s transcript to highlight the important issues and perspectives of the GPs. In Phase Two (Generating initial codes), documents are coded according to their appropriate name in NVivo. Each code was linked into nodes to facilitate the process of building main and sub-themes. In Phase Three (Searching for themes), after the initial arrangement and coding of the data, the symbols were classified into possible themes in addition to creating related sub-themes for the main themes. In Phase Four (Reviewing themes), the themes and their codes (established in the previous step) were checked and confirmed through comparing them with the interviews’ transcripts. In Phase Five (Defining and naming themes), this step expresses the final access to the main themes, their identification and approval regarding their relevance to the codes. This prompted a comprehensive analysis of every theme and determined an illuminating or descriptive name for each theme. Phase Six: (Producing the report), a detailed explanation of each theme was undertaken to facilitate understanding of each factor that influence the acceptance of CDSS.

5 Modified Proposed Framework The study results showed that the following factors influence the acceptance of CDSS by GPs. Performance Expectancy (Time, Alert, Accurate, Reduce Errors, Treatment Plan), Effort Expectancy and Facilitating Conditions (Training, Technical Support, Updating), Task-technology fit, Technology characteristics (Internet, Modern Computers), Task characteristics, Accessibility, Patient satisfaction, Communicability (With physicians) and Perceived Risk (Time risk, Functional performance risk of the system) influences the acceptance of CDSS by GPs. These are shown in Fig. 2. The results contributed to gain an insight into the factors that influence the acceptance and intention to use CDSS. Furthermore, more ideas and understanding of how to enhance the acceptance of CDSS and other advanced HIS systems were discovered and obtained.

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Fig. 2 Final modified framework

6 Discussion and Conclusion The CDSS is one of the advanced decision support mechanism that helps physicians to make more correct decisions using evidence-based facts or contents. Healthcare in developing countries needs more improved practices in this aspect, in order to better understand the health protocols, to reduce medical errors and to provide better health care services. The framework developed in this research provides a new approach that helps to understand the factors that influence the use of CDSS. This will greatly benefit researchers, developers and providers of medical systems by way of designing more successful systems implementations. In addition, the new framework provides a better understanding regarding the features and tools in CDSS that help health professionals to provide quality and effective medical services and care. Several HIS projects and systems have failed due to lack of consideration of the human side and the end user considerations while designing health systems [34]. Analysis and determination of the requirements of the end user of CDSS before its implementation and the final accreditation will save time, effort and money, and will also contribute to the adoption of a successful HIS [35]. Furthermore, Kabukye et al. [36] found that while health systems can improve health care, their adoption is still low because their systems do not meet the requirements of the user. A limitation of this study is that this research relied on participants in Saudi Arabia as a developing country. The focus was mainly in two cities: Riyadh, which is the largest city in Saudi Arabia in terms of population and is also the capital, and Qassim, as is it one of the closest areas to Riyadh [37]. According to UN-Habitat [37], the population of Riyadh is 8,276,700 people, while the population of Qassim has 1,464,800 people. These cities were chosen due to travel and location restrictions in addition to the time and cost factors.

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It was challenging to obtain enough participants due to the nature of their work and their concerns and being busy with patient care. The data collection process was interrupted due to longer waiting period for the GPs to agree to conduct the interview to obtain a suitable time for them. This research has only relied on a qualitative approach to explore the factors that affect GPs’ acceptance of CDSS. Consequently, a quantitative approach was not suitable. The aim of this research is to build and develop theory instead of testing in a real healthcare decision domain [38]. Therefore, applying a qualitative approach through conducting semi-structured interviews is appropriate for this research. This research provides an opportunity for future research to study and verify the study’s framework in studies that influence the acceptability of any new HIS design (for instance, using design science research [39–41]). This research was conducted in Saudi Arabia through utilising the interview technique, so it may be possible to conduct other similar studies using other research tools in other countries to determine if there are different or new factors. In addition, the focus of this study was on GPs, so other healthcare professionals would be of interests. It is possible to conduct further research that considers specialist or health professionals or consultants in different medical departments.

References 1. Khairat S, Marc D, Crosby W, Al Sanousi A (2018) Reasons for physicians not adopting clinical decision support systems: critical analysis. JMIR. Med Inform 6(2):e24-es 2. Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M et al (2017) What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci 12(1):113 3. Sambasivan M, Esmaeilzadeh P, Kumar N, Nezakati H (2012) Intention to adopt clinical decision support systems in a developing country: effect of Physician’s perceived professional autonomy, involvement and belief: a cross-sectional study BMC Med Inform Decis Mak 1(12): 142 4. Bawack RE, Kala Kamdjoug JR (2018) Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. Int J Med Informatics 109:15–22 5. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q: 425–78 6. Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236 7. Ali SB, Romero J, Morrison K, Hafeez B, Ancker JS (2018) Focus section health IT usability: applying a task-technology fit model to adapt an electronic patient portal for patient work. Appl Clin Inform 9(1):174–184 8. Gatara M, Cohen JF (2014) Mobile-health tool use and community health worker performance in the Kenyan context: a task-technology fit perspective: Proceedings of the Southern African institute for computer scientist and information technologists annual conference 2014 on SAICSIT 2014 Emp by Technology Association for computing machinery Centurion, South Africa. 229–40 9. Irick ML (2008) Task-technology fit and information systems effectiveness. J Knowl Manag Pract 9(3):1–5

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10. Lourdusamy R, Mattam XJ (2020) Clinical decision support systems and predictive analytics. In: Jain V, Chatterjee JM (eds) machine learning with health care perspective: machine learning and healthcare. Springer International Publishing, Cham, pp 317–355 11. Arts DL, Medlock SK, van Weert HCPM, Wyatt JC, Abu-Hanna A (2018) Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: a mixed methods evaluation. PLoS ONE 13(3):1–16 12. Lin C, Roan J, Lin IC (2012) Barriers to physicians’ adoption of healthcare information technology: An empirical study on multiple hospitals. J Med Syst 36(3):1965–1977 13. Narman P, Holm H, Hook D, Honeth N, Johnson P (2012) Using enterprise architecture and technology adoption models to predict application usage. J Syst Softw 85:1953–1967 14. Wu B, Chen X (2017) Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput Hum Behav 67:221–232 15. Usoro A, Shoyelu S, Kuofie M (2010) Task-technology fit and technology acceptance models applicability to e-tourism. J Econ Dev Manag IT Financ Mark 2(1):1 16. Afshan S, Sharif A (2016) Acceptance of mobile banking framework in Pakistan. Telemat Inf 33:370–387 17. Park J, Gunn F, Lee Y, Shim S (2015) Consumer acceptance of a revolutionary technologydriven product: the role of adoption in the industrial design development. J Retail Consum Serv 26:115–124 18. Mohamadali NAK, Garibaldi JM (2012) Understanding and addressing the ‘Fit’ between user technology and organization in evaluating user acceptance of healthcare technology: international conference on health informatics 1: 119–124 19. Joseph M, Chad P (2017) A manager/researcher can learn about professional practices in their workplace by using case research. J Work Learn 29(1):49–64 20. Mai CCC, Perry C, Loh E (2014) Integrating organizational change management and customer relationship management in a casino. UNLV Gaming Research & Rev J 18(2):1 21. Dick R (1990) Convergent interviewing, interchange, version 3, Brisbane 22. Remenyi D, Williams B, Money A, Swartz E (1998) Doing research in business and management: an introduction to process and method. Sage, London 23. Golafshani N (2003) Understanding reliability and validity in qualitative research. Qual Rep 8(4):597–606 24. Rao S, Perry C (2003) Convergent interviewing to build a theory in under-researched areas: Principles and an example investigation of Internet usage in inter-firm relationships. J Cetacean Res Manag 6(4):236–247 25. Cheek C, Hays R, Smith J, Allen P (2018) Improving case study research in medical education: a systematised review. Med Educ 480–487 26. Fàbregues S, Fetters MD (2019) Fundamentals of case study research in family medicine and community health. Fam Med Community Health 7(2):e000074-e 27. Johnson B, Christensen LB 4th ed (2012) Educational research: quantitative, qualitative, and mixed approaches. SAGE Publications, Thousand Oaks, CA 28. Gioia DA, Corley KG, Hamilton AL (2013) Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology. Organ Res Methods 16(1):15–31 29. Herm LV, Janiesch C, Helm A, Imgrund F, Fuchs K, Hofmann A et al (2020) A consolidated framework for implementing robotic process automation projects, Springer International Publishing, Cham p. 471–88 30. Maunder K, Walton K, Williams P, Ferguson M, Beck E (2018) A framework for eHealth readiness of dietitians. Int J Med Informatics 115:43–52 31. Christie HL, Schichel MCP, Tange HJ, Veenstra MY, Verhey FRJ, de Vugt ME (2020) Perspectives from municipality officials on the adoption dissemination, and implementation of electronic health interventions to support caregivers of people with dementia: inductive thematic analysis JMIR aging 3(1):e17255 32. Connolly M (2003) Qualitative analysis: a teaching tool for social work research. Qual Soc Work 2(1):103–112

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Research Trends in the Implementation of eModeration Systems: A Systematic Literature Review Vanitha Rajamany , J. A. van Biljon , and C. J. van Staden

Abstract The 2020 COVID-19 health pandemic has accelerated the trend towards digitizing education. Increased digitization necessitates a robust and regulatory framework for monitoring standards in a knowledge society, which requires adaptivity to the continuous changes in the quality assurance processes (moderation). This provides the rationale for an investigation into the literature trends in eModeration processes. This study draws on a systematic literature review as methodology to examine the extant literature on trends in eModeration research including the purpose of the research, methodologies and limitations regarding existing eModeration systems. The findings reveal that there is little, if any, empirical evidence of systems dedicated to online moderation of assessments specifically within the secondary school sector and that eModeration is mainly an emergent phenomenon with numerous adoption challenges, especially in resource constrained contexts. Keywords eModeration · eAssessment · Quality assurance · eSubmission · eMarking

1 Introduction Education is tasked with preparing students for economies that are experiencing turbulent changes [1]. The Fourth Industrial Revolution (4IR) has demanded an inevitable transformation in education, making Education 4.0 the buzzword within the educational fraternity [2]. Education 4.0, enabling new possibilities by aligning humans and technology, is a response to the needs of 4IR. A prediction of 4IR is V. Rajamany (B) · J. A. van Biljon · C. J. van Staden School of Computing, UNISA, Pretoria, South Africa e-mail: [email protected] J. A. van Biljon e-mail: [email protected] C. J. van Staden e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_12

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that, traditional methods and platforms for assessments may become irrelevant or insufficient [2]. Additionally, the global COVID-19 pandemic has accelerated 4IR predictions towards innovation and growth in digital solutions. The pandemic has refocused attention on eLearning and has necessitated a radical change in assessment processes. Tertiary institutions are increasingly adopting ICTs for online submission (e-submission) and electronic marking (e-marking). Increasing questions about the performance of eLearning systems have driven Higher Education Institutions (HEIs) to try different approaches to address the quality problems posed by the use of eLearning [3]. Moderation is a quality assurance process through which teachers share their knowledge and expectations concerning standards to improve the consistency of the judgement of assessments [4]. The moderator comments on the marking of a colleague and provides feedback on the consistency of the marking [5]. eAssessment needs innovative solutions to optimize the new moderation processes necessitated by the transformation from traditional paper-based moderation methods to electronic moderation [6]. A number of international studies claim generalizability in driving efforts at reforming moderation processes and increasing quality standards in education [7–9]. Prevailing research is generally supportive of a standards-based model, to develop moderation as a practical process in an attempt to raise standards [8, 10–12]. In contrast to online assessment and automated marking, which have been studied in depth and successfully applied in HEIs, the electronic moderation of school-based assessments is a relatively new phenomenon [13]. Based on the dynamic growth of online assessments, a usable, credible eModeration system is, therefore, critical. The research question can thus be stated as: What are the research trends regarding the implementation of eModeration systems? Assessment has traditionally been a process of written submissions [14]. Developments in access to, and advances in, ICT services have facilitated the area of digital assessment (eAssessment) [15] which is described as the use of technology to support and manage the assessment process life cycle [16]. eSubmission and eMarking technologies are gradually becoming the norm in UK Higher Education resulting in an increased interest in the electronic management of assessments [5]. This paper is structured as follows: Section 1 provides an introduction presenting the background, context and rationale for this paper. Section 2 indicates the literature review process. Section 3 outlines the findings and summarizes existing technological solutions for conducting moderation processes online. Section 4 concludes this paper.

2 Systematic Literature Review A Systematic Literature Review (SLR) is a rigorous, standardized methodology for the systematic, meticulous review of research results in a specific field [17]. The SLR is based on a detailed, well-articulated question. Furthermore, it isolates relevant studies, evaluates their quality and condenses the evidence by the use of an explicit

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methodology [18]. The search terms included, were: eModeration, digital moderation, digital moderation of assessments and digital platform for external moderation. Only English peer reviewed journal articles and articles published at conference proceedings from 2012 to 2020 were included. Given the dynamic nature of technology, there is a time lapse between the implementation of a system and when the system is, in fact, reported in academic literature. Restricting the search to a certain period of time is thus a limitation of this study as a system which has not yet been reported on, but could, in fact, exist. Literature focusing on studies in domains other than education were excluded. Within this group of papers, only papers that described implemented eModeration systems were included since this study focused on practical, evidence-based findings regarding the implementation of moderation systems. These exclusion criteria limited the number of papers retrieved. A further limitation arises from the search strategy focusing only on information system specific databases such as Scopus and Inspec. Specialized education databases such as ERIC were not specifically consulted. The search strategy followed is depicted in Fig. 1.

Fig. 1 Search strategy

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3 Results and Findings In this section, five systems/studies investigating eModeration will firstly be described individually. Secondly, the key focus of four of these five systems are summarized (cf. Table 1). The Digital Moderation Project [19] focused on teacher requirements prior to the creation of an actual eModeration system. Hence, the Table 1 Key focus of existing moderation systems System

Purpose

Proof of concept trial (SPARK) [10]

Improving peer review HEI processes of assessments in HEIs using technology to address quality assurance

Context

Findings An online tool should be context-sensitive; streamlined, efficient, cost-effective, sustainable and fit for purpose

Digital moderation project [19]

To determine teacher Secondary schools requirements for submitting assessments via an online digital platform

Inconclusive, no existing eModeration system could be found

User experience evaluation framework [21]

A framework for evaluating the user experience of an eModeration system

HEI

An eModeration system should enable moderators to upload marked scripts, download scripts, track the moderation process, provide security and notifications when moderation is complete

Adaptive Comparative Judgement System (ACJS) [20]

ICT system for social online moderation using comparative judgement of digital portfolios. Pairs of digital portfolios are dynamically generated for each assessor to judge. Area provided for assessors to record individual notes about each portfolio

HEI

It is feasible to use ICTs to support comparative judgements. An important finding is that the reliability of the final scores was not high

Computer assisted Machine learning evaluation system [11] techniques for solving problems of variances in evaluation

HEI

Machine learning can accurately predict scores of a second evaluator based on scores allocated by the first evaluator

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Digital Moderation Project was not included in the discussion. Based on the literature reviewed, preliminary findings are presented in Table 1. Newhouse and Tarricone [20] describe a system for pairwise comparison used in social online moderation to assist teachers with understanding of standards. A custom-made tool is used to store digital samples of assessments. The focus is on supporting social online moderation by generating groups of portfolios for each assessor to judge (cf. Table 1). The system calculates individual assessor scores to establish their reliability. System use is preceded and followed by standardization discussions using an online platform. Moderation takes the form of online scoring so that consensus is reached in awarding a grade rather than using the system to moderate assessments. The New Zealand Qualification’s Authority [19] conducted a survey to determine teacher requirements for an online platform for the submission of assessments. However, there is no further indication of the development of such a system (cf. Table 1). Van Staden [21] describes an eModerate system used and tested at two private Higher Education Institutions in SA. Assessors upload marked assessments and a moderator downloads these assessments for moderation. Stakeholders receive notification when moderation is completed. This study focused on a framework for evaluating the user experience of the eModerate system (cf. Table 1). Kamat and Dessai [11] present a system implementing machine language to establish the quality of the assessment and to validate consistency in evaluation. The system predicts a mark for each examiner to control variations in appraisals. Artificial Neural Network (ANN) modelling is then used on evaluations carried out by different examiners to predict the marks that would be obtained as though one examiner had performed all evaluations in the course (cf. Table 1). Durcheva et al. [14] describe the TeSLA system integrated into the Moodle platform and implemented in specialized courses. The emphasis in the TeSLA system is on the task design specifically focusing on ensuring academic integrity and eliminating opportunities for cheating by using photos, videos or audio recordings of registered students. The literature reviewed indicates that there are a limited number of studies applicable to the eModeration context. The findings indicate a focus on proof of concept systems and teacher requirements for using a digital platform to conduct moderation. Based on these findings, an online tool should be context-sensitive, streamlined, efficient, cost-effective, sustainable and fit for purpose. Only one of the five studies considered, i.e. the User Experience Evaluation Framework [21] provides comprehensive functionality which enables a moderator to access assessed scripts, annotate these scripts and upload them together with a report for the initial assessor to retrieve. The proof of concept (SPARK) system [10] only outlines the requirements for an eModeration system while Booth and Rennie [10] report only on the first phase of a seven-phase project. Van Staden [21] mentions a web-based eModerate System specifically designed for use at a HEI, but the actual moderation process is not necessarily an inherent function afforded by the eModerate System. Moderators are able to complete the

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moderation either using tools provided by a word processor or the sticky note functionality provided by Adobe products. Noteworthy amongst the findings is that the institution hosting the eModerate System should have adequate Internet connectivity and infrastructure, which is also a necessary prerequisite for 4IR. Additionally, technology limitations can hamper the digital moderation process [21]. The other systems namely (ACJS) and the Computer Assisted Evaluation System (cf. Table 1) focus on comparing the judgements provided by two evaluators either by generating a pair of portfolios or by using machine language to predict the accuracy of the judgements. However, the reliability of the final scores is dependent on teacher experience.

4 Conclusion This paper outlines a literature review investigating current trends on the use of technology in implementing moderation processes. The findings highlighted the importance of improving peer review processes using technology and machine learning techniques to determine variances in assessments. Notably, only two of the five studies focused on the implementation of technology in completing moderation processes. The five studies examined make use of qualitative and quantitative analyses of technological solutions, where the focus seems to be on quality assurance and the context predominantly that of HEIs. The lack of literature on the implementation of eModeration systems is the most pertinent finding of this paper, pointing to a knowledge gap on eModeration systems. It is, therefore, necessary for more research to be conducted on digital solutions for conducting moderation processes and, especially so in other educational contexts like the secondary school environment. Another important new direction is the improvement of peer review processes by using machine learning techniques to determine variances in assessments.

References 1. Motala S, Menon K (2020) In search of the “new normal”: reflections on teaching and learning during Covid-19 in a South African unversity. Southern African Rev Educ 26(1):80–99 2. Hussin AA (2018) ‘Education 4.0 made simple: ideas for teaching’, Int J Educ Lit Stud 6(3) 92. available at: https://journals.aiac.org.au/index.php/IJELS/article/view/4616 3. Farhan MK, Talib HA, Mohammed MS (2019) Key factors for defining the conceptual framework for quality assurance in e-learning. J Inf Technol Manag 11(3):16–28. https://doi.org/10. 22059/jitm.2019.74292 4. Handa M (2018) Challenges of moderation practices in private training establishments in New Zealand. Masters Dissertation, Unitec Institute of Technology 5. Vergés Bausili A (2018) From piloting e-submission to electronic management of assessment (EMA): mapping grading journeys. Br J Edu Technol 49(3):463–478. https://doi.org/10.1111/ bjet.12547

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6. Volante L (2020) ‘What will happen to school grades during the coronavirus pandemic?’ the conversation Africa, april. https://theconversation.com/what-will-happen-to-school-gradesduring-the-coronavirus-pandemic-135632?utm_medium=email&utm_campaign=Latest from the conversation for april 8 2020&utm_content=Latest from the conversation for april 8 2020+CID_1cd271e3ef246a59 7. Colbert P, Wyatt-Smith C, Klenowski V (2012) A systems-level approach to building sustainable assessment cultures: Moderation, quality task design and dependability of judgement. Policy Futur Educ 10(4):386–401. https://doi.org/10.2304/pfie.2012.10.4.386 8. Connolly S, Klenowski V, Wyatt-Smith CM (2012) Moderation and consistency of teacher judgement: teacher’s views. Br Edu Res J 38(4):593–614. https://doi.org/10.1080/01411926. 2011.569006 9. Wyatt-Smith C, et al (2017) ‘Standards of practice to standards of evidence: developing assessment capable teachers’, Assessment in Education: Principles Policy and Practice. Routledge, 24(2), 250–270. https://doi.org/10.1080/0969594X.2016.1228603 10. Booth S, Rennie M (2015) ‘A technology solution for the he sector on benchmarking for quality improvement purpose’s, In: Proceedings of the 2015 AAIR annual forum. Australasian association for institutional research Inc 22–32. https://doi.org/10.1145/3132847.3132886 11. Kamat VV, Dessai KG (2018) ‘e-moderation of answer-scripts evaluation for controlling intra/inter examiner heterogeneity’. In: IEEE 9th international conference on technology for education. T4E IEEE 130–133. https://doi.org/10.1109/T4E.2018.00035 12. Krause K et al (2013) Assuring final year subject and program achievement standards through inter–university peer review and moderation. http://www.uws.edu.au/latstandards 13. Van Staden C, Kroeze J, Van Biljon J (2019) Digital transformation for a sustainable society in the 21st century, IFIP international federatin for information processing 2019. Ed by IO pappas et al Cham: Springer International Publishing (Lecture Notes in Computer Science). https:// doi.org/10.1007/978-3-030-29374-1 14. Durcheva M, Pandiev I, Halova E, Kojuharova N, Rozeva, A (2019) Innovations in teaching and assessment of engineering courses, Supported by authentication and authorship analysis system. In: AIP conference proceedings, 1–9. https://doi.org/10.1063/1.5133514 15. Chia SP (2016) An investigation into student and teacher perceptions of, and attitudes towards, the use of information communication technologies to support digital forms of summative performance assessment in the applied information technology and engineering studies c. Doctor of Philosophy, School of Education, Edith Cowan University. https://doi.org/10.1057/ 978-1-349-95943-3_324 16. Moccozet L, Benkacem O, Tardy C, Berisha, E, Trindade RT, Burgi PY (2018) ‘A versatile and flexible framework for e-assessment in Higher-Education’. in 2018 17th International conference on information technology based higher education and training, ITHET 2018, 1–6. https://doi.org/10.1109/ITHET.2018.8424764 17. Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol Elsevier BV, 51(1):7–15. https://doi.org/10.1016/j.infsof.2008.09.009 18. Boell K, Cecez-Kecmanovic D (2015) On being ‘systematic’ in literature reviews in IS. J Inf Technol 30(2):161–173 19. New-Zealand–Qualifications Authority (2016) Digital Moderation Discussion Paper Wellington 20. Newhouse CP, Tarricone P (2016) Online moderation of external assessment using pairwise judgements. In: Australian council for computers in education 2016 conference refereed proceedings. Brisbane, 132–129 21. Van Staden C (2017) User experience evaluation of electronic moderation systems: a case study at a private higher education institution in South Africa. doctoral dissertation, school of computing, University of South Africa

From E-Government to Digital Transformation: Leadership Miguel Cuya and Sussy Bayona-Oré

Abstract In these times of pandemic, the world has witnessed the power of connectivity, and many organizations have seen the need to rethink their models and even reinvent themselves in response to this new form of global connectivity. This transformation has also changed the way public organizations deliver services to citizens. Successfully driving the digital transformation process requires a leader with skills and knowledge. Leadership has been considered as a critical factor that impacts egovernment. This paper presents the competences of those responsible for driving digital transformation (DT) in organizations. The results show that leadership is one of the most desired competences followed by technological knowledge, business vision, and customer orientation. These aspects must be considered as a fundamental basis, in any organization that takes on the challenge of this paradigm shift. Keywords Leadership · Digital transformation · Individual factors · E-Government

1 Introduction Currently, with the incorporation of Information and Communication Technologies (ICT), the use of the Internet, the massification of mobile technology has changed the way business is done, daily activities, and data processing [1]. The customer behavior and expectations are changing, and the transformation of the business model based on technology, innovation, and research it becomes necessary [2]. In this context, the practical “totality” acquires a more globalized meaning in all areas of business activity [3] and the top managers should understand digitalization and leading the change [4]. This situation forces the leaders of the organizations to adopt new digital M. Cuya · S. Bayona-Oré Universidad Nacional Mayor de San Marcos, Av. Germán Amézaga s/n, Lima, Lima, Peru e-mail: [email protected] S. Bayona-Oré (B) Universidad Autónoma del Perú, Av. Panamericana Sur Km. 16.3, Villa el Salvador, Lima, Peru © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_13

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technologies in their services that allow them to meet the demands of consumers [5]. In this way, organizations, regardless of the sector will face drastic changes in their corporate culture, in order to adapt their structures, processes, and models to face the new paradigms that respond to the context of the digital revolution, henceforth called Digital Transformation (DT) [6, 7]. Achievements or successes DT will depend on several factors; the stage of maturity, types of initiative, value of initiatives, and the structure of the teams focused on the customer experience [5]. It is worth mentioning the common mistake of understanding DT as the acquisition and use of cutting-edge technology, without considering the other factors, where despite the investment it is usually not possible to obtain the expected transformational impact. Experts attribute to this type of failure the lack of digital culture in leaders and decision-makers [8, 9]. Adopting DT will then be a priority for top management to seek new business models based on current customer needs, making the most of the organization’s talent pools, and finding a strategic response to the trend of the economy and digital technology [7, 10]. However, the success of these initiatives depends not only on the decision of the leader, but also on the commitment, capabilities, characteristics, and style of leadership of the leader throughout the transformation process [5]. These characteristics will be called individual factors, which will be studied under a constructivist and systemic approach. The study of technology from different perspectives is not at all unconnected with the problems of the foundation of knowledge [10, 11]. This work elaborates an epistemological analysis from a holistic and systemic approach in search of knowing the predominant factors which allow carrying out successfully the DT processes. The term systemic is related to the whole in a system, which in turn is defined as the set of elements functionally related to each other so that each element of the system is a function of some other element, and there is no single element [12]. Understanding the individual factors of a leader in DT within an epistemic framework leads us furthermore to review the causal transformations of the organization [13, 14]. Leading DT involves making organizational changes [15] and visible leadership support is required to drive change [16]. Another important point in the research is the methodology in finding new truths, these truths emanate from the relativistic analysis around a set of beliefs or paradigms [17]. A review of the specialized literature will be carried out focusing on the predominant factors regarding its role as a fundamental actor in the changes of culture and models required in a digital transformation. To this end, texts of obligatory reference in the field will be reviewed and those contributions of specialized academic articles between 2017 and 2020 will be included. In this work, we will focus on the individual characteristic of the leader and the relationship of this one in the organizations in the DT, present in the literature. Therefore, after the introduction, the rest of the paper is structured as follows: Sect. 2 presents the background of the research work; Sect. 3 presents the methodology used; Sect. 4 presents the results and discussion of results. Finally, conclusions and future work are included in Sect. 5.

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2 Background This section covers the basic concepts on which this research work is based such as digital transformation (DT), digital maturity, and leadership.

2.1 Digital Transformation (DT) Disruptive technological advances force organizations to find new ways of doing business so that they can adapt and evolve, thus avoiding the risk of going out of business [1]. The industrial ecosystem is being transformed into a digital one where globalized communication and artificial intelligence has been facilitating decentralization and automation in decision making [18]. This process of change to digitalization that organizations must adopt is seen by many professionals as a global mega-trend called Digital Transformation, which has the capacity to fundamentally change industries and their operations [5]. DT is the management process that guides the culture, strategy, methodologies, and capabilities of an organization from digital technologies [4]. It is essential to adopt the transformation in order to stay in the market, increasingly global, with more specific demands and constant change by consumers [6]. Likewise, the implementation of digital strategies for transformation will not be immune to the problems of the organization, whether structural or maturity problems [1]. However, it is important to consider that the DT can be a great opportunity to obtain better economic benefits [19]. The government must be able to attract new talents with skills, knowledge, experience, interpersonal abilities, and others with the purpose of address the challenge of digital transformation.

2.2 Digital Maturity Organizations that are not digital natives pursue digital maturity as an indispensable necessity to survive and succeed in today’s society [8]. Maturity is understood as a state of being complete or ready, based on the result of progress in the development of a system, and being specific in the DT we could use the term digital maturity to determine the state of readiness to successfully adopt the transformation [4]. Digital maturity is achieved through commitment, investment, and leadership. Companies with digital maturity set realistic priorities and commit to the hard work of achieving digital maturity. Digital maturity is acquired not only when the productive processes are digitized, but also when digital thinking or culture leads all actions [8]. Davenport and Redman [9] establish a model of digital maturity based on talent in four key areas: Technology, Data, Process, and Organizational Change (see Fig. 1).

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Fig. 1 Systemic construct of individual factors of the DT leader

2.3 Leadership In the business world, the term leadership has been and is being widely studied in the administrative sciences, so leadership is understood as the ability of a person to influence others to energize, support, or encourage them to achieve the goal of a common project and achieve improvements in the organization [20]. Leadership is the ability to influence a group to achieve a vision or set of goals, where the source of this influence could be formal, such as the managerial position in an organization [21]. In this business environment, leadership has been studied from two points of view; the first from the individual as part of a hierarchical position in the organization, and the other as a process of social influence occurring in a social system [22, 23]. Leadership is a process-oriented toward social transaction and interrelationship that occurs between leaders and collaborators in the fulfillment of common objectives [24]. Several studies agree that leadership in management teams plays a fundamental role in a digital integration process, since they are the main agents of change [23].

3 Methodology The present research is a review of the specialized literature corresponding to models, strategies, success, and failure cases experienced by organizations during the adoption of DT and focus on the predominant individual factors of the leader through inductive and systemic reasoning. For this purpose, academic and indexed articles, published in the period 2017–2020, were reviewed. To extract the relevant information from each of the scientific articles, an Excel format was designed with the following information, general data of the article, DT leader competences, leadership styles, competences definitions, and the influence of competences on DT. A total of

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22 peer-reviewed articles from journals or conferences were identified and analyzed in order to identify the predominant individual factors of the leader.

4 Results As a result of the literature review, a set of individual competences were identified that the leaders of today’s organizations should have developed by beginning the DT process. Table 1 shows the list of the individual competences identified. It can be seen that the most frequently mentioned competences required for digital transformation leaders are leadership, communication, business vision, technological knowledge, and agile capabilities. Most of the adoption case studies correspond to regions of developed countries in technological areas, which would indicate that despite the fact that technology is of global tendency, adoption of DT in less developed countries has some kind of paradigmatic brake. This could be a reason for future research. Leadership competence is the factor often mentioned in studies. It is achieved through organizational change, and this in turn through empowered leadership that directs change management. The process of DT requires consideration of the value of intellectual and human capital, and it is the leader who is responsible for managing change through those resources. In the same line, Brock [29] and Alunni [26] consider leadership as one of the success factors to be taken into account in the management capacities and in any organization that adopts a DT process. With this competence, the leaders focus on changing the organizational culture as part of the efforts to DT [32]. Communication is an important factor for the individual leader in overcoming the challenge of cultural and operational impediments that the organization presents in adopting DT. Alunni [26] considers communication as one of the three main factors for transformation. Communication is the competence that allows the strategy to be known inside and allows it to be adjusted through continuous communication with Table 1 Competences of the DT leaders Competences

Studies

Total

Leadership

[4, 5, 9, 26, 27, 28, 30, 31, 19, 33, 38, 39, 41]

13

Communication

[1, 5, 9, 19, 25, 26, 28, 36, 37]

9

Business vision

[5, 6, 9, 31, 32, 34, 37, 38]

8

Technological knowledge

[4, 6, 27, 28, 29, 31, 33, 38]

8

Agile capabilities

[29, 5, 33, 38, 40, 41]

6

Transparency

[1, 5, 25, 33, 36]

5

Customer orientation

[6, 26, 28]

3

Teamwork

[5, 33, 38]

2

Coaching

[5, 28]

2

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the client [34]. The business vision must be designed and communicated to the client so that he or she captures the value proposal. The vision as a leader’s competence is related to his strategic capacity to respond to the environment not only by thinking about what to do but also by proposing a new one [31]. “Leaders play a key, nodal role, guiding and providing vision on the new role implications, the focus of the new contribution and the impact it has on the client” [26]. For changes to be really successful, it must be understood that DT is not only a matter of technology change but a question of strategic vision that allows an adequate organizational change and process redesign [6]. That is why leaders must have the ability to focus their employees on a clear objective as part of the strategy [34]. It is important that the leader’s vision to have to face the change as something structural and only as a change of technology [5]. The systemic vision was one of the 11 competences identified in the work of developing management competences for the improvement of integrated care [35]. Brock [29] highlights the importance of the technological capabilities of leaders in organizations, to make them face the resistance of change and contribute to organizational agility under a digital model. Breuer [28] considers the digital competences of the leader as a necessary leadership evolution to face the changes in the industry. In one of the cases of Alunni’s work [26], they highlighted the importance of enhancing technological capabilities through training, thus reinforcing the importance of technological knowledge in the DT initiative. For her part, Meriño [31] indicates that one of the main obstacles in digital transformation is the lack of digital skills on the part of the leaders. To have the creativity, vision, and strategy, the leaders of these times have to be up to date with the technological changes, trends, and tools that exist today and even more so in the business world. Agile capabilities are related to the flexibility to accommodate small, medium, and large changes to processes [29]. Transparency would create trust and break down many fears that would smooth out the transformation and alignment [26]. In DT, as in any change, organizations face greater complexity and create uncertainty, so leaders should take into account this competence in the design of their communications [33]. The DT should prioritize employees’ and customers’ experiences with digitalization, requiring leaders to keep their digital skills up to date to take advantage of the opportunities that arise [25]. Customer orientation is related to know how to interact with new customer needs and how they access information in a digital context, to be considered in the digital strategy of the organization [34]. Teamwork allows us to strengthen and reinforce the synergy in the organization and encourage to exploit the individual potential for the benefit of the group, which are agile creating networks of contact either in formal or informal teams [33]. Teams must be agile and selected according to their skills [28, 33]. The leader must empower the team through a role of mentor, coach, and tutor [5, 28]. Leaders should influence even informal teams, creating networks of knowledge sharing through an inclusive environment [33]. The change process will require the leader’s ability to join the team as a coach, encouraged to bring out the best in each member for the benefit of the transformation [5]. In the e-government paradigm shift, the digital government promotes a new model of e-government [42]. A governmental reform precedes the DT of public services.

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Leadership is a critical success factor to implement local e-government [43]. The results of this review show that leadership is a competence for digital transformation.

5 Conclusions The digital transformation in public and private organizations is a process that requires adjust their products and services to the requirements of this digital trend where customer behavior and expectations are changing. Many factors are involved and must be considered in the transformation process. This paper presents the results of literature review focused on the importance of the leader’s role in order to make the change process successful, considering that DT is not only an improvement in the company’s technological resources, it is not only about improving a process, but about reviewing and rethinking a reengineering from the business model with a strategic and digital vision. Communication and technological knowledge are competences mentioned in the studies. For future work, we conduct a review using the Systematic Literature Review (SRL) method with the purpose of establishing the competences responsible for digital transformation and e-government of public institutions.

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Application of Machine Learning Methods on IoT Parking Sensors’ Data Dražen Vuk and Darko Androˇcec

Abstract Internet of things brings many innovations and will impact our everyday life. Smart parking is one of the important IoT usage scenarios in urban environments. In this work, we show how to use machine learning methods on Internet of things parking sensors’ data to detect free parking spaces. We have used about 100,000 instances of data from NBPS parking sensors provided by Mobilisis company. These are actual data from parking sensors with a magnetometer deployed all over the world. The data was preprocessed, normalized, and clustered, because temperature has a large effect on the value of the magnetometer. Next, the XGBoost algorithm and different architectures of artificial neural networks were used to predict whether the parking space is free or not. Used machine learning methods achieve better accuracy than the current classic algorithm based on the history data of a particular parking sensor that is currently used in production (Mobilisis smart parking solution). Keywords Machine learning · Parking sensor · Smart city · Artificial neural networks · Internet of things

1 Introduction Internet of things (IoT) extends the use of the Internet by using different physical objects—things (sensors, actuators, and microcontrollers). IoT services often use cloud computing to store sensors’ data and access it from remote locations. Smart city concepts use both mentioned technologies to improve the lives of citizens. One of the key issues for citizens is to find an available parking spot in the city. For that reason, many companies have decided to manufacture parking sensors and appropriate smart D. Vuk (B) Mobilisis d.o.o, Varazdinska ulica - odvojak II 7, 42000 Varaždin, Jalkovec, Croatia e-mail: [email protected] D. Androˇcec Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, 42000 Varaždin, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_14

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parking cloud solutions. One of such companies is Mobilisis d.o.o. with more than 4,000 parking sensors installed worldwide. In this work, we applied machine learning methods (XGBoost and artificial neural networks) to detect whether a car is above a specific parking sensor, which means a specific parking space is not available. The current detection is located using a classic software algorithm on the sensor itself, and an additional detection check is once again performed on the server based on the history of all parking for a particular sensor, and in this way, the algorithm concludes whether the space is free or occupied. In this way, the detection of NBPS sensors is 97% accurate, and in this work, we will show that more accurate prediction is possible by using machine learning methods. The remaining sections of this paper are organized as follows: In Sect. 2, a current state of the art is given. Section 3 explains used data and preprocessing. In Sect. 4, we give details on used machine learning methods and our models’ results. Finally, Sect. 5 concludes this paper and gives guidelines for future research.

2 Related Work Nowadays, smart city services are often driven by the Internet of things technologies. Mijac et al. [1] conducted a systematic literature review in order to investigate proposed smart city services driven by IoT. After obtaining the list of relevant papers, the papers were categorized by the smart city services into the following categories: traffic and transport; environment monitoring; accessibility and healthcare; waste management; public lighting; energy management; city infrastructure, and category other. Neirotti et al. [2] provided a comprehensive definition of the smart city term. They also explored various smart city initiatives. There are also existing works on applying machine learning methods in smart city scenarios. Mahdavinejad et al. [3] assess the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the smart city IoT data. Mohammadi and Al-Fuqaha [4] proposed a framework for semi-supervised deep reinforcement learning for smart city big data. They also articulated several challenges and trending research directions for incorporating machine learning to realize new smart city services. Chin et al. [5] tested the performance of four machine learning classification algorithms in correlating the effects of weather data on short journeys made by cyclists in London. Ullah et al. [6] performed a comprehensive review to explore the role of artificial intelligence, machine learning, and deep reinforcement learning in the evolution of smart cities. One of their topics was also machine learning method usage in intelligent transportation systems. Smart parking is the main theme of some existing works. Khanna and Anand [7] proposed an IoT-based cloud-integrated smart parking system. Lin et al. [8] performed a survey on smart parking solutions. Al-Turjman and Malekloo [9] classified the smart parking systems while considering soft and hard design factors. Bock

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et al. [10] described machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Saharan et al. [11] proposed an occupancy-driven machine learning based on-street parking pricing scheme. The proposed scheme uses machine learning based approaches to predict occupancy of parking lots in Seattle city, which in turn is used to deduce occupancy-driven prices for arriving vehicles. Amato et al. [12] proposed a CNN architecture for visual parking occupancy detection. Provoost et al. [13] examined the impact of the Web of things and artificial intelligence in smart cities, considering a real-world problem of predicting parking availability. Traffic cameras are used as sensors, together with weather forecasting web services. Yamin Siddiqui et al. [14] presented smart occupancy detection for road traffic parking using a deep extreme learning machine.

3 Data of Parking Sensors NarrowBand parking sensor (NBPS) was used in our work. It is an autonomous, wireless compact sensor for monitoring the occupancy of parking spaces, which allows cities to more easily manage the challenges of parking. The sensor uses earth magnetic field measurement technology to detect the presence of vehicles in the parking lot. These sensors are installed under a layer of asphalt in each parking space. In the event of any strong magnetic change, the sensor sends all changes to the server via the NBIoT network [20]. The packet sent to the server consists of a dozen of different data. The packet itself is 48 bytes in size. The data that are important for this work are the values of both magnetometers (x, y, z and x2, y2, z2) and the temperature for each magnetometer. The IoT NBPS sensor is manufactured by Mobilisis d.o.o. Raw data from parking sensors were obtained from Mobilisis d.o.o company. These are actual data from parking sensors with a magnetometer. The mentioned sensors are spread all over the world. The data obtained from the parking sensors are the values of two magnetometers and temperatures, seven values in total: (x, y, z), (x2, y2, z2), and temperature. Also, the detection of the presence/absence of the car above the sensor, the parking time, the location code, and the sensor MAC address for each data is obtained. The biggest problem with raw data is that each sensor gets completely different values from the magnetometer in the case when there is no car above the sensor or when the car is above the sensor, and this problem is mostly related to the calibration of the magnetometer and the problem of so-called “drifting”. Magnetic values when the temperature, sensor age, and environment change, the calibration is stable only at the temperatures at which the calibration was performed, but when the temperature changes the magnetometer changes its magnetic reading by more than 150 milligaus, which is equal to the change in a situation where the car is above the sensor. Also, it is important to mention that with each sensor this change in the reading of magnetic values in relation to temperature is different. The problem can also be manholes, metal fences, neighboring cars that do not park according to regulations, but park near or across the line, metal dust falling from the car, power lines, transformers,

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and everything else that can affect the magnetic change. Ideally, raw data should be prepared so that each sensor has similar values as all other sensors when there is no car above it. This is unfortunately impossible due to the orientation of the sensor as well as various other influences.

4 Application of Machine Learning and Results We have used Keras [15] framework (neural network method/deep learning) and XGBoost [16] (gradient boosting decision tree algorithm) to detect cars in parking spaces. For both machine learning methods, it will be explained in detail what the input data are, how we have prepared input data, and which combinations give the best results. Complete Python code used in this work is publicly available at https:// github.com/dvuk/primjena_metoda_strojnog_ucenja_na_podatke_interneta_stvari.

4.1 Normalization of Parking Sensor Data Examples of how to normalize the values from the NBPS parking sensor will be shown below. The values for normalization will be the values from the two magnetometers {x1, y1, z1} and {x2, y2, z2}, temperature, magnitude calculated by both sensors, and difference of vector from magnetometer M1 and M2. We have used the MinMax normalization algorithm. MinMax normalization is a normalization strategy that linearly transforms x into y = (x-min)/(max-min), where min and max are the minimum and maximum values in X. When x = min, then it is y = 0, and when x = max, then y = 1. This means that the minimum value in X is mapped to 0, and the maximum in X at 1. Thus, the entire range of X values from min to max is mapped to the range from 0 to 1.

4.2 XGBoost 100,000 instances of data have been prepared for car detection using the XGBoost algorithm [16], of which the first 90,000 data will be reserved for training while the second part of 10,000 will be used to test machine learning accuracy. The input parameters for machine learning will be the values from the first magnetometer (X, Y, Z), the values from the second magnetometer (x, y, z), temperature, magnitude from the first magnetometer, the difference of the vector between the first and the second magnetometer, and the sum of the vectors of the first magnetometer. The importance of features for the predictive modeling problem was created using the “sclearn.feature-selection” library in Python. Basically, this part tests the model by removing the features according to their importance. If the feature has no effect

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Fig. 1 Importance of different features

on the prediction or if it has a negative effect on the prediction, then such a feature should be removed. Figure 1 shows a graph with the importance of each input feature. 90,000 instances of data were used to train the XGBoost model. The GridSearchCV class was used to search for the parameters that give the best results. By looking for the parameters that give the best result, the accuracy increased from 90.59 to 94.19%, so the obtained result was increased by 3.6%. Executing our code to find the best parameters (−0.217778, max-depth = 8, n-estimators = 150), while the default or the default data in XGBClassifier is max-depth = 4, n-estimators = 100. With help of tuning of the XGBClassifier parameters and the training of the 90,000 instances, it has been achieved a score of 94.19%. It is important to note that the data with which the model was built are different from the data with which the prediction was tested. Normalizing the input did not help machine learning in the prediction and accuracy of car detection. When we trained a model with only one sensor, then machine learning gives slightly better results and the accuracy increases to 97.4, but on the other hand, it is almost practically impossible to train the model for each individual sensor. Therefore, the aim of this paper is to find a solution that will give the accuracy of the prediction for at least 97%, but for all sensors, not just one.

4.3 Neural Networks Models For training neural network models in Keras, we have used raw sensor data (90,000 instances for train set, and 10,000 instances for test set). The GridSearchCV [17] tool was used to tune the parameters. GridSearchCV works on the principle of searching for all combinations for a particular set of data and models. We have tuned the

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following parameters: optimizer algorithm, initializer, epochs, batches, and activation function. Using the GridSearchCV class for the activation parameter, the best result is obtained with softplus. The GridSearchCV class was also used for the batch-size, epochs, init, and optimizer parameters. For the most accurate detection the following parameters were obtained: “batch-size”: 20, “epochs”: 100, “init”: “uniform”, “optimizer”: “rmsprop”. These results took 19 h to GridSearchCV algorithm compare all combinations and show which parameters are best to use for the largest accuracy. The selection and number of layers for Keras neural network models cannot be calculated analytically. Basically one should start with trial and error. Different combinations need to be tried, depending on the domain problem. Our neural network model implementation is publicly available at https://github.com/dvuk/primjena_ metoda_strojnog_ucenja_na_podatke_interneta_stvari. We have used the following layers: sequential, LSTM [18], dense, dropout, and simple RNN [19]. Using the GridSearchCV tool to select the best parameters, setting the layers for deep learning and training models with 90,000 parking spaces achieved a result of only 68.91%. When we trained a model with only one sensor then deep learning gives much better results, accuracy increases to as much as 97.83%, but it is infeasible to train separately for each sensor.

4.4 Using Clustering to Improve Results To improve the existing results, which are still unsatisfactory both for Keras and XGBoost models, raw data should be prepared in a way that values of magnetometer when there is no car on a parking lot are similar regardless of their magnetometer calibration, temperature drift, or sensor orientation. The idea is to make an algorithm similar to data clustering and it is applied to each sensor individually. That would mean that any magnetic change should be stored in a memory or in a database. For each new magnetic value from the sensor, we should look for the most common value in the database with a specific temperature, because the temperature has a large effect on the value of the magnetometer. The most common value obtained for each axis separately should actually be a reference to when the car is not above the sensor. Preparing data in this way ensures that raw data is obtained from all magnetic sensors so that X, Y, and Z have almost the same values when there is no car above the sensor. As already mentioned, the most common value of the magnetometer is (−506, 120, −360) and if the new value obtained from the parking sensor is, for example, (−606, −64, −60) then by subtracting the vector we get the following value (−506–606, −120–64, −360–60) => (101, −56, −300). This example shows that X, Y, and Z still have large values and are far from the reference or most common value. From the above, it can easily be concluded that it is a car parked above this sensor. In theory, if such values are obtained from the parking sensor (−515, −135, −345) and subtract the reference or most common value as in the following example: (−506–515, −120–135, −360–344) => (9, 15, −16) it is clear that the values are

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small. By subtracting the most common reference value, values close to zero are obtained, which means that there is no car above a sensor. This method significantly increases accuracy and enable our XGBoost and neural network models to have a more accurate prediction. Using this preprocessing method, Keras model achieved an accuracy of 98,08%, and the XGBoost model achieved an accuracy of 98,57%, which is a better result than 97% achieved using current non-machine learning methods used in Mobilisis’s smart parking solution in production. Of course, there are also some downsides to this algorithm. Namely, for each sensor in the database or some other container, we must have a parking history stored in order to calculate the most common or a reference value. For example, if a new sensor is installed, it will not have any history and it will be necessary to make at least three parkings, so that the algorithm can learn which values are the most common.

5 Conclusion Smart parking initiatives are one of the most popular smart city use cases. Intelligent parking systems assist drivers to find an available parking space by using a mobile application or digital displays near to roads. In this work, we show how to use machine learning methods such as XGBoost and artificial neural networks in Keras on Internet of things parking sensors’ data to detect free parking spaces. We have used actual data from parking sensors with a magnetometer deployed all over the world from NBPS parking sensors provided by Mobilisis d.o.o. company. Using the preprocessing method, the Keras model achieved an accuracy of 98,08%, and XGBoost model achieved an accuracy of 98,57%, which are better results than 97% achieved using non-machine learning methods used in Mobilisis’s smart parking solution currently in production. Our models are publicly available at https://github. com/dvuk/primjena_metoda_strojnog_ucenja_na_podatke_interneta_stvari. In our future work, we plan to apply our model to other parking sensors’ data. We will also try other state-of-the-art machine learning algorithms (e.g., LightGBM, CatBoost), and various other architectures of artificial neural networks. Accuracy of machine learning methods is better than classical algorithms, but data availability and preparation are crucial here, so we will examine more on parking data preprocessing and post-processing in our future work.

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A Fast Algorithm for Image Deconvolution Based on a Rank Constrained Inverse Matrix Approximation Problem Pablo Soto-Quiros, Juan Jose Fallas-Monge, and Jeffry Chavarría-Molina

Abstract In this paper, we present a fast method for image deconvolution, which is based on the rank constrained inverse matrix approximation (RCIMA) problem. The RCIMA problem is a general case of the low-rank approximation problem proposed by Eckart-Young. This new algorithm, so-called the fast-RCIMA method, is based on tensor product and Tikhonov’s regularization to approximate the pseudoinverse and bilateral random projections to estimate the rank constrained approximation. The fast-RCIMA method reduces the execution time to estimate optimal solution and preserves the same accuracy of classical methods. We use training data as a substitute for knowledge of a forward model. Numerical simulations on measuring execution time and speedup confirmed the efficiency of the proposed method. Keywords Rank constrained · Pseudoinverse · Fast algorithm · Speedup · Image deconvolution

1 Introduction Image deconvolution is an image processing technique designed to remove blur or enhance contrast and resolution [1, 2]. If X ∈ Rm×n represents an digital image, then the mathematical model in image deconvolution can be written as Ax + η = c,

(1)

P. Soto-Quiros (B) · J. Jose Fallas-Monge · J. Chavarría-Molina Escuela de Matemática, Instituto Tecnológico de Costa Rica, Cartago 30101, Costa Rica e-mail: [email protected] J. Jose Fallas-Monge e-mail: [email protected] J. Chavarría-Molina e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_15

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where x ∈ Rmn is a vectorized version of X (i.e., convert X into a column by stacking its columns), A ∈ Rmn×mn is a matrix that models the forward process, η ∈ Rmn is an additive noise, and c ∈ Rmn is a vectorized version of the noisy image C ∈ Rm×n . Given c and A, the goal of the inverse problem is to reconstruct x. Entries in A depend on the point spread functions (PSFs) of the imaging system, and under some assumptions, matrix A may be highly structured so that an efficient algorithms can be used in the inverse problem to reconstruct x [1]. However, matrix A is unknown in most case, and only observed noisy image c is available. To solve this problem, training data is used as substitute for knowledge of the forward model by introducing a rank constrained inverse matrix problem that avoids including A in the problem formulation [2]. p×q Let Rr be the set of all real p × q matrices of rank at most r ≤ min{ p, q}. If S (k) S are training data of vectorized images x and c, respectively, {x }k=1 and {c(k) }k=1 then the goal of the rank constrained inverse matrix problem is to find  F ∈ Rrmn×mn that gives a small reconstruction error for the given training set, i.e., find  F such that S 1  Fc(k) − x k 22 , F = arg min mn×mn S F∈Rr k=1

(2)

where  · 2 is the Euclidean norm. Using relationships between Euclidean norm and Frobenius norm (i.e.,  ·  f r ), problem (2) can be reformulated as follows: 1  F = arg min FC − X 2f r , F∈Rrmn×mn S

(3)

where X and C are created using training data, i.e., X = [x (1) x (2) ... x (S) ] ∈ Rmn×S and C = [c(1) c(2) ... c(S) ] ∈ Rmn×S . Once matrix  F is computed, a matrix-vector multiplication is required to solve the problem, i.e., x =  Fc. Problem (3) is so-called the rank constrained inverse matrix approximation (RCIMA) problem, which is a generalization of the well know low-rank approximation problem proposed by Eckart-Young [3]. In [2], Chung and Chung present a solution of the RCIMA problem, which uses the SVD to estimate the pseudoinverse and the low-rank approximation. The SVD method is very accurate but is critically blocked by computational complexity that makes it impractical in the case of large matrices [4, 5]. In this paper, we propose a new and efficient method to compute a solution of the RCIMA problem (3), so-called the fast-RCIMA method. The approach of this new method uses fast algorithms to approximate the pseudoinverse and low-rank matrix. Most specifically, the fast pseudoinverse technique used in the fast-RCIMA method utilizes a special type of tensor product of two vectors [7] and Tikhonov’s regularization [8, 9] (see Sect. 3.1 below for further details). Besides, a bilateral

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random projection and its power-scheme modification [10, 11] are used in this paper to estimate a low-rank approximation. Moreover, in this paper we proposed a generalization of the method developed in [11] to estimate a low-rank approximation, which includes analysis of particular case rank(Y2 ) < r , where Y2 ∈ Rm×r is a right random projection (see Sect. 3.2 below for further details). The proposed implementation to estimate a solution of the RCIMA problem reduces the execution time to compute  F in (3) and, moreover, preserves the same accuracy of classical method developed in [2]. In this work, we choose methods already mentioned briefly in [7, 8, 10, 11] for their easy computational implementation and their high-speed and efficiency to calculate the pseudoinverse and low-rank approximation. However, there are other fast algorithms to estimate the pseudoinverse and low-rank approximation (see, e.g., [6, 12–15]). Throughout this paper, we use the following notation: M † denotes the pseudoinvserse of M, Im ∈ Rm×m is the identity matrix, and M(1 : i, :) and M(:, 1 : j) are formed with the first i rows and j columns, respectively, of M. The paper is organized as follows. Section 2 presents a solution of the RCIMA problem based on the SVD method given in [2]. Fast algorithms to estimate the pseudoinverse and low-rank approximation, respectively, are explained in Sect. 3. In Sect. 4, the fast-GRLMA method is proposed to approximate a solution to problem (3). A numerical example based on image deconvolution is presented in Sect. 5. Finally, Sect. 6 contains some concluding remarks.

2 RCIMA Method A solution of problem in (3) is proposed by Chung and Chung [2]. Let C = U V T be the singular value decomposition (SVD) of C. If k = rank(C) and P = X Vk VkT , where Vk = V (:, 1 : k), then a solution of the RCIMA problem is given by  F = Pr C † ,

(4)

where Pr is the optimal low-rank approximation of P, such that rank(Pr ) ≤ r (see Sect. 3.2 below for more details). Matrix P is known as the kernel of the RCIMA problem. The associated implementation of (4) is presented below in Algorithm 1. This implementation is so-called the RCIMA method.

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Algorithm 1: RCIMA method

1 2 3 4 5 6 7 8

S S Input : Training Data {x (k) }k=1 ⊆ Rmn , {c(k) }k=1 ⊆ Rmn and r ≤ mn Output:  F ∈ Rrmn×mn X = [x (1) x (2) ... x (S) ] C = [c(1) c(2) ... c(S) ] [∼, ∼, V ] = svd(C) k = rank(C) Vk = V (:, 1 : k) P = X Vk VkT ,  ] = svd(P) [U S, V  (:, 1 : r )]T C †  F = U (:, 1 : r ) S(1 : r, 1 : r )[V

3 Fast Algorithm to Compute Pseudoinverse and Low-Rank Approximation As mentioned in the above Sect. 2, the RCIMA method uses the SVD to compute pseudoinverses and a low-rank approximation, respectively. However, the SVD method is usually very expensive for high-dimensional matrices. Therefore, in this section, we show two fast algorithms to compute pseudoinverse and low-rank approximation.

3.1 Pseudoinverse and Tensor Product Matrix The pseudoinverse of Z ∈ Rm×n , denoted bv y Z † ∈ Rn×m , is the unique matrix T  T  satisfying the conditions Z Z † Z = Z , Z † Z Z † = Z † , Z † Z = Z † Z and Z Z † = Z Z † . If Z = U V T is the SVD of Z and k = rank(Z ), then the standard procedure to calculate Z † is as follows: Z † = U (:, 1 : k)[(1 : k, 1 : k)]−1 [V (:, 1 : k)]T .

(5)

Katsikis and Pappas [7] provides a fast and reliable algorithm in order to compute the pseudoinverse of full-rank matrices, which does not use the SVD method. This algorithm is based on a special type of tensor product of two vectors, that is usually used in infinite dimensional Hilbert spaces. The method to compute the pseudoinverse of a full-rank matrix Z in [7] is defined by  Z† =

(Z T Z )−1 Z T if m ≥ n . Z T (Z Z T )−1 if m < n

(6)

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In 2008, Lu et al. [8] extended the method in [7] for computing the pseudoinverse of rank deficient matrices, using an approximation of Tikhonov’s regularization in order to estimate Z † . If Z is a rank deficient matrix, then the method proposed in [8] to approximate Z † is defined by  Z ≈ ZP = †

(Z T Z + α In )−1 Z T if m ≥ n , Z T (Z Z T + α Im )−1 if m < n

(7)

where α is a positive number close to zero. Formula (7) is usefull to approximate Z † because, if α → 0 in (7), then Z † = Z P (see Theorem 4.3 in [9]). The method to estimate the pseudoinverse given by (6)–(7) is so-called the tensor product matrix (TPM) method.

3.2 Low-Rank Approximation and Bilateral Random Projection Given L ∈ Rm×n and r ≤ min{m, n}, the low-rank approximation problem is a minimization problem where the goal is to find  L r ∈ Rrm×n such that L −  L r 2f r = minm×n L − L r 2f r . L r ∈Rr

(8)

L r is given by If L = U V T is the SVD of L, then   L r = U (:, 1 : r )(1 : r, 1 : r )[V (:, 1 : r )]T .

(9)

An alternative method to estimate  L r was developed by Fazel et al. [10]. These authors show that a fast method to estimate  L r is  L r = Y1 (A2T Y1 )† Y2T ,

(10)

where Y1 = L A1 ,

Y2 = L T A2

(11)

are r -bilateral random projections (BRP) of L. Here, A1 ∈ Rn×r and A2 ∈ Rm×r are arbitrary full-rank matrices. Matrices Y1 and Y2 are called the left and right random projections of L, respectively. Comparing with randomized SVD in [14] that extracts the column space from unilateral random projections, the BRP method estimates both column and row spaces from bilateral random projections [11].

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If L is a dense matrix, then number of flops to compute  L r is less than that of the SVD based approximation [11]. However, if the singular values of L decay gradually, then the accuracy of (10) may be lost. To solve this problem, Zhou and Tao [11] propose a power-scheme to improve the approximation precision, when A2T Y1 is nonsingular. In this paper, we extend the method consider in [11] for computing the low-rank matrix, when A2T Y1 is singular. The revised method considers a power-scheme given by left and right random projection of X = (L L T )c L and L, respectively, i.e., Y1 = X A1 = (L L T )c L A1 ,

Y2 = L T A2 ,

(12)

where c is a nonnegative integer and A1 ∈ Rn×r , A2 ∈ Rm×r are arbitrary full-rank matrices. This power-scheme is useful because (L L T )c L A1 has the same singular vectors as L A1 , while its singular values decay more rapidly [14]. Besides, note 1 , where A 1 = that left random projection Y1 in (12) can be represented as Y1 = L A L T (L L T )c−1 A1 , i.e., formula (12) is a particular case of (11). Theorem 1 below shows a new alternative formula to estimate a low-rank approximation of L, using a particular choice of A2 . Theorem 1 Consider random projections given by (12), where A1 ∈ Rn×r is a arbitrary full-rank matrix and A2 = L(L T L)c−1 A1 . Then, estimation of low-rank approximation of L in (10) can be simplified to  L r = LY2 Y2† .

(13)

Proof Note that (L L T )c L = L(L T L)c , and therefore, left projection in (12) can †  be expressed as Y1 = LY2 . Further, from Proposition 3.2 in [9], Y2T Y2 Y2T = Y2† . Then  †  L r = Y1 A2T Y1 Y2T  † T = LY2 L(L T L)c−1 A1 LY2 Y2T



†  T c T L L A1 Y2 Y2T = LY2  † = LY2 Y2T Y2 Y2T = LY2 Y2† . Remark 1 Y2† in (13) can be computed by the TPM method explained in Sect. 3.2. Note that we only consider the case when number of rows is greater than or equal to number of columns, because n ≥ r .

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The method to estimate the low-rank approximation of L, given by (13) and the TPM method is so-called the modified bilateral random projections (MBRP) method. The pseudocode of the MBRP method is presented in Algorithm 2. Note that bilateral random projections Y1 = L A1 and Y2 = L T A2 = (L T L)c A1 can be computed efficiently using a loop in steps 1–4 of Algorithm 2.

Algorithm 2: MBRP Method for Computing Low-Rank Approximation

1 2 3 4

Input : L ∈ Rm×n , r ≤ min{m, n}, A1 ∈ Rm×r and c ∈ {1, 2, 3, ...} Output:  L r ∈ Rrm×n Y2 = A1 for i = 1 : c do Y1 = LY2 Y2 = L T Y1

if Y2 is full rank then  L r = LY2 (Y2T Y2 )−1 Y2T else if Y2 is rank deficient then α ∈]0, 1[  L r = LY2 (Y2T Y2 + α In )−1 Y2T 9

5 6 7 8 10

Remark 2 The Power-scheme is useful to improve accuracy of estimation of lowrank approximation. However, if MBRP method is executed in floating-point arithmetic and c is a large number, then all information associated with singular values smaller than μ1/(2c+1) L f r is lost, where μ is the machine precision [14]. Therefore, it is recommended to take a small value for c. Thus, numerical simulations in this paper are computed using c = 3.

4 Fast-RCIMA Method In this section, we propose a new implementation to compute a solution of the RCIMA problem on the basis of the TPM and MBRP methods explained in Sects. 3.1 and 3.2, respectively. A block diagram of this implementation, called the fast-RCIMA method, is presented in Fig. 1, which is explained below: Step 0:

S S and {c}k=1 . Obtain training data {x}k=1

Step 1: Create matrices X = [x (1) x (2) ... x (S) ] and C = [c(1) c(2) ... c(S) ]. Step 2: Compute pseudoinverse C P of C, using the TPM method. Step 3: Compute the kernel matrix P = X Vk VkT , where C = U V T is the SVD of C, Vk = V (:, 1 : k) and k = rank(C) . To avoid to compute the SVD of C, we use the fact that Vk VkT = C † C (see, e.g., equation (2139) in [16]). Therefore, kernel matrix P can be compute as P = XC P C.

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Fig. 1 Block diagram of the fast-RCIMA method

Step 4: Step 5:

r of P, using the MBRP method. Estimate low-rank approximation P r C P . Approximate the RCIMA solution  F=P

The associate pseudocode of the fast-RCIMA method is shown in Algorithm 3, where c = 3 is used at the power-scheme.

5 Numerical Simulation In this section, we show a numerical simulation of the proposed fast-RCIMA method for image deconvolution. The numerical examples were run on a desktop computer with a 2.30 GHz processor (Intel(R) Core(TM) i7-4712MQ CPU) and a 8.00 RAM.

5.1 Speedup and Percent Difference Consider two methods that solve the same problem, Method 1 and Method 2, with execution times T1 and T2 , respectively. The computational performance analysis of the fast-RCIMA method is evaluated using the following two metrics: • The speedup S (or acceleration) is the ratio between the execution times of both methods, i.e., S = T2 /T1 . If Method 1 is an improvement of Method 2, then speedup will be grater than 1. However, if Method 1 hurts performance, speedup will be less than 1 (see [17] for more details). • The percent difference P between Method 1 and Method 2, where T1 < T2 , is represented by P = 100(T2 − T1 )/T2 . Then, we say that Method 1 is P% faster than Method 2 (see [17] for more details).

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Algorithm 3: Fast-RCIMA Method

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

S S Input : Training Data {x (k) }k=1 ⊆ Rmn , {c(k) }k=1 ⊆ Rmn , r ≤ mn and A1 ∈ R S×r Output:  F ∈ Rrmn×mn /* Matrix Representation of Training Data X = [x (1) x (2) ... x (S) ] C = [c(1) c(2) ... c(S) ] /* Pseudoinverse of C with TPM Method if C is full rank then if mn ≥ S, then C P = (C T C)−1 C T if mn < S, then C P = C T (CC T )−1 else if C is rank deficient then α ∈]0, 1[ if mn ≥ S, then C P = (C T C + α I S )−1 Z T if mn < S, then C P = C T (CC T + α Imn )−1 /* Kernel Matrix P P = XC P C /* Low-Rank Matrix Approximation of P with MBRP Method Y2 = A1 for i = 1 : 3 do Y1 = PY2 Y2 = P T Y1

if Y2 is full rank then r = PY2 (Y T Y2 )−1 Y T P 2 2 else if Y2 is rank deficient then α ∈]0, 1[ r = PY2 (Y T Y2 + α Ir )−1 Y T P 20 2 2 21 /* Rank Constrained Inverse Matrix Approximation r C P F=P 22 

*/

*/

*/ */

16 17 18 19

*/

5.2 Numerical Example for Image Deconvolution This example illustrates the application of the RCIMA method to image deconvolution using training data [2, 18], which was briefly mentioned in Sect. 1. Specifically, we apply RCIMA and fast-RCIMA methods to the problem of filtering a noisyimage C ∈ R90×90 estimation on the basis of the set of a training images X = X (1) , ..., X (S) , where X ( j) ∈ R90 × 90 , for j = 1, ..., S. Our training set X consists of s = 2000 different satellite images taken from the NASA Solar System Exploration Database [19] (see Fig. 2a for 6 sample images). Let vect : Rm×n → Rmn be the vectorization transform. We write x ( j) = vec(X ( j) ) ∈ R8100 , for j = 1, ..., 2000. Instead of images in X , we observed their noisy version C = C (1) , ..., C (s) , where C ( j) ∈ R90×90 . If c( j) = vec(C ( j) ) ∈ R8100 , then c( j) = A( j) x ( j) + n ( j) , where A( j) ∈ R81000×81000 models the forward process and n ( j) ∈ R81000 is generated using the standard normal distribution. Here, A( j) = σ j I81000 and σ j is a constant generated using the standard normal distribution (see Fig. 2b for 6 sample images). We assumed that noisy image C does not

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(a)

(b) Fig. 2 a Some randomly selected satellite images. b Noisy versions of a Table 1 Execution time, MSE, speedup, and percent difference between RCIMA and fast-RCIMA methods cr RCIMA fast-RCIMA Speedup Percent Time (s) MSE Time (s) MSE Difference (%) 0.25 0.5 0.75 1

484.73 454.70 426.31 420.45

102.36 38.72 20.23 2.01

30.47 32.75 34.91 38.01

102.36 38.72 20.23 2.01

15.91 13.88 12.21 11.06

93.71 92.80 91.81 90.95

necessarily belong to C, but it is “similar” to one of them, i.e., there is C ( j) ∈ C such that C ( j) ∈ arg min C (i) − C f r ≤ δ, C (i) ∈C

for a given δ ≥ 0. Finally, to compute the optimal matrix  F ∈ R81000×81000 in the RCIMA problem in (3), we defined matrices X = [x (1) x (2) ... x (s) ] ∈ R8100×2000 and C = [c(1) c(2) ... c(s) ] ∈ R8100×2000 . The compression ratio in RCIMA problem is given by cr = r/(mn). Table 1 presents the execution time, MSE, speedup and percent difference between RCIMA and fast-RCIMA methods, for compression ratio cr ∈ {0.25, 0.5, 0.75, 1}, i.e., r ∈ {2025, 4050, 6075, 8100}. Figure 3 shows the estimates of C using both algorithms. Results in Table 1 suggest that fast-RCIMA method can obtain a nearly optimal approximation of the RCIMA problem in less time. Moreover, last column of Table 1 shows that the fast-RCIMA method is 90% faster, approximately, than the RCIMA method. Besides, Fig. 3 shows that the fast-RCIMA method compute optimal  F in (3) with the same accuracy as RCIMA method.

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(a)

(c)

(e)

(g)

(i)

(b)

(d)

(f)

(h)

(j)

Fig. 3 Illustration to the estimation of noisy image C by RCIMA and fast-RCIMA methods. a Source image X . b Noisy observed image C. c–d Estimation using the RCIMA and fast-RCIMA methods, respectively, for cr = 0.25. e–f Estimation using the RCIMA and fast-RCIMA methods, respectively, for cr = 0.5. g–h Estimation using the RCIMA and fast-RCIMA methods, respectively, for cr = 0.75. i–j Estimation using the RCIMA and fast-RCIMA methods, respectively, for cr = 1

6 Conclusions In this paper, we proposed in this paper a new and faster method for image deconvolution, based on the RCIMA problem in (3), using training data as a substitute for knowledge of a forward model. This new method, so-called the fast-RCIMA method, is based on tensor product and Tikhonov’s regularization to approximate the pseudoinverse, and bilateral random projections to estimate the low-rank approximation. Moreover, in Theorem 1 we present an alternative approach to compute the low-rank matrix approximation. Based on the numerical simulations in Sect. 5, the fast-RCIMA method reduces significantly the execution time to compute optimal solution and increases the speedup, preserving the same accuracy of the classical method to solve the RCIMA problem. Numerical simulation to filter a noisy image in Sect. 5 demonstrates the advantages of the proposed method. Acknowledgements This work was financially supported by the Vicerrectoría de Investigación y Extensión from Instituto Tecnológico de Costa Rica, Cartago, Costa Rica (Research #1440037).

References 1. Campisi P, Egiazarian K (2013) Blind image deconvolution: theory and applications. CRC Press 2. Chung J, Chung M (2013) Computing optimal low-rank matrix approximations for image processing. In: Proceddings IEEE 2013 Asilomar conference on signals, systems and computers, pp 670-674. https://doi.org/10.1109/ACSSC.2013.6810366

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3. Eckart C, Young G (1936) The approximation of one matrix by another of lower rank. Psychometrika 1(3):211–218. https://doi.org/10.1007/BF02288367 4. Wu T, Sarmadi S, Venkatasubramanian V, Pothen A, Kalyanaraman A (2015) Fast SVD computations for synchrophasor algorithms. IEEE Trans Power Syst 31(2):1651–1652. https://doi. org/10.1109/TPWRS.2015.2412679 5. Allen-Zhu Z, Li Y (2016) LazySVD: even faster SVD decomposition yet without agonizing pain. In: Advances in neural information processing systems, 974–982 6. Courrieu P (2005) Fast computation of Moore-Penrose inverse matrices. Neural information processing. Lett Rev 8(2):25–29 7. Katsikis V, Pappas D (2008) Fast computing of the Moore-Penrose inverse matrix. Electron J Linear Algebra 17:637–650. https://doi.org/10.13001/1081-3810.1287 8. Lu S, Wang X, Zhang G, Zhou Z (2015) Effective algorithms of the Moore-Penrose inverse matrices for extreme learning machine. Intel Data Anal 19(4):743–760. https://doi.org/10. 3233/IDA-150743 9. Barata J, Hussein M (2012) The Moore-Penrose pseudoinverse: a tutorial review of the theory. Brazilian J Phys 42:146–165. https://doi.org/10.1007/s13538-011-0052-z 10. Fazel M, Candes E, Recht B, Parrilo P (2008) Compressed sensing and robust recovery of low rank matrices. In: 42nd Asilomar conference on signals, systems and computers, pp 1043–1047. https://doi.org/10.1109/ACSSC.2008.5074571 11. Zhou T, Tao D (2012) Bilateral random projections. In: IEEE international symposium on information theory proceedings, pp 1286–1290. https://doi.org/10.1109/ISIT.2012.6283064 12. Telfer B, Casasent D (1994) Fast method for updating robust pseudoinverse and Ho-Kashyap associative processors. IEEE Trans Syst Man Cybern 24(9):1387–1390. https://doi.org/10. 1109/21.310515 13. Benson M, Frederickson P (1986) Fast parallel algorithms for the Moore-Penrose pseudoinverse. In: Second conference on hypercube multiprocessors. https://www.osti.gov/biblio/ 7181991-fast-parallel-algorithms-moore-penrose-pseudo-inverse 14. Halko N, Martinsson P, Tropp J (2011) Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev 53(2):217–288. https:// doi.org/10.1137/090771806 15. Deshpande A, Vempala S (2006) Adaptive sampling and fast low-rank matrix approximation. In: Approximation, randomization, and combinatorial optimization. Algorithms and techniques. Springer, pp 292–303. https://doi.org/10.1007/11830924_28 16. Dattorro J (2005) Convex optimization † Euclidean distance geometry. Meboo Publishing. https://ccrma.stanford.edu/~dattorro/0976401304.pdf 17. Cragon H (2000) Computer architecture and implementation. Cambridge University Press 18. Soto-Quiros P, Torokhti A (2019) Improvement in accuracy for dimensionality reduction and reconstruction of noisy signals. Part II: the case of signal samples. Signal Process 154:272–279. https://doi.org/10.1016/j.sigpro.2018.09.020 19. NASA (2020) NASA solar system exploration database. Online, Accessed 10 Sept 2020. https:// solarsystem.nasa.gov/raw-images/raw-image-viewer

On-Body Microstrip Patch Antenna for Breast Cancer Detection Sourav Sinha, Sajidur Rahman, Mahajabin Haque Mili, and Fahim Mahmud

Abstract Breast cancer is the most common invasive cancer for women. It is the second major cause of cancer that causes death after lung cancer in women. This paper portrays an on-body microstrip patch rectangular antenna, which is found to operate at ISM-Industrial, Scientific and Medical band of 2.4–2.4835 GHz after placing it on the surface of the human breast, designed in the CST microwave studio to specify the tumor in narrow bandwidth. Being highly flexible, FR4 is selected as a substrate, and copper is selected for both ground and patch. To guarantee the safety of the patient, a human breast phantom is constructed consisting of two layersskin and glandular tissue. The tumor is positioned at different locations on the breast phantom model to ensure the efficiency of the device. The S11 value without tumor is −49.405 dB and the voltage standing wave ratio is 1.0067957. Specific absorption rate is 1.18, total efficiency is −6.846 dB and radiation efficiency is −6.846 dB. To make the device biocompatible, all these parameters are experimented by comparing the cancerous tumor’s location and without the cancerous tumor. Keywords Human breast model · Glandular tissue · On-body antenna · SAR

1 Introduction Breast cancer is a type of disease that occurs when the cells in the breast grow excessively. Most breast cancers initiate in the lobules or ducts. Based on the capacity of extension and growth, tumors are classified into malignant and benign. According to the survey by National Cancer Institute, death rates due to breast cancer among

S. Sinha (B) Technische Universität München, Arcisstr. 21, 80333 Munich, Germany S. Rahman Universität Bremen, Bibliothekstr. 1, 28359 Bremen, Germany M. H. Mili · F. Mahmud American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_16

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women aging between 20 and 49 were more than double that of any other cancercausing death among women or men from 2012 to 2016. X-ray mammogram, ultrasound, and MRI-magnetic resonance imaging are traditionally implemented for the discovery of breast cancer. These methods possess some limitations as 4–34% of all breast cancers are unidentified due to the differentiation of malignant tissue or poor detection of cells. However, promising results are exhibited by microwave imaging (MI) [1]. The basic technique involved in the MI system is that it transmits and receives the scattered signal for diagnosis. Variant results between the electric and magnetic fields play a major role in recognizing the location of the tumor and its growth [2]. In 1953, microstrip radiators were initially found and extensive researches were made regarding their properties [3]. Ground, substrate, and patch resonator were the elements of this antenna. Being light, low-cost, ease of fabrication, and having a low profile, this antenna is employed for industrial and medical purposes [4]. Its properties would more be increased if substrate thickness, feed line dimension, and patch are optimized [5]. Researches are being continued for the past two decades to mitigate the issue of narrow bandwidth (low factorial bandwidth (FBW = 7%) and to enhance communication and receive more advantages in microstrip antennas [5]. The radiating element can be triangular, semicircular, circular, and square [6]. In this research, an on-body microstrip patch antenna is proposed which is to be placed on the surface of the breast skin and operating at ISM band, frequency of 2.4–2.4835 GHz. The antenna is designed for all human beings with flexible and cheap costs to detect the tumor in the early stage if present. Having several advantages, International Telecommunication Union declared radio communication at the frequency of 2.4–2.4835 GHz for the purpose of industrial, scientific, and medical [7]. The fundamental principle is that human organs containing different bio-tissues possess varied characteristics in terms of parameters such as conductivity and dielectric constant [8]. The on-body antenna is composed of ground, patch, and substrate. The dimensions (length, width, and thickness) are considered in such a way that they can be used by all kinds of people. A human breast phantom is designed with skin and glandular tissue being the two layers on the CST microwave studio. The skin encompasses the glandular tissue present inside the breast. All necessary dielectric material properties are maintained using library materials from CST microwave. A sphericalshaped tumor is designed using the above-mentioned software. For precise detection, the location of the tumor was changed and compared with the presence of tumor and absence of tumor. Additionally, return loss, operating frequency, bandwidth, directivity, radiation pattern, gain, voltage standing wave ratio, specific absorption rate, electric and magnetic field are determined. The structure and design method (Sect. 2), antenna characteristics without cancerous tumor (Sect. 3), and antenna characteristics with a cancerous tumor (Sect. 4) are demonstrated, respectively.

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2 Structure and Design Method 2.1 Design of Antenna The fundamental element in microwave imaging is an antenna. The proposed antenna is a rectangular shape with a length of 15 mm and a width of 15 mm to sustain biocompatibility. Radiating rectangular patch of the antenna fed by a rectangular feed line. The antenna consists of substrate, patch, and ground and power is supplied by the feed line. The antenna is experimented with copper in patch and ground of the thickness is 0.1 mm. FR4 was selected as a substrate with 0.8 mm thickness. The propounded antenna thickness is 1 mm. The propounded antenna dimension is (15 × 15 × 1) mm3. Hence, the CST microwave studio can calculate numerous monitors and have the capability to extract a high resolution of antenna data, the experiment was performed in the CST microwave studio [9]. The propounded antenna is designed for working on the surface of the human breast, therefore it is positioned on the lateral sides of the human breast phantom prototype to check its biocompatibility and performances. The spherical-shaped tumor of 5 mm radius is constructed in the CST microwave studio and positioned within the phantom. The results were compared based on the different locations of the tumor. Figure 1a demonstrates the propounded antenna’s geometrical view. The values which are labeled in Fig. 1a are in the unit of millimeter. Figure 1b shows the antenna along with the waveguide port and breast phantom. The power is supplied in a 2 mm wide feedline by the waveguide port. The red part marked in Fig. 1b represents the waveguide port. In Table 1, all parameters of the propounded on-body antenna are tabulated.

a.

b.

Fig. 1 Propounded microstrip patch antenna a Dimensions b with waveguide port and breast phantom

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Table 1 The antenna parameters

Antenna part

Length (mm)

Width (mm)

Thickness (mm)

Feed

5.5

2

0.1

Substrate

15

15

0.8

Ground

5.5

15

0.1

2.2 Equations Employed in Design of Propounded Antenna Different parameters are calculated using the following equations for the propounded on-body microstrip patch antenna [10]. Width C 

W = 2fr

(1)

εr +1 2

Here, fr = Operating Frequency c = 3 × 108 m/s (Speed of light) Er = 3.5 (The dielectric substrate’s relative permittivity) Dielectric Constant (Effective), εe f f

⎡ 1 εr + 1 εr − 1 ⎣  + = 2 2 1+

⎤ ⎦

(2)

12h w

W = Patch width h = Thickness of substrate (0.8 mm) Length (Effective) Lef f =

c √ 2 fr εe f f

(3)

Length Extension  L = 0.412h 

 + 0.264   − 0.258 wh + 0.8

εe f f + 0.3 εe f f

 w h

(4)

Actual length of the Patch L = L e f f − 2L

(5)

The calculated values obtained from solving the equation are not enough to reach the objective of the design. Therefore, the value of length and width are lessened by keeping the ratio unchanged until ISM band frequency is obtained.

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Fig. 2 Human breast phantom (Cross-sectional view)

2.3 Human Breast Phantom Model and Tissue Properties The human breast phantom is created consisting of skin and glandular tissue. All the dielectric properties are completely regulated, such as conductivity, relative permittivity, density, thickness, and loss tangent. The human breast phantom cross-sectional view is visible in Fig. 2. The semicircular breast phantom is constructed in the CST microwave studio, which resembles the human breast layout. The layout is shaped as half sphere and the antenna is placed on the phantom surface. A thickness of 1 and 23 mm outer radius is considered for the skin. Inside the skin, breast fatty tissue (fibro glandular) is placed with a 22 mm radius. The defected cell is located inside the fatty tissue with a 2 mm radius (Fig. 3).

3 Antenna Characteristics Without Cancerous Tumor 3.1 Reflection Coefficient or S11 Parameter Reflection coefficient determines the sum of power reflected or radiated from an antenna [11]. After placing on the surface of the breast phantom, radiation pattern is noted. The x-axis in Fig. 4 defines resonance or operating frequency, which is in the GHz range whereas the y-axis defines the return loss which is in the dB scale. The resonant of the propounded antenna is 2.48 GHz, which falls in the range of the ISM band. The return loss of −49.405 dB proves the performance of the antenna by

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Fig. 3 Antenna on the human breast phantom surface

Fig. 4 S11 parameter or return loss of the antenna (without cancerous tumor)

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Fig. 5 Far-field radiation pattern (3D view) of the propounded antenna (healthy tissue)

showing the maximum radiation. The bandwidth of the propounded design is noticed at 158.4 MHz (2.4038–2.5622 GHz), thus ensuring safety for all users.

3.2 Far-Field Radiation Pattern In Fig. 5, the radiation pattern of the propounded antenna is elucidated. Since the antenna is made to detect the cancerous tumor, therefore the directivity maintained is unidirectional. Despite being unidirectional, the radiation spreads throughout the organ part is as shown. Practically, the user can also rotate the antenna in all possible sides. Parameters such as directivity is 2.835 dBi, radiation efficiency is −6.846 dB, and total efficiency is −6.846 dB, respectively. Figure 6 is showing the polar view of the far-field radiation pattern of the propounded antenna with 2.61 dBi main lobe magnitude.

3.3 VSWR—Voltage Standing Wave Ratio It is basically a function of measurement of the power radiated from the antenna [12]. The propounded antenna’s VSWR is 1.006 at an operating frequency of 2.48 GHz. This specifies that the antenna’s impedance is matched perfectly with the transmission

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Fig. 6 Far-field radiation pattern (polar view) of the propounded antenna

line. For efficient performance, the value of VSWR should be between 1 and 2. The x-axis in Fig. 7 represents frequency in the range of GHz and the y-axis represents VSWR.

3.4 SAR—Specific Absorption Rate The radiation given out by the surrounding tissue is called SAR. It checks the safety level for all users [13]. According to FCC, the specific absorption rate should be less than 2 W/kg to meet the standard [14, 15]. SAR for the propounded antenna is noted 1.18 W/kg at the resonant frequency for 10 g tissue for 1mW of input power (Fig. 8).

4 Antenna Characteristics with Cancerous Tumor To examine the performance of the antenna, a spherical-shaped tumor is constructed in the CST studio suite with a 2 mm radius, and antenna is placed on the surface of the breast phantom. The dielectric property is followed by electric conductivity and permittivity are 0.7 S/m and 55, respectively. By placing it on the varied position of

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Fig. 7 VSWR of the antenna (healthy tissue––without cancerous tumor)

the phantom and replacing the position of the tumor, the effects are examined and compared. Figure 9a shows the condition where the tumor is absent. Figure 9b shows the condition where the tumor is in the center (position 1). Figure 9c represents the condition for position 2 (x = 45, y = 0, z = 0) and Fig. 9d represents the condition for position 3 (x = 90, y = 0, z = 0).

4.1 Reflection Coefficient or S11 Parameter Figure 10 depicts a vivid comparison of the S11 parameter by placing the tumor on a different position. Table 2 reflects the resonance frequency and return loss of all positions. From Table 2, it can be summarized that the operating frequency is identical during the absence and presence of a tumor. However, there is a major change in S11. Return loss or S11 is raised rapidly in presence of a tumor and inversely proportional with the distance of antenna and breast surface.

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Fig. 8 SAR (Specific Absorption Rate) for 10 g tissue for 1mW of input power

(a)

(b)

(c)

(d)

Fig. 9 Cancerous tumor (malignant) inside human breast in a Without tumor, b Position 1, c Position 2, d Position 3

4.2 Other Characteristics Table 3, represents the compared values of maximum main lobe magnitude, maximum E-field intensity, maximum H-field intensity, and surface current density

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Fig. 10 Reflection Coefficient or S11 parameter with and without cancerous tumor of the antenna

Table 2 Resonance frequency and return loss analysis Parameter

Without tumor

With cancerous tumor Position-1

Resonance frequency (GHz)

2.48 −49.405

Return loss or S11 (dB)

Position-2

Position-3

2.485

2.485

2.48

−36.062

−33.271

−24.79

Table 3 Comparison analysis of different characteristics Parameter

Without tumor

With cancerous tumor Position 1

Position 2

Position 3

Main lobe magnitude

2.61

2.67

2.7

2.75

Max. E-field intensity

73659

80138

79755

77259

Max. H-field intensity

560

576

570

562

Surface current density

317

332

329

321

between cancerous tumor and without cancerous tumor. It is identified that the value slightly increases in main lobe magnitude. The numbers of maximum E-field and H-field intensity increase with the presence of tumor and proportion with the distance of tumor [16]. But the surface current density slightly decreases.

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5 Conclusion In this research, a rectangular structured microstrip patch antenna with improved parameters has been presented. The purpose of designing the antenna is served in terms of efficiency, size, return loss along with making it functional at a resonant frequency of 2.48 GHz. The propounded design is more improved from the rest of the work in the field of frequency, SAR, radiation pattern, H-field, and E-field. The variation of the frequency curves shows an enormous response in identifying the cancerous and non-cancerous tissue. The VSWR of 1.007 proves that the impedance of the antenna is matched well with the transmission line. Besides, there are significant changes when the results are compared in the presence and absence of the tumor in both electric and magnetic fields. Considering the well-being of the user, SAR is computed as 1.18 W/kg at the operating frequency of 2.48 GHz for 10 g tissue. Hence, it can be said that the devised system is well efficient for the early diagnosis of breast cancer.

References 1. Alsharif F, Kurnaz C (2018) Wearable microstrip patch ultra wide band antenna for breast cancer detection. In: 41st international conference on telecommunications and signal processing (TSP), pp 1–5. Athens, Greece 2. Çalı¸skan R, Gültekin S, Uzer D, Dündar Ö (2015) A microstrip patch antenna design for breast cancer detection. Proc Soc Behav Sci 195:2905–2911 3. Cicchetti R, Faraone A, Caratelli D, Simeoni M (2015) Wideband, multiband, tunable, and smart antenna systems for mobile and UWB wireless applications. Int J Antennas Propagat 4. Gupta KC, Benalla A (1988) Microstrip antenna design. Technology & Engineering, Artech House 5. Saeidi T, Ismail I, Wen WP, Alhawari ARH, Mohammadi A (2019) Ultra-wideband antennas for wireless communication applications. Int J Antennas Propagat 6. Werfelli H, Tayari K, Chaoui M, Lahiani M, Ghariani H (2016) Design of rectangular microstrip patch antenna. In: 2nd international conference on advanced technologies for signal and image processing (ATSIP), pp 798–803, Monastir, Tunisia 7. Hasan RR, Rahman MA, Sinha S, Uddin MN, Niloy TR (2019) In body antenna for monitoring pacemaker. In: International conference on automation, computational and technology management (ICACTM), pp 99–102, London 8. Zhang H, Arslan T, Flynn B (2013) A single antenna based microwave system for breast cancer detection: experimental results. In: Loughborough antennas & propagation conference (LAPC), pp 477–481, Loughborough 9. Hirtenfelder F (2007) Effective antenna simulations using CST MICROWAVE STUDIO®. In: 2nd international ITG conference on antennas, pp 239–239, Munich, Germany 10. Sinha S, Niloy TR, Hasan RR, Rahman MA, Rahman S (2020) A wearable microstrip patch antenna for detecting brain tumor. In: International conference on computation, automation and knowledge management (ICCAKM), pp 85–89, Dubai, UAE 11. Hasan RR, Rahman MA, Sinha S, Uddin MN, Niloy TR (2019) In body antenna for monitoring pacemaker. In: International conference on automation, computational and technology management (ICACTM), pp 99–102. London

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12. Sinha S, Hasan RR, Rahman MA, Ali MT, Uddin MN (2019) Antenna design for biotelemetry system. In: International conference on robotics, electrical and signal processing techniques (ICREST), pp 466–469, Dhaka, Bangladesh 13. Islam NA, Arifin F (2016) Performance analysis of a miniaturized implantable PIFA antenna for WBAN at ISM band. In: 3rd international conference on electrical engineering and information communication technology (ICEEICT), pp. 1–5, Dhaka, Bangladesh 14. Safety E, Committee SC, Radiation N, Board IS (2008) IEEE recommended practice for measurements and computations of radio frequency electromagnetic fields with respect to human exposure to such fields 100 kHz –300 GHz. Measurement 2002 15. ICNIRP (2019) Guidelines for limiting exposure to time varying electric, magnetic, and electromagnetic fields. Health Phys 74:494–522 16. Sinha S, Niloy TR, Hasan RR, Rahman MA (2020) In body antenna for monitoring and controlling pacemaker. Adv Sci Technol Eng Syst J 5(2):74–79

Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru Juan J. Soria , Orlando Poma , David A. Sumire , Joel Hugo Fernandez Rojas , and Maycol O. Echevarria

Abstract Environmental pollution and its effects on global warming and climate change are a key concern for all life on our planet. That is why meteorological variables such as maximum temperature, solar radiation, and ultraviolet levels were analyzed in this study, with a sample of 19564 readings. The data was collected using the Vantage Pro2 weather station, which was synchronized with the time and dates of the electric field measurements made by an EFM-100 sensor. The Machine Learning analysis was applied with the Regression Learner App, from which the linear regression model, regression tree, support vector machine, Gaussian process regression, and ensembles of tree algorithms were trained. The most optimal model for the prediction of the maximum temperature associated with the electric field was the Gaussian Process Regression with an RMSE of 1.3436. Likewise, for the meteorological variable of solar radiation, the optimal model was Regression Tree Medium with an RMSE of 1.3820 and for the meteorological variable of UV level, the most optimal model was Gaussian Process Regression (Rational quadratic) with an RMSE of 1.3410. Gaussian Process Regression allowed for the estimation and prediction of the meteorological variables and it was found that in the winter season at low temperatures the negative electric field is associated with high variability in its behavior; while at high temperatures they are associated with positive electric fields with low variability. Keywords Machine learning · Electric field · Weather variables · Forecast · Regression learner app · Accuracy · Algorithms

J. J. Soria (B) · O. Poma · D. A. Sumire · J. H. F. Rojas · M. O. Echevarria Universidad Peruana Unión, Lima, Peru e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_17

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1 Introduction 1.1 Context for the Research Study The atmospheric electric field is a current research area in the East Lima area of Peru, associated with meteorological variables such as maximum temperature, solar radiation, and UV level. In this study, the predictive analytical method of Machine Learning was used to evaluate meteorological variables that influence the electric field in the area of East Lima, Peru. Recent studies in this area contribute to an understanding of the association between atmospheric electricity and the weather [6, 11]. Since 1753, Canton [1] “discovered that the air was generally positively charged with respect to the ground for good weather, with variations associated with different meteorological phenomena, such as precipitation and fog”, this shows the electrical behavior of the geospheric earth and is one of the first works to report the association with meteorological variables influencing the electric field. This close relationship between the electrical state of the atmosphere and changes in climate provided the motivation to continue the study of atmospheric electricity. Harrison suggests that the influence of the global circuit is affected by local factors such as contamination by aerosols, radioactivity, or meteorological disturbances. Harrison and Nicoll [5] and Takashima et al. [18] obtained temporary variations in the aerosol compositions made in the city of Fukuoka. With the development of technology, it became possible to apply modern sensors to monitor the electrical behavior of the atmosphere associated with climatic variation [1]. The data generated from the electric field measured with an EFM sensor was used [15]. According to [19], there is a global atmospheric electric circuit on earth. The variations of the current density of the ionosphere indicate that the current density flows downwards from the ionosphere to the ground as the ocean surface shows day by day the variations associated with the solar activity and with internal changes in the global atmospheric electric circuit. The ionosphere has a potential of approximately 250 kV, maintained by an upward current of approximately 1000 A, from the totality of storm clouds and other highly electrified clouds where the greatest variation in average current is a function of geographic location. According to the observations and models presented by [7], the relative geographic position is very important when considering the installation of electric field measurement (EFM) sensors. The confirmation of the presence of electric charge in the atmosphere raised other questions regarding its association with weather and storms. Thus was the case with Parsons and Mazeas, who confirmed in 1753 that the electric field recently identified in storms is associated with climatic conditions and the local processes of electrification occur in the atmosphere with the absence of appreciable convective clouds, according to Tacza et al. [17], and thus identifying the factors that determine an ecosystem [13]. The relationship between electric field and meteorological variables suggests that the Sun’s magnetic field has an influence. Variations in the solar magnetic field

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Fig. 1 Global atmospheric electric field

and its interplanetary extension, on time scales from days to decades, have been shown to significantly change meteorological variables such as tropospheric pressure, temperature, and lightning rates [15] (Fig. 1). During the past decade, a renewed interest in the problems of the Global Electric Field (GEF) has emerged due to climate applications [20].

2 Literature Review 2.1 Machine Learning Machine Learning is part of artificial intelligence [14] and seeks to provide machines with the ability to learn through data. This means that with quality data, appropriate technologies, and correct analysis, it is possible to create behavioral models to analyze data of high volume and complexity. The process of Machine Learning has interconnected processes, which are data collection, data preparation, model training, model evaluation, and performance improvement. This is also the case for the bayesian networks that are part of artificial intelligence [16]. Currently, in the context of technological convergence and digital transformation, information technologies intervene daily in the lives of people and organizations in a wide range of contexts, making them in many cases indispensable tools and catalysts for greater productivity and decision-making in different fields [14]. Machine learning is the process by which measurements are transformed into parameters for future operations [12]. The machine learning process has interconnected sub-processes, which are data collection, data preparation, model training, model evaluation, and performance improvement. The electric field data were collected with the EFM-100 sensor and the meteorological variables with the Vantage Pro2 sensor [2] equipment that has been installed in the Lima campus of the Universidad Peruana Unión. The study data were synchronized based on the dates and in time intervals of every 5 s, in such a way that they coincide and allow for greater precision. The model training was carried out with the MATLAB software using the Regression Learner App algorithm, obtaining

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adequate performance. The model was evaluated with efficient statistical indicators, which allowed for a high level of precision.

2.2 Regression Learner App The Regression Learner App models [9] are prediction structures that contribute according to the minimum value of the RMSE. The Linear Regression Model, the Regression Tree Model, the Support Vector Machine Model, the Gaussian Process Regression Model, and the Ensembles of Trees Model encompass a wide range within machine learning and seek better accuracy in their prediction which allow for the selection of an optimal model. First, it calculates a weighted average of the noisy observations and is defined by f (x∗ ) = k(x∗ )T (K + σ 2 I )−1 y,

(1)

which is a smoother linear combination of the y values, then the regression of the Gaussian process is also smoother linear [4]. In this study, the smoothing in matrix form helped to predict at training points and then in terms of the equivalent kernel, since the predicted values at the training points were calculated by f (x∗ ) = K (K + σn2 I )−1 y

(2)

3 Materials y Methods 3.1 Methodology This study used MATLAB’s Regression Learner App with Machine Learning prediction for the electric field, in which the Fine Tree, Medium Tree, Ensembles, and Gaussian Process Regression were the models that interacted to obtain the best predictive model [10] as shown in Fig. 2. This study used the Vantage Pro2 Console, Davis equipment, which recorded the measurements of the meteorological variables under study such as maximum temperature, solar radiation, and ultraviolet level [3]. The console works in five modes which are: configuration, current time, maximum graphic mode, minimum graphic mode, and final graphic. The WeatherLink software connects the Vantage Pro2 station to the computer located at the Universidad Peruana Union in the East Lima area as shown in Fig. 3. The electric field sensor installed in the eastern part of the Universidad Peruana Unión campus in Lima recorded electric field data during 2019. As in an earlier

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Fig. 2 Machine learning methodology for electric field prediction

Fig. 3 Location of the vantage Pro2 meteorological station on the Universidad Peruana Unión campus, Lima, Peru, in the East Lima Zone

study, the electric field measurements were made with the EFM-100 atmospheric Electric Field Monitor sensor [2]. New calibrations and measurements with its most powerful electric field sensor for new electro-optical investigations were developed by [21].

4 Results 4.1 Results of Machine Learning Models in Predicting the Electric Field Data collected throughout 2019 on temperature distribution and electric field were matched to exact dates and times by measuring 24 h a day, achieving a direct correlation between the study variables. The data were also normalized and then the Regression Learner App from MATLAB software was used, which obtained the best

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Table 1 Predictive model analysis matrix table Model

RMSE

R-squared

MSE

MAE

Fine tree

1.3869

0.42

1.9234

0.97695

Medium tree

1.3820

0.43

1.9099

0.98492

Ensemble

1.3604

0.44

1.8507

0.97428

Gaussian process regression (Exponential)

1.3462

0.46

1.8123

0.95908

Gaussian process regression (Rational quadratic)

1.3435

0.46

1.8049

0.95638

predictive model from Machine Learning with an RMSE of 1.3435, which optimizes the prediction of the maximum temperature with the electric field, which is shown in Table 1. Table 1 shows the prediction indicators of the Machine Learning models, which were applied in this study, in descending order according to RMSE. The Fine Tree model had an RMSE = 1.3869, the Medium Tree model an RMSE = 1.3820, the Assembly model an RMSE = 1.3604, the Gaussian Process Regression (Exponential) model an RMSE = 1.3462, and the Gaussian Process Regression (Rational Quadratic) model an RMSE = 1.3436. The model with the lowest RMSE was taken, which is Gaussian Process Regression (Rational Quadratic) and which shows a good performance with a determination coefficient of 46% for the comparison of the trained model versus the model where the response is constant and equal to the mean of the training response as shown in Fig. 4. Furthermore, it has a mean absolute error (MAE) of 0.95638.

Fig. 4 Optimal temperature prediction model with electric field

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Fig. 5 Scatter plot of the solar radiation and the electric field

4.2 Description of Solar Radiation with the Electric Field Figure 5 shows the scatter plot between the meteorological variable of solar radiation and the electric field with 19564 measurements, in which MATLAB’s Regression Learner App model predicted an optimal model through Regression trees, specifically a Medium Tree, with an RMSE of 1.382, a determination coefficient (R2 ) of 0.43, an MSE of 1.9099 and an MAE of 0.98492. This means that 43% of the information of the solar radiation was predicted with respect to the electric field.

4.3 Description of the UV Level with the Electric Field In Fig. 6, the scatter plot between the meteorological variable UV level and the electric field with 19564 measurements is shown, in which MATLAB’s Learner App Regression model predicted an optimal model called Gaussian Process Regression, with rational quadratic (GPR), with an RMSE of 1.3410, a coefficient of determination R2 of 0.46, an MSE of 1.806 and an MAE of 0.95606. This means that 46% of the information on the level of UV rays was predicted with respect to the electric field.

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Fig. 6 Scatter plot of the UV level with the electric field

5 Conclusions According to the application of Machine Learning carried out in this study, for the prediction of the meteorological variables in relation to the electric field estimated with the Regression Learner App models, two obtained the highest accuracy in prediction with Gaussian Process Regression models, the maximum temperature (R2 = 0.46) and the UV level (R2 = 0. 46), with an RMSE = 1.3435 and RMSE = 1.3410, respectively. The other main variable solar radiation (R2 = 0.43) provided an RMSE = 1.3820 in which the optimal model was found to be a Regression Tree. The training of the predictive Regression Learner App models with a run time of 6 h, generated a MATLAB 2018 version software code that predicts the electric field with respect to measurements and can be used in future research with the meteorological variables under study.

References 1. 2. 3. 4.

Bennett AJ, Harrison RG (2007) Historical background 62(10) Boltek N (2014) EFM-100 atmospheric electric field monitor guide. www.boltek.com Davis (2019) Vantage Pro2 Manuel de la console. https://www.davisnet.com/legal Eberhard J, Geissbuhler V (2000) Konservative und operative therapie bei harninkontinenz, deszensus und urogenital-beschwerden. Journal fur Urologie und Urogynakologie 7(5):32–46. MIT Press 5. Harrison RG, Nicoll KA (2018) Fair weather criteria for atmospheric electricity measurements. J Atmos Solar Terr Phys 179:239–250. https://doi.org/10.1016/j.jastp.2018.07.008 6. Harrison RG, Marlton GJ (2020) Fair weather electric field meter for atmospheric science platforms. J Electrostat 107. https://doi.org/10.1016/j.elstat.2020.103489 7. Hays PB, Roble RG (1979) A quasi-static model of global atmospheric electricity, 1. The lower atmosphere. J Geophys Res 84(A7):3291. https://doi.org/10.1029/ja084ia07p03291

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8. Lam MM, Freeman MP, Chisham G (2018) IMF-driven change to the Antarctic tropospheric temperature due to the global atmospheric electric circuit. J Atmos Solar Terr Phys 180:148– 152. https://doi.org/10.1016/j.jastp.2017.08.027 9. Mathworks C (2019a) Mastering machine learning a step-by-step guide with MATLAB 10. Mathworks C (2019b) Mastering machine learning a step-by-step guide with MATLAB. https://www.mathworks.com/content/dam/mathworks/ebook/gated/machine-learning-wor kflow-ebook.pdf 11. Nicoll KA, Harrison RG, Barta V, Bor J, Brugge R, Chillingarian A, Chum J, Georgoulias AK, Guha A, Kourtidis K, Kubicki M, Mareev E, Matthews J, Mkrtchyan H, Odzimek A, Raulin JP, Robert D, Silva HG, Tacza J, … Yaniv R (2019) A global atmospheric electricity monitoring network for climate and geophysical research. J Atmospheric Solar-Terrest Phys 184:18–29. https://doi.org/10.1016/j.jastp.2019.01.003 12. Paluszek M, Thomas S (2017) MATLAB machine learning. In: MATLAB machine learning. Apress. https://doi.org/10.1007/978-1-4842-2250-8 13. Rositano F, Bert FE, Piñeiro G, Ferraro DO (2018) Identifying the factors that determine ecosystem services provision in Pampean agroecosystems (Argentina) using a data-mining approach. Environ Dev 25:3–11. https://doi.org/10.1016/j.envdev.2017.11.003 14. Saboya N, Loaiza OL, Soria JJ, Bustamante J (2019) Fuzzy logic model for the selection of applicants to university study programs according to enrollment profile. Adv Intel Syst Comput 850:121–133. https://doi.org/10.1007/978-3-030-02351-5_16 15. Soria JJ, Sumire DA, Poma OSCE (2020) Neural network model with time series for the prediction of the electric field in the East Lima Zone, Peru, vol 2, pp 395–410. https://doi.org/ 10.1007/978-3-030-51971-1_33 16. Sperotto A, Molina JL, Torresan S, Critto A, Pulido-Velazquez M, Marcomini A (2019) A Bayesian networks approach for the assessment of climate change impacts on nutrients loading. Environ Sci Policy 100:21–36. https://doi.org/10.1016/j.envsci.2019.06.004 17. Tacza J, Raulin JP, Macotela E, Norabuena E, Fernandez G, Correia E, Rycroft MJ, Harrison RG (2014) A new South American network to study the atmospheric electric field and its variations related to geophysical phenomena. J Atmos Solar Terr Phys 120:70–79. https://doi. org/10.1016/j.jastp.2014.09.001 18. Takashima H, Hara K, Nishita-Hara C, Fujiyoshi Y, Shiraishi K, Hayashi M, Yoshino A, Takami A, Yamazaki A (2019) Short-term variation in atmospheric constituents associated with local front passage observed by a 3-D coherent Doppler lidar and in-situ aerosol/gas measurements. Atmospheric Environ X:3. https://doi.org/10.1016/j.aeaoa.2019.100043 19. Tinsley BA, Burns GB, Zhou L (2007) The role of the global electric circuit in solar and internal forcing of clouds and climate. Adv Space Res 40(7):1126–1139. https://doi.org/10.1016/j.asr. 2007.01.071 20. Williams E, Mareev E (2014) Recent progress on the global electrical circuit. Atmospheric Res 135–136:208–227. https://doi.org/10.1016/j.atmosres.2013.05.015 21. Zeng R, Zhang Y, Chen W, Zhang B (2008) Measurement of electric field distribution along composite insulators by integrated optical electric field sensor. IEEE Trans Dielectr Electr Insul 15(1):302–310. https://doi.org/10.1109/T-DEI.2008.4446764

Enhanced Honeyword Generation Method Using Advanced DNA Algorithm Nwe Ni Khin and Khin Su Myat Moe

Abstract Today, the security of password files is paramount. There are significant problems for users and companies in various fields. The dangerous attacks for password files are the brute force attack, DoS attacks, and dictionary attacks. Therefore, the researchers try to protect the password files using various algorithms such as honeyword generation algorithm, password hashing algorithm, MD5, and many other algorithms. Among them, the honeyword generation algorithm is one of the best algorithms for attacking the brute force attack. Honeywords generation algorithm is to prevent hackers from attacking the password file by mixing the real and fake passwords stored in the database. The existing honeywords generation algorithm uses the hashing and salting algorithm for creating real and fake passwords to get stronger security. We propose an improved honeywords generation process using an advanced DNA algorithm. The proposed process can save the processing speed. The current DNA algorithm has a weakness in security. Therefore, we apply an advanced DNA algorithm that uses five data lookup tables randomly in our proposed system. Therefore, we can secure and save time by using the improved generation process using an advanced DNA algorithm. We present case studies as a computation and testing results of the DNA algorithm in the paper. Moreover, we describe the process of generating honeywords. Finally, the comparison results of the proposed method and the existing honeywords generation method are described in this paper. Keywords Honeyword generation · DNA sequence · Brute force attack

N. N. Khin (B) · K. S. M. Moe Yangon Technological University, Computer Engineering and Information Technology, Yangon, Republic of the Union of Myanmar e-mail: [email protected] K. S. M. Moe e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_18

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1 Introduction Most organizations around the world want to secretly exchange secret messages through secure channels of communication. So, they use an encryption service with different encryption and decryption algorithms for their own data. Most of the security processes apply the password-based encryption (PBE) algorithm due to the user can easily pick and can’t forgot their passwords. However, current PBE algorithms are weak because most of the attackers using the various attacks, in particularly brute force attacks can easily obtain the keys. Distributing random passwords improves the security method. Setting a secure password is fundamental to keeping the information secure. Therefore, we will introduce an improved honey encryption algorithm that uses advanced DNA algorithm. Improved honeyword generation is a way to obstruct attackers from robbery password files and the password list is stored in the system. The improved honeyword generation algorithm generates the honeywords for deceiving the attackers. The user’s passwords and generating honeywords are stored in the password file in the database. Hackers can attack the password file using a brute force attack. If the attacker gets the password file, he can’t classify which one is the real password because the honeywords and real password are stored in the password file. DNA cryptography works on the concept of DNA computing. In the security area, the concept of the use of DNA computing has emerged as a potential technology that could create new hope for inviolable algorithms. DNA cryptography is used for secure data storage, authentication, digital certificates. The following Fig. 1 is the DNA algorithm. In our proposed system, we combine the two algorithms of improved honeyword generation and enhanced DNA algorithm for overcoming the weakness of the existing system. This proposed paper is made up of five sections. Review of literature on the process of honey encryption and DNA encryption. Section 2 discusses the previous work. Section 3 describes the process of DNA encryption and honeywords generation and the enhanced DNA code sequences. Section 4 focuses on the flowchart of the proposed system. Section 5 discusses the testing results and Sect. 6 presents a comparative study on the honeywords generation method and finally concludes the paper.

Password Hello123

Translate and Encrypt

DNA sequence TTTTAACTACTATGTCAACCCCG

Fig. 1 A figure shows DNA algorithm [1]

Translate and Decrypt

Password Hello123

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2 Literature Review Bonny et al. discussed [2]. Firstly, the plaintext is converted to ASCII value, ASCII value is converted to binary value and then the binary value is converted to DNA code. Secondly, random key is generated in the range of 1–256 which corresponds to the permutation of four characters, A, T, G, and C to produce the ciphertext. Thirdly, decryption is taken place. This system got data confidentiality and data integrity over data transmission. Moe and Win proposed [3] is solved the typo error, storage overhead, and time complexity using hashing algorithm in honeyword generation. Noorunnisa, N. S, Dr. Afree, K. R. discussed Honey Encryption combining with OTP (Vernam Cipher) to encrypt the original message. Increased security level and time consumption are similar to a blowfish [4]. The system of [5] was proposed by Pushpa, B. R. The system is the more powerful system for attacking the attacks. But the key length of ASCII is long. Mavanai et al. proposed [6]. This system performing transposition and folding operations to increase security and prevent brute force attacks but increase complexity. The proposed system [7] was introduced by Kumar, B.R., Sri, S., Katamaraju, G.M.S.A., Rani, P., Harinadh, N., and Saibabu, Ch. This paper includes several intermediate steps, to hide the data due to conceal information from attackers and hijackers and to disseminate their information securely. The proposed method mainly deals with two processes, the Honey Encryption process and the DNA Encryption Algorithm. Honeyword generation passwords using the Honey Encryption process and encryption keys from the user’s passwords are produced using a DNA encryption algorithm. In the key or password distribution section, the resulting DNA code is randomly mapped to seed space using the DTE process. In addition, we propose enhanced DNA encoding using five data lookup tables in the password encoding process. The proposed system protects the brute force attacks and saves processing time.

3 Background Theory The purpose of this idea is to overcome time complexity and to increase the security level. In addition, we sample the DNA encryption algorithm to secure the password file during the sweetwords encoding process.

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3.1 Enhanced Honeyword Generation Passwords are notoriously infamous and users often choose poor and repeated passwords. Therefore, the honeyword generation process generates the honeywords for deceiving the attackers. The purpose of enhanced honeywords generation methods is to issue an appeal to remove the attackers. In the enhanced honeywords generation process, the real password and generating honeywords are stored in the main server and honey checker. The storage process of the main server and honeychecker are shown in Tables 1 and 2. Honeychecker is a backup server that can be used to categorize the combination of real and honeywords (sweetwords) and that stores confidential information such as authentic passwords and DNA sequences. When the user enters the system, the main server checks the login passwords with the honeychecker. When entering a honeyword, system administration can be immediately identified by honeychecker. There are two main processes of honeychecker: the first step is to distinguish between the fake and actual passwords when entering into the system. Another task is to send a warning message to the administrator when entering honeywords [8]. Firstly, the original passwords are saved as indexes. The main server stores the username and indexes of honeywords. Honeychecker stores the index of real passwords only after converting to DNA sequence [9]. Table 1 Main server [9]

Table 2 Honeychecker [9]

User name

Index of honeywords

Sophia

(28,18,31,49)

Emily

(15,18,31,49)

Victor

(15,28,31,49)

Grace

(15,18,28,49)

Hazel

(15,28,18,31)

Index of real passwords

DNA sequence

15

AGTGGTCAG

28

GATGGATCA

18

CGAACTTGT

31

TAAGAGGTC

49

GCTCGACAT

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3.2 Enhanced DNA Algorithm DNA Encryption is the process of converting text containing numbers and special characters into DNA sequences [10]. Enhanced DNA Algorithm steps are followed: Step 1: Create the five DNA lookup tables for the passwords such as alphabets, numbers, special characters, and symbol as 64 * 5 = 320 words are randomly encoded. Step 2: The passwords are converted into the 3-based DNA sequence using a random DNA lookup table. For example, the password is “my secret!”. The DNA code of the password will be CCG CTT ACT CCT CCA TCT AGG CCA CTA CAC (see Tables 3, 4, 5, 6 and 7).

4 Flowchart of the Proposed System This proposed system flowchart includes the following two steps as shown in Fig. 2. The database file stores the sugarword and honeywords. For a new user, it is needed to register and it will generate a password for the new user. After the registration process, the user can enter the system by using his username and password. If it’s not the registered user, the password doesn’t exist in the database and the server is retrieving a fail login message. If the password is in the database, the server generates the passwords to honeychecker to distinguish between real passwords and honeyword. If the password is correct, the honeychecker allows this user to access it. Otherwise, the administrator sends an alert message to add honeywords to the honeychecker system. DNA algorithm is used for key generation. The first step is to Table 3 Data lookup Table 1 A = AAA B = ACA C = AGA D = ATA

E = GAA

G= GGA

H = GTA

I = AAC

M = GAC N = GCC O = GGC

P = GTC

J = ACC

K = AGC L = ATC

F = GCA

Q = AAG R = ACG S = AGG

T = ATG

U = GAG V = GCG W = GGG

X = GTG

Y = AAT

Z = ACT

1 = AGT

2 = ATT

3 = GAT

4 = GCT

5= GGT

6 = GTT

7 = CAA

8 = CCA

9 = CGA

0 = CTA

! = TAA

@ = TCA # = TGA

$ = TTA

* = CAC

? = CCC

/ = CGC

> = CTC

< = TAC

~ = TCC

Space = | = TTC TGC

\\ = CAG

_ = CCG

= = CGG + = CTG - = TAG

, = TCG

. = TGG : = TTG

; = CAT

% = CCT & = CGT ˆ = CTT

) = TCT

[ =TGT ] = TTT

( = TAT

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Table 4 Data lookup Table 2 ; = AAA

% = ACA & = AGA ˆ = ATA

( = GAA

) = GCA

[= GGA

\\ = AAC

_ = ACC

= = AGC + = ATC - = GAC

, = GCC

. = GGC : = GTC

* = AAG

? = ACG

/ = AGG

> = ATG

< = GAG

~ = GCG

Space = | = GTG GGG

7 = AAT

8 = ACT

9 = AGT

0 = ATT

! = GAT

@ = GCT # = GGT

$ = GTT

Y = CAA Z = CCA

1 = CGA

2 = CTA

3 = TAA

4 = TCA

5= TGA

6 = TTA

Q = CAC R = CCC

S = CGC

T = CTC

U = TAC

V = TCC

W= TGC

X = TTC

I = CAG

J = CCG

K = CGG L = CTG M = TAG N = TCG

O= TGG

P = TTG

A = CAT

B = CCT

C = CGT

G= TGT

H = TTT

D = CTT E = TAT

F = TCT

] = GTA

Table 5 Data lookup Table 3 A = AAA I = ACA

Q = AGA Y = ATA 7 = GAA * = GCA

\\ = GGA

; = GTA

B = AAC J = ACC

R = AGC

Z = ATC 8 = GAC

?= GCC

_ = GGC

% = GTC

C = AAG K = ACG S = AGG

1 = ATG 9 = GAG

/= GCG

= = GGG &| = GTG

D = AAT

L = ACT

2 = ATT

0 = GAT

>= GCT

+ = GGT ˆ = GTT

E = CAA

M = CCA U = CGA 3 = CTA

! = TAA

= AGT

0 = ATT

2 = GAT

T = GCT

L = GGT

( = CAA

- = CCA

= AGA

_ = ATA

. = GAA

I = AAC

B = ACC

@ = AGC < = ATC

= = GAC : = GCC ˆ = GGC

[ = GTC

P = AAG

J = ACG

C = AGG

# = ATG

~ = GAG

+= GCG

(=GTG

V = AAT

Q = ACT

K = AGT

D = ATT

$ = GAT

Space = - = GGT GCT

% = GTT

1 = CAA

W = CCA R = CGA

L = CTA

E = TAA

*= TCA

|=TGA

, = TTA

5 = CAC

2 = CCC

X = CGC

S = CTC

M = TAC F = TCC

? = TGC

\\ = TTC

8 = CAG

6 = CCG

3 = CGG

Y = CTG T = TAG

N= TCG

G = TGG / = TTG

0 = CAT

9 = CCT

7 = CGT

4 = CTT

U= TCT

O = TGT H = TTT

Z = TAT

; = GGG

5 Testing Results Python programming is used for the experimental results. The results are using with the processor AMD Ryzen 5 3500 U and memory 8.00 GB. Table 8 shows the process of the code of Python 3 compiler of the DNA program by testing the same password “hello123” three times, but the encrypted output of the process of DNA sequence is not the same.

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N. N. Khin and K. S. M. Moe Start Yes

Register

New Member? No

User Password No

Login Registration Success?

Choosing Data Lookup Table to produce DNA code Using Random Process

Yes

Server checks the key

Yes

DNA Code

Honeychecker classifies honeywords and sugarword

No

Honeyword

Unsuccess Login

Sweetwords are stored in Database (Honeywords + Sugarword)

Sugarword

Login Success

Raise an alarm

End

Fig. 2 Flowchart of proposed system

Table 8 Different outputs from “hello123” No

Password

DNA outputs

Execution time (ms)

1

hello123

TTTTAACTACTATGTCAACCCCGG

0.4947

2

hello123

CATCAAACTACTCCGATGATTCTA

0.3609

3

hello123

TTTTTAGGTGGTTGGGAGGATTAA

0.4886

6 Comparative Study We study the time complexity tasks with the different word lengths, typo safety, and storage overhead are compared to our proposed system with the current system.

Enhanced Honeyword Generation Method … Table 9 Time comparison of existing and our proposed algorithm

209

Password length

Existing method

Current method

7

3.050004

1.59

8

3.100004

2.29

9

3.140004

2.34

10

3.18005

2.37

Fig. 3 Results chart of time complexity

6.1 Time Complexity In this section, we compared other generations of honeywords, including our proposed models due to the complexity of DNA and password security. When trying to use different passwords of different lengths in the proposed method, the time complexity is less than the current method. Table 9 shows the results of experiments with different lengths of passwords, such as COSE789, Floral * 3, GRATIS75%, and silicon32G”. The existing method [3] is more complicated than the current method (see Fig. 3).

6.2 Flatness To test the level of security, the honeywords generation algorithm uses flatness. The flatness level determines which generation of beekeeping keywords is the best way to secure our applications. Flatness calculates how many times an attacker receives a password. Compare the 1/s results of the probability attacker that w is the attacker w and the probability of this attack. When w >= 1/s, the production of honeywords is approximately flat and an enemy can guess the correct random password. Otherwise, w < s is perfectly flat [11].

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Table 10 Comparison results of previous and our proposed method [3] No

Method

Flatness

Typo safety

Storage overhead

1

Chaffing by tweaking

1/s

Low

(s − 1) * n

2

Password model

1/s

High

(s − 1) * n

3

Take a tail

1/s

High

(s − 1) * n

4

Paired distance protocol

1/s

High

(1 + RS) * n

5

Improved honeywords generation

1/s

High

(1 * n)

6

Our proposed method

1/s

High

(1 * n)

Where s = Number of sweetwords in the password file, n = Number of users, RS = Random String

6.3 Typo Safety Typo safety is the major problem of existing honeywords generation algorithms. This honeywords generation algorithm causes similar problems due to similar honeywords creation [11].

6.4 Storage Overhead The previous honeywords production process produces at least 20 honeywords to deceive the attacker. As a result, attackers are in conflict and cannot easily identify any real or incorrect passwords. However, each user has at least 21 passwords and the database storage problem has become a problem. In our proposed method, we refer to it as a sweetword that contains a real password (sugarword) and honeywords. Because the user has only one password, honeywords becomes (s − 1). Under the current system, the Communication Distance Protocol (CDP) is the optional storage problem. Since our system uses member passwords as honeyindexes and can generally reduce storage overhead rather than the PDP algorithm as shown in Table 10.

7 Conclusion Our proposed honeywords production process using the new DNA algorithm can save more processing time than the existing system. We use the existing enhanced honeywords generation algorithm that can reduce the storage overhead problem. Therefore, our proposed system can solve the storage overhead problem. Moreover, we use an advanced DNA algorithm in the honeywords generation process, our proposed system can get better security. In the future, we will apply our proposed system in the transferring of a large amount of data in many organizations.

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References 1. Marlene, Bachand G (2020) Researchers storing information securely in DNA. 24 August 2020 from http://phys.org/news/2016-17-dna.html 2. Bonny BR, Vijay JF, Mahalakshmi T (2016) Secure data transfer through DNA cryptography using symmetric algorithm. Int J Comput Appl (0975–8887) 133(2) 3. Moe KSM, Win T (2017) Improved hashing and honey-based stronger password prevention against brute force attack. In: 2017 international symposium on electronics and smart devices. IEEE. 978-1-5386-2778-5/17/$31.00 4. Noorunnisa NS, Afree KR (2019) Honey encryption based password manager. JETIR 6(5). www.jetir.org (ISSN-2349-5162) 5. Pushpa BR (2017) A new technique for data encryption using DNA sequence. In: International conference on intelligent computing and control (I2C2) 6. Mavanai S, Pal A, Pandey R, Nadar D (2019) Message transmission using DNA crypto-system. Int J Comput Sci Mobile Comput 8(4):108–114 7. Kumar BR, Sri S, Katamaraju GMSA, Rani P, Harinadh N, Saibabu C (2020) File encryption and decryption using DNA technology. In: Second international conference on innovative mechanisms for industry applications (ICIMIA 2020). IEEE. Xplore Part Number: CFP20K58ART; ISBN: 978-1-7281-4167-1 8. Juels A, Revist RL (2013) Honeywords making password cracking detectable. In: MIT CSAIL 9. Moe KSM, Win T (2018) Protecting private data using improved honey encryption and honeywords generation algorithm. Adv Sci Technol Eng Syst J 3(5):311–320 10. Omer A (2015) DNA cryptography algorithms and applications. Hitec University 11. Gross D (2013) 50 million compromised in Evernote hack. CNN

A Review: How Does ICT Affect the Health and Well-Being of Teenagers in Developing Countries Willone Lim , Bee Theng Lau , Caslon Chua , and Fakir M. Amirul Islam

Abstract In developing agendas regarding teenagers’ use of information and communication technologies (ICTs) in developing countries, both health and wellbeing are typically ignored or assumed to have minor impacts on their lives. The purpose of this study is to describe the positive and negative effects of adopting ICT on teenagers’ health and well-being in developing countries. Several databases were searched to identify articles investigating the positive impacts, negative impacts, and evaluation of mobile health (mHealth) technologies in developing countries. The analyses concluded that teenagers in developing countries are leveraging mHealth applications to access health services, information and interact with physicians remotely. However, the long-term effect of ICT use can be seen in depressive symptoms, musculoskeletal pain, or even anxiety. Many researchers have yet to explore the potential of ICTs from different aspects of teenagers’ health and well-being; however, the negative impacts are more pervasive where ongoing studies have been conducted in the past years. The review provides insight into the benefits of ICT on teenagers in developing countries, but it is crucial to be aware of the negative implications on future health and well-being.

W. Lim (B) · B. T. Lau Faculty of Engineering, Computing and Science, Swinburne University of Technology, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia e-mail: [email protected] B. T. Lau e-mail: [email protected] C. Chua Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia e-mail: [email protected] F. M. A. Islam Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC 3122, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_19

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Keyword ICT · Impacts · Health · Well-being · Teenagers · Developing countries · Digital devices

1 Introduction The unstoppable advancement of information and communication technologies (ICTs) has brought sweeping changes to the new generation in terms of health and well-being. Implicitly, people between ages 13 and 19, referred to as teenagers or more commonly known as digital natives, are the first generation born in this evolving information age [1]. In other words, this generation was born into a world that was already technologically advanced, that are fundamental to the way that they communicate, learn, and develop. Internet, computer, mobile device, and televisions—all play a formative role in their daily activities [2]. Despite the encouraging sign of the ICT revolution, unfortunately, the inequalities or digital divides are still at large between developed and developing countries. According to United Nations, only 33.33% of the population has internet access in developing countries, compared to 82% in developed countries [3]. Although evidence of both positive and negative impacts of ICT are available, limited research has been conducted on the impacts of ICT, specifically in developing countries. Therefore, this review was conducted to understand the determinants, correlations, and consequences of ICT on health and well-being among teenagers in developing countries, which are critical for informing preventive interventions or measures that may benefit them. The current review is meant to provide insights on the impacts of ICT on health and well-being among teenagers and to identify the gaps for further research in developing countries. This review paper is divided into introduction, methodology, results, conclusion, and future works.

2 Methodology 2.1 Search Strategy The literature was gathered from leading academic databases, with 21,797 from ScienceDirect, 22,263 from Scopus and Google Scholar, to search for articles related to ICT impacts on teenagers’ health and well-being in developing countries. The initial search within each database returned a large number of articles; hence search terms were used with context-related terms such as “health and well-being”, “developing countries”, and “mHealth". The papers collected are only peer-reviewed journal research articles. Additional studies to examine for potentially relevant articles with three (3) from reference lists of all included articles and two (2) from websites. A total of 31 articles after inclusion and exclusion has been applied; were selected for further analysis.

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2.2 Inclusion and Exclusion Criteria Studies were included if they met the following criteria: (1) focused on teenagers between ages 13 and 19; (2) utilized technologies (mHealth) to improve health; (3) included positive or negative health and well-being outcome; (4) occurring in developing countries; (5) residing in urban and rural areas (6) were published between 2015 and 2020; and (7) written in the English language. In-depth analyses were conducted to eliminate irrelevant articles. The following data were abstracted (1) articles that do not cover the teenagers’ age group, (2) articles published before 2015 and after 2020, and (3) research from developed countries. Once the articles were collected, the exclusion criteria were applied as part of the title and abstract review.

3 Results and Discussions The study of the literature shows that there has been significant research done in the field of ICT, health, and well-being. Major findings have highlighted some main subjects: adoption of ICT, gender gaps, introduction to mobile health, and the detrimental impacts of ICT.

3.1 Adoption of ICT The effects of ICT on teenagers in developing countries have been more visible than ever. However, many questions have been raised about whether its effects are beneficial or damaging teenagers’ health and well-being [4–6]. Several studies have aimed to analyze the adoption of ICT, with different results; improved healthcare services through mobile applications [7], promoting health education [8]; in contrast, excessive use causes vision impairment [9], musculoskeletal pain [10, 11] and depression [12–16].

3.2 Gender Gaps Additionally, many policymakers and practitioners promote ICT in developing countries in the hope that to broaden access among teenagers in improving lifestyle [17]; even so, the gender inequalities are proven as a point of concern [18]. There has been considerable gender digital divide where girls are marginalized or excluded from accessing health resources [19–21]. Besides, teenagers from geographically disadvantaged areas often have limited benefits in using ICT to improve health and

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well-being. Despite the barrier, many teenagers from developing countries are willing and actively seeking digital health services [5, 22]. Given this omnipresent role of ICT, teenagers are more digitally vulnerable by its nature compared to the previous generation [23, 24]. Although it is evident that the introduction of ICT to teenagers in developing countries can boost their overall health and well-being, but the dependencies on technologies may have adverse health outcomes [25] and dangerous long-term effects on well-being.

3.3 Mobile Health The emergence of mobile health (mHealth) has been seen as an imperative steady growth, especially in developing countries, where the prevalence of adopting mobile health in the global landscape; Africa (67%), Eurasia (26%), and Latin America (13%) [7]. Mobile Health provides teenagers with opportunities to access healthcare services and information virtually [8]. It was widely used as part of a health intervention in low-income countries to promote public health campaigns [7, 26] and many teenagers are willing or intended to adopt mHealth services [27, 28].

3.4 Detrimental Impacts of ICT Health. A recent study was conducted to examine students’ habits of using electronic devices, average hours, viewing distance, and posture when using devices. The results showed that 33.3% of students use digital devices for 2–4 (43.6%) hours a day. Further study concluded that 27% of students experienced eyestrain when using devices while lying down and the prolonged usage may lead to new challenges of digital eyestrain [9]. Another article investigated the relationship between musculoskeletal symptoms and computer usage with the aimed to compare different variables; sociodemographic, musculoskeletal pain, and physical activity level. Their findings indicated 65.1% of students experienced musculoskeletal pain in the anatomical region, such as thoracolumbar spine (46.9%), upper limbs (20%), cervical spine (18.5%), and scapular region (15.8%) [11]. The study was conducted to determine the association between screen-time and body weight using a cross-sectional survey to investigate weight status, screen-time viewing, and students’ demographics. The results revealed a significant relation between screen-time usage and weight status, with 14.4% of students are categorized as overweight while 11.9% were obese and 36.8% among them has exceeded the recommended daily screen-time viewing of 2 h per day. In comparing urban and rural areas, students from urban areas are more likely to be obese than those from rural areas [29] (see Table 1). Well-being. A cross-sectional study reported on the association between mobile phone use and depressive symptoms. The authors described that depressive symptoms increased when the mobile phone is used extensively where 19.1% of students

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Table 1 Studies on ICT and health Publication

Scope

Age group

Region

Positive impacts

Negative impacts

Cai et al., 2017

Screen-time viewing and overweight

Students aged 10–18 years old

Asia (China)



14.4% were overweight and 11.9% obese

Hoque, 2016

mHealth adoption in a developing country

Students

Asia (Dhaka, Bangladesh)

Intention to adopt mHealth



Ichhpujani et al., 2019

Visual implications of digital device usage

Students aged 11–17 years old

Asia (India)



Eye strain after long hours of devices usage

Queiroz et al., 2017

Musculoskeletal pain related to electronic devices

Students aged 10–19 years old

South America (São Paulo, Brazil)



Females are more likely to have musculoskeletal pain

Silva et al., 2016

Computer use and musculoskeletal pain

Students aged 14–19 years old

South America (Pernambuco, Brazil)



Cervical and lower back pain

Asia (China)



High screen-time associated with poor health

Wang et al., High screen-time Students aged 2018 usage 13–18 years old

who used mobile phones for more than 2 h on weekdays and 18.3% who used mobile devices for more than 5 h during weekends are associated with the increased depressive disorder [14]. The authors aim to determine the relationship between the duration of using gadgets and mental-emotional health by conducting a cross-sectional study to investigate psychological attributes, emotional symptoms, behavioral problems, and prosocial behaviors. The study found that 55.2% of students are having an abnormal mental-emotional state when using gadgets for more than 10 h a week [30]. Based on the authors, the study examined the relationship between suiciderelated behaviors and the use of mobile phones. Results from the survey showed that extensive use of mobile phones indirectly causes depression with the risk of suicide-related and self-harming behaviors [31] (see Table 2).

4 Conclusion and Future Works The interaction with information and communication technology is increasing among teenagers, particularly in developing countries. It brings diverse effects on their

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Table 2 Studies on ICT and well-being Publication

Scope

Age group

Region

Positive impacts

Negative impacts

Chen et al., 2020

Mobile phone use and suicidal intention

Students aged 13–18 years old

Asia (China)



Devices usage causes suicidal actions

Demirci et al., Impact of 2015 smartphones on health

Students

Asia (Isparta, Turkey)



Use of smartphone causes depression

Liu et al., 2019

Prolonged mobile phone use

Students aged 15 years old

Asia (China)



Risk of getting depression

Van Der Merwe, 2019

ICT use pattern and well-being of open distance learning students

Students

Africa (South Africa)

Low risk of overuse symptoms



Wahyuni et al., 2019

Gadgets and mental health

Students aged 8–16 years old

Asia (Indonesia)



Digital gadgets affect mental health

Yan et al., 2017

Screen-time Students aged and well-being 13–18 years old

Asia (Wuhan, China)



High screen-time and increase in BMI

Zhao et al., 2017

Internet addiction and depression

Asia (China)



Internet addiction causes depression

Students

health and well-being. In terms of health perspective, mobile health application has the potential to deliver fundamental health support remotely, which is beneficial for teenagers from rural areas. However, the main findings in this literature review directed to the negative implications of ICT toward teenagers in developing countries. The extended use of digital devices has contributed to the risk of getting health complications such that musculoskeletal pain around the cervical and lumbar region [11], increase in weight among teenagers from urban areas [29], and eye strain causing discomfort, dryness as well as the blurring of vision [9]. This resulted in teenagers’ negative well-being that may lead to depression, mental-emotional health, or even suicidal behavior. Based on the studies reviewed, limited research was presented about the positive impacts of ICT on health and well-being—for instance, a significant underrepresentation on the adoption of mHealth and its potential benefits on teenagers. Hence, researchers have limited knowledge on the effectiveness of mHealth intervention,

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particularly in developing countries—besides, findings on how ICT contributed to teenagers’ well-being are noticeably low. A more extensive study should be carried out in addition to the future scope for researchers to understand the benefits of mHealth to teenagers in developing countries. Researchers could investigate the adoption of mHealth in developing countries globally to understand how different populations are leveraging the existing mHealth to improve health care. Nevertheless, future research is needed to study the impacts of ICTs on well-being, particularly in the context of underdeveloped countries, to provide information on how to improve the overall well-being or quality of life among teenagers. The findings in this review would be an important contribution to the growing body of evidence investigating the impacts of ICT on health and well-being among teenagers in developing countries.

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Multi-image Crowd Counting Using Multi-column Convolutional Neural Network O˘guzhan Kurnaz

and Cemal Hanilçi

Abstract Crowd density estimation is an important task for security applications. It is a regression problem consisting of feature extraction and estimation steps. In this study, we propose to use a modified version of previously introduced multi-column convolutional neural network (MCNN) approach for estimating crowd density. While in the original MCNN approach the same input image is applied to the each column of the network, we first propose to apply a different version of the same input image to extract a different mapping from each column. Second, original MCNN first generates an estimated density map and then performs crowd counting. Therefore, we adopt it for crowd counting and compare its performance with the proposed method. Regression task is performed by support vector regression (SVR) using feature vectors obtained from MCCNN. 2000 images selected from UCSD pedestrian dataset are used in the experiments. The regions of interest (ROI) are filtered out and the pixel values at the remaining regions are set to zero. In order to prevent distortion caused by camera position, perspective normalization has been applied as a pre-processing step which dramatically improves the performance. Keywords Crowd density estimation · Convolutional neural network · Crowd counting

1 Introduction As a natural consequence of rapidly growing urbanization, safety is becoming a basic human need. Therefore, the most visited places in cities are usually equipped with surveillance cameras by the authorities and often a human observer watches various O. Kurnaz (B) Mechatronics Engineering, Bursa Technical University, Bursa, Turkey e-mail: [email protected] C. Hanilçi Electrical and Electronic Engineering, Bursa Technical University, Bursa, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_20

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objects (especially a suspected person) and their activities for a long period of time. Thus, such monitoring can result with a failure or missing the target object in crowd scenes. In contrary to surveillance, many accidents caused by the large crowds of people (e.g., concerts, festivals, demonstrations, etc.) were previously observed at various locations and countries. Thus, utilizing computer vision techniques for such analysis has received great attention in recent years. Crowd density estimation −automatically estimating the level of a crowd− is a significant but challenging task for various purposes. Existing research on crowd density estimation can be divided into two categories [1]: (i) holistic and (ii) local approaches. In holistic approach, whole image is processed at once without any segmentation and the relation between the crowd size and feature space is calculated using global features of image [2]. In local approach, the local features extracted from the image are used for performing different approaches such as detection, tracking and pedestrian density estimation [1]. Chan et al. [3], introduced a privacy-preserving method to estimate the size of non-uniform crowds compose from pedestrians moving in various directions. An optimized convolutional neural network (CNN) was proposed for crowd density estimation in [4]. With the proposed method, estimation speed was increased by eliminating some network connections according to the presence of similar feature maps and accuracy was improved by employing two cascade connected CNNs [4]. Zhang et al. [5] proposed a method to train a CNN with two different switchable learning tasks: crowd density estimation and crowd counting, and it was reported that the proposed method gives a better local optimum for both tasks. Sindagi and Patel [6] introduced an end-to-end cascaded network of CNNs for crowd density estimation aiming at jointly learning the crowd count classification and estimation of density map. A combination of shallow and deep fully convolutional networks for predicting the density map for a given crowd image was used in [7]. This combination was shown for capturing high-level information (e.g., face or body detectors) and the low-level features (e.g., blob detectors). A deep residual network was proposed and found to be appropriate for coincident crowd density estimation, violent behavior detection and level classification of crowd density [8]. Hu et al. [9] proposed a deep learning-based approach for estimating the size of a high-level or mid-level crowd in a single image. In that method, a CNN architecture was used for extracting features of crowd and then crowd density and crowd count were employed to learn the crowd features to estimate the specific crowd density. Li et al. [10] proposed an architecture for congested scene recognition by providing a data-driven and deep learning method. This method was shown for understanding highly congested moments and performing accurate count estimation from its representative density maps. In [11], CNN was used to map a given crowd image to corresponding density map. Zhang et al. [12] proposed multi-column CNN (MCNN) to count the number of crowd from a single image with random crowd density and random camera position. This network was used to map the input image to the corresponding crowd density map. In this paper, we propose to use two different modified versions of the previously proposed MCNN approach [12] for crowd density estimation. In original MCNN, three different CNNs with different number of receptive fields were trained using

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the same input image and then the embeddings of the last convolutional layers were combined to estimate the corresponding density map. The main contributions of our work and its differences from [12] can be summarized as follows: • Rather than training each CNN with the same input image, we propose to use different pre-processed versions of the input image with each CNN. Our motivation for this approach is the fact that, although the parameters (e.g., number of filters, size of the convolutional filters, etc.) are different, each CNN is likely to learn similar embeddings when the same input image is used. However using different pre-processed versions of the same image as the input will result in different representations (therefore different level of information). With these different embeddings, the performance of the crowd density estimation can intuitively be improved. • Rather than using multi-column CNNs in an end-to-end type as proposed in [12], we propose to use it for feature extraction. • We use support vector regression (SVR) using the features extracted from MCCNN for crowd density estimation. • A slight modification on previously proposed MCNN [12] is introduced and we compare its performance with the proposed technique.

2 Methods As mentioned before, crowd density estimation is a standard regression problem consisting of training and test stages. Before extracting features from input images, a pre-processing step is applied where necessary arrangements (e.g., noise removal, background subtraction, and selection of the region of interest) are performed. In this study, selection of the region of interest and perspective normalization are used as pre-processing techniques. Pre-processed images are then used to extract features. Three different feature representations are obtained from the proposed multiimage multi-column CNN (MCCNN) consisting of three CNNs in parallel where each of them is trained using a different version of the input image. Finally, the feature mappings obtained from each CNN are combined to train the support vector regression model and estimate the crowd density. In this section, each step of the proposed crowd density estimation system is briefly explained.

2.1 Pre-processing: Region of Interest (ROI) Selection There may be regions in the images that do not contain useful information for crowd density estimation. Especially, on crowd density estimation, the regions in which no one appears do not contain useful information. Besides increasing the computational complexity, these regions mostly degrade the performance. Thus, using only the

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regions in which people appear (referred to as region of interest-ROI) in the images considerably improves the crowd density estimation performance [13]. Therefore, we first apply the selection of ROI as a pre-processing step to the images. To do so, relevant regions in the images are first determined and then the pixel values in the remaining regions are set to zero. The ROI in the images are obtained by applying an image filter that is designed to set the pixel values within the regions where people are not located to zero: f (x, y) = 0,

if (x, y) ∈ / ROI

(1)

2.2 Pre-processing: Perspective Normalization Previous studies in crowd density estimation showed that camera position has an important effect on the performance [3]. If the point of view is inclined, the size of the same object varies in different locations due to the effects of perspective. Since people closer to the camera look greater than further away, perspective normalization (PN) should be applied as a pre-processing step to the images for reducing the adverse effects induced by the perspective. Therefore, to tackle the effects of perspective, we apply PN method onto the ROI selected images as described in [3].

2.3 Feature Extraction: Multi-column Convolutional Neural Network (MCCNN) In this study, we propose to extract features using multi-image multi-column CNN (MCCNN) to count crowd. The overall structure of the proposed MCCNN and the number of parameters in each convolutional and fully connected blocks are shown in Fig. 1. Concretely, MCCNN is a deep neural network consisting of three CNNs in parallel. In contrast to the original MCNN approach, the input of each CNN column is a different pre-processed version of the same input image. Using a different pre-processed version of the same image at each column will result a different embedding conveying a different level of information. Extracting features using MCCNN intuitively will result better performance than using hand-crafted features. Because CNNs are known to be powerful in learning the best representative features. Therefore, we extract different feature mappings from each column of MCCNN and then combine these three feature vectors to form a single feature vector. Suppose that x1 , x2 , and x3 correspond to the bottleneck features obtained from the output of the last fully connected layer of each column of MCCNN, respectively. The final feature vector x is obtained by combining these three vectors as x = [x1 x2 x3 ]T . The first column of the MCCNN consists of five convolutional layers followed by three fully connected layers. The output layer of the network is a linear layer

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Fig. 1 Proposed MCCNN structure for crowd counting. The numbers below each convolutional layers block represent the number of convolutional filters in each layer and the size of convolutional filters, respectively. The numbers below each fully connected (FC) layer corresponds to the number of units in each FC layer

and performs the regression task. The input of this first column is the raw preprocessed image. Generally, each convolutional layer is followed by MaxPooling layer for dimensionality reduction. However, this may result in information loss. Therefore, we used convolutional layer with 2 × 2 kernel size and 2 × 2 stride size. While reducing the dimensionality, this helps to provide semantic information. The foreground image (difference image) obtained by applying the background subtraction process with the ROI selection and PN is used as the input to the second column of the MCCNN. The network is composed of six convolutional layers followed by two fully connected layers. The third column of the MCCNN is again a CNN architecture where the input images are obtained by applying Sobel edge detector followed by ROI selection and PN. The third column includes five convolutional layers and two fully connected layers. With the proposed approach we aim at obtaining different useful embeddings from each column and these embeddings will convey complementary information for crowd density estimation. Thus, intuitively combining these embeddings into a single feature vector will help to boost the crowd counting performance. In order to analyze the proposed MCCNN approach and to provide a deeper analysis we consider five sub-cases in the experiments: – MCCNN-I: Crowd counting is performed in an end-to-end fashion using only the first column of MCCNN structure. – MCCNN-II: Similar to MCCNN-I but the second column of the MCCNN architecture is used for crowd counting. – MCCNN-III: Similar to the MCCNN-I and MCCNN-II but the third column of the MCCNN is used. – MCCNN-IV: MCCNN structure is used for crowd counting in an end-to-end manner. To be more spesific, MCCNN is used for both feature extraction and regression tasks.

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Fig. 2 Modified multi-column convolutional neural network (MCNN) structure. The numbers below each convolutional layers block represent the number of convolutional filters in each layer and the size of convolutional filters, respectively

– MCCNN-V: MCCNN is used for feature extraction and the 80-dimensional feature vector obtained by combining the embeddings extracted from each column is used with support vector regression (SVR) to perform crowd counting.

2.4 Crowd Counting Using Modified MCNN Since multi-column convolutional neural networks (MCNN) was originally proposed for crowd density map estimation in [12] and reported a great success, we implemented a slightly modified version of MCNN for comparison. The structure of the modified MCNN for crowd counting is depicted in Fig. 2. In the original MCNN approach, each column consists of four convolutional layers with different number of filters and different kernel sizes. The output of the last convolutional layer from each column was then merged to form a feature map. Finally a convolutional layer with 1 × 1 kernel was used to convert feature map to density map. In the modified version of the MCNN in this work, the last convolutional layer at each column is followed by a flattening layer to convert the feature maps learned by each CNN into a single feature vector. Then the last convolutional layer in the original MCNN approach [12] is replaced by a linear output layer to perform the regression task. Similar to the proposed MCCNN, we analyzed the performance of MCNN method in five different ways: – MCNN-I: Given an input image, the first column of the MCNN is used in an end-to-end fashion for crowd counting. – MCNN-II: Similar to the MCNN-I but the second column of the MCNN is used. – MCNN-III: Similar to the MCNN-I and MCNN-II but the third column of the MCNN is used.

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– MCNN-IV: Rather than using MCNN for feature extraction, end-to-end crowd counting is performed using the modified MCNN. – MCNN-V: Features extracted from each column of the MCNN are combined into a single feature vector and then SVR is used for regression. With these five different ways of analysis, we aim at gaining a better understanding of MCNN approach and determining whether it is better to use it for feature extraction or in an end-to-end manner.

3 Experimental Setup 3.1 Dataset, Network Training, and Performance Criteria The experiments are carried on UCSD [14] dataset. 2000 images selected from the consecutive frames of the UCSD dataset are used in the experiments.1 The dataset is divided into three disjoint subsets (namely training, development, and test subsets) consisting of 1280, 320, and 400 images, respectively. Training set is used to train the models while development set is used for parameter tuning during the training. Finally, the test set is used to measure the performance of the system. Adam optimizer [15] is used for optimizing the parameters of the networks. Mean squared error loss is used as a loss function for training the models. Learning rate is fixed to 0.0001. Mean squared error (MSE) and mean absolute error (MAE) are generally used as the performance criteria for crowd density estimation [5, 12, 16]. Therefore, in order to make a reasonable comparison with the previous studies we used the MSE and MAE as the evaluation metric in the experiments which are defined as MSE =

n 1 (X i − Xˆ i )2 n i=1

(2)

MAE =

n  1    X i − Xˆ i  , n i=1

(3)

where X i is the ground truth (number of people in the image), Xˆ i is predicted value and n is total number of test images used in the experiments.

1

2000 images from UCSD dataset are used in the experiments because ground truth of these 2000 images are provided by [14] at http://www.svcl.ucsd.edu/projects/peoplecnt/.

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4 Results MSE and MAE values obtained using the proposed MCCNN approach and modified MCNN method are summarized in Table 1. From the results in the table, we first note that each column of the MCCNN yields considerably reasonable performance individually (MCCNN-I, MCCNN-II and MCCNN-III in the table). Interestingly, the first and the third columns (MCCNN-I and MCCNN-III) show similar performance in terms of both MSE and MAE criteria. This is possibly because while we apply preprocessed image (ROI selected and PN applied image) as input to the first column, pre-processed and edge detection applied image is applied as the input to the third column. However, the first column is possibly learning and revealing the information induced by the edge detection with the help of the receptive fields in the convolutional layers. Next, we observe that when MCCNN is employed for crowd counting in an end-to-end type (MCCNN-IV) MSE and MAE values slightly reduce in comparison to each individual column. Furthermore, using SVR with features extracted from MCCNN considerably improves the performance. Compared to end-to-end system, using SVR reduces the MSE value from 0.21 to 0.10 (approximately 39% reduction). This observation reveals that each CNN column yields highly complementary information for crowd counting. Next, we analyze the performance of the proposed modified MCNN. From the MCNN results given in Table 1, each column of the modified MCNN structure (MCNN-I, MCNN-II, and MCNN-III) yields reasonable performance. However, combining all columns and employing end-to-end system for crowd counting (MCNN-IV) yields considerably large improvement on the performance. For example, while MCNN-I gives 0.63 and 0.61 MSE and MAE values, respectively, MCNNIV yields 0.41 and 0.49 MSE and MAE values. This corresponds to approximately 34% and 19% improvement in terms of MSE and MAE, respectively. Comparing the multi-image multi-column CNN (MCCNN) and modified singleimage multi-column CNN (MCNN) results given in Table 1, MCCNN outperforms MCNN in all cases. This is possibly because we apply a different pre-processed version of the same input image to each column of CNN in MCCNN. Thus each column will result in a different feature mapping for crowd counting performance. However,

Table 1 Crowd counting results using MCCNN and modified MCNN. The best numbers in each row are given in boldface and globally best numbers are shown in boldface and underlined MCCNN MSE MAE MCNN MSE MAE methods methods MCCNN-I MCCNN-II MCCNN-III MCCNN-IV MCCNN-V

0.24 0.29 0.23 0.21 0.10

0.37 0.42 0.37 0.33 0.20

MCNN-I MCNN-II MCNN-III MCNN-IV MCNN-V

0.63 0.63 0.81 0.41 0.19

0.61 0.62 0.72 0.49 0.34

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Table 2 Comparison of the proposed method with previous studies on UCSD dataset Methods MSE MAE Zhang et al. [5] Sam et al. [11] Zhang et al. [12] Zou et al. [16] Zhu et al. [17] Proposed method

3.31 2.10 1.35 1.29 1.44 0.10

1.60 1.62 1.07 1.01 1.08 0.20

in MCNN the same raw input image is applied to each column and therefore each column is likely to learn the similar mapping. Thus MCNN is inferior to MCCNN. Finally, we compare the crowd counting results obtained in this study with the previously reported results. Although there exist many studies in literature addressing the crowd counting problem, we compare our results with the ones conducting their experiments on UCSD database in order to make a equitable comparison. Table 2 shows the previously reported MSE and MAE values and the values obtained in our work. As shown in the table both MCCNN and modified MCNN considerably outperform other methods.

5 Conclusion In this study, a multi-image multi-column convolutional neural network (MCCNN) approach is proposed for crowd counting. The proposed MCCNN consists of three CNNs in parallel and a different pre-processed version of the same input image was applied to each column. With this approach we aimed at obtaining different embeddings from each column conveying different level of information. A modified version of the previously proposed single-image multi-column CNN (MCNN) was also proposed in this study. Experimental results conducted on UCSD dataset revealed that proposed MCCNN considerably outperformed earlier studies and the proposed modified MCNN achieved reasonably good performance on crowd counting. Experimental results showed that crowd counting performance considerably improved by the proposed approach in comparison to a single CNN system.

References 1. Ryan D, Denman S, Sridharan S, Fookes C (2015) An evaluation of crowd counting methods, features and regression models. Comput Vis Image Underst 130:1–17 2. Velastin S, Yin JH, Davies A (1995) Crowd monitoring using image processing. Electron Commun Eng J 7(1):37–47

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3. Chan AB, Liang ZSJ, Vasconcelos N (2008) Privacy preserving crowd monitoring: counting people without people models or tracking. In: Proceedings of the CVPR 4. Fu M, Xu P, Li X, Liu Q, Ye M, Zhu C (2015) Fast crowd density estimation with convolutional neural networks. Eng Appl Artif Intell 43:81–88 5. Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the CVPR 6. Sindagi VA, Patel VM (2017) CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: Proceedings of the AVSS 7. Boominathan L, Kruthiventi SS, Venkatesh Babu R (2016) CrowdNet: a deep convolutional network for dense crowd counting. In: Proceedings of the MM’16, pp 640–644 8. Marsden M, McGuinness K, Little S, O’Connor NE (2017) ResnetCrowd: a residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification. In: Proceedings of the AVSS 9. Hu Y, Chang H, Nian F, Wang Y, Li T (2016) Dense crowd counting from still images with convolutional neural networks. J Vis Commun Image Represent 38 10. Li Y, Zhang X, Chen D (2018) CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the CVPR, pp 1091–1100 11. Sam DB, Surya S, Babu RV (2017) Switching convolutional neural network for crowd counting. In: Proceedings of the CVPR, pp 4031–4039 12. Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the CVPR, pp 589–597 13. Li M, Zhang Z, Huang K, Tan T (2008) Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection. In: Proceedings of the ICPR 14. Chan AB, Vasconcelos N (2008) Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans Pattern Anal Mach Intell 30(5):909–926 15. Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Proceedings of the ICLR, pp 1–15 16. Zou Z, Cheng Y, Qu X, Ji S, Guo X, Zhou P (2019) Attend to count: Crowd counting with adaptive capacity multi-scale CNNs. Neurocomputing 367:75–83 17. Zhu L, Li C, Wang B, Yuan K, Yang Z (2020) DCGSA: a global self-attention network with dilated convolution for crowd density map generating. Neurocomputing 378:455–466

Which Features Are Helpful? The Antecedents of User Satisfaction and Net Benefits of a Learning Management System (LMS) Bernie S. Fabito , Mico C. Magtira , Jessica Nicole Dela Cruz , Ghielyssa D. Intrina , and Shannen Nicole C. Esguerra Abstract The demand for Learning Management Systems (LMS) in Higher Educational Institutions (HEI) has increased dramatically due its flexibility in the delivery of education. While much literature has tried to explore the factors determining the LMS adoption of faculty members through the lenses of various Information (IS) Success Theories, little has been made to understand the relationship between the LMS features and the IS success variables. Hence, the study explored which features and success variables show a possible relationship, which may aid in the decisionmaking process of HEIs that are just starting to adopt an LMS. Results show that the communication features of LMS had the highest relationship with the IS success variables. Keywords Learning Management System (LMS) · Information System (IS) success variables · LMS features · User satisfaction · Net benefits

B. S. Fabito (B) · M. C. Magtira · J. N. D. Cruz · G. D. Intrina · S. N. C. Esguerra National University, Manila, Sampaloc, Philippines e-mail: [email protected] M. C. Magtira e-mail: [email protected] J. N. D. Cruz e-mail: [email protected] G. D. Intrina e-mail: [email protected] S. N. C. Esguerra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_21

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1 Introduction The rapid advancement in mobile and ubiquitous computing has paved the way for the increasing demand for Learning Management Systems (LMS). It is expected that by the end of 2020, the revenue for the eLearning Market will reach up to USD 31 Billion in comparison to the USD 107 Million in revenue obtained in 2015 [1]. A Learning Management System, when used in an educational setting, serves as a medium where learning can happen ubiquitously using mobile devices [2]. Through an LMS, the teachers can design and deliver instructional materials and provide meaningful assessments that can motivate students to learn [3, 4]. In the literature, a plethora of studies has been made to assert the factors contributing to students’ and faculty members’ adoption of Learning Management Systems. Through Information System acceptance theories like the Information System Success Model (ISSM) [5], researchers can identify the variables that lead to LMS adoption among students and faculty members. Some of these variables include Course Quality [6], Information Quality and System quality [7], Social Influence, Information Quality, and University Management [8], among others. The ISSM is a well-known theory that looks at the success of Information Systems (IS). Understanding the variables that drive IS success can help managers make better decisions to improve the system further. Over the years, this theory has been modified [9], extended, and applied to various industries, including Hotels [10], Hospitals [11], and Libraries [12], among others. The ISSM determinants include System Quality, Information Quality, Service Quality, System Use, User Satisfaction, and Net Benefits [5]. These variables play a role in determining what factors lead to the actual use and satisfaction of using an IS and how it benefits the organization. While studies have shown the external variables that affect the Information Success constructs (specifically on System Quality, Information Quality, and System Quality), little has been studied on how the features of an LMS affect the IS constructs. The features of an LMS define the functions that make an LMS stand out from other existing LMS. In a 2014 study, these features were classified into four components, namely, tools for distribution, tools for communication, tools for interaction, and tools for course administration [13, 13]. These components are present among LMS, and can be used to analyze any research endeavors. Hence, the features of LMS were grouped based on the components presented by [13]. Table 1 shows the different features of an LMS and its description, while Table 2 presents the categories and how the features fall under the categories. This study adds novelty to the existing body of knowledge in LMS adoption by understanding the IS success variables’ antecedents through the LMS features. Specifically, the study intends to discover the relationship between the faculty members’ perception of how helpful are the features of the LMS and the three (3) Information Success variables, namely, System Use, User Satisfaction, and Net Benefits. The conceptual framework shown in Fig. 1 shows the possible relationship between the variables. Actual use and satisfaction [17].

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Table 1 LMS features Features

Description

Chat support [14]

Allows interaction between the learners and faculty members

Course creation [15]

Allows the administration of courses/subjects in the LMS

Assignment submission [14] Allows the creation of the assignment module where students can view and submit assignments Assessment/quiz [14]

Allows the creation of quizzes and other assessments for the students

Message boards [15]

Allows the creation of a discussion board where students can communicate and provide feedback

Collaboration [16]

Allows the creation of private group boards or channels for interaction and feedback

Mobile integration [16]

Allows the seamless integration of the LMS to mobile devices

Gamification [14]

Allows the utilization of gamification (e.g., badges, coins, rewards) in an LMS

Video conferencing [15]

Allows the conduct of face-to-face meeting between students and faculty members synchronously

Table 2 LMS components [13] Components

Description

Tools for distribution

Allows professors to upload Assignment submission, learning materials and other related assessment/quiz module, resources for the students’ gamification consumption

Features

Tools for communication

Allows student-faculty, facultyfaculty, and student–student communication

Chat support, video conferencing

Tools for interaction

Facilitates feedback and reaction between students and faculty members

Message boards, collaboration

Tools for course administration

Allows course management of the LMS

Course creation, mobile integration

2 Methodology The study made use of a quantitative approach employing an online survey. A total of fifty-six (56) faculty members from a private Higher Educational Institution (HEI) answered the survey through Microsoft Forms Pro from June to July 2019. The survey was answered using a Likert scale of one (1) to four (4) with a verbal interpretation of Strongly Agree, Agree, Disagree, and Strongly Disagree for the IS Success variables. For the different LMS features, the verbal interpretation of Very Helpful, Somewhat helpful, Rarely Helpful, and not at all was used. A Spearman correlation was used to determine the relationship using Stata 11.0.

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Actual use and satisfaction [17]

System Use User Satisfaction

Interaction Message Board Collaboration

Net Benefits

Course administration Course Creation Mobile Integration

Fig. 1 Conceptual framework

3 Results and Discussion The survey shows that 48 or 85% of the respondents are using MS Teams as their LMS. The high number is attributed primarily because MS Teams is the official LMS used in the University where the study was conducted. Microsoft Teams is the Microsoft Office 365 tool for collaboration and communication. Although it is used mainly for business organizations, it can also be used for education. Classroom collaboration, end-to-end assignment management, and OneNote Class Notebooks are just some of the integrated features in the MS Teams for Education [18]. Table 3 presents the mean result of the LMS features and the IS Success variables. Analyzing the table, we can deduce that the features are perceived as somewhat helpful. The Assignment Submission, Chat Support, and the Message Boards had the highest mean result, suggesting that they are the most commonly used features in using an LMS. The result can be further observed in the visualization found in Fig. 2. For the IS Success variables, the respondents mostly agree with using the system, how it provides satisfaction to their work, and how it benefits delivering instruction with the students. Tables 4 and 5 show the Spearman correlation of the IS Success variables and the LMS Features. It can be inferred that there is a weak to moderate monotonic relationship between the variables, which are all statistically significant (p < 0.05). From the four (4) components presented, only communication had a consistent correlation to the three (3) IS success variables. Although the relationship ranges from weak to moderate, the report has shown a possible antecedent of the LMS features to the IS Success variables.

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Table 3 LMS features and IS success variables mean result LMS features and IS success variables

Mean

SD

Verbal interpretation

Chat support

1.55

0.63

Somewhat helpful

Assignment submission

1.50

0.63

Somewhat helpful

Course creation

1.73

0.79

Somewhat helpful

Assessment/quiz

1.57

0.63

Somewhat helpful

Message boards

1.55

0.60

Somewhat helpful

Mobile integration

1.82

0.83

Somewhat helpful

Collaboration

1.94

0.79

Somewhat helpful

Gamification

2.35

0.99

Somewhat helpful

Video conferencing

2.28

0.90

Somewhat helpful

System use

1.50

0.46

Agree

User satisfaction

1.81

0.50

Agree

Net benefits

1.73

0.49

Agree

Table 4 Spearman correlation result LMS features and IS success variables

Distribution rs

Communication p

rs

Interaction p

rs

Course administration p

rs

p

System use 0.38

0.00 0.50

0.00 0.38

0.00 0.30

0.022

User 0.37 satisfaction

0.00 0.53

0.00 0.44

0.00 0.33

0.010

Net benefits

0.01 0.48

0.00 0.43

0.00 0.32

0.016

0.34

Table 5 Spearman correlation result interpretation LMS features and IS Distribution success variables

Communication

Interaction

Course administration

System use

Weak

Moderate

Weak

Weak

User satisfaction

Weak

Moderate

Moderate

Weak

Net benefits

Weak

Moderate

Moderate

Weak

0.60–0.79 strong

0.80–1.0 very strong



0.00–0.19 very weak 0.40–0.59 moderate

Drilling the Spearman correlation for each LMS feature with the IS Success variables (Tables 6, 7, and 8) shows similar output. A weak to moderate monotonic relationship. It was clear that the chat support had the highest relationship with the three (3) IS success variables. This result is also similar to a research that showed

238 Table 6 Spearman correlation result between the LMS features and system use

Table 7 Spearman correlation result between the LMS features and user satisfaction

Table 8 Spearman correlation result between the LMS features and net benefits

B. S. Fabito et al. LMS features

rs

p

Chat support

0.509

0.000

Course creation

0.373

0.004

Assignment submission

0.396

0.002

Assessment/quiz

0.400

0.002

Message boards

0.393

0.002

Collaboration

0.307

0.201

Mobile integration

0.235

*0.081

Gamification

0.233

*0.083

Video conferencing

0.382

0.003

LMS features

rs

p

Chat support

0.492

0.000

Course creation

0.364

0.005

Assignment submission

0.287

0.031

Assessment/quiz

0.4220

0.001

Message boards

0.304,

0.022

Collaboration

0.4207

0.001

Mobile integration

0.291

*0.259

Gamification

0.259

*0.053

Video conferencing

0.3828,

lms features

rs

0.003

p

Chat support

0.483

Course creation

0.319

0.005

Assignment submission

0.287

0.031

Assessment/quiz

0.398

0.003

Message boards

0.293

0.028

Collaboration

0.424

0.001

Mobile integration

0.263

0.050

Gamification

0.214

*0.112

Video conferencing

0.378

0.004

*p

> = 0.05

0.002

Which Features Are Helpful? The Antecedents of User …

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that faculty members prefer to use an LMS that incorporates communication with the academic community [19]. The result of the present study would help provide input for organizations wanting to adopt an LMS. As mentioned in a study [20, 21], understanding the stakeholders’ needs in an LMS will guarantee its full utilization. To help validate the correlation, a Focus Group Discussion (FGD) with students from the same University was conducted. The FGD was used to determine if the students agree with the result. The interview has shown that the communication feature helps students as it allows them to communicate with concerned faculty members and classmates. Instead of using Social Media or an E-mail, a student can directly communicate with the professor for their academic concern through the LMS. This is one feature that students unanimously agree as an advantage over traditional face-to-face communication. Subsequently, a follow-up survey was conducted on a group of students after analyzing the result of the FGD. The survey includes rating the features that they find the most helpful in using an LMS. The result has shown similar output both from the correlation study and the FGD.

4 Limitations and Recommendations While the study has shown a possible relationship between the LMS features and the IS Success variables, caution is made with interpreting the study’s result due to the low number of respondents. Subsequently, since the study used a spearman correlation, causality between the LMS features and IS success variables cannot be made. The Spearman correlation was used due to the nature of the survey data. Future research endeavors in the same domain may include expanding the features of the LMS. The study may have failed to add other features that may exist in other LMSs that may serve as a predictor for the IS Success variables. New features include software integration to LMS, customized learning analytics reports, etc. Subsequently, exploring how the LMS features affect System Quality and Information Quality as external variables may also be considered to identify which features are desirable and can provide quality output.

References 1. [Infographic] Top learning management system statistics For 2020. https://elearningindustry. com/top-learning-management-system-lms-statistics-for-2020-infographic. Accessed 01 Sep 2020 2. Turnbull D, Chugh R, Luck J (2020) Learning management systems: a review of the research methodology literature in Australia and China. Int J Res Method Educ 1–15. https://doi.org/ 10.1080/1743727X.2020.1737002.

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3. Kattoua T, Al-Lozi M, Alrowwad A (2020) A review of literature on e-learning systems in higher education. https://www.researchgate.net/publication/309242990_A_Review_of_Litera ture_on_E-Learning_Systems_in_Higher_Education. Accessed 01 Sep 2020 4. Botha A, Smuts H, de Villiers C (2018) Applying diffusion of innovation theory to learning management system feature implementation in higher education: lessons learned. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 56–65. https://doi.org/10.1007/978-3-030-035 80-8_7 5. DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 9–30. M.E. Sharpe Inc. https://doi.org/10.1080/ 07421222.2003.11045748 6. Mtebe JS, Raisamo R (2014) A model for assessing learning management system success in higher education in Sub-Saharan countries. Electron J Inf Syst. Dev Ctries 61:1–17. https:// doi.org/10.1002/j.1681-4835.2014.tb00436.x 7. Shahzad A, Hassan R, Aremu AY, Hussain A, Lodhi RN (2020) Effects of COVID-19 in Elearning on higher education institution students: the group comparison between male and female. Qual Quant 1–22. https://doi.org/10.1007/s11135-020-01028-z 8. Fabito BS, Rodriguez RL, Trillanes AO, Lira JIG, Miguel P, Ana QS (2020) Investigating the factors influencing the use of a learning management system (LMS): an extended information system success model ( ISSM ). In: The 4th international conference on e-society, e-education and e-technology (ICSET’20). Association for Computing Machinery, Taipei. https://doi.org/ 10.1145/3421682.3421687. 9. Tajuddin M (2015) Modification of DeLon and McLean model in the success of information system for good university governance 10. Ojo AI (2017) Validation of the DeLone and McLean information systems success model. Healthc Inform Res 23:60–66. https://doi.org/10.4258/hir.2017.23.1.60 11. Ebnehoseini Z, Tabesh H, Deldar K, Mostafavi SM, Tara M (2019) Determining the hospital information system (His) success rate: development of a new instrument and case study. Open Access Maced J Med Sci 7:1407–1414. https://doi.org/10.3889/oamjms.2019.294 12. Alzahrani AI, Mahmud I, Ramayah T, Alfarraj O, Alalwan N (2019) Modelling digital library success using the DeLone and McLean information system success model. J Librariansh Inf Sci 51:291–306. https://doi.org/10.1177/0961000617726123 13. Jurado RG, Petterson T, Gomez AR, Scheja M (2013) Classification of the features in learning management systems. In: XVII Scientific Convention on Engineering and Architecture, Havana City, Cuba, Nov 24–28. XVII Scientific Convention on Engineering and Architecture, vol 53, pp 1689–1699. https://doi.org/10.1017/CBO9781107415324.004. 14. LMS features to improve usability-eLearning industry. https://elearningindustry.com/featuresto-improve-usability-of-lms. Accessed 26 Oct 2020 15. Important LMS features for your e-learning program. https://technologyadvice.com/blog/ human-resources/8-important-lms-features/. Accessed 26 Oct 2020 16. What is an LMS? (2020 Update) | LMS features | LMS use case. https://www.docebo.com/ blog/what-is-learning-management-system/. Accessed 26 Oct 2020 17. Joo YJ, Kim N, Kim NH (2016) Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educ Technol Res Dev 64:611–630. https://doi.org/ 10.1007/s11423-016-9436-7 18. Set up teams for education-M365 education | Microsoft Docs. https://docs.microsoft.com/enus/microsoft-365/education/deploy/set-up-teams-for-education. Accessed 30 Nov 2020 19. Alturki UT, Aldraiweesh A, Kinshuck (2020) View of evaluating the usability and accessibility of LMS “Blackboard” at King Saud University. https://clutejournals.com/index.php/CIER/art icle/view/9548/9617. Accessed 29 Oct 2020 20. Iqbal S (2011) Learning management systems (LMS): inside matters. Inf Manag Bus Rev 3:206–216. https://doi.org/10.22610/imbr.v3i4.935 21. Fabito B, Trillanes A, Sarmiento J (2021) Barriers and challenges of computing students in an online learning environment: insights from one private university in the Philippines. Int J Comput Sci Res 5:441–458. https://doi.org/10.25147/ijcsr.2017.001.1.51

Performance Analysis of a Neuro-Fuzzy Algorithm in Human-Centered and Non-invasive BCI Timothy Scott C. Chu, Alvin Chua, and Emanuele Lindo Secco

Abstract Developments in Brain-Computer Interface machines have provided researchers with the opportunity to interface with robotics and artificial intelligence; and, each BCI—Robotics system employed different Machine Learning algorithms. This study aimed to present a performance analysis for a Neuro-Fuzzy algorithm, specifically the Adaptive Network-Fuzzy Inference System (ANFIS), to classify EEG signals retrieved by the Emotiv INSIGHT in conjunction with the SVM algorithm as reference. Generation of EEG data was done through face gestures, specifically Facial and Eye Gestures. The generated data were fed to both algorithms for simulation experiments. Results showed that the ANFIS tends to be more reliable and marginally better than the SVM algorithm. Compared to SVM, the ANFIS took significant amounts of computational resources requiring higher specs and training time. Keywords BCI · BMI · Human-centered interface · ANFIS · SVM

1 Introduction The human brain interacts with limbs by coursing through electric signals along the nerves and synapses that serve as the bridge connecting the brain to every part of the body. These electrical activities can be captured by the Brain-Computer Interface (BCI) machines by conducting a test called an electroencephalogram (EEG) [1]. This test enabled medical professionals to observe and detect anomalies in a patient’s T. S. C. Chu (B) · E. L. Secco Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK e-mail: [email protected] E. L. Secco e-mail: [email protected] T. S. C. Chu · A. Chua Mechanical Engineering Department, De La Salle University, Manila, Philippines e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_22

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brain. Developments in BCI technology provided researchers not only access to affordable BCI machines, but also opportunities in robotic developments through the integration of robotics into BCI systems. Studies like [1, 2] explored the use of BCI systems in robotic arm applications. The prior study used raw EEG signals, which were visually processed by determining the spikes generated in an instance. The spikes served as the features which correspond to different gestures on each hand. The study employed a Linear Discriminant Analysis (LDA) as their classifier and was able to successfully distinguish separate sets of actions such as right, right neutral, left, and left neutral with an accuracy of close to 100%. Researchers in [2] took a different approach by employing the Steady-State Visual Evoked Potentials (SSVEP) technique where visual prompts were tied to a certain action command. This was achieved with a LED panel with 4 colors, blue, red, white, and green; additionally, each color was set to flicker at different frequencies. The study used a combination of Power Spectral Density (PSD) and Classification Tree Method (specifically ITR) to process the data and generate predictions with an average effective rate of 73.75%. The studies in [3, 4] all utilized the algorithms in the Emotiv provided Software Development Kit (SDK); however, the researcher in [3] particularly explored a different approach by using the Left and Right wink face gesture to control a robotic arm. Physically handicapped individuals experience hindrances from performing important tasks either at home or in their work. This can be addressed by developing a BCI-Robotics system as an attempt for individuals to regain their lifestyle. However, in the field of BCI control implementation, the distinguishing factor between studies is the performance of the algorithm used in the application. Common algorithms are usually Convolutional Neural Network or Artificial Neural Network, these algorithms offer high accuracy however take up a significant amount of computational time [5]. Another common algorithm utilized in this field is the Support Vector Machines (SVM); this algorithm can effectively classify signals efficiently, given that a proper kernel is provided by the user to create effective hyperplanes, often this becomes a huge challenge to inexperienced users [6]. Also, the SVM’s performance drops according to the size of the dataset, consequently taking up more computational time. The challenge is to utilize or develop an algorithm that offers a good balance between accuracy and computational time. In [7], it mentioned that a degree of overlap with the EEG signal features between similar gestures affected the performance of the machine learning algorithm. With this consideration, an algorithm capable of handling this overlap would be well suited in this application. The Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm is one form of Fuzzy Neural Network, a hybrid between Fuzzy Logic and Neural Networks, that utilizes a firstorder Sugeno model [5], which is capable of addressing this concern. This research explores the viability of the ANFIS algorithm in the BCI interface in conjunction with the Support Vector Machines (SVM), where the latter algorithm serves as a baseline comparison to put in perspective how well the ANFIS performs on some parameters such as accuracy.

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243

2 Theories Involved 2.1 Butterworth Band-Pass Filters Band-Pass filtering is a standard EEG pre-processing technique that uses the concepts of pass-band and stop-band. A pass-band allows information that is within the cutoff frequency to go through the filter, while a stop-band rejects information that is beyond the cut-off frequency. Butterworth Band-pass Filters extends the idea of band-pass filters and introduces the concept of a normalized gain; where instead of hard cut-off passes, it introduces a bell-shaped gain into the system. The Butterworth filters introduce two transfer functions which are represented by Eqs. 1 and 2. The two equations refer to the high-pass filter and the low-pass filter, respectively. |H (ω)| = 

1+

|H (ω)| =  1+

Ao  ωo 2n

(1)

Ao 

(2)

ω

ω ωo

2n

where H(ω) is the normalized gain, Ao as the max gain in pass-bands, ωo is the cut-off frequency, lower for low-pass filters (Eq. 2) and higher for high-pass filters (Eq. 1),

Fig. 1 a High-Pass b Low-Pass Butterworth filters of different orders

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ω is the frequency of the input signal, and n as the order of the filter. Figure 1 offers a graphical representation of the Butterworth Band-pass filters.

2.2 Adaptive Neuro-Fuzzy Inference System (ANFIS) The Adaptive Network-Fuzzy Inference-System (ANFIS), developed by JS Jang [5], combines both the concept of Fuzzy Inference System and Adaptive Network, making this a hybrid model. The Fuzzy Inference System is a core concept from Fuzzy Logic wherein it generally possesses four main functions namely, (i) Knowledge Base, (ii) Fuzzification, (iii) Defuzzification, and (iv) Decision-Making Unit as shown in Fig. 2. The Fuzzification process converts crisp input values to fuzzified values under a specified membership function. The fuzzified input is then fed to the DecisionMaking Unit where it runs the input to fuzzy operators (i.e., Min, and Max) and then compares them to a set of criteria established in the Rule Base to determine the appropriate output for the given set of input. Defuzzification returns the fuzzified output values to crisp values. An advantage of this system architecture is that it is capable of accepting vague inputs, also referred to as uncertain inputs, and returns sufficiently accurate outcomes. An Adaptive Network allows the algorithm to adapt to its mistakes and learn to increase the accuracy of the predictions made. This can be considered as a supervised learning algorithm, wherein design parameters are established and are integrated with the nodes to serve as efficacies; and, these efficacies are adjusted accordingly through training with a dataset. Theoretically, the algorithm is subjected to two passes of learning rules to obtain necessary parameter values. Table 1 summarizes the learning process of the algorithm. The ANFIS algorithm is composed of five layers; and, the initial layer is responsible for the fuzzification. Normally, a Gaussian membership function is utilized, however, other options such as triangular and trapezoidal membership function. Layers 2 and 3, are responsible for determining the parameters and normalized parameters using Eqs. 3 and 4, respectively.

Fig. 2 Fuzzy inference system [5]

Performance Analysis of a Neuro-Fuzzy Algorithm … Table 1 Hybrid learning process for ANFIS algorithm

245

Forward pass

Backward pass

Premise parameters

Fixed

Gradient descent

Consequent parameters

Least squares estimates

Fixed

Signals

Node outputs

Error rates

wi = μ Ai (x) × μ Bi (x), i = 1, 2 wi =

wi w1 + w2

(3) (4)

On the fourth layer, consequent parameters are calculated with Eq. 5. Oi4 = w¯ i f i = w¯ i ( pi x + qi y + ri )

(5)

where Oi4 is interpreted as the output of the ith node in layer 4. Finally, on the final thinking layer or fifth layer, all information from the previous layer is collated, calculated, and ‘defuzzified’ to produce a single output using Eq. 6. O15 = overall output =

 i

w¯ i f i =

wi f i i i wi

(6)

Figure 3 shows the sample ANFIS algorithm diagram with 2 inputs and a single output. A simple ANFIS algorithm generally follows two rules that are shown in Eqs. 7 and 8 below and can be expanded appropriately to fit a particular use case.   Rule 1 : If (x1 is in A1 )and (x2 is in B1 ), then yˆ = p1 x1 + q1 x2 + r1

(7)

  Rule 2 : If (x1 is in A2 )and (x2 is in B2 ), then yˆ = p2 x1 + q2 x2 + r2

(8)

Fig. 3 ANFIS diagram, 2 inputs, 1 output [5]

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where x 1 and x 2 are the values inputted in the algorithm, and Ai and Bi are the fuzzy sets of the data. The crisp output y results in a value corresponding to the input values and is computed together with the design parameters pi , qi , and r i .

3 Materials and Methods Figure 4 shows the methodology flowchart implemented in this research. The process begins with the Generation of EEG Raw Signals. The generated signals are then Retrieved and Transferred by the Emotiv INSIGHT neuroheadset to the computing hardware to the OpenViBE software. In OpenViBE, the data were processed and recorded into CSV files which are sent to the Machine Learning algorithms for training and evaluation.

3.1 Generation of EEG Raw Signals The generation of EEG data was achieved with two methods; the first was by making and holding a face gesture for 15 s, serving as the first EEG Dataset. The face gestures used were Neutral, Smile, Shocked, and Clench as shown in Fig. 5a.

Fig. 4 Methodology flowchart

Fig. 5 a Face gestures, b Eye gestures

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The second method of obtaining EEG data this time utilized eye gestures such as Neutral, Eyes Widen, Left Wink, Right Wink, and Closed, in Fig. 5b. Instances of the gesture are obtained, instead of holding. This composed the second EEG Dataset. Executing the mentioned face gestures generated brainwave activities on the Frontal Lobe, which were detected and captured by a BCI Machine.

3.2 Retrieving and Transferring of Generated EEG Data The Emotiv INSIGHT is a non-invasive Brain-Computer Interface (BCI) machine equipped with 5 electrode sensors that follow the international 10–20 system of electrode placements. The neuroheadset obtains the generated EEG signals and translates them into values measured in millivolts. Sensors AF3 and AF4 were observed to be most effective, due to the function of the Frontal Lobe. The Frontal Lobe is considered to be the section of the brain that manages motor skills, actions that require muscle movements to move different parts of the body. Cognitive functions such as learning, thinking, and memory are also managed by this lobe. This includes mental commands or actions where individuals ‘think’ or imagine performing a particular physical movement [8]. The INSIGHT has a sampling frequency of 128 Hz, consequently obtaining 128 samples of brain activity for each electrode per second.

3.3 Pre-processing of Data and Feeding to Algorithm The obtained data were sent to the OpenViBE software where it was processed and recorded. On the first EEG dataset, the research did not employ any filtering process to determine the capacity of the algorithms to manage raw EEG data. While on the second EEG dataset, a Fifth-Order Butterworth Band-Pass filter with the low-pass and high-pass frequencies to be 13 Hz and 43 Hz, respectively. This configuration blocked brainwaves under 13 Hz, which were observed to be noisy. The filtered data was further processed heuristically by locating the spikes detected on the sensors AF3 and AF4 of each instance. These values served as the key features in the sample that effectively define the gesture executed. The generated datasets were then fed to the algorithms for training and evaluation. Table 2 shows the details of the first and second EEG Datasets used in this experiment. Both EEG datasets are obtained with 1, 2, and 4 sample counts per block, affecting the number of features in the dataset.

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Table 2 Datasets generated and used Dataset

No. of features

No. of classifications

No. of row inputs

Total array size

First EEG dataset (Face gesture) 1 Sample count

5

4

800

800 × 5

First EEG dataset (Face gesture) 2 Sample count

10

4

800

800 × 10

First EEG dataset (Face gesture) 4 Sample count

20

4

800

800 × 20

Second EEG dataset (Eye gestures) 1 Sample count

5

5

250

250 × 5

Second EEG dataset (Eye gestures) 2 Sample count

10

5

250

4 Results and Discussion Both algorithms were tasked to classify and generate predictions with the obtained datasets. The tests were conducted five times and an average was obtained from all the runs. Results of the ANFIS algorithm were then analyzed together with the SVM.

4.1 Simulation Results for First EEG Dataset Immediately observed in Fig. 6, the ANFIS was not able to produce any predictions on the dataset with 2 and 4 sample counts as it has reached a ‘memory error.’ This Fig. 6. First EEG dataset—average accuracy of the 3 algorithms (4 face gesture classification)

Performance Analysis of a Neuro-Fuzzy Algorithm … Table 3 First EEG dataset overall algorithm training time

249

No. of sample counts

ANFIS (5 epoch)

ANFIS (10 epoch)

SVM

1 Sample count

17,832 s

37,025 s

0.0110 s

2 Sample count





0.0088 s

4 Sample count







implied that the ANFIS algorithm ran out of RAM to be able to train itself and create predictions with this dataset. The SVM was able to produce predictions with 85.83% and 82.43% accuracy with the dataset consisting of 2 sample counts and 4 sample counts, respectively. Focusing on the dataset with 1 sample count the SVM generated predictions with a 57.91% accuracy rating, while the ANFIS predicted with accuracies of 57.58% and 63.00% for 5 and 10 epochs, respectively. This experiment showed and consolidated 3 potential conclusions, namely: (i) (ii) (iii)

Based on the results of the dataset with 1 sample count, the performance of the ANFIS algorithm is indeed comparable to the performance of the SVM. Increasing the sample count in the dataset offers a degree of improvement in the performance of the algorithms. The ANFIS algorithm is not efficient in managing large datasets.

It was observed that the two sample counts in the dataset were sufficient to produce significantly more accurate results as shown in SVM’s performance results. Based on Table 3, it was observed that the duration SVM takes for training was only a fraction of a second. ANFIS, on the other hand, took significantly longer for training with 17,832 s around 4.95 h, while 10 epochs roughly doubled that amount. This inferred that the ANFIS took up significantly more computational resources than the SVM for this use case and causing the algorithm to crash.

4.2 Simulation Results for Second EEG Dataset Similar tests were conducted on the second EEG Dataset with its results shown in Fig. 7. The SVM generated predictions on the 1 sample count dataset with an accuracy of 52.00%. ANFIS on the other hand produced accuracy ratings of 74.22% and 80.88% for 5 and 10 epochs, respectively. For the dataset with 2 sample counts, all the algorithms performed satisfactorily with an 80.00% accuracy rating for the SVM and 89.33% and 90.13% for the ANFIS with 5 and 10 epochs, respectively. In this set of EEG data, it can be observed that the ANFIS performed significantly better as compared to the first set; this may be due to the size dataset to be smaller. In this experiment, the ANFIS was able to produce results for the dataset with 2 sample counts, which was not possible with the previous datasets. Between the two datasets of different sample counts, the same phenomenon was observed where the SVM algorithm benefitted the most from the increased number of features with an

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Fig. 7. Second EEG dataset—average accuracy of the 3 algorithms (all eye gesture classification)

Table 4 Second EEG dataset—overall algorithm training time

No. of samples

ANFIS (5 Epoch)

ANFIS (10 Epoch)

SVM

1 Sample count

264.99 s

590.88 s

0.0054 s

2 Sample counts

48,137 s

101,895 s

0.0037 s

increase in accuracy of 28%, while the ANFIS also showed at most a 15.11% increase in accuracy in the 5 epochs. In this set of experiments, the ANFIS algorithm was able to perform better than the SVM algorithm in both 1 sample count and 2 sample count datasets. Results from this set of experiments have validated the ideas presented in Sect. 4.1, specifically: (i) the performance of the ANFIS algorithm is comparable to the performance of the SVM, and (ii) increasing the sample count in the dataset offers a degree of improvement in the performance of the algorithms. Table 4 shows the time in seconds it took to train the algorithms. Focusing on ANFIS, the algorithm took significantly longer to train with the datasets. In 1 sample count, 5 epochs took 264.99 s or 4.42 min. There was an exponential growth in the duration of training for ANFIS with the dataset with 2 sample counts, doubling the number of features. For 5 epochs, the ANFIS took 48,137.76 s or 13.37 h to train, and nearly double for 10 epochs. Results recorded in Tables 3 and 4 supplement the idea, (iii) the ANFIS algorithm is not efficient in managing large datasets.

5 Conclusion and Recommendation This research explored the applicability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in BCI system implementations by analyzing the performance of the ANFIS algorithm, together with the SVM as a reference in managing EEG datasets. The research employed the use of facial gestures to generate EEG signals which are

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captured by the Emotiv INSIGHT. Raw EEG signals extracted from Face Gestures made up the first EEG Dataset while pre-processed EEG signals extracted from Eye Gestures composed the 2nd EEG Dataset. Both EEG datasets were fed into both algorithms to determine the performance of both algorithms. Overall, the ANFIS showed comparable or even better performance to SVM in terms of accuracy, however, the algorithm was not able to produce any results for the large datasets. With regards to training, the ANFIS took significantly more time and computational resources than the SVM. Results from the experiments confirmed that the ANFIS possessed comparable performance to the SVM algorithm in terms of accuracy. Both algorithms experienced a degree of improvement in the performance with more features in the dataset. Due to the time it took for training and the errors it experienced, the ANFIS also took up significantly more computational resources, consequently making ANFIS not efficient in managing large datasets. This concludes that the ANFIS is a viable algorithm for a BCI system implementation as its accuracy ratings are comparable to the SVM, requiring a relatively small dataset. However, feeding the ANFIS with large datasets, especially datasets with a lot of features will require large amounts of computational resources, making the algorithm inefficient. Researchers recommend that a direct interface between the neuroheadset and algorithms may yield better results for future researchers on this topic. Research findings are open to other methodologies where the intuitiveness of the interfaces can support the end-user while interacting with the Machine Interface [9–12]. Acknowledgments This work was presented in dissertation form in fulfillment of the requirements for the M.Sc. in Robotics Engineering for Timothy Chu under the supervision of E.L. Secco from the Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, UK, and Dr. Alvin Chua from the Mechanical Engineering Department, De La Salle University, PH.

References 1. Prince D, Edmonds M, Sutter A, Cusumano M, Lu W, Asari V (2015) Brain machine interface using Emotiv EPOC to control robai cyton robotic arm. In: 2015 national aerospace and electronics conference (NAECON). IEEE, pp 263–266 2. Holewa K, Nawrocka A (2014) Emotiv EPOC neuroheadset in brain-computer interface. In: Proceedings of the 2014 15th international carpathian control conference (ICCC). IEEE, pp 149–152 3. Aguiar S, Yanez W, Benítez D (2016) Low complexity approach for controlling a robotic arm using the Emotiv EPOC Headset. In: 2016 IEEE international autumn meeting on power, electronics and computing (ROPEC). IEEE, pp 1–6 4. Mamani MA, Yanyachi PR (2017) Design of computer brain interface for flight control of unmanned air vehicle using cerebral signals through headset electroencephalograph. In: 2017 IEEE international conference on aerospace and signals (INCAS). IEEE, pp 1–4 5. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

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6. Tavakoli M, Benussi C, Lopes PA, Osorio LB, de Almeida AT (2018) Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier. Biomed Signal Process Control 46:121–130 7. Li S, Feng H (2019) EEG signal classification method based on feature priority analysis and CNN. In: 2019 international conference on communications, information system and computer engineering (CISCE). IEEE, pp 403–406 8. How Your Brain Works, https://science.howstuffworks.com/life/inside-the-mind/human-brain/ brain8.htm. Accessed 11 Sep 2020 9. Elstob D, Secco EL (2016) A low cost EEG based BCI Prosthetic using motor imagery. Int J Inf Technol Converg Serv 6(1):23–36. http://arxiv.org/abs/1603.02869 10. Secco EL, Moutschen C, Tadesse A, Barrett-Baxendale M, Reid D, Nagar A (2017) Development of a sustainable and ergonomic interface for the EMG control of prosthetic hands. In: Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 192. Springer, pp 321–327. ISBN 978-3-319-58877-3 11. Secco EL, Caddet P, Nagar AK Development of an algorithm for the EMG control of prosthetic hand, soft computing for problem solving. In: Advances in intelligent systems and computing, vol 1139. Chapter 15. https://doi.org/10.1007/978-981-15-3287-0_15 12. Maereg AT, Lou Y, Secco EL, King R (2020) Hand gesture recognition based on near-infrared sensing wristband. In: Proceedings of the 15th international joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP 2020), pp 110– 117. ISBN: 978-989-758-402-2. https://doi.org/10.5220/0008909401100117

A Workflow-Based Support for the Automatic Creation and Selection of Energy-Efficient Task-Schedules on DVFS Processors Ronny Kramer

and Gudula Rünger

Abstract The performance of a task-based program depends strongly on the schedule and the mapping of the tasks to execution units. When the energy consumption is considered additionally, the selection of an advantageous schedule is time-consuming since many options have to be taken into account. The exploitation of frequency scaling increases the number of possible schedules even further. This article proposes a software framework which supports the selection of energy-efficient schedules for task-based programs by data analysis methods for performance data. The framework contains several components for the data acquisition and data management of performance data as well as components for analyzing the data and selecting a schedule based on the data. The software architecture of the framework is presented and the implementation is described. A workflow which selects a schedule for a given set of tasks on DVFS processors is presented. As example a set of task from the SPEC CPU 2017 benchmark is considered and the performance data for the resulting schedules is presented. Keywords DVFS · Power management · Energy efficiency · Frequency scaling · Multicriteria decision problem · Scheduling · SPEC benchmarks · Task-based programs · Data mining · Data analysis

1 Introduction Task scheduling methods are employed when a task-based application is to be executed on parallel hardware. The optimization goals of the scheduling can be quite different and influence the scheduling decision. In parallel computing, a low parallel R. Kramer (B) · G. Rünger Department of Computer Science, Chemnitz University of Technology, 09107 Chemnitz, Germany e-mail: [email protected] G. Rünger e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_23

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execution time is an important optimization goal, which is also called makespan in the context of scheduling. More recently, the energy consumption is an increasingly important goal, which has to be taken into account for parallel application codes [1, 2]. However, scheduling methods have to be modified to meet this goal. This article emphasizes on the scheduling for energy efficiency and proposes a software supported scheduling method based on an analysis of performance data for selecting such a schedule. Processors with dynamic voltage and frequency scaling (DVFS) provide the possibility to adapt the operational frequency such that the energy consumption as well as the execution time is affected. In general, the computational speed increases with increasing frequency, however the energy consumption also increases and vice versa [3–5]. Thus, using a fixed frequency or limiting the frequency scaling range for the execution of an application might be advantageous if the application program is waiting, for example, for input and output operations since the execution time does no increase but the energy consumption decreases. In this article, we consider the scheduling problem that a set of independent tasks is to be scheduled onto a set of processor cores which provide frequency scaling. Thus, the determination of such a Task-Schedule includes that for each task not only the start time and the core are determined but also the operational frequency with which it is executed. Since the tasks in the set of independent tasks have no dependencies, there exists a multitude of possible options for the mapping and the execution order of the tasks each of which can be executed in a different frequency mode. Each of the possible Task-Schedules provides possibly different execution time and energy consumption values. In order to support the selection of an energy-efficient schedule, we propose an approach based on data analysis of these performance data comprising execution time and energy consumption. This approach requires multiple software components which perform the management and analysis of performance data. These components comprise well-known components such as a systematic measurement of data but also a specific data storage and management of data. Most important is a specific workflow which uses the components such that a suitable schedule is selected as a result. This workflow can be designed by the application programmer and contains specific intermediate decisions for the workflow selection. The contributions of this article include the design and implementation of the schedule creation and evaluation framework (scefx) based on reusable components and functions as well as the design of a specific workflow implementing the schedule selection decision with the help of this software framework. Moreover, this workflow is applied to a set of independent tasks which stem for the SPEC CPU 2017 benchmark. Those tasks cover benchmarks which are solely based on integer operations as well as benchmarks based on floating-point operations and include the applications: bwaves, cactuBSSN, deepsjeng, exchange2, fotonik3d, gcc, imagick, lbm, leela, mcf, nab, omnetpp, perlbench, pop2, roms, wrf, x264, xalancbmk, xz [6]. Intensive experimental results are given and demonstrate the effectiveness of our data analytic approach for an energy-efficient schedule selection. The article is structured as follows: Sect. 2 describes the scheduling problem for task-based programs with the optimization goals for DVFS processors and proposes

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Table 1 Notation of the scheduling problem and the scheduling method Notation Meaning p P n T b B i Eb EB M Sb M SB

Number of execution units Set of p execution units, P = {P1 , . . . , Pp } Number of independent tasks Set of n independent tasks, i.e., T = {T1 , . . . , Tn } Bucket, which is a subset of tasks from T Set of p buckets, i.e., B = {B1 . . . , B p } Control variable when referencing in two sets Energy consumption for a bucket b  Energy consumption for a set of buckets B, i.e., E B = {b∈B} E b Makespan of bucket b Makespan of a set of buckets B, i.e., M S B = max{b∈B} M Sb

a workflow for selecting a schedule. Section 3 presents the software framework supporting the workflow and describes the implementation and usage of the framework. Section 4 shows the experimental performance results of the selected schedules for the SPEC CPU benchmark. Section 5 discusses related work and Sect. 6 concludes.

2 Energy-Efficient Task Scheduling The task scheduling problem considered is defined in Sect. 2.1. Section 2.2 introduces a bucket-oriented scheduling and the scheduling selection process based on data analysis of performance data. Section 2.3 describes how frequency scaling is included in the determination of an energy-efficient schedule. The notation used in this section is defined in Table 1.

2.1 Scheduling Problem for Independent Tasks The scheduling problem for a set of n independent tasks T = {T1 , . . . , Tn } to a set of p execution units P = {P1 , . . . , Pp } of a parallel system is to find an assignment of tasks to execution units such that a certain optimization criterion is fulfilled. The execution units can be processors or cores depending on the architecture considered. The optimization criterion can be a small parallel execution time, which is also called makespan in the context of scheduling. The makespan of an assignment of set T to set P is the maximum execution time that is required by one of the execution units. Since the task is independent of each other, any order of execution is possible. Also, each task can be assigned to any core. Thus, there is a multitude of different assignment

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possibilities of tasks to cores which have to be considered and from which the best one has to be selected. Scheduling algorithms or methodologies determine such an assignment. Since scheduling is an NP-completed problem, scheduling methods are usually heuristics. Search-based algorithms employ a search algorithm to select a schedule. In this article, we propose a method which determines a Task-Schedule by analyzing the performance data of the tasks and the different schedules. The data analysis works on specific data formats that are build up for the concept of buckets defined in the next subsection.

2.2 Bucket-Based Scheduling and Execution The scheduling method proposed in this article uses a set of p buckets B = {B1 . . . , B p } where each bucket is a subset of the task set T and p is the number of processors or cores available. When a schedule is executed, the buckets are assigned to the corresponding processors or processor cores. The resulting performance data for execution time and energy consumption are measured and represent the data basis for the analysis. The mapping of buckets to cores for execution is done by a specific software component which is called the executor. The executor software decides how the buckets are assigned to hardware. In this article the executor performs a one-toone mapping of buckets to processor cores so that each core is exclusively used for one bucket, i.e., the tasks in bucket Bi ∈ B are the tasks which are to be executed on execution unit Pi ∈ P, i = 1, . . . , p. Thus, a set of buckets is associated with a specific schedule. However, as the mapping is controlled by the executor, the usage of other executors could allow the assignment of a bucket to several cores, or distribute buckets over multiple machines. The data structure of buckets contains information for each task which comprises an identifier for the executor to request information on how to execute the task as well as cost information. The cost information includes the execution time as well as the energy consumption values for different processor frequencies. With this information the overall makespan M Sb of a bucket b as well as the overall energy consumption E b can be calculated. The makespan M S B of a schedule associated with a set of buckets B is equal to the maximum of all bucket makespans, i.e., M S B = max{b∈B} M Sb . The energy consumption of a set of buckets B is the sum of all energy consumption values for buckets b ∈ B as well as an additional idle power consumption M S B − M Sb for the idle times of the buckets b ∈ B. Since the idle power consumption is assumed to be much smaller than the power consumption of a processor working with its highest frequency, the idle power consumption is neglected during the scheduling decision, however, it still affects measured results and is included in an adaption. For the assignment of task to buckets, different algorithms are used. A makespanbased algorithm referred to as T-Schedule chooses the bucket to assign a task to according to the lowest makespan over all buckets, whereas an energy-based algo-

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rithm referred to as E-schedule chooses the bucked based on the lowest energy consumption over all buckets. The E-schedules help to provide a more evenly energy consumption distribution over all processor cores but may lead to larger idle times compared to T-schedules which, therefore, may have a larger energy consumption deviation between all cores. Examples visualizing this behavior can be seen in Sect. 4. A general rule which distribution algorithm is the most energy efficient cannot be provided, as the result differs from task set to task set.

2.3 Frequency Scaling for Energy Efficiency The overall power consumption E B of a schedule B can be reduced by reducing the frequency of a processor until the point of its highest efficiency [4]. Furthermore, another reduction is possible as not all processor cores may need to run on their full frequency. Because of idle times caused by different makespans M Sb of buckets, it is possible to reduce the frequency of processor cores without increasing the overall makespan M S B . Due to the fact that on real systems we also account the idle power consumption, the effect of reducing idle time can be even larger than the expected value in our case, which ignores the idle power consumption. This effect can be important for schedules which already use the processor frequency with the highest efficiency, where a lower frequency would increase the estimated power consumption despite the reduction of idle time. To find a Pareto-optimum where the makespan as well as the energy consumption are small at the same time, it is necessary to construct all possible schedules, calculate the execution costs, and then select the best schedule. The theoretical foundation for such a Schedule Selection Process (SSP) has been explored in [7]. This theoretical foundation is now extended to also cover the acquisition, evaluation as well as management of measured performance data and is implemented as a software framework, called the schedule creation and evaluation framework (scefx), which is presented in the following.

3 Schedule Creation and Evaluation Framework This section presents the schedule creation and evaluation framework (scefx), which supports the automation of data acquisition, data evaluation, data management as well as schedule creation and selection. The data is composed of performance data measurements of independent tasks executed on different machines using different processor frequencies. The goal is to support a workflow which determines TaskSchedules with different optimization targets such as the lowest makespan, lowest energy consumption as well as multicriteria optimizations for pareto-optimal combinations where both criteria are minimal [8].

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Fig. 1 Workflow for the collection of baseline data

3.1 Workflow to Determine a Schedule The workflow covers the steps data acquisition, data evaluation, data management, the determination of Task-Schedules and the execution of those schedules. More precisely, the workflow covers – – – –

The Schedule Selection Process (SSP) as presented in Sect. 2 The collection of baseline data (Fig. 1) The schedule execution and acquisition of performance data (Fig. 2) A cost determination (Fig. 3)

The workflow provides several interfaces or entry points which can either be used by a user or by the frameworks internals. In the following Figs. 1, 2 and 3 those entry points are colored in light gray. Collection of baseline data Baseline data are performance data resulting from measurements of specific tasks which are executed in isolation on one core while the other cores are idle. The execution of a single task can be interpreted as a schedule associated with a set of buckets which contains one bucket with that task and further empty buckets. Those schedules are executed using different processor frequencies to collect data about the performance behavior of the task on a specific machine which is stored in form of an application profile. The frequencies are selected by their importance for an interpolation. Harder to interpolate frequencies such as the min, max, and mean frequency have a higher priority than directly neighboring frequencies. However, the user may decide to collect data for all frequencies to improve quality of the application profile and therefore later estimations. Based on this baseline data, the execution costs M Sb and E b of a task set b and therefore a schedule B can be estimated. The sub-workflow for the collection of such

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Fig. 2 Workflow for the schedule execution and acquisition of performance data

Fig. 3 Workflow for the cost determination

data is shown in Fig. 1. In the case that a new machine is introduced, metadata about the machine is collected followed by the collection of baseline data. In the case of the introduction of new applications, idle machines are used to collect baseline data in the background. Schedule execution and acquisition of performance data To execute a schedule as well as record and analyze the performance data, a workflow visualized in Fig. 2 is provided. This workflow describes the process of monitoring the CPU and the memory usage of each task and combines it with energy measurement data for storage. To prepare the execution of tasks, the execution environment is setup according to the schedule description and hardware parameters such as processor frequencies are configured. While tasks are executed, the monitoring system records the machine load as well as the energy consumption and links them to the active tasks. This recording happens timestep based, however, the start or termination of a task causes an event which triggers the monitoring system to add an additional record.

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Cost determination The cost determination workflow in Fig. 3 uses cost information in form of (machine, application) combinations to determine the execution cost M S B and E B of a schedule B. Also the cost determination workflow is intended to be able to derive cost information for missing data points via interpolation in case of for example missing frequencies.

3.2 Framework Implementation To support and automate the usage of the workflow from Sect. 3.1 we have implemented the framework scefx. The framework scefx provides reusable functions required for task scheduling in general, but especially for the scheduling method of the case study SSP. The framework is written in Python and is built as a distributed system with components which are specialized. The software architecture enables an easy replacement of components or the creation of wrapper for other software systems as long as the interface is compatible with the interface of the replaced component. The interfaces follow the Representational State Transfer (REST) paradigm with JavaScript Object Notation (JSON) as data exchange format, as this reduces the complexity for rapid prototyping compared to other paradigms [9]. For the case study of the SSP, there are five components of which three implement the data acquisition, data evaluation, and data management. The fourth component implements the task-schedule creation and evaluation which in our use case implements the SSP. The fifth component is a user interface which is composed of a set of command line utilities which are able to access the data management as well as the task-schedule creation/evaluation via a web-based REST interface. This fifth component is the normal entry point for a user to work with the system. Via this component the user selects a target machine and provides a list of applications for execution and optimization targets to create a Task-Schedule. The Task-Schedule can be exported in various file formats to be reviewed or executed through the data acquisition component. In the following paragraphs more details for the three data handling components are given. Data Management To provide a central hub to connect all the following components and information, the data management provides an API to a database which stores all measured and generated data but also metadata about machines and applications. The machine metadata, such as information about the processor and memory layout or accelerators, is collected via the likwid library [10] by manually executing helper script when adding a new machine. For applications the metadata consists of general information such as the name and version number but also of information about the steps to reproduce and execution a specific application instance. Globally unique identifiers are used to reference machines, applications, and measurement data which allows components to collect data and communicate asynchronously. Data Acquisition The data acquisition component is a component to execute and monitor task schedules. Also, the component applies schedule machine settings such

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as the processor frequencies. The component stores all associations between executed tasks and measurement data as described in Sect. 3.1. The implementation of the components solely relies on Python libraries as well as read access to the Linux kernels interface filesystem and is therefore highly portable. However, most Linux distributions require root access to set processor frequencies and therefore the operating systems need to enable rootless access to the corresponding interfaces or to provide a sudo wrapper for root access. Data Evaluation The data evaluation component uses methods such as curve fitting, clustering, or interpolation to determine cost for the execution. For each task of a Task-Schedule, those costs are calculated based on measurements of previous executions. The task-schedule creation/evaluation component communicates with the data evaluation component and requests the estimated execution costs for a (machine, f r equency, application) combination. The data evaluation requests all information from the data management which fits the (machine, application) combination. The data evaluation then uses curve fitting to interpolate possibly missing data and to calculate the estimation for the requested combination.

4 Experimental Evaluation In this section the workflow described in the previous Sect. 3 is applied to a specific set of tasks from the SPEC CPU 2017 benchmark suite as defined in Sect. 4.1. The collected baseline data is used to construct Task Schedules and to estimate their makespan as well as energy consumption in Sect. 4.2. For the two schedules with the lowest makespan as well as the lowest energy consumption the processor frequencies are adapted to minimize the idle time and reduce the energy consumption which leads to two additional schedules. All four schedules are executed to measure their real energy consumption. The percentage of the energy consumption reduction for the schedules in both cases, the estimation as well as the real execution, is calculated and compared.

4.1 Definition of Set of Tasks and Baseline Data Collection For the definition of a set of tasks and the collection of baseline data, the intspeed and specspeed benchmark sets of SPEC CPU 2017 have been selected to have a mixed workload of integer as well as floating-point benchmarks to cover real-world use cases as close as possible. Among the integer benchmarks are a C compiler (gcc), a vehicle scheduling in public mass transportation (mcf), mail server work such as spam checking (perlbench) or a Monte Carlo simulation-based artificial intelligence for the game go (leela). Among the floating-point benchmarks is a simulation of blast

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waves (bwaves), a solver for the Einstein equations in vacuum (cactuBSSN), image operations performed on large using ImageMagick (imagick) or weather research and forecasting (wrf). All measurement data has been collected on an Intel machine with Skylake architecture. The processor supports 15 different operational frequencies F = {0.8, 1.0, 1.2, 1.4, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 2.8, 3.0, 3.2, 3.4}, given in GHz. For the collection of baseline data all tasks have been executed exclusively using all available operational frequencies where all processor cores have been activated and had the same fixed frequency. The collected baseline data can be seen in Fig. 4.

4.2 T- and E-Schedule Creation Based on the baseline data, time-oriented (T) and energy-oriented (E)-Schedules have been created which select the bucket a new task is assigned to by either the lowest bucket makespan or the lowest bucket energy consumption. The schedule creation has been performed for all available frequencies with six buckets. The number of buckets was chosen, as it provided the best visualization of the frequency adaptation effect. Despite of the evaluation being based on all supported frequencies of the processor, the following presentation is focused on the frequencies with the lowest energy consumption 2.3 GHz as well as the lowest execution time 3.4 GHz. Exemplary for both frequencies, Fig. 5 visualizes the T- and E-Schedule for 3.4 GHz. The T-Schedules provide an even execution time over all buckets while the energy consumption shows fluctuations. The E-Schedules provide a more evenly distributed

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energy consumption over all buckets which however results in a large fluctuation in the execution time. Frequency Adaptation Due to the fluctuation of the makespan for each schedule, the processors frequency for some buckets can be reduced (adapted) to reduce the difference between all makespans. In this set of T- and E-Schedules, the frequencyadapted E-Schedule provides the lowest total energy consumption for 3.4 GHz, which also happens to be the same case for 2.3 GHz. However, this does not apply for all frequencies, for example, for 3.2 GHz the T-Schedule provides the lowest total energy after the frequency adaptation. As 2.3 GHz is the frequency with the highest efficiency for the processor the frequency adaption leads to a slightly higher total energy consumption despite the fact that the idle time was reduced. This is due to the initial assumption of the idle power consumption to be zero as it was much lower than the power consumption when the processor is running at its highest frequency under load. After the analysis of all schedules with and without frequency adaptation two schedules where identified to be pareto optimal and have therefore been selected: 1. the E-schedule with a base frequency of 3.4 GHz as schedule with the lowest makespan and lowest energy consumption of all schedules with the same makespan. 2. the E-Schedule with a base frequency of 2.3 GHz as schedule with the lowest overall energy consumption. The frequency adaptation has assigned the frequencies F3.4 GHz = [3.4, 3.0, 3.2, 3.4, 3.2, 2.8] and F2.3 GHz = [2.3, 2.1, 2.1, 2.3, 2.1, 2.1], both given in GHz. Figure 6 visualizes the makespan resulting for these frequencies. The overall makespan of the schedule with 3.4 GHz as maximal frequency is lower than the schedule with 2.3 GHz, as expected. Furthermore, fluctuation within those schedules is different as well where for 2.3 GHz not only the makespan is larger but also the fluctuation. This can be explained by the frequencies supported by the processor where the common step size is 200 MHz. As a result, fluctuations are more likely to appear for 2.3 GHz than for 3.4 GHz as the relative frequency change in percent is lower for 3.4 GHz. The

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Table 2 Comparison of estimated energy consumption and measured energy consumption for the execution of E-Schedules without frequency adaptation (orig) and with frequency adaptation (adap) for 2.3 and 3.4 GHz Schedule Estimation Real Deviation 3.4 GHz orig 3.4 GHz adap Reduction 2.3 GHz orig 2.3 GHz adap Reduction

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resulting higher granularity raises the chance to find frequencies with a makespan close but not larger than the overall makespan. Measured Energy Consumption The energy consumption resulting from the execution of the schedules with and without frequency adaptation is shown in Table 2. The schedule is identified by its base frequency followed by “orig” for no frequency adaptation and “adapt” for schedules with frequency adaptation. For comparison the estimated and real power consumption are presented, the last column provides the deviation between the estimated and real power consumption in percent. The Table shows that the reduction of the idle time for the schedules executed with 2.3 GHz does indeed lead to a lower energy consumption even if it is only a small improvement of 2.03% which is a total saving of 6085 J or 1.69 Wh. The reduction of idle time for 3.4 GHz reduces the energy consumption by 10.75% which is a total saving of 22796 J or 6.33 Wh. Effect of the Idle Power Consumption As the data collection did not compensate for the idle power consumption of unused processor cores, a high deviation between the estimation and real power consumption was expected. For the schedules without frequency adaptation the deviation is between 11.47 and 16.94%. For the schedules

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with frequency adaptation the deviation is between 10.75 and 18, 87%. The percentile reduction shows, the measured reduction of the energy consumption is as expected larger than the estimated energy consumption. The observations show the importance of idle power consumption of the estimation of energy consumption. Thus, the initial assumption of the idle power consumption to be zero, while causing no issues with the makespan calculation, did lead to energy consumption estimations which are too high. However, for our use case the makespan is the main criteria to recognize and utilize possibilities to reduce the energy consumption while the estimation of energy consumption is mainly used during the distribution of tasks. For the distribution of tasks, the effective criteria for bucket selection are the relative difference between the tasks. Since the baseline measurements of all tasks have been performed under the same assumption, the error caused by this assumption is present as a constant factor which differs for each frequency. Therefore, as long as the comparisons happen within the same frequency, the error has no effect on the distribution of tasks because as it does not affect the relative difference [11].

5 Related Work Saving energy is an important issue for many applications in computer systems. It was found in [4] that manually setting a frequency for dynamic voltage and frequency scaling (DVFS) can lower the energy consumption but only to a minimum, lowering the frequency any further does increase energy consumption. An approach to achieve Intra-Task DVFS based on profile information is proposed in [12]. The energy aware distribution of workload in form of Virtual Machines is described in [13]. As described in [14] in cases of unbalanced MPI applications the energy consumption can be reduced by reducing wait times on synchronizations through a work estimation and active frequency control. The matter of reducing the energy consumption using data science is not limited to computer systems. A new approach to forecast the load on the electrical grid and schedule the power on time of large appliances is proposed in [15].

6 Conclusion In this article, we have proposed a software framework called schedule creation and evaluation framework (scefx), which is designed to support experiments to collect and evaluate a large variety of task performance data and metadata. An advantage is that existing performance data containing energy consumption and execution times of tasks can be used to adapt the processor frequencies to minimize idle times. We have performed experiments with scefx using a set of tasks from the SPEC CPU 2017 benchmark suite. Schedules with a theoretical minimal energy consumption

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for the highest and the most efficient processor frequency have been constructed. Those schedules have been executed with and without frequency adaption to measure and compare the real to the theoretical energy saving. For the schedules based on the shortest overall execution time the energy saving was 10.75%. For the schedules based on the lowest energy consumption the energy saving was 2.03%. The framework scefx and the specific workflow automate many parts for the collection and evaluation of schedule performance data which allows the execution of more performance experiments in less time. Due to the design of scefx based on reusable components and functions more advanced scheduling techniques can be added and evaluated with minimal additional programming effort. Acknowledgements This work has been supported by the German Ministry of Science and Education (BMBF) project SeASiTe Self-adaption of time-step-based simulation techniques on heterogeneous HPC systems, Grant No. 01IH16012B.

References 1. Aupy G et al (2015) Energy-aware algorithms for task graph scheduling, replica placement and checkpoint strategies. In: Handbook on data centers. Springer New York, New York, NY, Chap. 2, pp 37–80. ISBN: 978-1-4939-2092-1. https://doi.org/10.1007/978-1-4939-2092-12 2. Rocha I et al (2019) HEATS: heterogeneity- and energy-aware task-based scheduling. CoRR arXiv:1906.11321 3. Fedorova A et al (2009) Maximizing power efficiency with asymmetric multicore systems. In: Queue 7.10, pp 30–45. ISSN: 1542-7730. https://doi.org/10.1145/1647300.1658422 4. Le Sueur E, Heiser G (2010) Dynamic voltage and frequency scaling: the laws of diminishing returns. In: Proceedings of the 2010 international conference on power aware computing and systems. HotPower’10. USENIX Association, Vancouver, BC, Canada, pp 1–8 5. Saxe E (2010) Power-efficient software. In: Communication ACM 53.2, pp 44–48. ISSN: 0001-0782. https://doi.org/10.1145/1646353.1646370 6. Bucek J, Lange K-D, Kistowski JV (2018) SPEC CPU 2017: next- generation compute benchmark. In: Companion of the 2018 ACM/SPEC international conference on performance engineering. ICPE ’18. Association for Computing Machinery, Berlin, Germany, pp 41–42. ISBN: 9781450356299. https://doi.org/10.1145/3185768.3185771 7. Rauber T, Rünger G (2019) A scheduling selection process for energy- efficient task execution on DVFS processors. In: Concurrency and computation: practice and experience 31.19. e5043 cpe.5043, e5043. https://doi.org/10.1002/cpe.5043. https://onlinelibrary.wiley.com/ 8. Ehrgott M (2005) Multicriteria optimization, 2nd edn. Springer, Berlin, Heidelberg. OnlineRessource (XIII, 323 p. 88 illus, digi-tal). ISBN: 9783540276593 9. Tihomirovs J, Grabis J (2016) Comparison of SOAP and REST based web services using software evaluation metrics. Inf Technol Manag Sci 19(1):92–97. https://doi.org/10.1515/itms2016-0017 10. Treibig J, Hager G, Wellein G (2010) LIKWID: a lightweight performance- oriented tool suite for x86 multicore environments. In: 2010 39th international conference on parallel processing workshops, pp 207–216 11. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques. The morgan kaufmann series in data management systems. Elsevier Science. ISBN: 9780080890364 12. Qin Y et al (2019) Energy-efficient intra-task DVFS scheduling using linear programming formulation. IEEE Access 7:30536–30547

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13. Shojafar M et al (2016) An energy-aware scheduling algorithm in DVFS- enabled networked data centers. In: Proceedings of the 6th international conference on cloud computing and services science-volume 2: TEEC, (CLOSER 2016). INSTICC. SciTePress, pp 387–397. ISBN: 978-989-758-182-3. https://doi.org/10.5220/0005928903870397 14. Kappiah N, Freeh VW, Lowenthal DK (2005) Just in time dynamic voltage scaling: exploiting inter-node slack to save energy in MPI Programs. In: SC ’05: Proceedings of the 2005 ACM/IEEE conference on supercomputing, p 33 15. Park S et al (2020) A two-stage industrial load forecasting scheme for day- ahead combined cooling, heating and power scheduling. In: Energies 13.2, p 443. ISSN: 1996-1073. https://doi. org/10.3390/en13020443

Artificial Intelligence Edge Applications in 5G Networks Carlota Villasante Marcos

Abstract In recent years, the Fifth Generation of mobile communications has been thoroughly researched to improve the previous 4G capabilities. As opposed to earlier architectures, 5G Networks provide low latency access to services with high reliability. Additionally, they allow exploring new opportunities for applications that need to offload computing load in the network with a real-time response. This paper analyzes the feasibility of a real-time Computer Vision use case model in small devices using a fully deployed 5G Network. The results show an improvement in Latency and Throughput over previous generations and a high percentage of Availability and Reliability in the analyzed use case. Keywords 5G · URLLC · Computer vision · Artificial intelligence · E2E latency · OWD · E2E service response time · Availability · Reliability

1 Introduction Mobile communications have experienced a continuous transformation process, from mobile phone calls and SMS with 2G to video calls and data consumption anywhere with 4G [1]. Nevertheless, the increase in video streaming and high-data-consuming use cases have caused an impact on the network. In addition, data consumption is rapidly rising, new number of connections appear each day and new technologies such as Internet of things (IoT) need better energy efficiency [2]. The Radiocommunication Sector of the International Telecommunication Union (ITU-R) set up a project named IMT-2020 [3], which has established further stricter requirements to provide high speed, high reliability, low latency, and energy efficiency mobile services that could not be achieved with previous generations. Within that project, ITU-R defined 5G usage scenarios depending on the requirements addressed on the network: enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low Latency Communications (URLLC), and massive machine-type communications (mMTC). C. Villasante Marcos (B) Ericsson España SA, Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_24

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Every 5G use case or application can be allocated to one or several usage scenarios, depending on the requirements established on the key capabilities of IMT-2020 [4]. The requirements set, and the progress made on the standardization of 5G, offer network operators the possibility to provide new services to a high number of areas such as healthcare, industry, education, tourism, etc. [5]. Computer Vision (CV) can be found nowadays in several real examples and Use Cases (UCs) of those areas, as self-driving car testing, health diagnostics, security and surveillance cameras, or even in stores [6]. Not only are Artificial Intelligence (AI) techniques present on new UCs but they also help optimize network performance, enhance customer experience, boost network management by controlling network slicing and solve complex data problems [7]. Most of the solutions with AI rely on heavy machinery, as it requires high computational load for some of the tasks involved. More often, examples of CV can be seen in lighter devices like smartphones or Virtual Reality (VR) glasses, and other personal devices where the workload is done on the device itself, but it still requires quite complex dedicated hardware and software. 5G Networks provide infrastructure and resources, such as edge computing [8], low latency, and high reliability, in order to move the computation load to another location. In this manner, the devices could be lighter, simpler, and cheaper, as the intelligence relies on the network and it can become a distributed solution [9]. Offloading the devices’ intelligence into the network will also ease to request for more computer capacity, fix any encountered problem, and update the software more easily since the software is in a centralized server. The appearance of so many new UCs has encouraged the launch of several projects and research programs, such as 5G Public–Private Partnership (5GPPP) to explore the possibilities of 5G. Focusing on a European scenario, we can find 5G-EVE “5G European Validation platform for Extensive trials” project [10], whose main purpose is to facilitate a way to validate 5GPPP projects network Key Performance Indicators (KPIs) and services. 5G-EVE evaluates real use cases in a 5G network environment [11] by trying to evaluate which 5GPPP scenario they fit in and the requirements needed to fulfill necessities, showcasing them with a final demo. 5G-EVE gives an end-to-end facility by interconnecting European sites in Greece, Italy, France, and Spain. Specifically, in Spain, 5TONIC [12] was designated as the Spanish laboratory site for 5G-EVE, and where the realization of this study takes place. In this paper, we introduce the analysis of real-time CV with image recognition on small connected devices in 5G networks. For this proposal, a fully deployed 5G NonStandalone (NSA) network located in the 5G-EVE Spanish site, based on Ericsson’s 5G portfolio, is used to give coverage to a device with an incorporated camera which will be continuously streaming video to the CV application located on the network. The results will demonstrate the benefits of the new generation network over the previous mobile generation regarding latency, reliability, and throughput.

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2 Materials and Methods 2.1 Goals The main goal of this work is to evaluate the feasibility of Computer Vision with image recognition UCs on the new generation of mobile networks. As a baseline, a camera is used, streaming video through the network to a server where CV image recognition is being applied and a control command is sent back to the device. On the one hand, edge computing, a technique that enables to perform computation offload in the network near the connected devices, is analyzed to check if there is any advantage. On the other hand, we carry out a comparison between 4 and 5G-New Radio (NR), since the latter ensures low latency and high reliability on the network, enabling real-time response applications.

2.2 Environment The environment for the study is described in Fig. 1, composed by a Camera, a 5G NR NSA Customer Premise Equipment (CPE), a 5G NR Radio Access Network (RAN), option3 or NSA NR [13], a Packet Core (PC) with 5G NSA support and a Server where the CV Application (APP), with a trained Neural Network (NN), is running. As the study was done at the 5TONIC laboratory, the infrastructure used was provided by the project [14]. As shown in Fig. 2, it consists of Raspberry pi with a camera module connected to a 5G CPE as the User Equipment (UE), a RAN (with 4G, 5G NSA and Narrowband-IoT (NB-IoT) access), a Packet Core (with 4G and 5G NSA support) and a Transport layer, which enables to simulate several environments Fig. 1 High-level environment representation

Fig. 2 Use case environment representation of 5Tonic equipment

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and conditions. Furthermore, the RAN was configured with a bandwidth of 50 MHz in B43(4G)/n78(5G) and a Time-Division Duplexing (TDD) pattern of 7:3 to improve the achievable Uplink throughput.

2.3 Measurements The use case evaluated in this paper is a good example of a URLLC service and the most important KPI is Latency or End-to-End (E2E) Latency, as considered in 5GEVE [15]. Availability and Reliability, related to the percentage of correctly received packets, must be evaluated. Computer vision use cases usually come alongside with real-time response applications where immediate reactions and non-packet-loss are needed. For example, if a pedestrian steps in the way of a vehicle, it needs to react as fast as possible to avoid crashes by slowing down or changing directions. Considering that in these types of UCs the UE needs to send a great amount of data through the network, we should also analyze User Data Rate and Peak Data Rate. E2E Latency as a 5G EVE KPI is measured as the duration between the transmission of a small data packet from a source node and the reception of the corresponding answer at the same point. In this paper, we take several measurement methods of Latency to evaluate the use case, such as Ping in order to evaluate such latency as an emulated control packet, Round Trip Time (RTT), shown in (1), and Smooth RTT (SRTT) in (2), as defined in [16] to measure the E2E Latency on a small TCP packet and on the transmission of a video frame. The latter measurement is considered the most important KPI about latency in this study, as it measures the real E2E Latency of the UC on the network. RTT = TimeTCP ACK − TimeTCP Packet

(1)

SRTT = (1 − α) ∗ SRTT + α ∗ R’

(2)

In addition, One-Way Delay (OWD) is also presented. OWD is understood as the time difference between the time of transmission and reception. It could be measured in both uplink and downlink, but in this use case, the relevant path is uplink, since the video is being sent from the UE to the application layer. Until now, the KPIs correspond to network propagation; however, to calculate the complete E2E Service Response Time and analyze the feasibility of these kinds of UCs, the processing time of the video frame on the application must be added, as shown in Fig. 3.

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Fig. 3 Representation of E2E latency, OWD, and E2E service response time

2.4 Methodology The User Equipment used in this study is composed of a Raspberry Pi 3 model B+ [17] with a camera module [18] connected to a 5G NR CPE. In order to have a live video streaming, we used the Linux project UV4L [19] with the raspicam driver [20], where several parameters of the video can be set easily, such as image encoding and resolution, and streaming Server options. In this study, the parameters used were: MJPEG and H264 encoding, 20 frames per second (fps), 640 × 480 (VGA), 720 × 576 (D1), and 1280 × 720 (HD) image resolution [21]. The CV APP Server consists of a dockerized application with a Caffe [22] NN, which receives the images sent by the UE and processes them to perform image recognition to later immediately send a command to the UE. The NN was trained to recognize people and objects that could be found in the lab as shown in Fig. 4. Furthermore, to make the evaluation of the use case and retrieve the measurements and interesting KPIs, the traffic on the network is captured with TCPdump [23], and three specific positions on the environment were selected: the UE, the Core, and the APP Server. In order to extract the KPI of E2E latency, the measurement on UE is the only one needed, but to evaluate OWD delay it is necessary to extract the timestamp of the packet in different locations and subtract them, as shown in (3) for a TCP uplink packet. OWD(TCP)Uplink = Time(TCP)APP Server − Time(TCP)UE

(3)

When trying to measure both E2E latency and OWD of a video frame, another dimension appears, since it is not only measured for one packet but several packets. Fig. 4 Recognition of a chair shown in the image processed by the CV APP Server at 5Tonic

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Fig. 5 Video frame packet sequence to measure Service E2E Latency and OWD. Representation of synchronization NTP Server-Client mechanism, measurement extraction points on UE, Packet Core, and APP Server

The video frame can be defined as the group of packets between two packets with a payload value string containing “–Boundary”. Despite being TCP, not all the packets have an Acknowledgement (ACK) packet, but instead, it is used as a receipt of a group of packets, not necessarily the group corresponding to a video frame. Hence, in this case, the response packet considered to measure E2E Latency is the next ACK packet to appear after a video frame (see Fig. 5). To calculate E2E Service Response Time (E2ESRT ), the OWD of a video frame in the uplink direction OWDVU , the Processing time τ, and time of transmission of the response OWDRD are needed, represented in (4). E2ESRT = OWDVU + τ + OWDRD

(4)

To have the minimum possible error and the highest precision, lab synchronization is one of the key components of the study, and we achieved it by using a Network Time Protocol (NTP) Server-Client mechanism, where all devices and nodes are set by the same clock. In addition, we evaluated several scenarios to have a better understanding of the feasibility of this use case on a 5G network when compared to 4G. We used Netem to add additional latency and simulate the location of the Core and APP Server with respect to the RAN. Edge computing, or local range scenario, is a new scenario that is only present in 5G. Below we considered edge computing as a range of 0 km, regional range scenario of 200 km, and the national range scenario of 400 km. In order to do this analysis and comparison between the results in different scenarios, we added a delay in milliseconds (ms) on the packet core interface, 2 ms for a regional scenario and 4 ms for the national scenario.

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The experiments of this work aim to assess the feasibility of a Computer Vision use case in a 5G network. Depending on the application where CV is present, the delay might be crucial, in an automotive scenario that time is directly related to the velocity (v) of a vehicle or space (s) traveled when braking, as shown in (5). v = s/t = s/E2ESRT

(5)

3 Results The main measurement of this analysis is E2E Latency. First, in order to set a baseline, we measured Latency with the Ping utility for an ICMP packet, which is considered in this study as a control packet. Second, we had to consider that the video stream in this use case is sent over Transmission Control Protocol (TCP) and, since it is a connection-oriented protocol, it guarantees that all sent packets will reach the destination in order and allows to take trustworthy measurements. We took the first measurements for a video stream with an MJPEG encoder and 640 × 480 resolution. Both Latency for a control packet and TCP packets are exposed in Fig. 6. Third, as previously explained, it is important to consider the Service E2E latency. This is the case for the transmission of a whole video frame, shown in Fig. 7. Depending on the type of traffic, control packet, TCP packet, or whole video frame, the E2E latency values differ, but we can observe that all 5G scenarios have lower values than on both 4G regional and national scenarios. 5G Edge computing scenario is the fastest and it reduces in 23% the latency for the presented UC, 66% for control packets, and 44% for TCP packets. As mentioned in Sect. 2.3, OWD can be measured in both directions: uplink and downlink. However, in this scenario, the important measurement is on uplink as the traffic load and the corresponding delay is greater when transmitting video uplink than sending the confirmation of receipt. To make the comparison, we set

Fig. 6 Left: control packet latency boxplot measured with Ping on each scenario. Right: TCP packet latency boxplot measured with SRTT on each scenario

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Fig. 7 Service video frame latency boxplot measured with SRTT on each scenario

Fig. 8 Left: comparison of RTT, OWD uplink, and Ack transmission on a 5G network. Right: Video frame OWD boxplot per encoder and size

three different image resolutions (VGA, D1, and HD) with two different encoders (MJPEG and H264) to observe a possible variation on the OWD (see Fig. 8). As mentioned before, all nodes and devices were synchronized with a common clock in order to achieve minimum error and higher precision. To calculate the propagation error in this environment, we measured the ntp offset in every node every 10 s. If a node is UE, b node is the Core and the dockerized CV APP server is c, we must apply (6) and (7) as defined in [24] to calculate the propagation error. The total error can be approximated as a sum of normal distribution errors. Q =a+b+c

(6)

σ Q2 = (σa )2 + (σb )2 + (σc )2

(7)

For each node, we calculated the standard deviations of the ntp offsets, being 0.387 ms on the UE, 0.317 ms on the Core, and 0.117 ms on the Container, which results in a standard propagated error of ±0.821 ms. The CV application consists of different computing actions: transformation of the images received to NN input known as Blob, the NN detection, interpretation of the detections, and command creation. During the tests, the processing time was observed to be similar with both MJPEG and H264 encoding and with different image resolutions, a total of 20.3 ms. In addition, if we calculate the E2E Service

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Fig. 9 Left: time distribution of each action performed on the CV application. Right: time distribution of E2E Service Response Time

Response Time, as defined in (4), for an MJPEG video stream with VGA resolution and assuming an OWDRD of 5 ms, the result is 87 ms. In Fig. 9, the time distribution of each action in the CV APP and the time distribution of the E2E Response Time can be appreciated. Furthermore, Availability is defined by the percentage of packets that were successfully delivered through the network. In this study, no packet loss was found which corresponds to a 100% of Availability in 5G. Regarding Reliability, it is defined as the percentage of successfully delivered packets within the time constraint required by the service. In Fig. 10, the Video frame OWD Latency Cumulative Distribution Functions (CDFs) for the VGA resolution with MJPEG encoding can be observed. Based on (5), the maximum velocity of a vehicle was calculated in both networks and scenarios, considering that it must detect an object and command an order within 1 m. Assuming E2ESRT as before but with an OWD for upper 95% reliability, the results for the calculated velocity are shown in Table 1. Finally, in order to determine the maximum achievable bandwidth, we used iperf3 [25] on both 5G and 4G networks, achieving 54.6Mbits/sec and 32.2Mbits/sec, respectively. In Fig. 11, the demanded throughput with each type of image resolution is shown. It can be observed, for both D1 and HD resolutions, that the demanded

Fig. 10 Video frame OWD latency CDFs for the VGA resolution on each scenario and reliability set to 95%

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Table 1 Table captions should be placed above the tables Velocity (km/h)

5G edge

5G regional

5G national

4G regional

4G national

40,31 km/h

39,43 km/h

37,7 km/h

35,19 km/h

34,51 km/h

Fig. 11 Demanded throughput per image resolution and maximum achievable throughput thresholds

throughput is above the maximum achievable bandwidth in a 4G network, this implies that to maintain a good Quality of Service a downgrade is required.

4 Conclusions This paper presents the improvements of 5G NSA for a Computer Vision model UC with image resolution. As appreciated on the results, all explored scenarios on 5G have lower values on the calculated E2E latency for each type of traffic, being Edge computing the fastest one. In this UC, the predominant direction of traffic is found to be the uplink traffic and a slight difference is appreciated on the OWD results when the resolution of the video is changed. Despite the processing time on the calculation of the E2E Service Response time, it is observed that 5G Edge scenarios allow a faster velocity on a vehicle, in order to detect an object within 1 m and being able to react with a reliability higher than 95% in the network. In addition, the maximum throughput achievable in a 4G network is below the observed demanded throughput for higher resolutions. This implies that a 5G network is needed to obtain a video stream with high resolutions and, at least, 20fps. Acknowledgements This Master Thesis was supported by Ericsson and Universidad Carlos III de Madrid. I would like to thank Marc Mollà Roselló for his assistance as the Thesis supervisor, and the colleagues from the 5G-EVE project who provided insight and expertise that assisted the research. Also, the friends and family who provided comments that greatly improved the manuscript.

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References 1. What is 5G? https://www.ericsson.com/en/5g/what-is-5g?. Accessed 22 Oct 2020 2. ETSI 5G, https://www.etsi.org/technologies/5g. Accessed 22 Oct 2020 3. ITU towards “IMT for 2020 and beyond”, https://www.itu.int/en/ITU-R/study-groups/rsg5/ rwp5d/imt-2020/Pages/default.aspx. Accessed 22 Oct 2020 4. Elayoubi SE et al (2016) 5G service requirements and operational use cases: analysis and METIS II vision. In: 2016 European Conference on networks and communications (EuCNC), Athens, pp 158–162 5. Discover the benefits of 5G, https://www.ericsson.com/en/5g/use-cases. Accessed 22 Oct 2020 6. Faggella D Computer vision applications–shopping, driving and more, https://emerj.com/aisector-overviews/computer-vision-applications-shopping-driving-and-more. Accessed 22 Oct 2020 7. What role will Artificial Intelligence have in the mobile networks of the future? https://www. ericsson.com/en/networks/offerings/network-services/ai-report. Accessed 22 Oct 2020 8. Ericsson Edge computing-a must for 5G success, https://www.ericsson.com/en/digital-ser vices/edge-computing. Accessed 22 Oct 2020 9. Image recognition applications in the era of 5G, https://www.ericsson.com/en/blog/2019/6/dis tributed-ai-image-recognition-applications. Accessed 22 Oct 2020 10. 5G EVE, https://www.5g-eve.eu/. Accessed 22 Oct 2020 11. Canale S, Tognaccini M, de Pedro LM, Ruiz Alonso JJ, Trichia K, Meridou D et al (2018). D1.1 requirements definition & analysis from participant vertical industries 12. 5TONIC, https://www.5tonic.org/. Accessed 22 Oct 2020 13. 3GPP release 15 overview-IEEE spectrum, https://spectrum.ieee.org/telecom/wireless/3gpprelease-15-overview. Accessed 22 Oct 2020 14. Legouable R, Trichias K, Kritikou Y, Meridou D, Kosmatos E, Skalidi A et al (2019). D2.6 participating vertical industries planning. Zenodo 15. Ruiz Alonso JJ, Benito Frontelo I, Iordache M, Roman R, Kosmatos E, Trichias K et al (2019) D1.4 KPI collection framework. Zenodo. 16. Paxson V, Allman M, Chu J, Sargent M (2011) Computing TCP’s retransmission timer. RFC 6298. https://doi.org/10.17487/RFC6298, https://www.rfc-editor.org/info/rfc6298. Accessed 22 Oct 2020 17. Raspberry Pi 3 Model B+, https://www.raspberrypi.org/products/raspberry-pi-3-model-bplus/. Accessed 22 Oct 2020 18. Camera Module V2, https://www.raspberrypi.org/products/camera-module-v2/. Accessed 22 Oct 2020 19. User space Video4Linux, http://www.linux-projects.org/uv4l/. Accessed 22 Oct 2020 20. Uv4l-raspicam, http://www.linux-projects.org/documentation/uv4l-raspicam/. Accessed 22 Oct 2020 21. Resolution, https://elinetechnology.com/definition/resolution/. Accessed 22 Oct 2020 22. Caffe, https://caffe.berkeleyvision.org/. Accessed 22 Oct 2020 23. TCPDUMP & LIBPCAP, https://www.tcpdump.org/. Accessed 22 Oct 2020 24. Harvard, A Summary of Error Propagation, http://ipl.physics.harvard.edu/wp-uploads/2013/ 03/PS3_Error_Propagation_sp13.pdf. Accessed 22 Oct 2020 25. Iperf3, http://software.es.net/iperf/. Accessed 22 Oct 2020

A Concept for the Use of Chatbots to Provide the Public with Vital Information in Crisis Situations Daniel Staegemann , Matthias Volk , Christian Daase , Matthias Pohl, and Klaus Turowski

Abstract In times of crisis, at which many people experience insecurity, fear, and uncertainty, the government plays a critical role when it comes to civil protection and the distribution of vital information. However, in many cases, misleading or contradictory information are spread by various sources, exacerbating the desperation of the local population and leading to wrong decisions, which might worsen the situation. To counteract this problem, a single system that is highly trustworthy and supports the citizens in gathering tailored high-quality information to overcome the crisis appears to be sensible. In this paper, a conceptual approach is presented that attempts to provide this single point of interaction by combining chatbots, robotic process automation, and data analytics for the tailored provisioning of important information, sourced from pre-selected institutions. This assures the highest possible degree of information quality and therefore acceptance in the potential user base, while also allowing for a fast dissemination of new findings and statistics as well as the ability to monitor the users’ behaviour to gain additional insights. Keywords Chatbot · Robotic process automation · Civil protection · Emergency communication · Big data

D. Staegemann (B) · M. Volk · C. Daase · M. Pohl · K. Turowski Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany e-mail: [email protected] M. Volk e-mail: [email protected] C. Daase e-mail: [email protected] M. Pohl e-mail: [email protected] K. Turowski e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_25

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1 Introduction While the responsibilities of modern governments are multifarious, one of the primary tasks is the avoidance of serious harm to their citizens. Even though, this applies at any time, it especially holds true in times of crisis [1]. Exemplary occurrences that belong to this category would be terrorist attacks, earthquakes, tsunamis or pandemics. Besides the primary mission of dealing with the situation by means of the corresponding operatives, another highly important challenge is to quickly establish a way to effectively communicate important information from a variety of concerned domains to the public [2]. Especially in consideration of the multitude of rivalling sources of information like news outlets, social media, blogs, and others who might follow their own agenda instead of the best interest of the recipients, the government can be compared to an enterprise that competes to distribute its goods (trustworthy information) to as many customers (citizens) as possible [3]. For this purpose, it is necessary to make the government’s proposition as attractive as possible. However, most competitors as well as the government itself do not directly charge for the provided information. Therefore, the price as a distinctive feature of an information provisioning service is ruled out and other properties like the perceived quality of information and the ease of use of the offered solutions remain as the deciding factors for the decision making. Furthermore, also, outdated official information or those that are aimed at another audience might distract from the latest relevant insights. Apart from that, even not obtaining any information at all or only part of it constitutes a real possibility that might, however, result in negative consequences for the regarded person or its surroundings. This is exacerbated by the fact that, it is not necessarily apparent to the individual, at which point the entirety of the relevant information are acquired or if something essential is still missing. For this reason, considering the duty of ensuring the safety of their citizens as well as the public order, even in case of a crisis, many governments are tasked to provide the public with a solution that is suitable to overcome those aforementioned challenges. Consequently, the aim should be to establish a single point of contact as a comprehensive and reliable source of vital information that is easy to access and therefore reaches a wide audience. This, however, culminates in the following research question. RQ: How can a quickly to activate, scalable, easy to use, always up-to-date communication system be designed to constitute a single point of interaction to a highly heterogeneous audience that requires reliable information from a variety of domains to overcome crisis situations? While it may be undeniable that the correct understanding of the information provided is crucial to fully exploit a system that is suited to the research question, an opportunity for users to communicate further questions can be essential. As noted by Pourebrahim et al. [4], governments in disaster situations are usually prominent users of the microblogging service Twitter, but they use it primarily as a one-way communication platform rather than communicating directly with their audience. Assuming that one or more people who work for the government are responsible for the Twitter account, it is no surprise that they cannot answer every single question

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posed by each user. Therefore, we present a solution that uses a reasonable selection of robotic technologies to provide two-way communication to reduce the problem of ambiguity. The key concepts here are the structuring and analysis of the huge amounts of information by using emerging big data analytics approaches, the collection and updating of data on different systems by incorporating software robots from the field of robotic process automation (RPA) and finally the automated bidirectional distribution of information with the help of traditional chatbots.

2 Background Disasters such as pandemics or attacks on national security pose the problem that they occur unexpectedly and that each emergency protocol usually has to be slightly adapted to the specific scenario [5]. The thirst for information of the population rises, and the amount of available data can be vast and distributed [6]. In order to save lives, a single information system that provides people with everything they need to know is beneficial, especially the rules of conduct in their respective regions. Time is a precious resource in a crisis, and human manpower might be limited as well [2, 5, 6]. Thus, the development of a whole new software solution each time a sudden global or national threat occurs is impractical, especially if the distributed information sources, such as government agencies or research institutions [5], remain largely the same. As an easier alternative, a highly adaptable automation technology should be used in the back end to link the individual sub-processes of data acquisition and processing in order to relieve the human workforce for more complex administrative tasks that cannot be performed by a robot [5, 6]. The front end, in turn, requires automation technology that enables the citizens to access the required data quickly and intuitively. One way to achieve this is a combination of software robots and BDA in the back end to acquire and process important data from various sources, and traditional chatbots in the front end to receive the requests and communicate the corresponding information.

2.1 Chatbots Although the capabilities of language processing have come a long way since Joseph Weizenbaum presented the first chatbot called ELIZA in 1966 [7], the basic principle is still the same. Chatbots mimic a human conversation by analyzing a given input for specific keywords [8]. The chatbot then follows a predefined conversation path to return an appropriate response. By integrating a smart and intuitive interface, it is not only possible to automate complex business processes including the involvement of external users such as customers [9], but also the scalability of the number of users simultaneously using a chatbot is advantageous compared to the one-to-one usefulness of a service employee. Especially in scenarios such as disaster situations,

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where the fast distribution of information could be crucial for the potential saving of lives [2], public interest in currently important details can be very high at an early stage of the crisis, but may decline over time as the situation becomes less confusing. The use of non-human interlocutors such as chatbots enables an extremely flexible question and answer solution compared to purely human service personnel, as they do not need to be hired or dismissed after the initial rush. Although it could be argued that users of social networks in particular could help each other with questions, these platforms also offer the opportunity for anti-social behaviour such as the deliberate dissemination of false information [10], which is why a centralized reliable contact point can be considered advantageous even if the answering of questions is automated and impersonal. With the possibility of extending a chatbot with artificial intelligence and algorithms of machine learning [8], this kind of human-like communication is a promising opportunity to improve the distribution of information in chaotic and volatile time periods.

2.2 Robotic Process Automation While RPA is primarily intended to increase the efficiency and accuracy of backoffice workflows in business companies organizations [11], it is not limited to this task. The core principle, in contrast to tailor-made individual software solutions, is that software robots work exactly as a human would work [9, 12]. Tasks involving a variety of different information systems can be handled by the robots without having to redevelop a single piece of software [13], which greatly facilitates the revision of the entire process cycle. Furthermore, software robots do not require in-depth programming knowledge [8], since they are largely built by recording or entering the sequence of steps required for a process. Besides the key feature of relieving the strain on human workers [13], which can be even more precious in serious situations, software robots bring several other advantages of traditional manufacturing robots to the office area, including round-the-clock accessibility, high efficiency, and low error susceptibility [8, 9]. In this regard, RPA is ideally suited for data-intensive and repetitive tasks [11], which would be easy for a human employee to learn, but can lead to mistakes during their frequent execution. Recent research indicates that the integration of additional technologies such as artificial intelligence and big data analysis may lead to even more complex workflows performed by software robots in the future [13]. From processing unstructured inputs such as natural language to advanced learning skills to resolve unexpected problems, robots could come closer to real human-like behaviour [9]. Especially in unforeseen scenarios with numerous different systems from which necessary data must be collected and updated at high frequency, RPA offers a fast and easy to integrate solution [13].

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2.3 Big Data With an improving capability to produce and capture data, not only the ability to acquire meaningful information increased, but also the accompanying challenges. As a result, the term big data was coined, reflecting those increased demands. According to the National Institute of Standards and Technology (NIST), big data “consists of extensive datasets primarily in the characteristics of volume, velocity, variety, and/or variability that require a scalable architecture for efficient storage, manipulation, and analysis” [14]. While the engineering of the corresponding big data analytics solutions is a highly sophisticated task [15], the benefits are still widely acknowledged [16, 17]. The list of application areas that already profit from the purposeful and timely analysis of data comprises, but is not limited to, sports [18], business [19], education [20], but also healthcare [21]. One further domain that already benefits from big data, but also has plenty of room for further advancements [22–27], is civil protection and disaster management, where timely and correct decisions can be crucial.

3 The Proposed Approach While the clear communication of information from a government to its citizens is always important, this applies even more in times of a crisis, when the correct decisions could make the difference between life and death [28]. Since the majority has no primary sources of information and is also no expert on the relevant topics, they are reliant on the insights of others as a basis for their own behaviour. However, oftentimes it can be time-consuming to obtain the corresponding information, with the evaluation of actuality, applicability to the respective situation and veracity posing additional challenges, especially in times of fake news and the information deluge on social media [29]. As a result, a lot of important information does not reach all of the relevant recipients and the benefits of obtained insights are not fully realized. Therefore, it appears sensible to tackle this issue by positioning validated sources as a single point of information for their respective domain, without increasing the barrier by introducing a high degree of complexity for the user, which would otherwise contribute to the establishment of digital inequality [30]. Due to the government being seen as the most trusted source of information, it is the evident administrator for this kind of solution [31]. Furthermore, since the dissemination of devices like PCs, tablets or smartphones is usually high and they allow for interactive applications, they appear to be an optimal vehicle for such a solution. As the range of potentially important information is wide and those might even vary depending on the user’s location, it is usually impossible to present them on just a few static pages. Yet, a complex navigation to find the desired information reduces the usability and increases the risk of mistakes that lead to incorrect results. Therefore, a more intuitive way of obtaining them is necessary. This is facilitated through the use of chatbots, allowing the user to reach his information goals by

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Fig. 1 The schematic overview of the proposed concept

using natural language instead of following complicated navigation paths or being forced to remember distinct commands. As Fig. 1 indicates, this approach also allows for the correct interpretation of slightly erroneous statements and sentence structures, increasing the accessibility for those who might otherwise be excluded, since language barriers can be a serious issue in crisis situations [32]. However, to always convey up-to-date insights, those bots must be linked to reliable sources that are constantly updated. For this purpose, RPA is an ideal means with its combination of high flexibility and simple setup, allowing to rapidly adapt to any arising necessities [12]. The chatbot tasks the software robot with the retrieval of a piece of information that corresponds to the user’s intent and subsequently incorporates it into its next answer. In that manner, new insights are automatically taken into account and only new types of questions have to be implemented once to be afterwards permanently available. To improve performance, an interim data storage could hold frequently requested information for a predefined amount of time, avoiding the necessity to freshly obtain them every time. However, it is absolutely vital for the system’s success that the selection of the organizations that provide the incorporated information is conducted thoroughly. This assures on the one hand the quality of the input and on the other hand, it also strengthens the confidence of the recipients regarding the system. Additionally, it is necessary to clearly define each sources responsibility according to their expertise and other potentially significant factors (e.g. location) and to also reflect it in the system’s data flow and modelling. This again increases the quality and also helps to avoid contradictions or ambiguities. Since the expertise for operating

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databases might not be available in every organization and they also lack the complete overview of the solutions architecture, the administration is centralized and all the information are stored in a comprehensive, scalable data hub. The data suppliers are just provided the possibility to modify or add entries regarding predefined aspects and to request the addition of new ones that they deem lacking. Besides the possibility to allow the public easy on-demand access to relevant and correct information, the system offers another benefit by allowing for big data analytics. Depending on local regulations, the user’s requests could be aggregated and analyzed to find aspects that quicken interests and that might have not been sufficiently clarified by other channels like news or press conferences to subsequently address this grievance. This, in turn, would not only have a beneficial impact for the users through presentation of highly demanded information but also the government itself. Potential trends, the identification of hotspots of the ongoing crisis and sentiment analysis could be facilitated. Hence, by evaluating the requests that could not be adequately served, it is also possible to detect, which additional information needs to be incorporated into the systems to meet the public’s information needs. Furthermore, in an attempt similar to Google Flu Trends [33] or the mining of twitter and web news to make predictions regarding COVID-19 [34], the analysis of those requests could possibly be used to obtain additional insights when it comes to disease outbreaks and pandemics, thus facilitating a better strategic planning. A schematic overview of the envisioned concept is depicted in Fig. 1, with dotted arrows indicating request and the others symbolizing responses or the entering of new data.

4 Concluding Remarks In the contribution at hand, a single point of interaction that combines the capabilities of chatbots, RPA and data analytics is proposed that provides an accessible platform for people to inform about ongoing crises. In times of insecurity, fear, and uncertainty, the intended approach shall increase the protection of the civil population by a clear and accurate provision of tailored information. At the current stage, the previously presented approach constitutes, for now, a conceptual model without being technically specified and implemented for a concrete country. However, possible pitfalls and thorough considerations have been identified that should be recognized in the future. This includes aspects, for instance, related to not only the general planning and implementation, but also the subsequent use and acceptance by the users. At the final stage, one has to keep in mind that the general awareness and use of such a solution might strongly vary between different countries. Unique cultural tendencies, the individual attitude of the population and existing legislations may influence the actual use. While in many countries, residents believe in a high trustworthiness of their government and, thus, in the goodwill of such a solution, within others the situation can be different. In some cases, people may develop the feeling that, due to the nature of a single point of interaction, the general information independencies could be distracted or even manipulated. Especially in those countries with natural higher

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privacy concerns, the massive storing, aggregation, and analyses of public data may be met with scepticism. For that reason, sophisticated technical concepts are required that prevent malicious and unauthorized access of third parties to generally increase the overall trustworthiness and reliability of such a solution. Apart from the single point of interaction, this also includes the data hub. Since the latter comprises all of the relevant data made available for the system, not only effective anonymizing techniques, but also comprehensive resilience and fault tolerance strategies should be used. Since, when faced with an actual crisis and the potentially high use of the system, it is vital to avoid failures or outages of the system. Furthermore, in the context of the development and integration of the system, researchers of adjacent domains should be consulted. This applies also for large-scale evaluations of the system as well as observations of the interaction between the people and the system. Due to the comprehensive data storage and the data analytics that can be performed, a suitable balance between the results that could and those, which should be presented needs to be identified. A system that might cause additional fear and panic in case of a disaster would not have the desired effect and might even be counterproductive.

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Fuzzy Reinforcement Learning Multi-agent System for Comfort and Energy Management in Buildings Panagiotis Kofinas, Anastasios Dounis, and Panagiotis Korkidis

Abstract In this paper, a Multi-agent System (MAS) is proposed to maintain the comfort of a building in high levels and simultaneously reduce the overall energy consumption. The multi-agent system consists of three independent agents each one dedicated to one comfort factor. These factors are the thermal comfort, the visual comfort and the air quality. Fuzzy Q-learning algorithm is utilised in all the agents in order to deal with the continuous state-action space. Simulation results highlight the superiority of the system compared to a simple on-off algorithm, as a reduction of 3% is observed and the comfort index remains high throughout the entire simulation. Keywords Multi-agent system · Building · Fuzzy reinforcement learning · Q-learning · Energy management · Comfort management

1 Introduction Energy management is critical in modern societies, as far as environmental and financial aspects are concerned. Since people spent most of their lives inside buildings, the maintenance of the indoor environment in good levels, to assure health and productivity [1], under an energy-efficient framework, is a crucial task. Three major factors affecting the overall comfort in a building are the thermal comfort, the indoor quality and the visual comfort. The temperature of the indoor environment is an indicator of the thermal comfort, while the concentration of the CO2 and illumination level are indicators of indoor air quality and visual comfort, respectively [2]. These indicators are characterised by the following indexes [3]: Thermal Comfort Index (TCI), P. Kofinas (B) · A. Dounis · P. Korkidis Department of Biomedical Engineering, University of West Attica, Athens, Greece e-mail: [email protected] A. Dounis e-mail: [email protected] P. Korkidis e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_26

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Indoor Air Quality (IAQ) and Visual Comfort Index (VCI), respectively. In order for the indicators to be kept in acceptable levels, the heating/cooling system, the ventilation system and the artificial lighting system, acting as actuators in the control framework, have to be controlled. There are many methodologies on the thermal comfort and energy management in buildings with HVAC actuators; however, most of these studies focus only on the temperature control [4] or temperature/relative humidity control [5–10]. Few papers consider the maintenance of visual comfort’s high levels by using artificial lighting [11, 12]. In addition to that, few papers propose approaches for ventilation control to improve the indoor air quality in the building [1, 13]. None of these papers consider the building’s indoor comfort as a unified system of the three aforementioned factors. Some approaches on fuzzy logic controllers [14–16] and multi-agent systems (MAS) [17–19] have been proposed, for the maintenance of high-level indoor comfort and simultaneously reducing the overall consumption of the buildings by controlling the indoor temperature as well as the CO2 concentration and the indoor illuminance. These approaches mainly depend on expert knowledge for setting the parameters and they do not deploy any learning mechanism for adapting their behaviour when the building’s dynamics change. Moreover, in most cases, a central agent acts as a coordinator of all the local agents, which can lead to failure if the central agent fails. Reinforcement learning methods have been introduced, in the context of energy management, to enhance the proposed systems with learning mechanisms; however, these approaches focus on a single agent system [20]. Some of them aim to achieve thermal comfort and energy saving, by controlling only the indoor temperature [21, 22] or to achieve visual comfort and energy saving by controlling only the artificial lighting system [23]. The single agent control scheme is also used in cases where more than one control variable is used in order to control both the thermal comfort and the air quality [24]. Specifically, in this case, that state-action space becomes very large. This paper proposes a fully decentralised reinforcement learning MAS for controlling the temperature, the illuminance and the air quality of an office building. The MAS consists of three agents: each responsible for a key factor, e.g. one agent responsible for achieving thermal comfort, one for visual comfort and one for improving air quality. Each agent performs a modified Q-learning approach with fuzzy approximators [25] to deal with the continuous state-action space. The aim of the MAS is to optimise the overall comfort of the building and simultaneously reduce the overall energy consumption. The contribution of this paper is summed up as follows: – A fully decentralised MAS for improving the overall comfort of an office building, where agents act independently of each other is deployed. This control framework is robust since any faults or failure of an agent will not affect the overall system performance. – Q-learning is implemented in each agent in order to adapt its behaviour in environment changes.

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– Fuzzy approximation is considered in each agent to deal with the continuous stateaction space. – Both indoor comfort and the energy consumption are embedded in the reward signal for achieving simultaneously indoor comfort and energy saving. We organise the paper so forth as follows: reinforcement learning, fuzzy Qlearning and MAS preliminaries are discussed in Sect. 2. Sections 3 and 4 provide an analysis of the building’s model and the control architecture. Simulation results are illustrated and discussed in Sect. 5. Finally, conclusions and future work are given in Sect. 6.

2 Reinforcement Learning Reinforcement learning (RL) is a family of algorithms that have been inspired by the way that humans and living beings are learning. Reinforcement learning has a target to extract a policy through the exploration of possible pairs of state-action. Policy means the mapping between states and actions. The signal that defines whether an agent acts well or not is defined as the reward signal.

2.1 Fuzzy Q-Learning Q-learning [26] is a tabular form of reinforcement learning in which the agent gradually builds a Q-function. This function estimates the future discounted rewards for actions in given states. For a given state x and the action α, the Q(x, a) is the definition of the Q-function output. The values of the Q-function are stored in a table. The following equation gives the update of the Q-table: Q  (x, α) ← Q(x, α) + g(R(x, α, x  ) + γ max Q(x  , α) − Q(x, α)) α

(1)

where Q  (x, α) is the new value of the Q-function output for the given state when the action α is applied. The new value is updated by receiving the reward signal R(x, a, x  ). In principle, the agent, governed by an exploration/exploitation algorithm performed at the current state x, selects an action α and transits into the next state x  . It receives the reward by the transition R(x, α, x  ) and updates the Q-value of the pair (x, α), with the assumption that it performs the optimal policy from the state x  and onwards. In the update equation, g denotes the learning rate and γ the discount factor. Learning rate values can lie in the range of [0, 1]. Small learning rate values result in small changes of agent’s prior knowledge while higher values make the agent consider more the newer information. If g is set to 0 the agent does not acquire new knowledge while if g is set to 1 the agent considers exclusively the new information. The discount factor γ determines how important the future rewards are. If γ is set to 0, the agent considers only the current rewards and if γ is set to 1 the agent will strive for a long-term high reward. In our study, the learning rate is set to

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0.1 and the discount factor is set to 0.9. This method has two main drawbacks. The first one is that is impractical when the state-action space is very large and the second one is that it cannot be applied for continuous state-action space. Fuzzy logic systems (FLSs) can be used in order to overcome these drawbacks. FLSs can achieve good approximations in the Q-values and can modify the Q-learning into fuzzy Q-learning [27] which can be used both for continuous state-action space and for reducing the state-action space. The algorithm of fuzzy Q-learning can be described as follows: 1. State x observation. 2. Output selection, for each fired fuzzy rule, based on the exploration/exploitation algorithm. 3. Global output α(x) and corresponding value of Q(x, α) computation by N i=1 α(x) =  N

αi (x) αi

i=1

  Q x, α =

N

αi (x)

αi (x) αi q[i, i † ] N i=1 αi (x)

i=1

(2)

(3)

where N is the number of the fired fuzzy rules, αi (x) is the fired degree for each fuzzy rule, αi is the action that is selected for each fuzzy rule and q[i, i † ] is the corresponding Q-value for the pair of the fuzzy rule i and the action i † . The action corresponds to the action that the exploration/exploitation algorithm selects. 4. The global action α(x) is applied and the next state x  is observed. 5. Reward R computation. 6. Q-values are updated according to the formula: αi (x) q[i, i ∗ ] = gQ  N i=1 αi (x)

(4)

where Q = R + γV (x  ) − Q(x, α) and   V x =

N

αi (x  ) αi q[i, i ∗ ] N  i=1 αi (x )

i=1

(5)

and q[i, i ∗ ] is the corresponding Q-value for the pair of the fuzzy rule i and the action i ∗ . The action i ∗ corresponds to the action that has the maximum corresponding Q-value for the fuzzy rule i. A schematic diagram describing the method of this paper is given in Fig. 1.

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Fig. 1 Fuzzy Q-Learning agent interacting with environment

2.2 Multi-agent System (MAS) and Q-Learning A system which is composed of two or more agent is called MAS. The agents have the ability to interact with the environment and commonly have the ability to interact with each other [28]. A MAS is commonly used in problems that a single agent cannot solve. Specifically, MASs are used in problems where the solution is very difficult to be found by one agent or in problems that are physically distributed and its target is to maximise the total discounted reward [29]. The most common approaches of Q-learning in MAS are – Markov Decision Process (MDP) learning [30], where the MAS is a single agent, i.e. it has multiple state variables and a vector action. – Independent learners, where each agent ignores the presence of the other agents and learns its own policy [31]. – Coordinated RL, where an agent coordinated its actions with only their neighbours and ignores the presence of the other agents [32]. – Distributed value function, where each agent has a local Q-function based on its own actions and updates this function by embedding its neighbours Q-functions [33].

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3 Building Modelling and Description The building’s model consists of three subsystems: the Heating/Cooling Balance Model, the Daylighting and Artificial Lighting System and the Ventilation System. Let us start by considering the heating/cooling balance model. In the current study, we consider a space of 50 m2 with a 3 m2 window located on the north side of the building. Details and characteristics of the building can be found in Table 1. The heating system of the building introduces hot air into the space [34]. The heat flow into the space arises from the below equation: dQ = (Theater − Troom ) · M˙ · c · u h/c , where u h/c ∈ [0, 1] dt

(6)

and d Q/dt is the heat flow rate into the space, Theater is the temperature of the hot air, Troom is the temperature of the room’s air, M˙ is the air mass flow rate in the air from the heating system, c is the heat capacity of the air at constant level of pressure and u h/c is the control signal. The derivative of the space temperature over time is expressed as 1 d Q heater d Ql dTroom = ( − ) dt Ma · c dt dt

(7)

where Ma is the mass of the air inside the space and Troom − Tout d Q losses = dt Req

(8)

where Req is the equivalent thermal resistance of the space and Tout is the ambient temperature. The cooling system introduces cold air into the space [35]. The heat flow into the space arises from: dQ = (Tcooler − Troom ) · M˙ · c · u h/c , where u h/c ∈ [−1, 0] dt

(9)

and Tcooler is the temperature of the cold air. Tout − Troom d Q losses = dt Req

(10)

dTroom 1 d Q losses d Q cooler = ( − ) dt Ma · c dt dt

(11)

Details about the parameters of the heating/cooling system can be found in Table 1.

Fuzzy Reinforcement Learning Multi-agent System … Table 1 Thermal parameters of the building Parameter M˙ Theater c Mα Req Tcooler House dimensions (length, width, height)

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Value 2600 kg/h 40 ◦ C 1005.4 J/kg.K 183.75 kg 1.0229 × 10−6 K/W 16 ◦ C 10 m, 5 m, 3 m

Let us now discuss the daylighting and artificial lighting system. In order to calculate the diffused horizontal illuminance provided by the sky into the building, Eq. 12 is used. Specifically, this equation calculates the horizontal illuminance in a point p inside the space [36]: E p,d

 x w + ww − x p −1 x w + x p  tan + tan  h 2p + z 2 h 2p + z 2  x w + ww − x p z tan−1  − + 2 2 (h p + h w ) + z (h p + h w )2 + z 2  x p + xw −1 + tan  (h p + h w )2 + z 2

z rw L  = 2 h 2p + z 2



−1

(12)

where E p,d is the horizontal diffused illuminance of the point p, L is the Luminance from the sky, z is the horizontal distance between the point p and the window, h p is the height between the lower edge of the window and the point p, h w is the height of the window, ww is the width of the window, xw is the distance between the left wall and the left edge of the window, x p is the distance between the point p and the left wall and rw is the transparency of the window. In order to compute the diffused horizontal illuminance, a sensor is installed outside the building close to the window. In this way we can compute the diffused horizontal illuminance at a point which is located close to the south wall of the building which is among the most shaded points inside the building. According to this value we assume that the diffused horizontal illuminance provided by the sky is distributed uniformly and the control decisions are taken according to this value. The actuators are fluorescence lamps, where the relation between the consumed power and the diffused horizontal illuminance is 100 lm/W [37] and the window, which is an electro-chromic window [33], can change its transparency provided a small value of voltage. The installed power of the artificial lighting is 400 W which can provide a maximum illuminance of 800 lux in a surface of 50 m2 . The relation between the lux and the consumption is assumed to be linear. We also assume that

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Table 2 Parameters of the building Parameter Number of windows Window dimensions (width, height) rw z hp hw ww xw xp

Value 1 3 m, 1 m 0.78 10 m 0.5 m 1m 3m 3.5 m 2.5 m

the diffused horizontal illuminance provided by the artificial lighting is distributed uniformly all over the surface of the building. Details about the lighting system can be found in Table 2. As far as the ventilation system is concerned, the differential equation, which governs the generation and decay of CO2 , based on mass balance considerations and for constant volume of air inside the building is expressed as follows: q(Cout − C) + R N dC = dt V

(13)

where V is the volume of the space, R is the generation rate of CO2 by a single occupant, N is the number of occupants inside the space, Cout is the outdoor concentration of CO2 and q is the ventilation rate. For a building office, according to DIN 1946, the air renewal per hour must be between four and six times. For a space of 150000 L, the ventilation rate of the ventilation system must be at least 600000 L/h. The installed power for such a system is approximately 165 W by assuming a relationship of 0.5 W/(L/s). The generation rate per occupant equals 29.88 l/h [19] and for the number of occupants inside the building a repeating sequence is used in the range of [0, 10] (Fig. 2 and Table 3).

Table 3 Parameters of the ventilation system Parameter R q (maximum value) Cout V

Value 29.88 l/h 600000 L/h 400 ppm 150000 L

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Fig. 2 Number of occupants inside the building

4 Multi-agent System (MAS) The MAS consists of a group of three agents A = {AG 1 , AG 2 , AG 3 }, where AG 1 is the temperature agent, AG 2 is the illuminance agent and AG 3 is the CO2 agent. Figure 3 illustrates the MAS. Black arrows represent set points defined by the users and outdoor measurements from sensors. Blue arrows represent the control signals produced by the agents. Red arrows represent measurement signal from the actuators (power consumption) and green arrows represent indoor measurements. The signals from the black and the green arrows define the states of the agents. Furthermore, the signals from these two groups in combination with the signals from the red arrows constitute the reward signal. All signals are normalised in [0, 1] range for signals with positive values and in [−1, 1] range for signals with both positive and negative values. The states of the agents are defined by a total number of six fuzzy state variables X i : two variables for each agent. For each positive value input five membership functions (MFs) are used. In addition to that, for each both positive and negative value inputs, seven MFs are used (Fig. 4). We denote membership functions as linguistic variables PVB, PB, PM, PS, Z, NS, NM and NB, where P stands for Positive, V:Very, B:Big, M:Medium, S:Small, Z:Zero and N:Negative. The temperature agent has two inputs as follows: – The outdoor temperature which is in the range [0, 1]; – The error eT between the set point of the users and the indoor temperature normalised in [−1, 1].

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Fig. 3 Proposed MAS

Fig. 4 Membership functions of inputs

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An input with five MFs and an input with seven MFs are used, resulting in 35 states corresponding to an equal number of fuzzy rules. Five singleton fuzzy sets 1 1 1 1 + −0.01 + 01 + 0.01 + 0.1 } are used for the output vector of the temperA1 = { −0.1 ature’s agent. The global action defines the percentage of the air flow rate by the heating/cooling system according to its maximum capacity. Positive signal actuates the heating system while negative signal actuates the cooling system. The reward R A1 of the agent is given by R A1 (x, α, x  ) = −|eT | − 0.1· PH C

(14)

where PH C is the power consumption of the heating/cooling system. The illuminance agent has two inputs as follows: – The indoor horizontal illuminance which is in the range [0, 1]; – The error e L between the set point of the users and the total indoor illuminance normalised in [−1, 1]. Again, there is one input with five MFs and a second input with seven MFs, resulting in 35 states corresponding to an equal number of fuzzy rules. The illuminance 1 1 1 + −0.15 + 01 + 0.15 + agent’s output vector has five singleton fuzzy sets A2 = { −0.45 1 }. The global action defines the percentage of the power change to be consumed 0.45 by the artificial lighting system according to its nominal operating power for positive signal while negative signal change the transparency of the electro-chromic window. The reward R A2 of the agent is given by R A2 (x, α, x  ) = −|e L | − 0.1· PAL

(15)

where PAL is the power consumption of the artificial lighting system. The CO2 agent has two inputs as follows: – The number of occupants inside the building normalised in the range [0, 1]; – The error eco between the set point of the users and the indoor CO2 concentration normalised in the range [−1, 1]. There are two inputs with five MFs, which result to 25 states corresponding to an equal number of fuzzy rules. The output vector of the CO2 agent has five singleton 1 1 1 1 fuzzy sets A3 = { −0.3 + −0.06 + 01 + 0.1 + 0.15 }. The global action defines the percentage of the power change to be consumed by the ventilation system according to its nominal operating power. The reward R A3 of the agent is given by R A3 (x, α, x  ) = −|eco | − 0.1· PV

(16)

where PV is the power consumption of the ventilation system. The same exploration/exploitation algorithm is used by all the agents. For the agent’s exploration capability to be increased, when visiting a new state, we allow the agent to explore for 500 rounds/state and then check and perform the actions that

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has not been performed at all. The agent exploration is set to 1% while the agent exploitation is set to 99%. The consequents of the fuzzy rules are chosen by trialand-error method. The fuzzy singleton sets, of the output vector, provided by each agent represent different levels of the continuous control signal.

5 Simulation Results A one year period, with a simulation step of 0.001 h, is chosen as the time of our numerical experiments. The data, concerning the outdoor temperature and the diffused horizontal illuminance of the sky, is acquired from the database of software EnergyPlus for the location Athens, Greece with 2 h sample time for the temperature and 1 h for illuminance. The illuminance values are multiplied by a factor of 0.5 that corresponds to the amount of illuminance that is provided outside a north window. For the outdoor CO2 concentration, a constant value of 400 ppm is used. In Figs. 5 and 6, the outdoor temperature, the indoor temperature and the control signal for heating/cooling system for the whole year and for one random day, respectively, are illustrated. The control signal is produced by the corresponding agent and drives the heating/cooling system. The set point of the indoor temperature is not constant and depends on the outdoor temperature. If the outdoor temperature exceeds the value of 29 ◦ C the set point is set to 27 ◦ C. If the indoor temperature falls under the value of 20 ◦ C the set point is set to 22 ◦ C . In the beginning, the agent focuses more on exploration than exploitation and the indoor temperature gets various values from 3 to 33 ◦ C. After the extensive exploration, it is obvious that the indoor temperature is stabilised close to CO2 for the winter and CO2 for the summer. Small deviations from the set points come from the exploration phase which has reduced to only 1%. Similar behaviour can be observed on the agents of the indoor illuminance’s and the CO2 concentration’s agents. Figures 7 and 8 depict the outdoor horizontal diffuse illuminance, the indoor diffused horizontal illuminance provided by the sky, the total indoor illuminance, the control signal of the agent, the provided illuminance by the artificial lighting system, and the transparency of the electro-chromic window for the whole year and for one random day. The set point for indoor horizontal illuminance is set to 700 lux. After the extensive exploration, the indoor horizontal illuminance remains close to the set point. This happens by increasing the indoor horizontal illuminance by the artificial lighting when the illuminance provided by the sky is not sufficient and by decreasing the indoor illuminance by changing the window transparency when the indoor luminance provided by the sky exceeds the set point. This is more obvious in Fig. 6 where these quantities are depicted for only 1 day. Figures 9 and 10 illustrate the number of occupants inside the building, the control signal provided by the agent and the indoor CO2 concentration for the whole year and for one random day, respectively. The CO2 concentration remains under the value of the 1000 ppm in most of the time. During the extensive exploration, the CO2 concentration does not exceed the limit of 1000 ppm but a continuous operation of

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Fig. 5 a Outdoor temperature. b Indoor temperature. c Control signal for heating/cooling system. All plots correspond to 1 year

Fig. 6 a Outdoor temperature. b Indoor temperature. c Control signal for heating/cooling system. All plots correspond to a random day

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Fig. 7 a Outdoor/Exterior illuminance. b Indoor illuminance provided from the sky. c Total indoor illuminance. d Control signal. e Provided illuminance by the artificial lighting system. f Transparency of the electro-chromic window. One-year time period

Fig. 8 a Outdoor/Exterior illuminance. b Indoor illuminance provided from the sky. c Total indoor illuminance. d Control signal. e Provided illuminance by the artificial lighting system. f Transparency of the electro-chromic window. One random day time period

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Fig. 9 a Number of occupants in the building. b Agent’s control signal. c Indoor CO2 concentration. All plots correspond to 1-year period

Fig. 10 a Number of occupants in the building. b Agent’s control signal. c Indoor CO2 concentration. All plots correspond to one random day period

the ventilation system is observed. This operation leads to energy waste. After the extensive exploration, the ventilation system operates when there are people inside the building. The proposed MAS is compared with a single on-off control system regarding the provided overall comfort and the energy consumption. For the overall comfort, three indexes are combined, one for the thermal comfort, one for the visual comfort

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Fig. 11 a Membership function of thermal index. b Membership function of illuminance index and c Membership function of air quality index

and one for the air quality. Figure 11 depicts the trapezoidal membership functions that assign values to the corresponding indexes according the indoor temperature, the indoor horizontal illuminance and the CO2 concentration. The total comfort index equals to C I = 0.4 · T C I + 0.4 · V C I + 0.2 · I AQ (17) Figures 12 and 13 illustrate the total comfort index with respect to time for the proposed MAS and the on-off control system, respectively. The comfort index for the on-off control system remains high with deviations mainly in the range of [0.8, 1] while the comfort index for the MAS remains close to 1 (except in the beginning where extensive exploration is applied). Sudden reductions to the index value are observed to both cases because of the change of the temperature set point. The total energy consumption of the MAS for the whole year equals to 4059 kWh while for the on-off control system equals to 4185. This means a reduction of 126 kWh which corresponds to 3% further energy saving. Specifically the energy requirements of the MAS are: the heating/cooling system consumes 1736 kWh, the artificial lighting system consumes 1732 kWh and the ventilation system consumes 590 kWh. For the on-off control system, the energy requirements are: the heating/cooling system consumes 1829 kWh, the artificial lighting system consumes 1735 kWh and the ventilation system consumes 621 kWh.

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Fig. 12 Comfort index with MAS

Fig. 13 Comfort index with on-off control

6 Conclusion A multi-agent system, as a solution to the distributed problem of indoor comfort for a building office while reducing the overall energy consumption of the building, is proposed. The building’s energy is managed by a multi-agent system. The MAS controls actuators such as heating/cooling system, ventilation system, artificial lighting and electro-chromic window. We deploy a modified Independent Learners approach

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in order to reduce that states space and enhance a learning mechanism. Local rewards and state information relevant to each agent are used. In addition to that, fuzzy Qlearning is utilised to handle the continuous state and action space of each agent. The MAS consists of three agents and the total number of the fuzzy states is 95. Each agent learns through the same exploration/exploitation algorithm demonstrating good performance. The comfort index, after the initial extensive exploration phase of the agents, remains very high and in most of the time equals to 1, highlighting the superiority of the MAS compared to a simple on-off control system. Additionally, a further reduction of 3% to the overall energy consumption is observed. Simulation results, of our study, justify the agent’s individual performance as well as the MAS’s total performance. The trained algorithm can be applied in any similar building system and avoid the initial intense exploration of the proposed MAS. A trained MAS can be directly applied on a real building. In future work, the combination of fuzzy Q-learning with evolutionary algorithm can lead to even better performance by optimising parameters regarding the membership function, learning rate (g) and discount factor (γ). These parameters have been chosen by trial-anderror method and an optimisation algorithm can exploit values which will lead to even better performance of the whole system regarding both the occupant comfort and the energy consumption. Additionally, fuzzy stochastic equations can be used for predicting occupancy which will lead to further reduction of the consumed energy and improvement of the comfort index. Acknowledgements This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020 in the context of the project ‘Intelligent Control Techniques for Comfort and Prediction of Occupancy in Buildings—Impacts on Energy Efficiency’ (MIS 5050544). The publication of the scientific work is funded by the University of West Attica.

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Discrete Markov Model Application for Decision-Making in Stock Investments Oksana Tyvodar

and Pylyp Prystavka

Abstract Understanding of the stock market and ability to forecast the price move play the key role in the wealth generation for every investor. This paper attempts to apply Markov chain model to forecast the behavior of the single stocks from S&P 100 index. We provide the description of the discrete Markov model that aims to forecast upward or downward move based on historical statistics of stocks’ visit to particular state which is constructed using technical analysis. S&P 100 data from January 2008 to December 2015 was used to build the model. The analysis of the model on real-life out-of-sample data from January 2016 to August 2020 provides the proof that use of proposed model will generate higher profits in comparison with the buy-and-hold investment approach. Keywords Markov chains · Discrete Markov model · Technical analysis · Stock movement forecast · Stock price · Cumulative profit · Stock market · Technical analysis · Absolute return · Moving average

1 Introduction Billions of dollars are traded every day on the financial markets around the world. The main priority of the traders behind these financial operations is to predict the future behavior of the financial instrument which they have interest in. But due to the dynamic and noise structure of the markets, this problem has not been solved for many years. Deterministic models cannot be used for the tasks with stochastic nature, so statistical methods based on information from the previous data are used. The Markov chain is one of the simplest cases of the random events sequence, but despite its simplicity it can be used to describe complex phenomena. Markov chains have been also used repeatedly by different researchers in financial forecasting tasks. Matle, Quaya (2014) used Markov chains to analyze changes in the stock price on the Ghana Stock Exchange, which improved the method for portfolio construction [1]. O. Tyvodar (B) · P. Prystavka Department of Applied Mathematics, National Aviation University, pr. Lubomir Husar, 1, Kyiv 03058, Ukraine © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_27

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Zhang et al. (2009) applied Markov chains to predict China stock market dynamics [2]. Choji, Iduno, and Kasem (2013) applied the Markov chain model to predict the stock price of two Nigerian banks, which was described by three states: rise, fall, and no change in price [3]. This article provides a Markov-based model for predicting stock price movements in the S&P 100 portfolio [4]. First, the definition of the discrete Markov model for the price movement process is introduced. Then, the efficiency of the proposed model is compared to the buy-and-hold strategy. This article firstly introduced the discrete Markov model with the states described by the values of technical indicators and previous changepoints of the price chart. It is proved that the use of the proposed model can yield to significant profit in comparison with buy-and-hold investment approach.

2 Discrete Markov Model Definition Let us consider the process p, which takes place in the system S, and describes the behavior of the given stock price.

2.1 System State Definition Firstly, we define the local changepoints (cp) of a size X as the up-and-down streaks of greater than X% for time series. We calculate them as the series of uninterrupted up-and-down movements in a price series. Uninterrupted is meant in the sense that no countermovement of +X% (−X%) percent or more occurs in down (up) movements. Changepoints correspond to the action which should be done to maximize the profit, so when cp = −1, one should sell short [5] company’s stocks, and when cp = 1 one should buy the corresponding stocks. The changepoints of the Bank of America (BAC) stock prices from January 2018 to January 2020 are shown in Fig. 1. Let us define the states, that the system can be in, using the following information: 1.

Relative Strength Index (RSI). RSI is a technical indicator developed by J. Welles Wilder [5]. We calculate RSI on 14-day timeframe and discretize its values into the following groups: – – – –

2.

Low, if the RSI value is less than 45. High, if the RSI value is greater than 65. Lower than average, if the RSI value is between 45 and 50. Higher than average, if the RSI value is between 50 and 65.

Signal of Moving Average Convergence/Divergence (MACD). MACD is a technical indicator developed by Gerald Appel and is designed to develop the changes in the strength, direction, momentum, and duration of a trend in a

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Fig. 1 Local changepoints detection for BAC in 2018–2019: triangle up-local minimums (cp( pi ) = 1), triangle down-local maximums (cp( pi ) = −1)

stock’s price [6]. We use 14-day moving average (MA) as “fast” moving average and 28-day MA as “slow” moving average. We use double exponential moving average [7] to calculate MAs. The MACD signal is discretized into two groups: – Fast, if the “fast” moving average is greater than the “slow”; – Slow, otherwise. 3.

The number of days since the last local changepoint (cp = 0), which are discretized into the three groups: – Long Trend, if the number of days since the last changepoint to the current calculation date is greater than 75% percentile of the time between changepoints’ distribution. – Short Trend, if the number of days since the last changepoint to the current calculation date is less than 25% percentile of the time between changepoints’ distribution. – Average Trend, otherwise.

4.

Return since the last changepoint (cp = 0), which are discretized into six groups: – Lowest, if the absolute return is less than 10% percentile of the distribution of the absolute returns between changepoints. – Medium Low, if the absolute return is between 10 and 30% percentile of the distribution of the absolute returns between changepoints. – Higher Low, if the absolute return is between 30 and 50% percentile of the distribution of the absolute returns between changepoints. – Average, if the absolute return is between 50 and 70% percentile of the distribution of the absolute returns between changepoints. – Above Average, if the absolute return is between 70 and 90% percentile of the distribution of the absolute returns between changepoints. – Highest, if the absolute return is above 90% percentile of the distribution of the absolute returns between changepoints.

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Fig. 2 State transition of AAPL stock

The percentile values of the distributions of time and absolute returns between changepoints are calculated on historical data before 2010 for each separate stock, so no leakage of the future data is done. We then combine the four features explained above to define the state of the system S. Figure 2 shows the state transition of AAPL stock (Apple Inc.) in January 2020. In general, the system S can be in one of the 144 states, for example: S0 = RSI − Low_MACD − fast_Long Trend_Lowest Return.

2.2 Dataset Labeling The most common way to label the dataset is to assign a 1 to a positive move of a threshold %, −1 to a negative move of a -threshold % and a 0, if the stock move is less than threshold %. This technique has flaws due to heteroskedastic nature of the stock returns: the volatility of the stock returns is not constant so the constant threshold value cannot account for this [8]. To perform the labeling of upward and downward move of the analyzed stock, the triple barrier method is used [9]. For each analyzed day, we define three barriers: upper and lower horizontal barriers, and the vertical barrier, which defines the maximum holding period of a trade. To define the upper and lower barriers, we calculate the 10-day historical volatility [10] and set the lower barrier to Close[i] − 1.95 · SD[i], upper barrier to Close[i] + 1.95 · SD[i], where Close[i] is the analyzed day’s close price, SD[i]—volatility of previous 10 days. We assign a label −1, if the lower barrier is the first hit in 20-day ahead interval, 1 if the higher barrier is the first hit in 20-day ahead interval. If the vertical barrier is hit first, we assign 1, if the Close[i + 20] > Close[i], −1—otherwise.

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In the raw form, 10-year data of the stock price represent only one sequence of many events leading to the last quoted price. In order to get more sequences and, more important, get training set for stock behavior prediction, for each analyzed day we generate a sequence of nine consecutive states drawn from previous changepoints and a state on the analyzed day. For example, below are the sequence of states drawn on June 01, 2020 for the OXY stock. These sequences can be thought of as a pattern leading to a final price move. RSI − Low_MACD − slow_Short Trend_Average Return → . RSI − Low_MACD − slow_Short Trend_Higher Low Return → . RSI − Higher than Average_MACD − slow_Average Trend_Highest Return → . RSI − Low_MACD − slow_Average Trend_Above Average Return → . RSI − Higher than Average_MACD − slow_Short Trend_Average Return → . RSI − Low_MACD − slow_Short Trend_Average Return → . RSI − Higher than Average_MACD − slow_Short Trend_Average Return → . RSI − Lower than Average_MACD − slow_Average Trend_Higher Low Return → . LABEL 1 (UPWARD MOVE)

2.3 Transition Matrices Definition To generate the transition matrices [11] from the sequences, we split the events into two separate datasets based on the label. As we predict the triple barrier outcomes, one dataset will contain the sequences which led to the label 1 (hit of the higher barrier) and other, label −1. The transition matrix A+ for price move that ends with the label 1 is determined as follows: c+ Si S j a+ =  + , S j c Si S j where c+ Si S j is the number of positions in a training set of label 1 at which the state Si is followed by the state S j . We obtain the matrix A− for the label −1 from empirical data in a similar way. We calculate these counts in a single pass over the sequences and store them in 144 × 144 matrices A+ and A− . When we have two models, we can ask which one explains the observation better. To compare the models, we calculate the log-odds ratio:   L  a S+i S j P Si |A+ R(Si ) = log = log . P(Si |A− ) a S+i S j i=0

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The higher this score is, the higher the probability is that the sequence leads to the upward move (label 1). To generate the transition matrices and calculate the threshold Tstock for the score R(Si ) for each analyzed stock, the stock price data from 201001-01 to 2015-12-31 is used. To determine the value of threshold Tstock , Youden’s J statistic [12] is employed. The optimal cut-off is the threshold that maximizes the distance to the identity (diagonal) line of the ROC curve. The optimality criterion is max(sensitivities + specificities) [13].

3 Model Efficiency Testing To evaluate the adequacy of the model, we will conduct an experiment with the simulation of trading for January 2016–August 2020. For each trading day, the sequences of nine states are generated using the features explained in Sect. 2.1 and the decision to Buy is made if R(Si ) is greater than threshold Tstock , to Sell R(Si ) ≤ Tstock . Since the last Close price is used in the indicators’ calculation, we assume that the trade is opened on the next trading day using its Open as enter price. The triple barrier is also used in the process of trade generation. The trade can be closed in the following situations: • The holding period of 20 days is over. • The take profit is hit. For the long position, the take profit is placed on the top barrier explained in Sect. 2.2, for the short position—bottom barrier. • The stop loss is hit. For the long position, the stop loss is placed on the bottom barrier, for the short position—top barrier. To compare the proposed model and buy-and-hold strategy we will use the cumulative $ profit: 2020−08−01  Cumulative$Profit = signald · (exit − enter), d = 2016−01−01

where exit—close price of the trade; enter—open price of the trade; signal = 1—for buy decision, signal = −1—for sell decision. The cumulative $ profit for stocks in each economic sector is shown in Fig. 3. When analyzing the profitability of the Markov model strategy, we can notice that the communication sector is outperforming the others. Figure 4 shows the comparison of buy-and-hold versus Markov model strategy on communication’s stocks. The second outperformer is consumer discretionary, whose high returns can be explained by firstly, the outlook on American consumer spending [14] appeared to be solid in 2019; secondly, the sector is formed with the such market giants as Amazon, Target, Home Depot, and Walt Disney. Materials sector showed the lowest performance which can be explained by its sensitivity to the fluctuations in the global economy and

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Fig. 3 Cumulative $ profit for discrete Markov model for S&P 100 stocks by sector

Fig. 4 Comparison of buy-and-hold versus Markov model strategy on communication’s stocks

the US dollar concerns about the US–China trading relationships and the COVID-19 pandemic in 2020. To prove that buy-and-hold strategy generates lower returns, we apply Kolmogorov–Smirnov test with the main hypothesis: H0 : F(x) > G(x)

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and the alternative H1 : F(x) ≤ G(x), where F(x) and G(x) are the distribution of cumulative $ profit of the Markov model and the buy-and-hold model, respectively. The p-value of the conducted test is equal to 1, so we can conduct that the proposed model is outperforming the market.

4 Conclusion The paper proposes a model based on Markov chains and its use in problems of decisions-making in investing in market traded shares. It was shown that proposed Markov model outperforms buy-and-hold approaches in the analysis of S&P 100 stocks data. It is shown that communication and consumer discretionary sectors show the highest performance due to the rapid growth of its holdings, such as Amazon, and growth in consumer spendings. Further research may be applied to the analysis of Russell 1000 data with the outlook on credit rating of the analyzed companies and development of the automated system with states defined by the indicators of user’s choice.

References 1. Mettle F, Quaye E, Laryea R (2014) A methodology for stochastic analysis of share price of markov chains with finite states. In: SpringerPlus. https://doi.org/10.1186/2193-1801-3-657 2. Zhang D, Zhang X (2009) Study on forecasting the stock market trend based on stochastic analysis method. Int J Bus Manag 3. Choji DN, Eduno SN, Kassem GT (2013) Markov chain model application on share price movement in stock market. J Comput Eng Intell Syst 4 4. Wikipedia-S&P 100. https://en.wikipedia.org/wiki/S%26P_100 5. Wilder JW (1978) New concepts in technical trading systems. ISBN 0-89459-027-8 6. Appel G (2005) Technical analysis power tools for active investors. Financial Times Prentice Hall, p 166. ISBN 0-13-147902-4 7. Patrick GM (1994) Smoothing data with faster moving averages. Tech Anal Stocks Commod 8. Singh A, Joubert J (2019) Does meta labeling add to signal efficacy? https://hudsonthames. org/does-meta-labeling-add-to-signal-efficacy-triple-barrier-method/ 9. de Prado ML (2018) Advances in financial machine learning. Wiley 10. Sinclair E (2008) Volatility trading. Wiley 11. Gagniuc PA (2017) Markov chains: from theory to implementation and experimentation. Wiley, USA, NJ. pp 1–235. ISBN 978-1-119-38755-8 12. Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35 13. Powers DMW (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J Mach Learn Technol 2(1):37–63 14. U.S. Bureau of Economic Analysis, Personal Consumption Expenditures [PCE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCE

Howling Noise Cancellation in Time–Frequency Domain by Deep Neural Networks Huaguo Gan, Gaoyong Luo, Yaqing Luo, and Wenbin Luo

Abstract With the wide applications of sound reinforcement system, howling has become a major problem affecting system performance due to the acoustic coupling between the speaker system and the microphone when there exists a positive feedback loop. To suppress the howling noise, in recent years, researchers have proposed many acoustic feedback control methods such as frequency shift method, notch filtering, and adaptive feedback cancellation method. However, current methods mainly involve using adaptive filters in either time or frequency domain, which can suppress howling to some extent but may lead to sound distortion, or have limited suppression ability. In this paper, we propose a novel method to suppress howling noise from speech signal by training deep neural networks (DNN) as an adaptive filter in time–frequency domain, where short-time Fourier transform (STFT) is performed to convert the signal from the time domain to time–frequency domain, and to extract complex values as signal features, so that a supervised end-to-end DNN is constructed which can nonlinearly map the complex values of the howling speech to the complex values of the clean speech, aiming for cancelling the howling noise from the feedback signals. Experimental results have demonstrated that the proposed method can suppress the howling noise effectively, and at the same time greatly improve the quality and intelligibility of the processed speech. Keywords Sound reinforcement system · Acoustic feedback · Howling · STFT · Supervised · End-to-end DNN · Time–frequency domain

H. Gan · G. Luo (B) · W. Luo School of Physics and Materials Science, Guangzhou University, Guangzhou 510006, China Y. Luo Department of Mathematics, London School of Economics and Political Science, London WC22AE, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_28

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1 Introduction Sound reinforcement system is widely used in daily life, such as in publicaddress systems, in an auditorium or conference/meeting room, where acoustic or audio/speech signals are picked up by microphones. In a closed environment, the structure of a single-channel closed-loop sound reinforcement system is shown in Fig. 1, where the source signal v(t) and the acoustic feedback signal x(t) obtained through the feedback path (F) are collected by the microphone to generate microphone signal y(t), and then the loudspeaker signal u(t) is obtained through the electro-acoustic forward path (G), which is played out by the loudspeaker. In such a positive feedback process, according to the Nyquist stability criterion [1], the amplitude of some frequency components of the microphone output signal y(t) will be larger and larger, resulting in that the closed-loop system will become unstable and an oscillation producing acoustic howling will occur. Since the use of sound reinforcement system, howling has been affecting the system performance due to the acoustic feedback and has attracted the attention of numerous researchers. Acoustic feedback causes the coupling between the loudspeaker signal and microphone signal [2–4], also known as the Larsen effect [1]. In the past few decades, researchers have proposed different approaches to suppress the howling noise. For example, designing special building structures and using particular building materials can reduce the reflection of sound, thus decreasing the coupling effect between the loudspeaker signal and microphone signal. However, these methods are often costly, requiring high expertise to operate, and are not suitable for wider applications. In addition to the manual method of suppressing howling, many acoustic feedback control methods based on digital signal processing techniques such as adaptive filtering, have also been proposed to automatically suppress howling. There are mainly three methods presented, namely the frequency shift (FS) [2, 5], the notch-filter-based howling suppression (NHS) [6, 7], and the adaptive feedback cancellation (AFC) [8]. However, both FS and NHS methods can cause signal distortions due to frequency shift on the input signal, or wide bandwidth of notch filter to suppress the amplitude of the howling frequency point, destroying the Fig. 1 Single channel closed-loop sound reinforcement system

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amplitude condition of howling. While the AFC estimates the feedback signal by adaptive algorithm, which is subtracted from the microphone output signal to reconstruct and approximate the original one. In practical applications, due to the correlation between the feedback signal and the desired signal, it is required to perform decorrelation operations including time-varying processing [9], noise injection [9], nonlinear processing [10], and forward path delay [11], which can also cause signal distortion. Moreover, when the frequency of the howling signal changes rapidly, the AFC method may not respond quickly enough to signal changes. As discussed, current methods mainly using adaptive filters in either time or frequency domain, can generally suppress howling to some extent but may lead to sound distortion, or have limited suppression ability. To solve the complex howling problem, we propose a novel method to suppress howling noise by training deep neural networks (DNN) as adaptive filters in time–frequency domain.

2 Theoretical Analysis 2.1 System Model The structure of a single channel closed-loop sound reinforcement system is shown in Fig. 1, where the corresponding impulse response of the system is as follows: y(t) = x(t) + v(t) x(t) = F(t) ∗ u(t), u(t) = G(t) ∗ y(t) Therefore v(t) = y(t) ∗ (1 − F(t)G(t)) where “∗” represents convolution. Thus, the frequency response of the closed-loop path from the source signal to the loudspeaker signal, including the acoustic feedback, can be expressed as transfer function: G(ω)Y (ω) G(ω) U (ω) = = V (ω) Y (ω)(1 − F(ω)G(ω)) 1 − F(ω)G(ω)

(1)

where G(ω) is the frequency response of the equipment between the microphone and the loudspeaker, F(ω) is the frequency response of the acoustic path from the loudspeaker to the microphone, F(ω)G(ω) denotes a sloop response of the closedloop acoustic system. According to the Nyquist stability criterion, when the system is unstable, the following conditions are satisfied:

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|G(ω)F(ω)| ≥ 1

(2)

∠G(ω)F(ω) = n2π, n ∈ Z

(3)

where the magnitude response |G(ω)F(ω)| and phase response ∠G(ω)F(ω) denote loop gain and phase, respectively. If there is a radial frequency ω in the source signal that satisfies both conditions (2) and (3), the acoustic system will produce an oscillation at this radial frequency, which can be detected by human ears as howling. To suppress howling, an adaptive filter can be designed to avoid the above conditions to be satisfied.

2.2 Adaptive Filtering 2.2.1

Adaptive FIR Filter

The adaptive filter parameters or weights can be adjusted automatically according to the statistical characteristics of the input signal. The filtering includes two processes: one is the filtering process, in which the parameters of the filter are convoluted with the input sequence to obtain the filtered output; the other is the adaptive process, in which the parameters of the filter are adjusted by the adaptive algorithm. The typical time-domain least-mean-square (LMS) adaptive FIR filter is shown in Fig. 2. According to LMS algorithm, the weight vector w(n) of the adaptive filter is updated by

Fig. 2 Adaptive FIR filter

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y(n) = x(n)wT (n)

(4)

e(n) = d(n) − y(n) = d(n) − x(n)wT (n)

(5)

w(n + 1) = w(n) + 2μe(n)x(n)

(6)

where x(n) is the input sequence and the error signal e(n) is the difference between the desired signal d(n) and the output signal y(n).

2.2.2

Frequency-Domain Adaptive Filter

Frequency-domain adaptive filter (FDAF) [12] is used here to compare with the proposed DNN method. As shown in Fig. 3, the input signal X (n) and the desired signal D(n) are data blocks with N data points at n time, which are converted into frequency-domain signals as X (k) and D(k) by fast Fourier transform (FFT). Then the output Y (k) of FDAF is given by   W T (k) = W1 (k)W 2 (k) · · · W N (k) ⎡

⎤ X 1 (k) 0 · · · 0 ⎢ 0 X 2 (k) · · · 0 ⎥ ⎢ ⎥ X (k) = ⎢ . .. . . .. ⎥ ⎣ .. . . . ⎦ 0 0 · · · X N (k) Y (k) = X (k)W (k)

Fig. 3 Structure of frequency-domain adaptive filter

(7)

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E(k) = D(k) − Y (k)

(8)

where E(k) denotes the error between D(n) and Y (k), and W (k) is the frequencydomain weight vector. According to LMS adaptive algorithm, the weight update equation of FDAF is W (k + 1) = W (k) + μX ∗ (k)E(k)

(9)

where the asterisk * denotes conjugate. Next, we calculate the optimal weight of FDAF. According to the minimum mean square error criterion, we have   R X X = ε X ∗ (k)X (k)   R X D = ε X ∗ (k)D(k) W0 = R −1 X X RX D

(10)

where ε indicates mathematical expectation, R −1 X X is the inverse of the autocorrelation of X (k), and R X D is the cross-correlation between X (k) and D(k). It is noted that the optimal weight W0 of FDAF is the form of Wiener optimal solution. We then transform the operation formula of the frequency-domain adaptive algorithm into an equivalent time-domain operation formula to compare. According to the definition of cyclic convolution, the equivalent time-domain form of formula (7) can be expressed as ⎡

⎤ · · · x(n + 1) ⎢ · · · x(n + 2) ⎥ ⎢ ⎥ X (n) = ⎢ ⎥ .. .. ⎣ ⎦ . . x(n + N − 1) x(n + N − 2) · · · x(n) x(n) x(n + 1) .. .

x(n + N − 1) x(n) .. .

Y (n) = X (n)W (n)

(11)

where X (k) whose first column is X (n), denotes cyclic matrix of the input data block X (n). The computational speed of FDAF is faster because it transforms convolution operation in the time domain into multiplication operation in the frequency domain, and FFT is a fast implementation of Fourier transform. According to formulas (7)–(9) and (11), the equivalent time-domain form of frequency-domain weight updating formula can be obtained: E i (n) = Di (n) − X i (n)

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E i (n)X i (n)

(12)

i=1

where X i (n) is the ith data point of data block X (n). FDAF can process the signal in blocks, so that the same weight can be used in a data block, updated with the gradient of the whole data block, leading to better adjusted weights with higher accuracy.

3 Howling Noise Cancellation by DNN 3.1 Feature of Complex Coefficients To suppress howling noise, we propose to use the complex coefficient values of the signal as inputs of DNN, where signal features are extracted in the time as well as frequency domain with phase information by short-time Fourier transform (STFT). The sampling frequency of all speeches in this paper is 16 kHz. Assuming that the time-domain signal of a speech is s(t), the number of points of each frame of the signal is 256, and the number of overlapping points between frames is 128, and then the FFT of 256 points is used for each frame of data to obtain a group complex coefficient values, in the time–frequency domain, the signal at the mth frame is given by s(m, f ) =

  a f1 + b f1 i , . . . , a fk + b fk i . . . , a f256 + b f256 i k = 1, 2, 3, . . . , 255, 256

(13)

where (a fk +b fk i) is the complex value of the kth frequency bin. Due to the symmetry of FFT, only a half of the values s(m, f ) is taken. At the same time, the real part and imaginary part of each frequency bin are taken out to form a new complex coefficient vector: snew (m, f ) = [a f1 , . . . , a fk , . . . , a f129 , b f1 . . . , b fk . . . , b f129 )] k = 1, 2, 3, . . . , 128, 129

(14)

It is noted that the speech of the current frame is related to adjacent frames, so we combine the complex coefficients of the current frame and the five frames before and after the current frame into a vector, which is the input of the DNN feature vector: S(m, f ) = [snew (m − 5), . . . , snew (m), . . . , snew (m + 5)]

(15)

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The S(m, f ) calculated from the howling speech is used as the input feature vector of DNN. The snew (m, f ) of the current frame is calculated by clean speech as the desired output y(m, f ) to train DNN.

3.2 DNN Network The deep neural network (DNN) used in this paper is a multi-layer perceptron (MLP). By establishing an end-to-end network model, the time–frequency features of howling speech extracted by STFT are directly mapped to the time–frequency features of clean speech. The output of the network is the estimation of the time– frequency characteristics of clean speech. The network structure is shown in Fig. 4. As previously discussed, the dimension of the input vector of DNN is 129 * 2 * 11 = 2838, and that of the output vector is 129 * 2 = 258. The number of hidden layers of the network is 3 layers, each layer has 500 neurons. In this study, the backpropagation (BP) algorithm is developed to train the network with mean squared error as the loss function. By repeating the two processes of forward propagation and backward propagation, the weight of the network is adjusted, so that the value of the loss function of DNN is minimized until it reaches a reasonable range or a preset value, then the training stops. The forward propagation can be represented by ‡(l) = W (l) a(l) + b(l)

(16)

a(l) = f ‡(l)

(17)

The back propagation is also the process of weight updating: Fig. 4 Diagram of DNN structure

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W (l) = W (l) − μ b(l) = b(l) − μ

∂E ∂ W (l)

(18)

∂E

(19)

∂ b(l)

where f (·) refers to the activation function, ‡(l) and a(l) denote the state value vector and the activation value vector of all neurons in layer l. The bias vector and weight matrix from layer l − 1 to layer l are represented as b(l) and W (l) , respectively. E is the loss function. The desired output of DNN is y(m, f ), and the output vector of DNN ¯y(m, f ) is equal to F(X(m, f )), then its loss function can be expressed as 1 || y − F(X)|| k

E=

1

= (yn − Fn (X))2 k n=1 k

(20)

Here k = 258 is the number of DNN output neurons, and F(·) represents the mapping relationship from the input to the output of DNN. We use the exponential linear unit (ELU) as the activation function and He initialization as the weight initialization to prevent the gradient from vanishing, and Adam as optimization technology to accelerate the training speed of DNN. The activation function adds a nonlinear factor to enable the DNN to represent the nonlinear system model. The nonlinear ELU activation function is

α(exp(z) − 1) if z < 0 ELUα (z) = (21) z if z  0 where the parameter α is generally set to 1. We summarize the network parameters of DNN as in Table 1. MLP neurons of the DNN network shown in Fig. 4 are fully connected to each other. The output of the DNN is the estimation of the time–frequency feature of Table 1 Network parameters of DNN

Parameters

Choice

Input layer neuron

2838

Hidden layer

3

Neuron in each hidden layer

500

Output layer neuron

258

Activation function of hidden layer

Elu

Optimization algorithm

Adam

Weight initialization

He initialization

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clean speech. DNN adjusts the weights of the network through the BP algorithm to minimize the error between the predicted values and the desired values, by completely mapping the input to output with nonlinear relationships. When compared with the FDAF method, the advantages of the DNN method lie in the fact that it has a higher dimensional structure and employs time–frequency analysis of input signal. It is interesting to note the similarity between the forward propagation process of DNN and the filtering process of adaptive filtering, and that the back-propagation process of DNN is similar to the adaptation process of adaptive filtering. From theoretical analysis, it is noted that the best weight of FDAF is the form of Wiener filter solution. If the acoustic environment changes rapidly, it is difficult for the adaptive filter to track the changes of the environment quickly. While DNN learns through a large number of samples, and the correct mapping law of input space and output space is stored in complex weights, by which the DNN filtering can rapidly respond to signal changes. Furthermore, the traditional howling suppression methods usually require a model of the howling problem to work out a solution, and particularly the multi-channel model can be much more complex and may be difficult to build. With the proposed DNN method, however, there is no need to build a mathematical model.

4 Experimental Results and Discussions The time–frequency features of howling speech are used as the input of DNN. In order to generate the howling noise, we first simulate the single-channel sound reinforcement system as shown in Fig. 1. For the sake of simplicity, the electro-acoustic forward path adopts fixed gain. Then howling is caused by the coupling of the microphone signal and loudspeaker signal. The acoustic feedback path F is represented by room impulse response (room acoustic characteristics). The feedback signal x(t) is equal to the convolution of room impulse response and loudspeaker signal u(t), and the room impulse response can be replaced by a finite impulse response. To obtain the howling speech, a clean speech is used as the original input of the simulation system. Only one sampling point is taken and put into the input buffer in each cycle. After the amplification by electro-acoustic forward path, the loudspeaker signal at this moment is obtained. As described in Sect. 2.1, the feedback signal x(t) is convoluted with the room impulse response. The obtained feedback signal is added to the sampling point of the next cycle to obtain the microphone signal.

4.1 Dataset We found more than 100 sentences from the Internet for experiments, and all sentences were sampled at 16 kHz. We selected 100 sentences to form the test/training set, 10 sentences to form the validation set. All the sentences in the sets use the room impulse response shown in Fig. 5 to generate howling speech.

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Fig. 5 Measurement results of PESQ and STOI in test set

4.2 Evaluation Method Speech quality and intelligibility are the evaluation criteria for many speech processing technologies, particularly for speech recognition. In this study, objective methods are used to measure speech quality and intelligibility. The objective measurement method of speech intelligibility is a short-term objective intelligibility measure (STOI) [14], which calculates the correlation between the time envelope of clean speech and processed speech in a short period of time. STOI has been shown to be highly correlated with human listeners’ speech intelligibility, ranging from 0 to 1. While the objective measurement method of speech quality is the perceptual evaluation of speech quality (PESQ) [15], which calculates the difference between clean speech and processed speech, and obtains the MOS-LQO (mean opinion score listening quality objective) value of speech samples. PESQ values are set from −0.5 to 4.5.

4.3 Results and Discussions 4.3.1

Measurement of PESQ and STOI

The PESQ and STOI values of the test set processed by FDAF and DNN are calculated respectively, and the values are averaged. The results are shown in Fig. 5, where DNNcomplex means by the proposed DNN method, its input feature vector is the complex coefficient values (containing phase information) of the signal in the time–frequency domain. While DNN-magnitude uses the same DNN structure, but the input feature vector is the magnitude spectrogram of howling signal in the time–frequency domain. The phase information of the clean signal is not used directly for training, while the phase of the howling signal is used when reconstructing the processed signal. It can be seen from the evaluation results in Fig. 5 that by both the PESQ and the STOI measurements, the values obtained by the DNN-complex method are the highest, indicating that its howling suppression performance is the best. Based on the DNN method, regardless of the input feature vectors, the values of PESQ and STOI are both higher than those by the FDAF method. This shows that the howling suppression ability of DNN is better than that of conventional adaptive filtering. As for the DNN-based methods, we also changed the input feature vector of the network to study the influence of different input features on the suppression ability. It is found

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that the DNN method based on complex coefficient value input is 0.0324 higher on STOI and 0.3613 higher on PESQ than that based on magnitude spectrogram input. This confirms that adding phase information to train DNN can greatly improve the processed speech quality.

4.3.2

Signal Reconstruction

In order to observe the spectrogram of reconstructed speech processed by DNNcomplex and FDAF, we select a sentence from the test set to calculate. The following spectrograms plotted in Fig. 6 are based on the processing of that sentence. It can be seen from the area selected by the red rectangle that the speech produces an obvious howling around 550 Hz with a large amplitude in Fig. 6b. When compared Fig. 6c with that in Fig. 6a, the spectrum of reconstructed speech processed by FDAF has a serious loss in the high-frequency part. While the spectrum of the reconstructed speech processed by DNN in Fig. 6d is very similar as in Fig. 6a, indicating that the frequency of howling is obviously removed, and signal features are well preserved. This explains why the method based on DNN has a better ability to suppress howling than the method based on FDAF.

(a)

(b)

(c)

(d)

Fig. 6 Spectrogram of speech signal: a Spectrogram of clean speech. b Spectrogram of howling speech. c Spectrogram of reconstructed speech processed by FDAF. d Spectrogram of reconstructed speech processed by DNN-complex

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5 Conclusions Howling in sound reinforcement system has become a major problem affecting system performance due to the acoustic coupling between the speaker system and the microphone when there exists a positive feedback loop. To suppress the howling noise, researchers have proposed many methods such as the frequency shift method, notch filtering, and adaptive feedback cancellation method. However, current methods mainly using adaptive filters in either time or frequency domain, can suppress howling to some extent but may lead to sound distortion, or have limited suppression ability. In this paper, a DNN-based method with a higher dimensional structure and nonlinear activation function is proposed by using complex coefficient values in the time–frequency domain as DNN input features that can cancel the howling noise and greatly improve the quality and intelligibility of the processed speech. The experimental results demonstrate that the performance of the proposed DNN-based method with minimized sound distortion is much better than the conventional adaptive filtering for howling cancellation.

References 1. Van Waterschoot T, Moonen M (2011) Fifty years of acoustic feedback control: state of the art and future challenges. Proc IEEE 99(2):288–327 2. Siqueira MG (2000) Steady-state analysis of continuous adaptation in acoustic feedback reduction systems for hearing-aids. IEEE Trans Speech Audio Process 8(4):443–453 3. Wang G, Liu Q, Wang W (2020) Adaptive feedback cancellation with prediction error method and howling suppression in train public address system. Signal Process 167:107–279 4. Sankowsky-Rothe T, Schepker H, Doclo S, Blau M (2020) Acoustic feedback path modeling for hearing aids: comparison of physical position based and position independent models. J Acoust Soc Am 147(1):85–100 5. Schroeder RM (2005) Improvement of acoustic-feedback stability by frequency shifting. J Acoust Soc Am 36(9):1718–1724 6. Leotwassana W, Punchalard R, Silaphan W (2003) Adaptive howling canceller using adaptive IIR notch filter: simulation and implementation. In: International conference on neural networks & signal processing, vol 1, pp 848–851 7. Deepak S (2008) Feedback cancellation in a sound system, US 8. Van Waterschoot T, Rombouts G, Moonen M (2004) On the performance of decorrelation by prefiltering for adaptive feedback cancellation in Public Address systems. In: Proceedings of the 4th IEEE benelux signal processing symposium, pp 167–170 9. Schmidt G, Haulick T (2006) Signal processing for in-car communication systems. Signal Process 86(6):1307–1326 10. Waterschoot TV (2004) Instrumental variable methods for acoustic feedback cancellation. Katholieke Universiteit Leuven, Belgium 11. Estermann P, Kaelin A (1994) Feedback cancellation in hearing aids: results from using frequency-domain adaptive filters. In: IEEE international symposium on circuits & systems (ISCAS), vol 2, pp 257–260 12. Wu S, Qiu X (2009) A windowing frequency domain adaptive filter for acoustic echo cancellation. IEICE Trans Fundam Electron Commun Comput Sci 10:2626–2628 13. Williamson DS, Wang Y, Wang DL (2017) Complex ratio masking for monaural speech separation. IEEE/ACM Trans Audio Speech Lang Process 24(3):483–492

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14. Taal CH, Hendriks RC, Heusdens R, Jensen J (2011) An algorithm for intelligibility prediction of time-frequency weighted noisy speech. IEEE Trans Audio Speech Lang Process 19(7):2125– 2136 15. Rix AW, Beerends JG, Hollier MP, Hekstra AP (2001) Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In: 2001 IEEE international conference on acoustics, speech and signal processing, vol 2, pp 749–752

Daily Trajectory Prediction Using Temporal Frequent Pattern Tree Mingyi Cai, Runze Yan, and Afsaneh Doryab

Abstract Prediction of future locations from traces of human mobility has significant implications for location-based services. Most existing research in this area focuses on predicting the next location or the destination rather than the entire route. This paper presents a temporal frequent-pattern tree (TFT) method for predicting future locations and routes. We evaluate the method using a real-world dataset containing location data from 50 users in a city. Our results show that for more than 91% of the users, the accumulated average distance between the actual and predicted locations is less than 1000 m (46 m < range < 1325 m). The results also show that the model benefits from similarities between users’ movement patterns. Keywords Temporal series · Frequent pattern mining · Trajectory · Temporal frequent pattern tree

1 Introduction Location-aware systems provide services based on the current or predicted location of the users. Location prediction algorithms often focus on the immediate next location or destination location [1–4] rather than the entire route. However, in some applications such as peer-to-peer and reciprocal services (e.g., ridesharing), which involve coordination between multiple people, predicting future location trajectories of the parties involved in the transaction becomes crucial in order to connect and match them efficiently at their convenience. M. Cai (B) Carnegie Mellon University, Pittsburgh, PA 15213, USA e-mail: [email protected] R. Yan · A. Doryab University of Virginia, Charlottesville, VA 22904, USA e-mail: [email protected] A. Doryab e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_29

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This paper addresses the problem of predicting future locations and routes at different timeslots throughout the day. Given the start time and location, we aim to find the likely routes to predict a user’s locations at certain time slots. Our method constructs a temporal frequent-pattern tree (TFT) from users’ location history and uses sequence matching to predict a series of locations in a given time segment. We show our prediction models perform well on users whose movement patterns are relatively fixed and that the performance is reasonable for users with variable movement patterns. Moreover, we show the temporal tree structure efficiently stores data and makes it more convenient to exploit the similarity of movement patterns between users. The main contributions of our paper are as follows: • We introduce a temporal tree structure for storing historical location patterns and for fast retrieval of likely future patterns. • We explore the impact of different customization settings on our method, including temporal clustering, outlier removal, global predictor, and the length of the initial trajectory on the route prediction.

2 Related Work The fast development in location tracking technology has given rise to studying human mobility patterns. Some research studies have focused on predicting such patterns ranging from predicting the immediate next location to predicting the movement trajectory. Methods for predicting the next immediate location often use the given historical data from locations visited by the user. We discuss two main approaches for next location prediction: Markov modeling and Neural Networks. Chen et al. [5] presented a next location predictor with Markov modeling (N L P M M), which integrates a Personal Markov Model (P M M) and a Global Markov Model (G M M). P M M only uses each user’s data for modeling, while G M M utilizes all users’ trajectories based on the assumption that users often share movement patterns. The results show that N L P M M outperforms P M M, G M M, and other state-of-the-art methods. However, each state in N L P M M represents an abstract location, and its performance can degrade when applied to real locations. Asahara et al. [6] proposed a mixed Markov-chain model (M M M) to predict pedestrian movement. M M M categorized the pedestrians into groups and assumed the pedestrians in the same groups behave similarly. The performance of M M M is better than Markov Models and Hidden Markov Models. However, since the above methods are based on Markov models, they are instrumental in predicting the next immediate location, but they are not suitable for predicting a long-term trajectory. Another weakness of Markov models is the increased complexity and computational cost as the number of states increases. This problem becomes more prevalent in trajectory prediction because locations are modeled as states; thus, Markov models can only model a minimal number of locations.

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Since the development of deep learning techniques, there have been more approaches to address the next location prediction problem using neural networks. Fan et al. [7] encoded locations and trajectories into feature vectors and built multiple Convolution Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM). A real-world traffic dataset with 197, 021, 276 vehicle passing records was used for the evaluation, and their proposed models outperform Markov models. Users’ intents captured from calendar events and text messages have also been used for location prediction. For example, Yao et al. [8] proposed a method named semantics-enriched recurrent model (SERM) to learn the joint embeddings of multiple factors such as user and time. They evaluated their model on two real-world datasets and showed that it could better capture sequential transition regularity and long-term dependencies than existing models. One weakness in their approach is that, they discretize the locations, and so the precision of the results is only within each grid. Although Neural Networks have been widely used in this area, they require many computationally expensive training instances. However, location data is often difficult to collect due to user privacy issues. The continuous trajectory prediction problem aims to predict the user’s future route in the remaining time under the condition of the user’s historical and initial trajectories. Sadri et al. [9] presented a method combining similarity metrics and temporal segmentation to estimate the rest of the day location trajectory. They also implemented temporal correlation and outlier removal to improve the method’s performance and reliability. Chen et al. [10] designed a continuous personal route prediction system using the Continuous Route Pattern Mining (CRPM) algorithm and decision tree-based algorithms. The client-server architecture of the system can protect personal privacy. In [11], the same authors analyzed user movement patterns from historical data and then predicted the destination and continuous future routes given the start routes. However, they did not consider time factors, which are essential for route prediction since most users’ movement patterns are repetitive.

3 Methods 3.1 Temporal Frequent Pattern Tree (TFT) Han et al. [12] developed Frequent Pattern Growth (FP-growth) algorithm and an efficient data structure (FP-Tree) for mining frequent pattern objects. Unlike the Apriori candidate set generation-and-test approach, the FP-growth adopts a patternfragment growth method to avoid the costly generation. The efficiency of mining is also achieved by compressing a large database into a FP-Tree, avoiding repetitive scans of the database. They have shown that the FP-growth method is efficient and scalable for mining both long and short frequent patterns and is about an order of magnitude faster than the Apriori algorithm. This algorithm was mainly designed for efficient mining of patterns in customer transactions without considering the

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time sequence. However, our modeling of frequent routes requires considering the temporal and sequential aspect of the route, i.e., if location spot l1 is visited at time t, then l2 must be visited at time t + , where  > 0. Therefore, we extend the FP-Tree method to include temporal series data that we refer to as Temporal Frequent-Pattern Tree (TFT). The following describes the method in detail. Let P = {(ti , s j )} be a general temporal series, with 0 ≤ i ≤ m and 0 ≤ j ≤ n, where m is the total number of time slots and n is the total number of distinct items (location spots in our case). Given a temporal series database D B = {P1 , ..., PK } which consists of K temporal series, our goal is to design a structure for efficient mining of temporal sequences and to identify their frequency. A temporal frequent pattern tree has the following structure: 1. It consists of a root labeled as “null”, a set of subtrees, and a frequent-item headertable. 2. Each node in the tree structure consists of four fields: item, timeslot, count, and a node-link. The timeslot field can be any time segment, e.g., hours during the day, days of the week, etc. In our paper, the timeslot field represents hours of the day, an integer between 0 and 23, inclusive. The count field registers the count of the item-timeslot appearing in the database, and node-link links to the next node in the tree carrying the same item and a larger timeslot or null if there is none. 3. Each entry in the frequent-item headertable consists of three fields: item, head of node-link in the TFT carrying the same item-name or null if there is none, and frequency. This frequency field is the frequency of some item appearing in the database as opposed to the count of the item-timeslot appearing in the database. Given a temporal data series D B, and for each tuple (ti , s j ) in D B, there are three cases: Case 1: If the node (ti , s j ) already exists in the tree, then 1) increment its node count by one, and 2) increment the frequency of the edge from the root to (ti , s j ) by 1. Case 2: If the node (ti , s j ) does not exist in the tree but the node (ti  , s j ) exists in the tree with ti  < ti , then 1) create a new node (ti , s j ), 2) create a node-link from (ti  , s j ) to (ti , s j ), and 3) increase the frequency of the edge from the root to (ti , s j ) by 1. Case 3: If the item s j does not exist at all in the tree, then 1) create a new node of (ti , s j ), as well as a new entry of (s j , f ) in the headertable, where f = 1, and 2) create an edge from the root to the new node (ti , s j ). We set the root to node (ti , s j ) after each iteration. Table 1 shows an example dataset that consists of three trajectories P1 , P2 , and P3 , where each contains location spots that span six hours. We first instantiate our temporal frequent pattern tree with the new data. Then we build the corresponding tree by processing the user’s data from each temporal series. For each series, we start from the root of the tree. For each time slot and location item, we check if there is already a node in the tree with the same location and timeslot. If there is, we increase the frequency of that node by one and connect the current node and that node by an edge. Starting from the root for each series, we recursively process all

Daily Trajectory Prediction Using Temporal Frequent Pattern Tree Table 1 Example database Hours t0 t1 Trajectories P1 P2 P3

l1 l1 l1

(a) Tree with P1

l3 l2 l2

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t2

t3

t4

t5

l4 l4 l4

l1 l2 l4

l2 l3 l1

l1 l1 l1

(b) Tree with P1 and P2

(c) Tree with P1 , P2 , and P3

Fig. 1 Construction process of TFT tree for the data in Table 1

data. The frequencies of l1 , l2 , l3 , and l4 appearing in D B are 8, 4, 2, 4, respectively. As demonstrated, the frequencies of the sequence l1 → l2 and l1 → l2 → l4 are relatively high compared to others. The process of constructing the TFT tree for the data in Table 1 is shown in Fig. 1a–c.

3.2 Route Prediction Modeling Our goal is to predict the route of future locations from the constructed TFT. We first characterize a route through the following definitions. Definition 1 A spot v ∈ V is a point of location. Note that such a location could either be an outdoor location or an indoor location. An outdoor location is usually represented as a geo-location in the GPS, whereas an indoor location is a contextual location such as an office or kitchen. We assume that both outdoor and indoor locations could be encoded as a numerical latitude-longitude pair. Definition 2 A significant spot denoted as s is a spot or a clustering of spots that indicate a location that the user is likely to visit regularly.

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Definition 3 A route Er is an ordered sequence of significant spots, where ∀e ∈ Er , e ∈ E, is a path from a significant spot s1 to another significant spot sn . Such a path follow the standard mathematical definition of a path in a graph. In particular, Er := [e1 , e2 , e3 , ..., em ], e1 = (s1 , s2 ), e2 = (s2 , s3 ), e3 = (s3 , s4 ) ... em−1 = (sm−1 , sm ), em = (sm , sm+1 ). Following the terminology in previous section, a route is an instance of a temporal series P = {(ti , s j )} (0 ≤ i ≤ m and 0 ≤ j ≤ n) where the item fields are significant spots, m = 23 is the total number of hours during a day, and n is the total number of significant spots. As an example, P = {(7, s1 ), (7, s2 ), (8, s3 ), (9, s3 )} might be a person’s route in the morning. We also define Ppred as the route of the current day, the day that needs prediction. Similarly, if the database D B = {P1 , ...PK }, then Pk stands for the route of the k-th day in the database. Definition 4 A sub-route P ti ∼t j is a contiguous sub-sequence of route P between timeslot ti and t j with timeslots between 0 ≤ ti ≤ t j ≤ 23. For example, if we want 10∼13 . to predict current day’s trajectory between 10 : 00 and 13 : 00, we aim to find Ppred Definition 5 The length of a route P is the length of its sequence. For example, the length of the route trajectory given above is 4. In general, the length of the route P ti ∼t j is t j − ti + 1. Our route prediction method uses historical routes constructed as a TFT to predict the current day’s future location route. The algorithm assumes the first part of the route is known. This can be one starting point and time or a sequence of location spots with corresponding timeslots. The algorithm then finds a subset of candidate routes that may predict the rest of the sequence. The final step chooses the route with maximum frequencies. Algorithm 1 describes how we use TFT to predict routes.

Algorithm 1: Route Prediction t ∼t

i j Input: (T , Ppred ) T := T F T of historical routes ti ∼t j Ppred := The known part of the route to be predicted

t

j+1 Output: Ppred

∼m

t ∼m {Pk j+1

set T  := : Pk ∈ T } set P∗ = {} for t j+1 ≤ t j  ≤ m do add(t j  , s j  ) to P∗ , where s j  is the location of maximum frequency at time t j  r etur n P∗

The algorithm by default uses the built TFT from each individual user’s data to predict their future routes. However, in some cases, the lack of enough historical data may affect the prediction results. To accommodate for this limit, we take advantage of similarity between movement patterns that may exist between users in same spatial

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regions or clusters. Therefore, in addition to building individual TFTs for each user, we build a community tree with from all users. Definition 6 A Community tree (C-TFT) is a TFT as described in Sect. 3.1 except the database D B consists of the data from all users from the same region. We then use this tree to predict individual routes. We call this approach Global Prediction which we test in our experiments as described in the next section.

4 Experimental Evaluation In this section, we introduce how we use past historical location GPS data and the initial routes on the current day to predict trajectories during the rest of the day. We collected the GPS coordinates from 50 users in a city for five consecutive weeks. The data was collected at three-minute intervals and continuously uploaded to our server. It was processed daily to extract and update location and route patterns for each participant. We then performed a clustering method on all the location spots in the dataset and replaced each location coordinate with a corresponding intersection coordinate. We built the routes with intersections as significant spots. To test our prediction algorithm, we used portions of data from each user to build the TFT tree and used the rest to test the prediction performance. Since different users have a different amount of data, for each user, we selected the first 45 of the total days as historical trajectories and the last 15 as test sets. We then built TFT models with the historical trajectories and used our route prediction algorithm on the test set to predict future location trajectories. For each test day, we ask the model to predict users’ locations at every timeslot after the end timeslot of the known trajectory (as 0∼7 , then we ask long as there is data for that timeslot). For example, if we are given Ppred the model to predict locations for that user starting at eight that day. Once the model has made the prediction, we calculate the distance between predicted locations and actual locations for each timeslot and obtain an average. In the end, we calculate the average for all days to obtain the overall distance. Table 2 shows a sample route from a user along with the model prediction results. The first row represents the times during the day, and the last row is the distance between the predicted location and the actual location. The predicted location and the actual location are the same from timeslots 4 am to 9 am. From the table, the model’s prediction is accurate before 7 pm. The predicted location at 7 pm is l0 , while the actual location is l2 . In this scenario, l0 is likely to be the user’s home since they most frequently visit it from 20 to 6 each day. Similarly, l1 is probably the user’s workplace since it appears mostly during 10–17. The location l2 only appears in the database once, and among all other locations in the user’s database, l0 is the closest to l2 with a distance of 1.19 km. One possibility in this scenario could be that the user stayed at work until 18 on that particular day and then ran errands on the way back home on a different route. This divergence resulted in lower prediction accuracy on that particular day (see Table 3).

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Table 2 Sample route Time (hours) 4–9 Actual_loc Predicted_loc Distance (m)

l0 l0 0

10–18

19

20–23

l1 l1 71.4

l2 l0 1189.6

l0 l0 0

Table 3 Global and individual predictors. Distance is in meter and time is in millisec Mean dist. Max dist. Std dist. Mean time Individual Global

505.5 446.0

1325.0 824.0

340.6 270.8

1267 13613

We examined different settings, including the number of historical routes, outlier removal, and global predictors, to understand the effect of different factors on the prediction performance. We then explored the role of variation in movement patterns in prediction performance. The results are described in the following sections.

4.1 Impact of the Number of Historical Routes We explored how different numbers of historical routes in constructing TFT impact the prediction outcome by creating a comparison. For the first model (model 1), the historical routes included all the routes in the first 45 days. In the second model (model 2), only part of the routes was chosen as historical routes. Our hypothesis was that, model 1 would be more accurate than model 2, but model 2 would be faster. For model 2, we selected routes on the same weekdays as the prediction day. For example, if the prediction day were Wednesday, the model would only look at routes on previous Wednesdays. As shown in Table 4, the models with more data are indeed more accurate. In terms of performance, the average time for model 1 to make predictions is 1267 ms, whereas it takes 1096 ms for model 2. The performance speed of the two models is very close. We believe this is because the dataset contains only around five weeks of data for each user, and thus the difference between the number of historical routes in the two models is modest. Despite this small difference in data, the two models differ significantly in terms of accuracy. We believe that there are too little data for model 2, which only looks at the same weekdays. If there are four weeks of historical routes in total, then the TFT for model 2 will be built from around one week of data. This data may not accurately model a user’s frequent routes. When the size of the dataset increases, this tradeoff becomes more worth considering.

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Table 4 Impact of the number of historical routes and outlier removal. Model 1 and 2 are built with outliers removed, whereas model 3 and 4 include outliers. Model 3 is built with all historical routes, and model 4 takes partial data to build the TFT and make predictions Dist. ≤ Dist. ≤ 750 Dist. ≤ 500 Max (m) Min (m) Time (ms) 1000 m (%) m (%) m (%) Model 1 Model 2 Model 3 Model 4

91.2 76.5 73.0 68.9

78.4 64.7 63.5 50.8

51.0 37.3 49.2 34.4

1365 2383 2625 3403

46 59 72 79

1267 1096 1175 953

4.2 Outlier Removal Since our focus is mostly on frequent (routine) routes, we try to identify outlier routes (e.g., a business trip) and measure the model’s performance with and without the outlier routes. However, outlier detection requires a distance threshold that might differ from user to user depending on travel patterns. To obtain a personalized threshold for each user, we first calculate the individual distance threshold based on each person’s average distance traveled per day for five weeks. Then, we cluster those averages to find the distance threshold for each user. This threshold is used to identify outlier locations. Our method is summarized in the following steps: 1. Scan D B. Sum up (lat ∗ f, lon ∗ f ) for each distinct location in D B, where lat and lon are latitude and longitude of the location, respectively, and f is the frequency of the location visits by the user. 2. Calculate the centroid coordinates by dividing the above coordinates by the total frequencies. 3. Calculate the average distance d between the centroid and other locations. 4. If the distance between l and the centroid is greater than the d above, then output true and false otherwise. Table 4 summarizes the results from models with and without outliers. Models 1 and 2 are built with outliers removed, whereas models 3 and 4 include outliers. Model 3 is built with all historical routes and model 4 takes partial data to build the TFT and make predictions. By comparing model 1 with model 3 and model 2 with model 4, we can see that regardless of the number of historical routes, the model without outliers is much more accurate but slightly less efficient. As mentioned in previous sections, the reason behind this is that since we measure model accuracy in terms of average distance, outliers can have a significant impact on accuracy.

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4.3 Global Predictor As previously mentioned, we construct a Community TFT (C-TFT) to accommodate the sparsity in users’ data and evaluate its performance in predicting future routes for users in the same region. Such users may have similar movement patterns, so data from other users may cover the user’s missing routes. To use the global route predictor, we traverse the community TFT instead of the individual TFTs. Table 3 is a comparison between the individual predictor and the global predictor. The performance of the global predictor is slightly better than the individual predictor in terms of residual distance. However, the average time to make predictions is more than ten times higher than the individual model. This is because the community tree’s size can be large, and the amount of data that needs to be processed is also substantially larger than the individual tree.

4.4 The Role of Variable Movement Patterns in Prediction Performance We further explore the relationship between model performance and the user movement patterns to understand the impact of variation in those patterns on predictions. We separate the users into four groups: Group 1 are users with a predicted distance above 1000 m, Group 2 are users with a predicted distance between 1000 and 750 m, Group 3 are users with a predicted distance between 750 and 500 m, and Group 4 are users with a predicted distance less than 500 m. We hypothesize that a lower average distance implies lower movement variation. To test this, we first fix four time slots 7, 8, 17, and 18 when the users are likely to have the most varied movement patterns. Then for each time slot, we count the number of distinct locations ever visited for each user at that time. This gives us a different list of numbers for each group. We then take the standard deviation within each list to measure how varied movement patterns are within each group. We repeat this process for all four time slots to obtain the results in Table 5. As expected, users in groups 1 and 2 have a larger variation in the number of significant spots than users in groups 3 and 4, which implies relatively more variant movement patterns.

5 Conclusion We designed a temporal tree structure that efficiently stores location data from users and helps predict future location trajectories. We built different data models and examined the effect of outlier routes and the number of routes used in building the TFT. Our evaluation of real-world data from 50 users collected over five consecutive weeks showed that in the best case, in 91.2% of the time, the accumulated average

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Table 5 Comparison between different user groups based on Standard Deviation of the number of different locations. Group 1 are users with predicted distances above 1000 m, Group 2 are users with predicted distances between 1000 and 750 m, Group 3 are users with predicted distances between 750 and 500 m, and Group 4 are users with predicted distances less than 500 m Std. of locations 8 9 17 18 time slots Group 1 Group 2 Group 3 Group 4

42.0 38.2 30.5 34.1

62.3 60.0 49.5 29.5

105.0 92.4 66.3 29.2

139.2 109.4 43.8 52.5

distance between the predicted location trajectories and actual routes was less than 1000 m (min = 46 m and max = 1365 m). Our future steps include testing with a larger dataset collected over a longer time and evaluating the ridesharing real-world application method.

References 1. Do TMT, Dousse O, Miettinen M, Gatica-Perez D (2015) A probabilistic kernel method for human mobility prediction with smartphones. Pervasive Mob Comput, 20(C):1328 2. Jeung H, Liu Q, Shen HT, Zhou X (2008) A hybrid prediction model for moving objects. In: 2008 IEEE 24th international conference on data engineering, pp 70–79 3. Jeung H, Yiu ML, Zhou X, Jensen CS (2010) Path prediction and predictive range querying in road network databases. VLDB J 19(4):585602 4. Scellato S, Musolesi M, Mascolo C, Latora V, Campbell A (2011) Nextplace: a spatio-temporal prediction framework for pervasive systems, vol 6696, pp 152–169 5. Chen M, Liu Y, Nlpmm XY (2014) A next location predictor with Markov modeling. In: Tseng VS, Ho TB, Zhou Z-H, Chen ALP, Kao H-Y (eds) Advances in knowledge discovery and data mining. Springer International Publishing, Cham, pp 186–197 6. Asahara A, Maruyama K, Sato A, Seto K (2011) Pedestrian-movement prediction based on mixed Markov-chain model, pp 25–33 7. Fan X, Guo L, Han N, Wang Y, Shi J, Yuan Y (2018) A deep learning approach for next location prediction, pp 69–74 8. Yao D, Zhang C, Huang J, Serm JB (2017) A recurrent model for next location prediction in semantic trajectories, pp 2411–2414 9. Sadri A, Salim FD, Ren Y, Shao W, Krumm JC, Mascolo C (2018) What will you do for the rest of the day? An approach to continuous trajectory prediction. In: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, vol 2, no 4 10. Chen L, Lv M, Ye Q, Chen G, Woodward J (2011) A personal route prediction system based on trajectory data mining. Inf Sci 181(7):1264–1284 11. Ling C, Mingqi L, Gencai C (2010) A system for destination and future route prediction based on trajectory mining. Pervasive Mob Comput 6(6) 12. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, SIGMOD 00. Association for Computing Machinery, New York, NY, USA, p 112

Quick and Dirty Prototyping and Testing for UX Design of Future Robo-Taxi Dokshin Lim

and Minhee Lee

Abstract People increasingly view mobility as a service and want more choices for traveling between points A and B. Designing user experiences of future robo-taxi needs to be seen from a broad perspective and consider an extended user journey. Our work explores the application of quick and dirty prototyping and usability testing to design UX of future robo-taxi services from the ground up. We made low-fidelity prototypes early in the design process and quickly tested with users iteratively to answer initial questions arisen on critical touchpoints in the user journey. Two critical touchpoints are rider-driverless car match and in-car environment configuration. Five optional eHMI (external HMI) were tested, and our results suggest combining options depending on the distance between the vehicle and the passenger. We also tested two environment templates and found that preference depends on travel time regardless of the purpose of the journey. Finally, we suggest a service scenario composed of 14 scenes. Keywords Autonomous vehicles · Robo-taxi · eHMI · Quick and dirty prototyping · Usability · Co-creation · UX

1 Background Consumers, who increasingly view mobility as a service, want more choices for traveling between points A and B, including ride hailing, car sharing, and perhaps even self-driving “robo-taxis.” [1]. A robo-taxi, also known as a self-driving taxi or a driverless taxi, is an autonomous vehicle of level 4 or 5 (levels defined by SAE international) operated for ridesharing services like NuTonomy and Waymo. Public opinion on self-driving vehicles, in general, is regarded as skeptical in many published reports D. Lim (B) Department of Mechanical and System Design Engineering, Hongik University, Seoul, South Korea e-mail: [email protected] M. Lee Samsung Electronics, Suwon, Gyeonggi, South Korea © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_30

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[2]. There are studies that suggest how to better offer fully autonomous vehicles in a way to improve people’s trust. For example, there exist gender differences in fearing autonomous vehicles depending on the size of the cars [3]. In terms of appearances, it is demonstrated by ‘Firefly’, the first hardware prototype of Waymo, that robo-taxi design in the early phase of adoption is better to be a small and completely different style than conventional cars [4]. Another study [5] pointed out specific user interfaces such as information display, handles, digital blinkers, camera, and mobile application’s presence affected the feeling of trust. User experience design of autonomous vehicles is being developed under different subjects and this study place the focus on designing user interfaces needed in critical situations while using a future robo-taxi. Lim et al. [6] view the user journey in a simple framework from a case study of T5 Pod (available at Heathrow Terminal 5 in the UK) and indicate that users feel anxious when they are not able to anticipate and prepare for the next step at every step. Lee also simply breaks down the user journey of robo-taxi services into five steps: 1. Calling a service, 2. Waiting, 3. Boarding, 4. Moving, and 5. Getting off [5]. The issues in common arise when users want to make the best use of their time either to prepare for the next step or to enjoy the best of being in the journey. “Quick and dirty” is the term that became popular by Brooke [7] and his approach is still widely practiced. Quick and dirty usability tests require less effort while still yielding useful results. These approaches are quite revealing in the early stages of a project as a quick sense check [8]. The main idea of quick and dirty prototyping is to start with cheap and fast prototypes. It can be achieved by using low-cost, readily available materials on the spot in early-stage and using (creating) these lowfidelity prototypes. It is important to make sure that the low-fidelity prototype has just the level of detail required, never too much. Also, it is important to accept that the prototype is to break, completely destroy or throw away once the questions they pose are answered. Thus, “$1 prototyping” [9] or “Rough Prototyping” (Service Design Tools [10]) is appropriate. Quick and dirty methods have sampling bias though. Therefore, the results should be counted mindfully [11].

2 Research Methods 2.1 Our Process This research is composed of two main iterations of prototyping and usability testing, which resulted in our first user scenario. Then, a co-creation workshop is organized to refine and develop our final service scenario. Figure 1 illustrates our process.

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Fig. 1 Our process

2.2 First Iteration The first iteration deals with the rider-driverless car (robo-taxi) match situation. The rider-driver match is a perplexing experience even today. How future robo-taxis could make the rider-car match experience simple and easy? In each iteration, we define four key components (people, objects, location, and interactions) of prototyping and testing [12], which make decision-making of each step clear. Nine subjects are passengers who are waiting for a robo-taxi (see Table 1). Table 1 People; participants of the first iteration No.

Age

Gender

Job

Major

1

21

M

A second-year undergraduate

Visual design

2

22

F

A junior undergraduate

Visual design

3

21

M

A first-year undergraduate

Visual design

4

23

M

A second-year undergraduate

Automobile design

5

24

M

A senior undergraduate

Mechanical engineering

6

24

M

A senior undergraduate

Mechanical engineering

7

24

M

A senior undergraduate

Mechanical engineering

8

24

M

A junior undergraduate

Mechanical engineering

9

25

F

A web designer

Graphic design

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Fig. 2 Objects; user interfaces of the robo-taxi and passenger’s mobile phone

Users’ mobile and a vehicle can communicate in real time through a network and near-field communication. Based on this, information is displayed on the vehicle’s user interface, assuming that the front glass is displayed so that the passenger can scan it before boarding. The front-facing screen of robo-taxi and passenger’s mobile phone screens are prototyped as shown in Fig. 2. The two interfaces are consistent. The interaction takes place in the following steps. A passenger calls a robo-taxi via a mobile phone. A robo-taxi approaches from a distance. The passenger compares the interfaces on both devices and identifies the taxi. Hypothesis and Determinations. A hypothesis is that people will prefer a minimum amount of information on an abstract level. The following determinants were applied differently (see Table 2).

2.3 Second Iteration The second iteration studies in-car user experiences. In the era of autonomous vehicles, we expect to be able to perform various tasks during the journey. How future robo-taxis could offer an in-car environment optimized for the tasks that users want? Six subjects assumed moving to a club to attend a party with friends at night (see Table 3). A combination of dashboards, lighting, and music defines the “environmental templates” of future robo-taxi. Robo-taxi analyzes the passenger’s context and sets the most optimized environmental template. The interaction is as follows. In order to go to a party, passengers ride a taxi. The back-end system of taxis analyzes the context of passengers. Based on the analysis, the taxi system optimizes the template for the journey.

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Table 2 Five low-fidelity porotypes of the first iteration No. Determinants 1

Text

2

3

Information

Robo-taxi

Mobile

User data Unprocessed – User Name – Destination – Time of request – Time of departure – Car number Processed

Random code

4

Graphic Image

5

Color

– User ID – Car ID

– Ex: 64WPQL

– Icon

– Ex: Yellow

Hypothesis and Determinations. A hypothesis is that people will prefer that the user interfaces of robo-taxi are automatically set depending on the passenger’s context of use. The determinants of this experiment are whether or not custom the in-car UI is provided based on contextual awareness. We conducted two tests with two groups. Three subjects played the role of friends, who went to the party together. When passengers got into the robo-taxi, the system set the “Party” UI template based on

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Table 3 Profile of second test participants No.

Group

Age

Gender

Job

Major

1

A

23

M

A graduate student

Visual design

2

A

23

M

A graduate student

Visual design

3

A

23

M

S/W engineer

Web S/W

4

B

23

M

S/W engineer

Mobile S/W

5

B

23

M

Project manager

Web service

6

B

23

M

H/W engineer

Mobile H/W

Fig. 3 Low-fidelity protype of the second iteration

the passengers’ destination, time, passenger’s personal information, etc. Lighting, music, and dashboard display were set up according to the theme (see Fig. 3).

2.4 Co-creation Workshop In this study, experts in autonomous vehicles co-create the UX scenario to make up for the limitation of quick and dirty prototyping. When the problem is complicated, designers experience limitations in solving problems. They inevitably accept cocreation with others [13]. Two external experts (in Table 4) and four internal project members worked together in the workshop. Based on the previous process, all participants have identified what the pain points are. Then, they build new scenarios that can solve the problems.

Quick and Dirty Prototyping and Testing for UX Design … Table 4 Profile of semi-professionals who participated in co-creation

No.

Position

1 2

351 Company

Career

UX designer

Electronics company

15 Years

Project manager

Automotive AI S/W company

3 Years

3

Product planning

Electronics company

14 Years

4

Graphic designer

E-Book service

4 Years

5

Product designer

Electronics company

11 Years

6

A graduate student

N/A

N/A

3 Results 3.1 First Iteration The subjects preferred to identify seamlessly at a glance. The lower the complexity, the higher the preference, and the most preferred type was the color (option no. 5 in Table 2). Option no. 1 and 2 were repulsed in terms of personal information being displayed. However, right before boarding, many wanted to confirm with specific information. As a result, six out of nine subjects suggested a combined approach. From the distance, they preferred to identify the vehicle with implicit information such as color. When the vehicle got closer enough, they preferred explicit data such as their id to confirm.

3.2 Second Iteration Three out of six did not prefer contextual UI’s automatic configuration. The negative opinion revealed that they just wanted to sleep or rest regardless of the destination. Also, they mentioned that the travel time was not long to consider environmental configuration. Three people who preferred this feature predicted that it would be useful if the templates were diversified and refined.

3.3 First User Scenarios Based on the results of two iterations, user scenarios are defined as in Fig. 4. Important visual elements are well emphasized in the form of storyboards. The scenario is in the following order: (Scene #1) Main character A prepares to go out. (Scene #2) A calls a robo-taxi using his mobile. He adds his friends as passengers and depicts the final destination. The system identifies each passenger’s location and calculates the optimized route to pick up everyone. (Scene #3) A receives a notification that his car

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Fig. 4 First user scenario after quick and dirty prototyping

is close. (Scene #4) A gets in the car and moves to the location of B. (Scene #5) B is waiting on the boulevard. Simple visual information is displayed on the front glass of the robo-taxi. The same visual information is shown on B’s mobile phone, making it easy for B to identify that the car is for him. (Scene #6) The taxi is carrying A and B and heading to C’s location. (Scene #7) C, ready early, is moving without waiting. The taxi’s back-end system tracks C’s location in real time and moves to where C is. (Scene #8) After C gets on board, the taxi moves to the final destination of the group.

3.4 Co-creation Workshop The above user scenario was discussed to supplement the following design issues: Rider-driverless car match scenario. Both agreed to a simple test through color. P2 noted that it is practical to derive a way to distinguish the specificity of information services by distance. Professional 2 noted, “It accurately reflects the needs of users who do not want to get much attention when boarding a taxi, but want to avoid errors just before boarding,” Professional 1 said, “Random code should be simplified to 2–3 digits or emoticons to reduce user stress.” Professional 1, who is a UX designer, noted that a designer should consider user psychology. In the introduction of autonomous driving technology, users will have anxiety about the new high-end technology, so it is necessary to consider emotional aspects. “I think it would be better to use emoticons rather than random numbers and letters.” Professional 2 assumed that the passenger was also moving toward the car. A scenario is possible to ask the occupant to move to the vehicle to optimize the route. Scenarios for exception cases are needed, such

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as when the passenger’s location is not known. AI technology will also be introduced in robo-taxi, so the vehicle can actively call the passenger. Customized UI for the vehicle. This feature is useful, but preferences vary depending on individuals and context, so every professional agreed to suggest and have users choose the template themselves. Both professionals said that situations, not passenger feelings, should be prioritized to define UI. Defining UI based on other objective factors, excluding analogy to emotion, can minimize errors. When robo-taxi automatically configure the user interface, the voice can also be specialized according to preference or situation. There was also a mention of the limits of quick and dirty prototyping. Professional 1 commented, “In addition to typical situations such as parties, reviews are needed based on various situations. If the research and definition of space itself are preceded in the following study, more sophisticated results will be produced.” Co-creation of the final service scenario. Two semi-experts and researchers improved the user scenario by reflecting on the issues discussed earlier. Final service scenarios are shown in Table 5. Discussing each scene on the storyboard with semiprofessionals, the service scenario became sophisticated. Various methods have been proposed to improve low-fidelity prototypes into high-fidelity services applied to the real market.

4 Conclusion The test showed the need of combining options depending on the distance. People preferred to identify their car all the way from a distance till the car is very close. The most preferred option was the color. People showed their concern of privacy regarding the interface showing unprocessed user data. However, they wanted to have clear confirmative information when the car is very close and were willing to use explicit user data. Our second test revealed clear personal preferences. Three out of six did not prefer the contextual UI’s automatic configuration, so the hypothesis could not be verified. Some people who were negative about this feature said that they might do things that were not related to the destination (for example, sleep or rest). Another important finding is that it was also influenced by the length of travel time. When it does not take long, there is no interest in adaptive UI. By applying a series of quick and dirty prototyping and testing, we could validate our hypothesis in two days at an ultra-low cost. The co-creation workshop definitely supplemented the shortcomings of the quick and dirty methods. Our results and final service scenarios may give implications in designing eHMI(external human–machine interface) or in the interior design of future robo-taxis.

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Table 5 Service scenarios created in the co-creation workshop No.

Scenes

Scenario description

1

Joe Frey decides to go to a club with his friends, getting ready! He turns on the robo-taxi app with his mobile and requests a taxi. Joe adds two more people as passengers

2

Robo-taxis analyzes the locations of Joe, Kelvin, and Steve to optimize the route. The first pickup is Joe’s house: Joe gets a close-up notification and goes out to the boulevard in front of the house

3

When robo-taxi enters a near distance, it displays a specific color (blue), which is the same color displayed on Joe’s mobile. It is vivid, so that can be seen from a long distance

4

As the vehicle enters a closer distance, the color is blurred, and a simple emoticon code occurs. The color of Joe’s mobile is also blurred, and the same emoticon is shown

5

The second passenger, Kelvin, is waiting for the taxi on the road early. As the vehicle nears, a simple emoticon code co-occurs as the mobile and the vehicle, making it easy for Kelvin to recognize which vehicle to take

6

Nearby communication occurs between the two devices as Kelvin approaches the vehicle door, which automatically unlocks the vehicle. At this time, haptic and notification occur on mobile as well. Kelvin checks his name and gets on the taxi

7

The robo-taxi picks up Joe Frey and Kelvin and heads to Steve, the last passenger

8

Steve, the third pick-up target, is in a hurry. So, he keeps walking, looking at the taxi’s moving position. While he is on the move, the notification arrives on mobile that the vehicle has entered the perimeter

9

The robo-taxi has entered a close distance from Steve, but the final route is not accessible due to one-way traffic. Nevertheless, since it takes quite a while to go back, the vehicle system analyzes whether it will go back or ask Steve to come (continued)

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Table 5 (continued) No.

Scenes

Scenario description

10

After analyzing that it is much faster for him to walk, Robo-taxi calls Steve. Robo-taxi asks Steve to come back about 20 m through an AI-based call and sends maps to Steve’s mobile

11

Steve also seamlessly identifies the taxi and gets into the car after auto-unlock

12

Robo-taxi analyzes the UI that passengers are expected to prefer depending on their destination, arrival time, day of the week, Etc

13

Based on the analysis, the taxi first recommends a mood template named “Party” to the dashboard, and various templates are presented below it

14

When Kelvin selects the Party template, the lights on the vehicle change, and the music plays. The dashboard plays live-streaming of the destination club. Joe, Kelvin, and Steve go to the club with excitement!

Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. NRF2018R1D1A1B07045466).

References 1. Gauging the disruptive power of robo-taxis in autonomous driving|McKinsey. (October 4, 2017). https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/gaugingthe-disruptive-power-of-robo-taxis-in-autonomous-driving. Accessed 22 Nov 2020 2. Public Opinion Polls Show Skepticism About Autonomous Vehicles by Advocates for Highway and Auto Safety. (April 26, 2018). https://saferoads.org/wp-content/uploads/2018/04/AV-Pub lic-Opinion-Polls-4-26-18.pdf. Accessed 22 Nov 2020 3. Lim D, Lee H (2018) A study on technology acceptance of fully autonomous cars for UX design. J Integr Des Res 17(4):19–28 4. Newcomb D (2018) Will cute cars make you less scared of autonomous tech?| PCMag. (May 1, 2018). from https://www.pcmag.com/opinions/will-cute-cars-make-you-less-scared-of-aut onomous-tech. Accessed 22 Nov 2020

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5. Lee M, Lee Y (2020) UI proposal for shared autonomous vehicles: focusing on improving user’s trust. In: Krömker H (ed) HCI in mobility, transport, and automotive systems. Driving behavior, urban and smart mobility. Springer International Publishing, pp 282–296. https://doi. org/10.1007/978-3-030-50537-0_21 6. Lim D, Lee JH, Han SY, Jung YH (2019) Deriving insights to design UX of fully autonomous vehicles from contextual user interviews on T5 POD. In: Korean society of design science conference proceeding, vol 5, pp 300–305 7. Brooke J (1996) SUS: a “Quick and Dirty” usability scale. usability evaluation in industry. (1996, June 11) 8. Moule J (2012) Killer UX design: Create user experiences to wow your visitors, 1st edn. SitePoint, Australia 9. Nudelman G (2014) The $1 prototype: lean mobile UX design and rapid innovation for material design, iOS8, and RWD, 1.2 ed. Design Caffeine Press, San Francisco. California 10. Rough Prototyping|Service Design Tools. https://servicedesigntools.org/tools/rough-protot yping. Accessed 22 Nov 2020 11. Hall E (2013) Just enough research. A Book Apart, New York 12. Dam RF, Siang TY (2020) Prototyping: learn eight common methods and best practices. The Interaction Design Foundation. https://www.interaction-design.org/literature/article/pro totyping-learn-eight-common-methods-and-best-practices. Accessed 22 Nov 2020 13. Sanders EB-N, Stappers PJ (2008) Co-creation and the new landscapes of design. CoDesign 4(1):5–18. https://doi.org/10.1080/15710880701875068

Iterative Generation of Chow Parameters Using Nearest Neighbor Relations in Threshold Network Naohiro Ishii, Kazuya Odagiri, and Tokuro Matsuo

Abstract Intelligent functions and learning are important issues, which are needed in the application fields. Recently, these technologies are extensively studied and developed using threshold neural networks. The nearest neighbor relations are proposed for the basis of the generation of functions and learning. First, the these relations are shown to have minimal information for the discrimination and to be the basis of the inherited information for threshold functions. Second, for the Chow parameter problems, we developed fundamental schemes of the nearest neighbor relations and performed their analysis for the Chow parameters. The sequential generation of the Chow parameters is proposed which is caused by small changes of the connecting weights in threshold neurons. Keywords Nearest neighbor relation · Sequential generation of chow parameters · Boundary vertex · Minimal information of nearest neighbor relation

1 Introduction Neural networks are the current state-of-the art technologies for developing the AI and machine learning. It is well known that the network is composed of many neurons. Intelligent and active functions of neural networks are based on that of the respective neurons. As the application of threshold function to learning theory, the Chow parameters problem is extensively studied using complexity theory [1–3]. The Chow parameter problem is given as follows.—Given the Chow Parameters [4] of a N. Ishii (B) · T. Matsuo Advanced Institute of Industrial Technology, Tokyo 140-0011, Japan e-mail: [email protected] T. Matsuo e-mail: [email protected] K. Odagiri Sugiyama Jyogakuen University, Nagoya 464-8662, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_31

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threshold function, output a representation in the weights and threshold, which realize the function. These sophisticated analyses do not necessarily show the constructive procedures or algorithms to obtain the final solutions. Any concrete procedures are expected to obtain the solutions. For the Chow parameter problems, we developed fundamental schemes of the nearest neighbor relations [5–8] and performed their analysis for the application to threshold networks. The nearest neighbor relation (NNR) consists of the adjacent pair vertices between the true and false vertices through the hyperplane. Through the nearest neighbor relations (NNRs), the sequential generation and learning of the Chow parameters can be analyzed. First, we have shown the basic characteristics of the NNRs in threshold function, which includes fundamental properties studied up to now. Next, it is shown that the NNR has the minimal information for generation of threshold function. Third, for the generation of threshold functions and Chow parameters [1, 4], conditions of the NNRs are analyzed. Finally, the Chow parameters are obtained sequentially. As the Chow parameter problem, a solvable procedure was proposed through the NNRs. In this paper, the nearest neighbor relation (NNR) is introduced in Sect. 2. As the basis of the generation of the threshold function, the NNRs are important role for the inherited information of the function, which are shown in Sect. 2. The boundary vertices for the Chow parameters are shown in Sect. 3 and the sequential generation of the parameters is described in Sect. 4.

2 Nearest Neighbor Relations in Threshold Function The NNR is applicable for the generation of threshold functions. The threshold function is a Boolean function on the n-dimensional cube with 2n vertices, which is divided by a hyperplane. The threshold function f is characterized by the hyperplane W X − θ = 0 with the weight vector W (= (w1 , w2 , . . . , wn )) and threshold θ . The X is a vertex of the cube 2n . The threshold function is assumed here to be a positive and canonical function, in which the Boolean variables satisfy the partial order [9]. Definition 1 The NNR (X i , X j ) in the threshold function f is defined to be vertices satisfying the Eq. (1), {(X i , X j ) : f (X i ) = f (X j ) ∧ |X i − X j | ≤ δ(= 1)},

(1)

where δ = 1 shows one bit difference between X i and X j in the Hamming distance (also in the Euclidean distance). Definition 2 The boundary vertex X is defined to be the vertex which satisfies the Eq. (2) |W X − θ | ≤ |W Y − θ | for the X (= Y ∈ 2n )

(2)

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Fig. 1 Boundary vertices and NNRs

Theorem 1 The boundary vertex X becomes an element of the NNR in the threshold function. Theorem 2 The vertices X i and X j in the NNR (X i , X j ) are the adjacent vectors, each of which belongs to a different class through the hyperplane. This is proved easily from the Definition 1, since the vertices X i , X j are the nearest to the hyperplane, which divides the true and the false data (Fig. 1).

2.1 Discrimination Using Nearest Neighbor Relations The discrimination between true vertices and false ones in threshold function is developed using nearest neighbor relations. In Fig. 2, a three-dimensional cube for the discrimination between true vertices ( 余 , black circles) and false ones ( ○ , white circles) is shown. We consider the difference between the true vertex X 7 (111) and the false one X 0 (000), to be X 7 − X 0 = {(111) − (000)}. The difference becomes {(111)}, which implies the x1 component to be 1, the x2 to be 1 and x3 to be 1. In Fig. 2, four nearest neighbor relations are obtained, which are indicated in arrows of the thick solid lines. These are {(111), (011)}, {(101), (001)}, {(101), (100)}, and {(110), (100)}. When there exists different component between data in the nearest relation   neighbor {(111), (011)}, the difference is described as {(111) − (011)} x − x  = 1. We can Fig. 2 Pathways between true vertex (111) and false one (000)

X 5 (101)

X 7 (111)

X1 (001) X 3 (011)

X 4 (100) X 6 (110)

X 0 (000)

X 2 (010)

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show the difference between the true vertex and the false one in the sequence as (111) → (110) → (010) → (000) in the dotted line in Fig. 2. The difference information between the true vertex (111) and the false one (000) is derived using the sequence as follows: First, the difference of the x 1 component between (111) and (000) is expanded as {(111) − (000)}(x1 −x1 )=1 = {(111) − (110)}(x1 −x1 )=0 + {(110) − (010)}(x1 −x1 )=1 + {(010) − (000)}(x1 −x1 )=0

(3)

Equation (3) shows the pathway with three parenthesis steps indicated in arrows of the dotted lines from (111) to (000). Similarly, the difference of the x3 component from (111) to (000) is equal to the NNR {(011), (001)}. The discriminative information is represented by the NNRs. Theorem 3 The difference information between true vertices and false ones in threshold function is inherited from that of the NNRs.

2.2 Boolean Operations for the NNRs The threshold function is generated using the NNRs. As an example, the threedimensional vertices in the cube are shown in Fig. 2. As true vertices, (011), (110), and (111) are indicated in the black circle, 余 , which belongs to +1 class. As false vertices, (000), (010), (100), (001), and (101) are indicated in the circle, ○, which belongs to 0 class. The generation of threshold functions through the NNRs is performed in the Boolean operations. As an example, five directed arrow vectors are introduced here for the NNRs. In Fig. 2, the true vertex (011) has the NNRs as {(011), (010)} and the true one (11) has also {(010), (100)}. Then, the directed arrow vector indicates the vector from the true vertex to the false one for the NNR, which is shown as {(011), (010)} Two directed arrow vectors, {(011), (001)} and {(011), (010)} generate one plane in the Boolean operation AND in Fig. 3. The directed arrow vector generates the x 3 variable between the true vertex and the false

(011) ‫ە‬

Fig. 3 Boolean operations for the NNRs

x3

AND

‫ۑ‬ (010)

(110) ‫ە‬

x2

x1

AND

‫ۑ‬ ‫ۑ‬ (001) (010)

(111) ‫ە‬

x2 ‫ۑ‬ (100)

x2 ‫ۑ‬ (101)

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one. Thus, {(011), (010)} generates x 3 variable, while {(011), (001)} generates x 1 variable. By the operation AND of x 2 and x 3 , the Boolean product x2 · x3 is generated. Similarly, {(110), (100)} and {(110), (010)} generates another plane, which is shown in Fig. 3. From this plane, the Boolean product x1 · x2 is generated. Since these two planes are perpendicular, the Boolean operation OR is used to connect these two planes. The remained directed arrow vector in Fig. 3, is {(111), (101)},which is included in either planes as the vector. Thus, by the OR operation for the two perpendicular planes, the threshold function x2 · x3 + x1 · x2 is obtained. Thus, the function is represented in the Eq. (4). f = x1 · x2 + x2 · x3

(4)

3 Boundary Vertices for Generation of Chow Parameters 3.1 Characterization of Boundary Vertices The condition of the linear separability of the Boolean function is stated in the following theorem derived from the linear inequality conditions [9, 10]. Theorem 4 To realize the linear separability for the Boolean function with n weights and a threshold of {(n + 1) variables}, there exist (n + 1) inequalities, which are independent inequality equations among 2n vertices inputs Since these (n + 1) independent inequalities are replaced to the equality equations having the value +ε or −ε, the (n + 1) vertices corresponding to these equations become boundary vertices in the threshold function. Applying Theorem 6 to the nearest neighbor relations in threshold functions, the following theorems are derived. Theorem 5 There exist at least (n + 1) nearest neighbor relations in the threshold function with n input variables and threshold. Further, at least one boundary vertex is included in the respective NNR. Theorem 6 Assume the boundary vertex X in the threshold function f with n variables and assume the following Eq. (5) holds, g(X ) = 1 − f (X ), g(Y ) = f (Y ) for (Y = X and X, Y ∈ 2n )

(5)

Then, the function g becomes a threshold function. Further, when functions f and g satisfy the Eq. (5), the X becomes a boundary vertex for both functions, f and g. Theorem 6 is proved in the following. Assume the X is a true vertex of f , i.e., f (X ) = 1. Assume the X has the m (≥ 1) components of 1 and weights components

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a ji , i = 1 ∼ m of the weight A corresponding to the m components. Replace these weights {a ji } of the m components to the weights {a ji }, where a ji = a ji −δ, i = 1 ∼ m and δ > 0. Other components weights except these m ones are kept as the same valued components. This changed weights and threshold is indicated as (A , θ  ). The weight change δ is computed from the Eqs. (6), (7), (8), and (9). For the X , the Eq. (6) holds, A X − θ  = a j1 + a j2 + · · · + a jm − θ − mδ = ε − mδ < 0

(6)

ε = |AX − θ | = (a j1 + a j2 + · · · + a jm ) − θ < |AY − θ |

(7)

and

In the components of ji , i = 1 ∼ m, assume the true input vertex Y has at most (m − 1) components to be 1. Then, for the Y , the following equation holds A Y − θ  ≥ AY − θ − (m − 1)δ > ε − (m − 1)δ > 0

(8)

Then, the δ satisfying both Eqs. (6) and (8) is derived as 0 < (ε/m) < δ < (ε/(m − 1)δ),

(9)

where m ≥ 2 is assumed. For the true vertex Y (= X ), which has the components, ji , i = 1 ∼ m to be 1 and other components to be 1 in the jk . Since f is assumed to be a positive function, the weight a j for any j, a j ≥ 2ε. From the Eq. (9), 0 < δ < ε holds. Thus, using the Eq. (8) A Y − θ  = ε − mδ +



a jk > ε − (m − 2)δ > 0

(10)

k

Thus, for m ≥ 2 and all the true vertices {Y },the Eq. (8) is satisfied. For all the false vertices, the Eq. (11) holds. A Y − θ  ≤ AY − θ < −ε

(11)

In case of m = 1, from the Eq. (6), A Y − θ  = a ji − θ − δ = ε − δ < 0

(12)

For the true vertex Y , which has the component to be 1 of the X and other components jk with 1,

Iterative Generation of Chow Parameters Using Nearest …

A Y − θ  = ε − δ +



363

a jk > 3ε − δ > 0

(13)

k

From the Eqs. (12) and (13), ε < δ < 3ε is derived.

3.2 Boundary Vertices in the NNRs The boundary vertices in the NNRs play an important role for the generation and learning of Chow parameters [4]. The boundary vertex in the NNR is characterized using 2-assumability [9] in the following: Theorem 7 In the threshold function f , the necessary and sufficient condition of the true(false) vertex X for the NNR to be a boundary vertex is to satisfy the 2-assumable condition for the changed false(true) vertex. In the above, the first parenthesis (false) corresponds to the latter one(true). Theorem 8 In the threshold function f, any NNR has at least one boundary vertex, i.e., the true vertex or the false vertex becomes a boundary vertex or both of them become boundary vertices. This is proved showing a simple case without the loss of the generality. Assume that a nearest neighbor relation {(101), (111)} is shown in the directed arrow in Fig. 4, in which the (101) is a false vertex, while (111) is a true vertex. The NNR is included in the dotted ellipse. The vertex (111) is checked using Theorem 6. The true vertex (111) is changed as the false vertex indicated in the white circle by the arrow in Fig. 4. Then, the 2-assumability (two crossed diagonals) is not satisfied among 4 vertices in Fig. 4. Thus, the true vertex (111) does not become a boundary vertex. Theorem 9 Assume the total number of m NNRs specified in the i-th component (i = i 1 , i 2 , . . . , i m ) of the threshold function f , in which there exits one NNR for the respective component i s , (s = 1 ∼ m). Among the m NNRs, the vertex Z is common. Then, all the vertices among the m NNRs become boundary ones. Fig. 4 Boundary vertex in the NNR

X 5 (101)

X 7 (111)

X1 (001) X 3 (011) X 4 (100) X 6 (110)

X 0 (000)

X 2 (010)

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This is proved as follows: Assume the common vertex Z is the true one. If the common vertex Z is not a boundary one, there exists the true vertices Y ,which satisfy Z > Y by 2-assumability. As an example, this is shown to be the case of Z = X 7 (111) in Fig. 4. But, Y = X 3 (011) and X 6 (110) are false vertices generating the nearest neighbors with Z , which contradicts the assumed Y to be true vertices.

4 Iterative Generation of the Chow Parameters In Fig. 5, the Chow parameters with 5 variables are generated sequentially based on the NNRs of threshold functions. Then, the Chow parameter consists of 6 tuples [m : s1, s2, s3, s4, s5 ], which m shows the summed number of true vertices, while s1, s2, s3, s4, s5 indicate the summed number of the first 1’s component, that of the second 1’s component„ that of the fifth 1’s component of the true vertices [4], respectively. Based on the Chow parameter [m : s1, s2, s3, s4, s5 ] = [6:11111], the new Chow parameter [7:22111] is generated changing the false boundary vertex (11000) to the true vertex using the NNR {(10000), (11000)} from the Chow parameter [6:11111]. In Fig. 5, the generation of Chow parameter is generated sequentially by the small change of small weights of the element. The relations of the Chow parameters are given in the Theorem 10. In Fig. 4, assume that the Chow parameter at T is indicated as the ChowT and that at S as Chow S , which is generated in the advance of T with m steps. Also, assume here their threshold functions f T and f S , respectively.

Fig. 5 Chow parameters with 5 variables are generated sequentially through NNRs. Parenthesis indicates false vertex to generate the next Chow parameter

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Theorem 10 Between the Chow parameters at T and at S, the following equation holds. The ChowT = the Chows +

m 

false vertex i

(14)

i=1

in the NNR, where the first component of the (ChowT )1 becomes (ChowT )1 = (Chow S )1 + m

(15)

As an example, in Fig. 5, consider the case of the Chow parameter T to be 9: 33311 and S to be 6: 11111. Then, the false vertices of the NNRs between them are given as m = 3 and {(11000), (10100), (01100)}.From the Chow parameter S, 6:11111), by the Eq. (14), 6 + 3: (11111) + {(22200)} = 9: 33311 is obtained. Corollary 11 Between the threshold functions f T and f S , the Eq. (16) holds. f T = f S + Boolean sum

 m 

 false vertex i in the NNR of the respective step

i=1

(16) As a solvable procedure for the Chow parameter problem, the sequential generation of the Chow parameters is applicable. When the Chow parameter with m variables, T is given, we start from the function of the most simple Chow parameter, S0 with m variables at the 0-th step as shown in Fig. 6. At the 1st step, the Chow parameter S1k generated from S0 using the boundary vertex in the NNR selected by the minimization between the Chow parameter T j and the selected S jk in Fig. 6. Similarly, at the 2nd step, the Chow parameter S2k  generated from S1k is obtained

Fig. 6 Selection of the Chow parameter at the j-th step

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similarly by the minimization. By the iteration of steps in Fig. 6, the given Chow parameter, T is obtained. The Boolean function of the T is obtained from the Eq. (16), which generates the weight and threshold [9].

5 Conclusion For the Chow parameter problems, we developed fundamental schemes of the nearest neighbor relations and performed their analysis for the application to threshold networks. The NNR consists of the adjacent pair vertices between the true and false vertices. Through the NNRs in the threshold function, the iterative generation and learning of the Chow parameters can be analyzed. In this paper, first, we have shown the basic characteristics of the NNRs in threshold function, which unifies fundamental properties studied up to now. Second, we have shown the solvable procedure of the iterative generation of the Chow parameters.

References 1. O’Donnell R, Servedio R (2011) The Chow parameters problem. SIAM J Comput 40(19):165– 199 2. De A, Diakonikolas I, Feldman V, Servedio RA (2014) Nearly optimal solutions for the Chow parameters problem and low-weight approximation of halfspaces, 61(2):11.1–11.36 3. Diakonikodas I, Kane DM (2018) Degree-d Chow parameters robustly determine degreed PTFs (and algorithmic applications) electronic colloquium computational complexity, 25(189):1–29 4. Chow CK (1961) On the characterization of threshold functions. In: Proceedings of the symposium on switching circuit theory and logical design (FOCS), pp. 34–38 5. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27 6. Ishii N, Torii I, Iwata K, Odagiri K, Nakashima T (2017) Generation and nonlinear mapping of reducts-nearest neighbor classification. In: Chapter 5 in advances in combining intelligent methods. Springer, pp 93–108 7. Ishii N, Torii I, Mukai N, Nakashima T (2017) Generation of reducts and threshold function using discernibility and indiscernibility matrices. In: Proceedings of ACIS-SERA IEEE computer society, pp 55–61 8. Ishii N, Torii I, Iwata K, Odagiri K, Nakashima T, Matsuo T (2019) Incremental reducts based on nearest neighbor relations and linear classification. In: Proceedings of IIAI-SCAI IEEE computer society, pp 528–533 9. Hu ST (1965) Threshold logic. University of California Press 10. Fan K (1966) On systems of linear inequalities, linear inequalities and related systems. In: Kuhn HW, Tucker AW (eds). Princeton University Press, pp 99–156

Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm Jayshree Ghorpade-Aher and Balwant Sonkamble

Abstract Individual feature selection algorithms, used for processing highdimensional multi-source heterogeneous data may lead to weak predictions. The traditional single method process may not ensure the selection of relevant features. The selections of features are susceptible to the changes in input data, and thus fail to perform consistently. These challenges can be overcome by having a robust feature selection algorithm that generates a subset of original features and evaluates the candidate set to check for its relevance. Also, it determines the feasibility of the selected subset of features. The fundamental tasks of selecting feature subset minimize the complexity of the model and help to facilitate the further processing of the model. The limitations of using single feature selection technique can be reduced by combining multiple techniques to generate the effective features. There is a need to design efficient approaches and technique for estimating the feature relevance. This ensemble approach will help to include diversity at input data level, as well as the computational technique. The proposed method—Ensemble Bootstrap Genetic Algorithm (EnBGA)—generates the effective feature subset for the multi-source heterogeneous data. Various univariate and multivariate base selectors are combined together to ensure the robustness and stability of the algorithm. In this pandemic of COVID-19, it’s observed that patients already diagnosed with diseases such as diabetes had an increased mortality rate. The proposed method performs feature analysis for such data, where the Genetic Algorithm searches the feature subset and extracts the most relevant features. Keywords Feature selection · Genetic algorithm · Ensemble · Machine learning · Heterogeneous data · Bootstrap

J. Ghorpade-Aher (B) P.I.C.T, MIT World Peace University, Pune, India B. Sonkamble Pune Institute of Computer Technology, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_32

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1 Introduction The existing data processing methods face challenges to process heterogeneous data due to computational power, statistical accuracy, and algorithmic stability [1]. To handle these problems, good feature selection techniques must be used that will maximize the relevance of the features with the target variable. The feature selection method comprises of selecting most relevant features and improving the performance of the model with the selected feature subset. The data oriented architecture lays various challenges [2] for processing the data and extracting the significant features. The association of multiple base selector techniques, yield better features as compared to the individual traditional feature selection techniques. Ensemble learning can incorporate with better features for feature selection and provide robustness to the model by introducing variations in the input data. Removal of redundant features avoids making incorrect decisions based on noisy data [3], thus reducing the overfitting problem. Also, the removal of constant, quasi-constant or low variance features contributes in dimensionality reduction. The lowered training time of algorithm fastens the process of modeling. The relevant feature subset is deduced using various univariate and multivariate feature selection techniques [4]. Further, optimization is one of the important aspects of algorithmic calculations. Minimizing or maximizing a function depending upon the problem statement can provide with optimal results, which will fine-tune the decision-making strategy. The biological behavioral algorithms such as Genetic Algorithm (GA) and Swarm Intelligence (SI) are mostly used for extracting optimal features. Various univariate techniques like Chi-square, Information Gain, Receiver Operating Characteristics (ROC) scores, and multivariate algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting are used to compute the scores. The univariate techniques are mostly used for analyzing single variable, whereas multivariate techniques emphasize the study of correlation among more than two variables. Genetic Algorithm (GA) [5] follows the biological evolution with stochastic behavior that produces the optimal solution of the given fitness function. It uses the Darwin’s theory called ‘Survival of the Fittest’, where the successive generations try to adapt with the environment. Genetic algorithm processes the population of individuals, and thus generates better approximations towards the expected outcome. The proposed method called Ensemble Bootstrap Genetic Algorithm (EnBGA) include selection of the significant top ‘k’ features and then extracting the effective best features. Diabetic Mellitus (DM) disease is increasing continuously and has become one of the most prevalent diseases. The mortality rate is increasing in the current pandemic of COVID-19. Patients already diagnosed with disease such as diabetes had a death rate of more than 7% as reported by the Centre for Disease Control and Prevention [6]. The experimental analysis of multisource heterogeneous data is performed using the proposed algorithm for the Type-2 Diabetes Milletus (T2DM) disease. The effective diagnosis provides meaningful insights to the domain experts for further research.

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The literature review is discussed in Sect. 2. Section 3 depicts the research study of the proposed method and Sect. 4 states the experimental discussions. Finally, the concluding remarks are stated in the last section.

2 Literature Review Most of the Machine Learning applications involving multi-source data with various input features possess complex relationships. Each application with its smallest point as datum has its own set of features or characteristics or traits [1, 4]. The expected results can be obtained by selecting the feasible feature subset with proper decisionmaking, which still is an open challenge while constructing the compact predictive models [7]. As it’s difficult to control the size of the input data, the subset of unique features must be considered to improve the performance of the model and also minimize the resource utilization. These challenges can be overcome by having a robust feature selection algorithm that generates a subset of original features and evaluates the candidate set to check for its relevance. Feature engineering [8] is one of the important tasks as it identifies the relevant traits for an application. Each single learning technique has some limitations and cannot perform consistently for a varied set of input data [3]. Ensemble technique collectively considers the processing of different base selectors [9] and tries to make the model more generalized for new data. The literature study revealed that different learning techniques use its own strategy for calculating the feature selection accuracy. These techniques can be integrated with average mean functions or geometric mean functions. The univariate techniques [10] such as Chi-square (Chi2) calculate the significance value (pvalue) that gives the probability under the null hypothesis. It is observed that minimum Chi2_Score(1pvalue) will yield best features. Entropy is said to be the measures of uncertainty, impurity, noise, etc. The Information Gain (IG) maximizes with reduction in entropy. The higher values of IG, states the effectiveness of an attribute in classifying training data. Feature selection techniques helps to maximize the classification accuracy of a classifier by reducing the computational complexities. The ROC (Receiver Operating Characteristics) method examines each one of the variables ‘x’ or feature ‘F’ against the target variable ‘y’. The significant features are identified depending on the significance value and weight scores of the features. A boosting algorithm is a multivariate technique that changes the weak learners to strong learners by adjusting their weights along the way. The Extreme Gradient Boosting [11] (XGBoost) algorithm implements ensembles of decision tree. The comparative importance of an attribute increases as its use in constructing key decisions increases. XGBoost is the regularization of Gradient Boosting Machine and posses L1 (Lasso Regression) & L2 (Ridge Regression) which prevents the model from overfitting. Random Forest (RF) derives the importance of each variable on the tree decision to provide a good predictive performance. In Random Forests, the

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impurity decrease from each feature can be averaged across trees to determine the final importance of the variable. The Genetic Algorithm is a technique that evolves over a period with better solutions by adapting with the environment. The fitness function acts as an optimization benchmark. The measures of model performance with best features are functions such as accuracy scores or root mean square error. The fitness values [7] with larger scores are considered to be better. The next generation is produced by randomly combining the individuals having best fitness scores resulting in crossover and mutation processes. The selected feature subset has the individuals as either included (TRUE or ‘1’) or not included (FALSE or ‘0’). Thus, Genetic Algorithm [8] performs better than the traditional feature selection algorithms. Genetic Algorithm can handle datasets with multiple features, even if it requires more computations to converge during each population.

3 Proposed Ensemble Bootstrap Genetic Algorithm (EnBGA) The motivation of the research is to study and analyze the feature selection algorithms to generate the features that would maximize the outcome of the proposed model. Figure 1, depicts the proposed technique for feature selection. The proposed feature selection technique is designed to perform data processing [12] to obtain the optimal features. The proposed EnBGA technique for selecting the relevant features is as follows:

3.1 Data Preprocessing Data preprocessing is a crucial part as it handles the missing data-values by using imputer method, removing ‘null’ or ‘nan’ values, selecting the mean, median or mode of the attribute, etc. It deals with the multiple observations of same entry, thus tackling the problem of data repetition along with the quasi-constant features.

Fig. 1 Proposed feature selection technique

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The statistical techniques like standard deviation, central tendency computations, interquartile ranges, etc., help to explore, analyze, and understand the data with its respective features.

3.2 Feature Creation Data transformations are performed on the heterogeneous data to convert categorical data [4] to numerical data or ordinal data. The scaling of categorical data is performed by converting it into dummies. The feature creation and selection helps to extract the relevant feature subset. Various techniques such as Variance threshold scores, One-hot binarizer, Count vectorizer, etc., are used to find the scores of the feature relevance. Data standardization and normalization improves the feature selection process.

3.3 Feature Selection The various feature selection techniques explores the significance of each feature along with its correlation with the target variable. Various techniques such as Filter methods, Wrapper methods, and Ensemble approaches can be used for feature selection. The wrapper method performs extraction of relevant features by forming a feature subset. The undesired features of a model, slows its performance. Filter method finds the ranking of individual features as per the relevance criteria with the response variable. Among these methods, the Ensemble approach [13] yields the optimal features, those results in better predictive performance. The appropriate features help to facilitate the Machine Learning algorithm to train faster. Features from different sources are used to predict the target variable. The proposed method implements these significant characteristics of the Ensemble approach to generate the optimal features [14]. The main objective of this approach is to generate a more reliable and robust set of features by integrating the results of various base feature selection vectors. These base selectors can be functionally similar or may have heterogeneous approach. The proposed method includes the heterogeneous [15] selectors by considering different selection algorithms to exploit the features of sampled data. Also, the knowledge of fine-tuning of the parameters of the algorithms is required to select the ‘best’ threshold values. Further, data transformations are performed with various data aggregation techniques [9] to generate the optimal ensemble feature subset. The proposed method known as Ensemble Bootstrap Genetic Algorithm (EnBGA) works in two phases as discussed below. Selection of the significant top ‘k’ features: The first module inserts variation at data level by having bootstrap samples and implements the ensemble techniques for best feature selection using univariate and multivariate algorithms. The univariate techniques such as Chi-square, Information gain, and ROC are used to identify the

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relevant features possessing strong relationship with the output variable. The multivariate techniques like Extreme Gradient Boosting, Random Forest, and Gradient Boosting are implemented to obtain the significance of the features. It’s observed that a higher score of a feature implies an increase in the importance of that feature in the data with respect to the target variable. Let ‘D’ be the input data, with a set of bootstrap data samples. The reduced feature set ‘FR ’ is a subset of the total features ‘FN ’. The ‘TEn ’ represents combined ensemble techniques. The Algorithm 1, processes ‘D’ to generate the top ‘k’ features ‘Fk ’. Step-1: Step-2:

Step-3: Step-4: Step-5:

D ← {di | i ∈ (1,2,…, 6)}} for each di ∈ D ◯ di ← [input(X1 ), target(y1 )] ◯ Fj (s) ← {feature-score for j ∈ FR where FR ⊆ FN } Franked ← {descending [Fj (s)]} Faggregate ← {mean [Xranked ] for t ∈ TEn } Fk ← {Fm | m ∈ (1,2,…, k)}

Extracting the optimal best features: The parameter tuning for feature selection using Genetic Algorithm (GA) helps to obtain effective optimal results. The GA outperforms the traditional feature selection techniques and explores the best features of the datasets. Selecting significant features as the relevant subset is a combinatorial optimization problem [8]. Mathematically, an exhaustive feature selection technique requires 2N different combinations for computations where ‘N’ is the number of features. Thus, huge computations are involved to evaluate the data with large number of features. Hence, an intelligent technique is needed to perform better selection of features in practice with stochastic selection method. The Genetic Algorithm [10] does not depend on the specific knowledge of the problem and is inspired by the idea of natural selection theory proposed by Charles Darwin. The GA algorithm initializes the population in the dataset and evaluates the fitness function which signifies the effectiveness of the selection. It involves selection, crossover, mutation, and termination processes. The selection process is based on the concept of survival of the fittest as part of the evolutionary algorithm. GA allows the emergence of the optimal features that improves the selection over time from the best of prior features. The selected k-features ‘Fk ’ are fed to Algorithm 2 for extracting the effective features. Let P(n) be the population and FF(n) evaluates the fitness function. Step-1: Step-2: Step-3:

Step-4:

P(n) ← {initialize Pi where i ∈ (1,2,….,n)} FF(n) ← {fitness function FFj where j ∈ (1,2,….,n)} while {termination = True} ◯ Pp (t) ← {select parents with best fitness} ◯ Pc (t) ← {crossover for generating new individuals} ◯ Pm (t) ← {mutation[Pc (t)]} FGA ← {fittest individuals/features where FGA ⊆ Fk }

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4 Experimental Discussion The applications such as medical diagnosis have features with complex relationships. The data is captured from multiple sources. This heterogeneous data is explored to extract optimal best features which are relevant to the target variable and improve the performance of the model. The experimentation is executed using spyder tool and python programming. The proposed method, Ensemble Bootstrap Genetic Algorithm (EnBGA), is implemented and executed using multi-source data. The statistical analysis of the selected features helps to understand the significance of each feature towards the target variable. The public dataset from EHR (Electronic Health Records) is used for the experimentation. The dataset is a multisource dataset with various sources from Diagnosis data (disorders as approved by the World Health Organization.), Medication data (medicines, e.g., voxy, lisinopril, cozaar, etc.), Transcripts data (Vitals, e.g., weight, height, blood pressure, etc.), and Lab data. Table 1, shows the feature score analysis for Diagnosis dataset. The optimal features selected are (i) F1: Cardiovascular_Disorders, (ii) F2: Genitourinary_Disorders, (iii) F3: Endocrine_Disorders, (iv) F4: Hematologic_Disorders, and (v) F5: Musculoskeletal_Disorders. Similarly, the feature analysis was performed for the other datasets. The Gradient Boost (GB) algorithm is used for comparative performance analysis of these selected effective Genetic Algorithm (GA) features and Top-10 features as shown in Table 2. It’s observed that GA yields more accuracy (AUCScore) with significant features. Table 1 Feature score analysis for Diagnosis dataset Techniques

Features F1

F2

F3

F4

F5

Chi2_Mean

1.00000

1.00000

1.00000

1.00000

0.97709

IG_Mean

0.06785

0.01001

0.00467

0.00736

0.00152

ROC_Mean

0.10795

0.02677

0.04818

0.04442

0.04818

xGB_Mean

0.65972

0.01756

0.00885

0.01055

0.00760

RF_Mean

0.08399

0.03792

0.03776

0.03436

0.05377

GBC_Mean

0.49867

0.04204

0.02170

0.01644

0.01090

Wt_Mean

0.40303

0.18905

0.18686

0.18552

0.18318

Table 2 The performance results for Diagnosis dataset Classifier

Type

AUCScore

Sensitivity

Specificity

GB

GA

0.806533

0.808468

0.166667

GB

TOP10

0.805528

0.808468

0.166667

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5 Conclusion The proposed algorithm, Ensemble Bootstrap Genetic Algorithm (EnBGA), is a robust technique that selects the relevant effective features. These effective features improve the predictive power of the model. The Ensemble approach includes diversity at input data level and provides stability to the model. Genetic Algorithm explores the effective features and enhances the performance of the model. Genetic Algorithm is a stochastic method that may require more computations to converge during each population with increased computational time. Thus, the proposed algorithm EnBGA, will help to select the appropriate features that will contribute to proper decision-making and predictions.

References 1. Ding W, Lin C, Pedrycz W (2020) Multiple relevant feature ensemble selection based on multilayer co-evolutionary consensus mapreduce. IEEE Trans Cybern 50(2):425–439 2. Zhao Y, Duangsoithong R (2020) Empirical analysis using feature selection and bootstrap data for small sample size problems. In: IEEE 16th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTICON), Pattaya, Chonburi, Thailand, pp 814–817. (January 2020) 3. Ghorpade J, Sonkamble B (2020) Predictive analysis of heterogeneous data–techniques & tools. In: 2020 5th international conference on computer and communication systems (ICCCS), Shanghai, China, pp 40–44. (May 2020) 4. Pes B (2019) Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. In: Neural computing and applications. Springer pp 1–12. (February 2019) 5. Wang J, Xu J, Zhao C, Peng Y, Wang H (2019) An ensemble feature selection method for high-dimensional data based on sort aggregation. Syst Sci Control Eng IEEE Access 7(2):32–39 6. Woodward A (2020) What to know about the coronavirus outbreak. World Economic Forum. (March 2020) 7. Yamada Y, Lindenbaum O, Negahban S, Kluger Y (2020) Feature selection using stochastic gates. In: 37th international conference on machine learning, Vienna, Austria, PMLR 119, pp 1–12. (July 2020) 8. Khair U, Lestari YD, Perdana A, Hidayat D, Budiman A (2019) Genetic algorithm modification analysis of mutation operators in max one problem. In: IEEE, third international conference on informatics and computing (ICIC), Palembang, Indonesia, pp 1–6 (August 2019) 9. Yu Z et al (2019) Adaptive semi-supervised classifier ensemble for high dimensional data classification. IEEE Trans Cybern 49(2):366–379 10. Nag K, Pal NR (2020) Feature extraction and selection for parsimonious classifiers with multiobjective genetic programming. IEEE Trans Evol Comput 24(3):454–466 11. Palhares P, Brito L (2018) Constrained mixed integer programming solver based on the compact genetic algorithm. IEEE Latin Am Trans 16(5):1493–1498 12. Aruna Kumari GL, Padmaja P, Jaya Suma G (2020) ENN-ensemble based neural network method for diabetes classification. Int J Eng Adv Technol (IJEAT) 9(3). ISSN: 2249–8958 13. Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(06), pp 1–45 14. Ditzler G, LaBarck J, Ritchie J, Rosen G, Polikar R (2018) Extensions to online feature selection using bagging and boosting. IEEE Trans Neural Netw Learn Syst 29(9):4504–4509

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15. Thomas J, Sael L (2015) Overview of integrative analysis methods for heterogeneous data. In: IEEE international conference on big data and smart computing, Jeju, pp 266–270 16. Seijo-PardoI B, Porto-Díaz I, Bolón-Canedo V, Alonso-Betanzos A (2017) Ensemble feature selection: homogeneous and heterogeneous approaches. In: Knowledge-based systems, vol 118. Elsevier, pp 124–139 (February 2017)

A Generalization of Secure Comparison Protocol with Encrypted Output and Its Efficiency Estimation Takumi Kobayashi and Keisuke Hakuta

Abstract Secure comparison protocol outputs an unencrypted or encrypted comparison result. In this paper, we focus on a secure comparison protocol with an encrypted output. In recent works, since the computation of such protocols proceeds bitwise, the efficiency problem has not yet been solved. In this study, we propose a new secure comparison protocol with an encrypted output, which is a generalization of one proposed by Kobayashi and Hakuta (2018). As an interesting feature, a computation of our proposed protocol proceeds w bits-by-w bits for any positive integer w to compute an output. We discuss the security under semi-honest model. Furthermore, we estimate for the efficiency. Keywords Cryptography · Homomorphic encryption · Privacy · Privacy enhancement techniques · Secure comparison · Secure multi-party computation · Semi-honest model

1 Introduction Secure comparison protocol can be applied to various applications of secure multiparty computation. We call it a SCP (Secure Comparison Protocol) in this paper. Many researchers have developed SCPs which output an encrypted comparison result by using several building blocks [1, 2]. On the other hand, some SCPs using one building block also have been proposed [3, 4]. It has ease to perform security analysis for SCPs using one building block compared to that of SCPs using several ones. Moreover, since SCPs in recent works proceed bitwise to compute the output, many SCPs still have an efficiency problem. To solve that, Kobayashi and Hakuta focused on a SCP which proceeds multi bits-by-multi bits to compute it [4]. In this paper, we call such a SCP multi bitswise SCP for short. A goal of their study is developing a w bits-wise SCP for any T. Kobayashi (B) · K. Hakuta Shimane University, 1060 Nishikawatsu-cho, Matsue, Shimane, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_33

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positive integer w (w ≥ 2). For the goal, they proposed two SCPs. In this paper, we call the first protocol [4, Protocol 1] (resp. the second one) KH Protorol 1 (resp. KH Protorol 2). KH Protocol 1 proceeds bitwise to obtain an encrypted output, while KH Protocol 2 is a 2 bits-wise SCP. They claimed that “the generalization of KH Protocol 2 could make the SCP more efficient” [4]. Therefore, we tackle the generalization. A notion “(a > b)” indicates a truth value of a proposition “a > b” for the input integers a and b. Moreover, the other cases are defined as well. For instance, if a proposition “a > b” is truth then a ciphertext of a comparison result (a > b) equals that of “1”. Throughout this paper, we consider a scenario as follows: “Party A has a private integers a, while Party B has a private integers b. They want to compare their private integers and do not leak their integers and the comparison result each other. Now Party A’s plaintext is encrypted with Party B’s public key. On the other hand, Party B has Party B’s public key and the secret key. As a result, Party A gets an encrypted comparison result (a > b).” In this study, we propose a SCP with an encrypted output. Now we call it Our protocol for short. Our protocol (Algorithm 1) is a generalization of KH Protocol 2 [4, Protocol 2]. KH Protocol 2 is a 2 bits-wise SCP to obtain the output. On the other hand, Our protocol is a w bits-wise SCP for any positive integer w (w ≥ 1) to do that. This feature of Our protocol is so interesting. We also discuss the security under semi-honest model and estimate for the efficiency of Our protocol. Moreover, homomorphic encryption is commonly used in a SCP. We call it HE (Homomorphic Encryption). Since we use subprotocols which are constitute of only HE, Our protocol is constitute of only HE. Our protocol has one advantage that it has ease to perform the security analysis, compared to that of ones using several building blocks. This paper is organized as follows. In Sect. 2, we fix some notation. In Sect. 3, we propose a SCP and prove the correctness (a protocol works to provide the outputs using the inputs correctly). Moreover, we discuss the security and estimate for the efficiency. In Sect. 4, we conclude the paper.

2 Mathematical Preliminaries Throughout this paper, we use the following notation. We reserve the notation Z to denote the set of rational integers. We represent the set of non-negative integers by Z≥0 , that is, Z≥0 := {n ∈ Z | n ≥ 0}. Let us denote by Zn := Z/nZ the residue class ring modulo n for a positive integer n ≥ 2. We reserve the notation p and q to denote (l/2)-bit primes. We represent a multiplication of p and q by N . Now we assume that N is a l-bit RSA modulus. We denote by Z∗N 2 the set {a ∈ Z/N 2 Z | gcd(a, N 2 ) = 1}. We describe the definition of a public key encryption scheme as follows [5, Definition 7.1]. We reserve the notation E to denote a public key encryption scheme. Let us denote by κ a security parameter of the scheme E Moreover, ( pk, sk) indicates an output by Gen(1κ ) and a combination of public key pk and secret key sk. Party B’s public key is denoted by pk B . The corresponding secret key is denoted by sk B . Let

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us denote by PK and SK the public key space and the secret key space, respectively. We reserve the notation P and C to denote the plaintext space and the ciphertext space. Enc pk (m; r ) means a ciphertext of the plaintext m ∈ P with a public key pk and a random number r , while Decsk (c) means a result by decrypting the ciphertext c ∈ C with a secret key sk. In this study, we define maps Enc and Dec as follows: Enc : P × PK → C, (m, pk) → c, Dec : C × SK → P, (c, sk) → m. Our protocol needs a semantically secure and additive HE scheme. Any additive HE scheme [1, 2, 6, 7] can be applied to Our protocol. In this paper we apply Paillier encryption scheme [6] to Our protocol. Paillier encryption scheme is commonly applied to many applications of SMC. For more details, we refer the reader to [6]. – Encryption: Given a plaintext m ∈ Z N , choose a random number r ∈ Z∗N . A ciphertext of the plaintext m is c = Enc pk (m; r ) = g m r N mod N 2 . – Homomorphic property: Given plaintexts m 1 and m 2 ∈ Z N , choose random numbers r1 , r2 ∈ Z∗N . Ciphertexts are represented by c1 = Enc pk (m 1 ; r1 ) = g m 1 r1N mod N 2 , c2 = Enc pk (m 2 ; r2 ) = g m 2 r2N mod N 2 . Then we have c1 · c2 = Enc pk (m 1 ; r1 ) · Enc pk (m 2 ; r2 ) = Enc pk (m 1 + m 2 ; r1r2 ).

(1)

3 Proposed Protocol We propose a SCP which outputs an encrypted comparison result (a > b). We describe the overview of Our protocol as follows: Our protocol is a generalization of KH Protocol 2. KH Protocol 2 is a 2 bits-wise SCP to obtain the encrypted output, while Our protocol is a w bits-wise SCP for any positive integer w to do that. These protocols compute a ciphertext of comparison result (a > b). Maps G and Gˆ are used in this paper. These return a comparison result for the inputs. We define a map G : Z2≥0 → {0, 1} and a map Gˆ : Z2≥0 → {0, 1} as 

1, if a ≤ b, G(a, b) = 0, otherwise,

 0, if a ≤ b, ˆ and G(a, b) = 1, otherwise.

Next, we put a positive integer w (w ≥ 1). We define the following sets Γ˜ and Γ , and the following element γ in Γ : Γ˜ := {0, 1, . . . , 2w − 1}  Z, γ ∈ Γ := Γ˜ \{0} = {1, . . . , 2w − 1}  Z. The non-negative integer a is represented as follows: a = (ah−1 , . . . , a0 )2 = (A H −1 , . . . , A0 )2w ∈ Z≥0 , and Ai := (awi+(w−1) , awi+(w−2) , . . . , awi )2 ∈ Γ˜  Z. We represent the non-negative integer b similarly. We assume that h is divisible by w in Our protocol using padding zeros to the integers if necessary. Now we have H = h/w obliviously. Furthermore, let us represent an integer representation of (Ai−1 , . . . , A0 )2w ∈ Z≥0 as A(i) . An integer representation of (Bi−1 , . . . , B0 )2w ∈ Z≥0 is represented as B (i) similarly. Moreover, we put ti for

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Algorithm 1 Our protocol Inputs: Party A : a = (ah−1 , . . . , a0 )2 = (A H −1 , . . . , A0 )2w , d := 2w and pk B . Inputs: Party B : b = (bh−1 , . . . , b0 )2 = (B H −1 , . . . , B0 )2w , d := 2w , pk B and sk B . (A) Outputs: Party A : Enc pk B (t H ; rt H  ) such that t H = Gˆ (a, b), Party B : Not applicable.

1: Party A encrypts “0”, “1” and d and obtains Enc pk B (0; r X(A) ), Enc pk B (1; rY(A) ) and (A) Enc pk B (d; r Z ). (A) 2: Party A computes Enc pk B (1; rY )−1 . (A) (A) 3: Party A substitutes Enc pk B (1; rY ) into Enc pk B (t0 ; rt0 ). 4: for i from 0 to H − 1 by +1 do 5: Party A chooses ci such that ci ∈ {0, 1} at random. 6: if ci = 0 then (A) (A) (A) 7: Party A substitutes Enc pk B (ti ; rti ) and Enc pk B (0; r X ) into Enc pk B (si ; rsi ) and 8: 9:

10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25:

26:

Enc pk B (ci ; rc(A) i ), respectively. else (A) (A) (A) −1 Party A does: Enc pk B (si ; rsi ) ← Enc pk B (1 − ti ; rY rti ). (A)

(A) (B) (A)

29: 30:

31: 32: 33: 34:

(A)

They perform a SDP: Party A inputs d and Enc pk B (d + Bi + ci − 1; r Z r Bi rci (rY )−1 ) while Party B inputs d and sk B . Finally Party A outputs Enc pk B ( (d + Bi + ci − (A) ). 1)/d ; r D (A) Party A does: Enc pk B (ti+1 ; rti+1 ) ← Enc pk B (ti + Bi − u i + (d + Bi + ci − (A) (B)

27: 28:

(A)

Party A substitutes Enc pk B (1; rY ) into Enc pk B (ci ; rci ). end if (A) Party A sends Enc pk B (si ; rsi ). (B) Party B encrypts Bi and obtains Enc pk B (Bi ; r Bi ). if Bi = 0 then Party B encrypts “0” and obtains Enc pk B (0; r X(B) ) and substitutes Enc pk B (0; r X(B) ) into (B) Enc pk B (u i ; ru i ). else (B) (A) (B) Party B does: Enc pk B (u i ; ru i ) ← Enc pk B (si + Bi ; rsi r Bi ). end if (B) (B) Party B sends Enc pk B (Bi ; r Bi ) and Enc pk B (u i ; ru i ) to Party A. if ci = 0 then (B) (B) (B) (A) Party A does: Enc pk B (u i ; ru i ) ← Enc pk B (2Bi − u i + 1; (r Bi )2 (ru i )−1 rY ). end if if Ai = 0 then (A) (B) (A) (A) Party A computes: Enc pk B (d + Bi + ci − 1; r Z r Bi rci (rY )−1 ).

(B)

(A)

1)/d ; rti r Bi (ru i )−1 r D ). else (A) Party A encrypts Ai and obtains Enc pk B (Ai ; r Ai ). (A) (B)

(A)

(A)

Party A does: Enc pk B (d + u i − Ai − 1; r Z ru i (r Ai )−1 (rY )−1 ). They perform a SDP: Party A inputs d and Enc pk B (d + u i − Ai − (A) −1 (A) −1 (rY ) ) while Party B inputs d and sk B . Finally Party A out1; r Z(A) ru(B) i (r Ai )

(A) puts Enc pk B ( (d + u i − Ai − 1)/d ; r D  ). (A) (A) Party A substitutes Enc pk B ( (d + u i − Ai − 1)/d ; r D  ) into Enc pk B (ti+1 ; rti+1 ). end if end for (A) (A) (A) Party A does: Enc pk B (t H ; rt H  ) ← Enc pk B (1 − t H ; rY (rt H )−1 ). (A)

35: Party A outputs Enc pk B (t H ; rt H  ).

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i = 0, . . . , H as follows:  ti :=

1, G(A(i) , B (i) ),

if i = 0, otherwise.

Remark that we have t H = G(A(H ) , B (H ) ) = G(a, b). We describe Our protocol as Algorithm 1. Algorithm 1 computes the value ti+1 for each i from 0 to H − 1. The objective of Algorithm 1 is as follows: Party A outputs a ciphertext of a comparison result t H = G(a, b) using their private input integers a and b. Algorithm 1 can achieve the goal by computing a ciphertext of t H = G(A(H ) , B (H ) ) = G(a, b) throughout the iteration between lines 4–33 of Algorithm 1 and carrying out bit inverse the ciphertext between lines 34–35. A notation “←” means that computes an element of right side and substitutes it into one of left side. We use a notation Enc pk B (m; r ) to describe a ciphertext of a plaintext Mul using Party B’s public key pk B and a random number r . Now we explain each lines of Algorithm 1. In line 1–3, Party A performs an ). In line 4, they carry initialization and substitutes Enc pk B (1; rY(A) ) into Enc pk B (t0 ; rt(A) 0 out iterations on i from 0 to H − 1. In lines 5–12, Party A masks ti (= G(A(i) , B (i) )) then it is sent to Party B. Due to the mask, Party B is unable to determine if si = ti or ). In lines 13–19, Party si = 1 − ti if Party B decrypts the ciphertext Enc pk (si ; rs(A) i (B) (B) B computes Enc pk (Bi ; r Bi ) and Enc pk (u i ; ru i ) depending on Bi and sends the ciphertexts to Party A. In lines 20–22, Party A solves the mask. In lines 23–32, Party ) depending on Ai . After line 33, since we have the A computes Enc pk (ti+1 ; rt(A) i+1 following equation, Party A gets a ciphertext of G(a, b). t H = G(a, b).

(2)

ˆ b). In Finally, by using bit inverse in lines 34–35, Party A gets a ciphertext of G(a, lines 3, 7, 9, 10, 15, 17, 21, 24, 26, 28, 29, 31, and 34, Party A or Party B denotes by one ciphertext other one. In particular, in lines 9, 17, 21, 24, 26, 29, and 34, they can obtain each ciphertext by the property (1) of HE scheme. Furthermore, as a subprotocol, a SDP is needed in Algorithm 1. The SDP is described as follows: “Party A inputs an encrypted integer X and a divisor D while Party B inputs a divisor D and sk B . As a result, Party A outputs the encrypted division result X/D .” We use a SDP proposed by Veugen [8, Protocol 1] as the subprotocol. They need to perform the SDP in line 25 and 30 of Algorithm 1. Meanwhile, they do not need to do that in lines 26 and 31 because Party A can store the encrypted division result. Furthermore, a SCP is required in Veugen’s SDP. This SCP needs to provide an encrypted comparison result (a > b) from the inputs a and b where the size is log2 d . We apply KH Protocol 1 [4, Protocol 1] to the subprotocol of the SDP. For more details, we refer the reader to KH Protocol 1 [4, Protocol 1] and [8, pp. 169–170]. Next, we describe the correctness of Our protocol (Algorithm 1). First of all, we need the following proposition (Proposition 1) for the correctness.

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Table 1 Value of variables in one iteration (1/2) ci Ai Bi Case si u i after line 18 0 0 0 0 0 0 1

0 0 γ γ γ γ 0

0 γ 0 γ γ γ 0

1

0

1

←0 ← ti ←0 ← ti ← ti ← ti ←0

ti ti ti ti ti ti 1 − ti

ui ui ui ui ui ui ui

γ

1 − ti

u i ← 1 − ti + Bi

γ

0

1 − ti

ui ← 0

1

γ

γ

(Ai < Bi )

1 − ti

u i ← 1 − ti + Bi

1

γ

γ

(Ai = Bi )

1 − ti

u i ← 1 − ti + Bi

1

γ

γ

(Ai > Bi )

1 − ti

u i ← 1 − ti + Bi

(Ai < Bi ) (Ai = Bi ) (Ai > Bi )

+ Bi + Bi + Bi + Bi

u i after line 22 ui ← 0 u i ← ti + Bi ui ← 0 u i ← ti + Bi u i ← ti + Bi u i ← ti + Bi u i ← 2Bi − u i + 1 = 0−0+1=1 u i ← 2Bi − u i + 1 = 2Bi − (1 − ti + Bi ) + 1 = ti + Bi u i ← 2Bi − u i + 1 = 0−0+1=1 u i ← 2Bi − u i + 1 = 2Bi − (1 − ti + Bi ) + 1 = ti + Bi u i ← 2Bi − u i + 1 = 2Bi − (1 − ti + Bi ) + 1 = ti + Bi u i ← 2Bi − u i + 1 = 2Bi − (1 − ti + Bi ) + 1 = ti + Bi

Proposition 1 For given integers Ai ∈ Γ˜ and Bi ∈ Γ˜ (1 ≤ i ≤ H − 1), we have ⎧ ⎪ ⎨1, if Ai < Bi , (d + ti + (Bi − Ai − 1))/d = ti , if Ai = Bi , ⎪ ⎩ 0, otherwise (Ai > Bi ). Proof The proof is straightforward.

(3)



From Proposition 1, we depict that what values are substituted into each variables in one iteration between lines 4–33 as Tables 1 and 2. Moreover, we need the following lemma (Lemma 1) for the correctness of Algorithm 1. Lemma 1 For any integer i (0 ≤ i ≤ H − 1), we have

ti+1

⎧ ⎪ ⎨1, if Ai < Bi , = ti , if Ai = Bi , ⎪ ⎩ 0, otherwise (Ai > Bi ).

(4)

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Table 2 Value of variables in one iteration (2/2) ci Ai Bi Case ti+1 0

0

0

0

0

γ

0 0

γ γ

0 γ

(Ai < Bi )

0

γ

γ

(Ai = Bi )

0

γ

γ

(Ai > Bi )

1

0

0

1

0

γ

1 1

γ γ

0 γ

(Ai < Bi )

1

γ

γ

(Ai = Bi )

1

γ

γ

(Ai > Bi )

ti+1 ← ti + Bi − u i + (d + Bi + ci − 1)/d = ti + 0 − 0 + 0 = ti ti+1 ← ti + Bi − u i + (d + Bi + ci − 1)/d = ti + Bi − (ti + Bi ) + 1 = 1 ti+1 ← (d + u i − Ai − 1)/d = (d − (Ai + 1))/d = 0 ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = 1 ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = ti ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = 0 ti+1 ← ti + Bi − u i + (d + Bi + ci − 1)/d = ti + 0 − 1 + 1 = ti ti+1 ← ti + Bi − u i + (d + Bi + ci − 1)/d = ti + Bi − (ti + Bi ) + 1 = 1 ti+1 ← (d + u i − Ai − 1)/d = (d − Ai )/d = 0 ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = 1 ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = ti ti+1 ← (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d = 0

Proof We consider three cases: Ai < Bi , Ai = Bi , and Ai > Bi . Case 1. Ai < Bi . From Tables 1 and 2, we consider the four subcases with ci , Ai and Bi . Case 1–1. ci = 0, Ai = 0 and Bi = γ . Since u i = ti + Bi , we have ti+1 = ti + Bi − u i + (d + Bi + ci − 1)/d = (d + (Bi − 1))/d . Since 0 ≤ (Bi − 1) < d and d ≤ d + (Bi − 1) < 2d, we have ti+1 = (d + (Bi − 1))/d = 1. Case 1–2. ci = 0, Ai = γ , Bi = γ and Ai < Bi . Since u i = ti + Bi , we have ti+1 = (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d . From Proposition 1, we have ti+1 = (d + ti + (Bi − Ai − 1))/d = 1. Case 1–3. ci = 1, Ai = 0 and Bi = γ . Since u i = ti + Bi , we have ti+1 = ti + Bi − u i + (d + Bi + ci − 1)/d = (d + Bi )/d . Since d < (d + Bi ) < 2d, we have ti+1 = (d + Bi )/d = 1. Case 1–4. ci = 1, Ai = γ , Bi = γ and Ai < Bi . Since u i = ti + Bi , we have ti+1 = (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d . From Proposition 1, we have ti+1 = (d + ti + (Bi − Ai − 1))/d = 1. Case 2. Ai = Bi . From Tables 1 and 2, we consider the four subcases with ci , Ai , and Bi .

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Case 2–1. ci = 0, Ai = 0 and Bi = 0. Since u i = 0 and Bi = 0, we have ti+1 = ti + Bi − u i + (d + Bi + ci − 1)/d = ti + (d − 1)/d = ti . Case 2–2. ci = 0, Ai = γ , Bi = γ and Ai = Bi . Since u i = ti + Bi , we have ti+1 = (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d . From Proposition 1, we have ti+1 = (d + ti + (Bi − Ai − 1))/d = ti . Case 2–3. ci = 1, Ai = 0 and Bi = 0. Since u i = 1 and Bi = 0, we have ti+1 = ti + Bi − u i + (d + Bi + ci − 1)/d = ti − 1 + d/d = ti . Case 2–4. ci = 1, Ai = γ , Bi = γ and Ai = Bi . Since u i = ti + Bi , we have ti+1 = (d + u i − Ai − 1)/d = (d + ti + (Bi − Ai − 1))/d . From Proposition 1, we have ti+1 = (d + ti + (Bi − Ai − 1))/d = ti . Case 3. Ai > Bi . This proof is similar to that of Case 1. This completes the proof of Lemma 1.



Again we recall some notation. h is a bit-length of binary representation for the input integers. H is a bit-length of 2w -ary representation for the input integers. In other words, H indicates the number of blocks when h-bit binary representation is separated by w bits. Now we have H = h/w. The correctness in Algorithm 1 is as follows: Party A computes a ciphertext of ˆ b) from the inputs a and b. In other words, if a > b then Party A outputs a G(a, ciphertext of t H = 1, while if a ≤ b then Party A outputs a ciphertext of t H = 0. We describe it as Theorem 1. From Theorem 1, a ciphertext of t H = G(a, b) can be )−1 computed by Party A after line 33. By computing Enc pk (1; rY(A) ) · Enc pk (t H ; rt(A) H ˆ b) is output by Party A. in lines 34–35, a ciphertext of G(a, Theorem 1 For any integer H (H ≥ 1) that stands for a bit-length of 2w -ary representation for the inputs integer, after H -th iteration in Algorithm 1, We have Eq. (2). Proof We prove by induction on the number of blocks H . At first, we consider H = 1. Now two integers a and b are represented as follows: a = (ah−1 , . . . , a0 )2 = A(1) = (A0 )2w and b = (bh−1 , . . . , b0 )2 = B (1) = (B0 )2w . From Lemma 1, if A0 < B0 then we have t1 = 1 after line 32 in Algorithm 1. Hence, if A(H ) < B (H ) then t H = G(A(H ) , B (H ) ) = 1. From Lemma 1, if A0 = B0 then we have t1 = t0 after line 32. Since t0 = 1, we have t1 = t0 = 1. Hence if A(H ) = B (H ) then t H = G(A(H ) , B (H ) ) = 1. From Lemma 1, if A0 > B0 then we have t1 = 0 after line 32. Hence if A(H ) > B (H ) then t H = G(A(H ) , B (H ) ) = 0. Thus, we have Eq. (2) after line 32 for H = 1. Next, we assume that we have Eq. (2) after (H − 1)-th iteration in Algorithm 1 for any positive integer H . Now two integers a and b are represented as follows: a = (ah−1 , . . . , a0 )2 = A(H −1) = (A(H −1)−1 , . . . , A0 )2w and b = (bh−1 , . . . , b0 )2 = B (H −1) = (B(H −1)−1 , . . . , B0 )2w . Finally, we prove that we have Eq. (2) after H -th iteration in Algorithm 1 for any positive integer H . We have a = (ah−1 , . . . , a0 )2 = A(H ) = (A H −1 , A(H −1)−1 , . . . , A0 )2w , b = (bh−1 , . . . , b0 )2 = B (H ) = (B H −1 ,

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B(H −1)−1 , . . . , B0 )2w . To give the proof, we consider three cases as follows: A(H ) < B (H ) , A(H ) = B (H ) , and A(H ) > B (H ) . Case 1. A(H ) < B (H ) . Now we consider the following two subcases to prove the correctness. Case 1–1. A(H ) < B (H ) and A H −1 < B H −1 . A H −1 and B H −1 are most significant bits of A(H ) and B (H ) , respectively. Since A H −1 < B H −1 , from Lemma 1, t H = 1. Thus we have t H = G(A(H ) , B (H ) ) = 1. Case 1–2. A(H ) < B (H ) and otherwise. We have ∃ i 0 ∈ {0, . . . , H − 2} s.t. Ai0 < Bi0 , Ak = Bk (i 0 + 1 ≤ k ≤ H − 1). From Lemma 1, since tk+1 = tk for k = H − 1, we have t H = t H −1 . Since Ak = Bk (i 0 + 1 ≤ k ≤ H − 2), we have tk+1 = tk for k = i 0 + 1, . . . , H − 2. Since Ai0 < Bi0 , we have ti0 +1 = 1. Thus, we have t H = t H −1 = t H −2 = · · · = t(i0 +1)+1 = ti0 +1 = 1. Since t H = 1, we have t H = G(A(H ) , B (H ) ) = 1. Hence, in Case 1, we have Eq. (2) after H -th iteration. Case 2. A(H ) = B (H ) . Now it is obvious that A j = B j for all j (0 ≤ j ≤ H − 1). From Lemma 1, we have t H = t H −1 . we have tk+1 = tk for all k (0 ≤ k ≤ H − 2). Namely, we have t H −1 = t H −2 = · · · = t1 = t0 . Moreover, we have t0 = 1. Since t H = t H −1 = t H −2 = · · · = t1 = t0 = 1, we have t H = G(A(H ) , B (H ) ) = 1. Hence, in Case 2, we have Eq. (2) after H -th iteration. Case 3. A(H ) > B (H ) . This proof is similar to that of Case 1. Consequently, we have Eq. (2) after H -th iteration in Algorithm 1. This completes the proof of Theorem 1. 

3.1 Efficiency Estimation We estimate for the efficiency of Our protocol. Now we describe some notation for the computational cost. We denote by Dec, Enc, E x p, I nv, Mul, and Sqr the cost of one decryption, encryption, exponent, inverse, multiplication, and squaring, respectively. We use a 2048-bit RSA modulus N (this satisfies 112-bit security level [9, pp. 51– 56]) for our estimation. Since Our protocol is a generalization of KH protocol 2, the estimation for Our protocol is similar to that of KH Protocol 2 [4, Sect. 4.3]. For our estimation, we have the following two assumptions. First, in lines 24–26 of rc(A) (rY(A) )−1 ) Algorithm 1, if ci = 1 then Party A computes Enc pk B (d + Bi ; r Z(A)r B(B) i i and carries out the rest lines using the result. Otherwise Party A computes Enc pk B (d + Bi − 1; r Z(A)r B(B) rc(A) (rY(A) )−1 ) and carries out them as well. In addition, Party A does i i not need to encrypt “0” in line 1. Since Party A computes the above ciphertexts depending on the value of ci , the changes do not affect the correctness of Our protocol. Second, Party A and Party B can store their ciphertexts they obtain in their lines, respectively.

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Table 3 Estimation for computational cost (Total) Total Enc Mul Cost 1 Cost 2 Cost 3 Cost 4 Cost 5

3 1.5w + 1 1.5w H + 4H 1.5H + 2 1.5w H + 5.5H + 2

3 2.5w + 1 2.5w H + 4H 5.875H + 1 2.5w H + 9.875H + 1

I nv

Exp

1 1.5w + 1 1.5w H + 2H 1.875H + 2 1.5w H + 3.875H + 2

1 0.5w 0.5wH+H 0 0.5w H + H

Table 4 Computational cost among similar protocols (Total) Protocol Total Enc Mul I nv KH Protocol 1 KH Protocol 2 Algorithm 1

1.5h + 1 8.5H + 2 1.5w H + 5.5H + 2

2.5h + 1 14.875H + 1 2.5w H + 9.875H + 1

1.5h + 1 6.875H + 2 1.5w H + 3.875H + 2

Exp 0.5h 2H 0.5w H + H

First of all, we estimate for the computational cost of subprotocols in Our protocol. Our protocol uses a secure division protocol [3, Protocol 1] which needs a SCP with an encrypted output. We call it a SDP (Secure Division Protocol). In [3], the author claimed that “Except for one execution of a SCP, Party A requires 2 Enc, 3 Mul and 1 I nv, and Party B requires 1 Enc and 1 Dec”. By precomputing, we may assume that 1 Dec ≈ 1 E x p [6]. For one execution of the SDP without a SCP, the average cost is depicted as Cost 1 in Table 3. Moreover, we use KH Protocol 1 as a subprotocol for the SDP. The SDP requires the input size log2 d = log2 2w = w against the SCP. For one execution of the SCP in the SDP, the average cost is depicted as Cost 2 in Table 3. For H -times executions of the SDP including the subprotocol, the average cost is depicted as Cost 3 in Table 3. Next, we estimate for the cost of Our protocol without subprotocols. From [4, Table 3], the average cost of Our protocol without subprotocols is depicted as Cost 4 in Table 3. Finally, we estimate for the cost of Our protocol including subprotocols is depicted as Cost 5 in Table 3. We compare the total computational cost among Our protocol, KH Protocol 1, and KH Protocol 2 with the number of multiplications. Again we use a 2048-bit RSA modulus. From [4, Sect. 3.2, Sect. 4.3], we depict the cost of KH Protocol 1 and KH Protocol 2 in Table 4. From Table 3, we depict that of Algorithm 1 in Table 4. Moreover, we convert the cost of 1 I nv, 1 Sqr , 1 E x p, and 1 Enc into the number of Mul, respectively. According to some works by Cohen et al. [10], we have 9Mul ≤ 1I nv ≤ 30Mul and 0.8Mul ≤ 1Sqr ≤ 1Mul. In this paper, we assume that 1I nv ≈ 9Mul and 1Sqr ≈ 1Mul.

(5)

Number of Multiplications (Thousand)

A Generalization of Secure Comparison Protocol with Encrypted Output … 1400

387

Our proposed protocol KH Protocol 1 KH Protocol 2

1200 1000 800 600 400 200 0 1

2

5

10

15

20

25

30

35

40

45

50

w

Fig. 1 Comparison of computational cost (Total)

We use a modified binary method for an exponent [11, Algorithm 14.83]. In the algorithm, we set the exponent part t  = 2048. Moreover, we choose the window size k  = 7 because the parameter is the most efficient. From [11, Table 14.16], since    the algorithm requires (t  + 1) Sqr and ( t  /k  × (2k − 1)/2k + 2k −1 − 1) Mul on average, we assume that 





1E x p ≈ (t  + 1) Sqr + ( t  /k  × (2k − 1)/2k + 2k −1 − 1) Mul ≈ 2402 Mul. (6) Furthermore, we estimate for the computational cost for 1 Enc of Paillier encryption scheme. According to [7, Fig. 1], we may assume that 1Enc ≈ 3 × (l/2) + 1 ≈ 3073Mul.

(7)

Note that the security parameter in [7] is equivalent to (l/2)-bits in this paper. For our estimation, we need to set a bit-length of inputs. As one application, Our protocol can be applied for a secure face recognition system proposed by Erkin et al. [12]. Since they set 50-bit integers for a SCP as the inputs [12, Sect. 5], we assume that h = 50 similarly. Note that 2048-bit integers can be applied as the inputs at most. From Eqs. (5), (6), (7), and Table 4, KH Protocol 1 needs a thousand of 294.4 Mul and KH Protocol 2 needs a thousand of 781.2 Mul approximately. The total computational cost of Our protocol is as follows: a thousand of 1264.9 Mul, 781.2 Mul, 491.0 Mul, 394.2 Mul, 362.0 Mul, 345.9 Mul, 336.2 Mul, 329.70 Mul, 325.1 Mul, 321.7 Mul, 319.0 Mul, 316.8 Mul for w = 1, 2, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, respectively. Furthermore, we illustrate the cost among these protocols in Fig. 1. From Fig. 1, Our protocol is more efficient as w is increased. Our protocol is more efficient than KH Protocol 2 for w ≥ 3 and is not more efficient than KH Protocol 1.

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3.2 Security We discuss the security of Algorithm 1. Our analysis is similar to that of KH Protocol 2 [4, Protocol 2] and Veugen’s one [3, Protocol 2]. For more details, we refer the reader to [4, Sect. 4.4] and [3, Sect. 2.3]. Moreover, we confirm the security for their lines of Our protocol. In lines 5–12, ) by a coin toss ci and sends Enc pk (si ; rs(A) ) to Party Party A masks Enc pk (ti ; rt(A) i i B. Because of the mask, Party B is unable to determine if si = ti or si = 1 − ti if ). In lines 13–19, Party B computes Party B decrypts the ciphertext Enc pk (si ; rs(A) i (B) Enc pk (u i ; ru i ) depending on Bi . Furthermore, Party B sends Enc pk (Bi ; r B(B) ) and i Enc pk (u i ; ru(B) ) to Party A. Thus, they do not leak any private information each other. i As as result, we can see that a semi-honest model is acceptable in Our protocol.

4 Conclusion We proposed a secure comparison protocol. It outputs an encrypted comparison result and is based on only an additive homomorphic encryption. We archived to generalize a protocol proposed by Kobayashi and Hakuta in WISA 2018. As an interesting feature, our proposed protocol proceeds w bits-by-w bits for any positive integer w to compute an output. Moreover, we discussed the security under semi-honest model and estimated for the efficiency of our proposed protocol.

References 1. Damgård I, Geisler M, Krøigård M (2008) Homomorphic encryption and secure comparison. Int J Appl Crypt 1(1):22–31 2. Damgård I, Geisler M, Krøigård M (2009) A correction to efficient and secure comparison for on-line auctions. Int J Appl Crypt 1(4):323–324 3. Veugen T (2011) Comparing encrypted data. In: Technical Report, Multimedia Signal Processing Group, Delft University of Technology, The Netherlands, and TNO Information and Communication Technology, Delft, The Netherlands 4. Kobayashi T, Hakuta K (2019) Secure comparison protocol with encrypted output and the computation for proceeding 2 bits-by-2 bits. In: Kang B, Jang J (eds) The 19th world conference on information security applications-WISA 2018. LNCS, vol 11402. Springer, Cham, pp 213– 228 5. Goldwasser S, Bellare M (2008) Lecture notes on cryptography 1996–2008. http://cseweb. ucsd.edu/mihir/papers/gb.html 6. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Stern J (ed) Advances in cryptology-eurocrypt 1999. LNCS, vol 1592. Springer, Heidelberg, pp 223–238 7. Damgård I, Jurik M (2001) A generalisation, a simplification and some applications of Paillier’s probabilistic public-key system. In: Kim K (ed) Public-key cryptography-PKC 2001. LNCS, vol 1992. Springer, pp 119–136

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8. Veugen T (2014) Encrypted integer division and secure comparison. Int J Appl Crypt 3(2):166– 180 9. National Institute of Standards and Technology, Recommendation for Key Management Part 1: General (Reversion 4), National Institute of Standards and Technology Special Publication 800–57, January 2016 10. Cohen H, Miyaji A, Ono T (1998) Efficient elliptic curve exponentiation using mixed coordinates. In: Ohta K, Pei D (eds) Advances in cryptology-ASIACRYPT 1998, international conference on the theory and applications of cryptology and information security. LNCS, vol 1514. Springer, Heidelberg, pp 51–65 11. Menezes AJ, Oorschot PCV, Vanstone SA (1997) Handbook of applied cryptography. CRC Press, Boca Raton 12. Erkin Z, Franz M, Katzenbeisser S, Guajardo J, Lagendijk RL, Toft T (2009) Privacy-preserving face recognition. In: Goldberg I, Atallah MJ (eds) Privacy enhancing technologies symposiumPETS 2009. LNCS, vol 5672. Springer, Heidelberg, pp 235–253

Conceptualizing Factors that Influence Learners’ Intention to Adopt ICT for Learning in Rural Schools in Developing Countries Siphe Mhlana , Baldreck Chipangura , and Hossana Twinomurinzi

Abstract Despite the positive contribution of Information and Communication Technologies (ICT) to learning outcomes in education, its adoption, integration, and use remain a challenge for rural school learners in developing countries. To understand why ICT adoption by rural school learners has been slow, a systematic literature analysis was undertaken in this study. An analysis of twenty-nine peer reviewed and published papers selected from five electronic databases was conducted to identify the key factors that influence rural school learners’ adoption patterns. The findings revealed the key factors as ICT infrastructure, technical support, access to resources, and social influence. Deductively, infrastructure was found to have a direct obstruction and negative consequences on the adoption of ICT technologies in learning. The findings are valuable for illustrating factors that affect the adoption of ICT technologies by rural learners in developing countries. The results inform educational policy strategies for providing rural schools with infrastructure and resources that promote the use of ICT in learning. Providing ICT infrastructure is fundamental to rural schools, especially in this era where the COVID-19 pandemic has made it compulsory for learners to adopt online learning. Keywords ICT · Adoption · Learner intention · Secondary schools · Developing countries

S. Mhlana (B) · B. Chipangura University of South Africa, Johannesburg, South Africa e-mail: [email protected] B. Chipangura e-mail: [email protected] H. Twinomurinzi University of Johannesburg, Johannesburg, South Africa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_34

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1 Introduction In the past decade, teaching and learning has evolved from approaches that are classroom and teacher-centered to approaches that are learner-centered [1, 2]. The migration to learner-centered approaches have been facilitated by the adoption of technologies that include communication devices, computers, teaching and learning software applications, and social media [3–8]. As of recently, the adoption of ICT technologies in learning have immensely contributed to online teaching and learning as an intervention for breaking the disruption brought by the COVID-19 pandemic [9]. Additionally, the other benefits of ICT technology adoption in teaching and learning include the acquisition of twenty-first century technological skills by learners [10], learner motivation, breaking boundaries of space and time in learning [11, 12], and improved quality of education [13, 14]. Despite the benefits of ICT technology adoption in teaching and learning, literature documented challenges that hinder adoption at rural schools, which include lack of ICT infrastructure, technical support, cost of ICT devices, intermittent electricity and network connectivity [15]. Apart from the pros and cons, existent literature is silent on the actual ICT technology adoption by rural learners in developing countries. Literature is biased on ICT technology interventions that focus on bridging the digital divide and technology adoption at school level with a focus on school management and teachers [16]. Deductively, it can be inferred that ICT technology adoption at schools exclude the learners even though they are fundamental stockholders. If learners are excluded from the adoption equation at schools, this contradicts the basic definition of adoption. Adoption is defined as a judgment or choice that individuals make every time they want to take part in an innovation project [17]. Therefore, the aim of this research paper is to investigate factors that influence rural school learners’ intention to adopt ICTs for learning in developing countries. To uncover these factors, the investigation was undertaken as a systematic literature analysis study. The rest of the paper is structured as follows. Section 2 presents the Methodology, Sect. 3 presents the Analysis and discussion of the findings, and Sect. 4 presents the Conclusion, limitations, and future work.

2 Methodology This study adopted the Lage and Filho [18] systematic review protocol and the reporting approach used by Brereton et al. [19] and Amui et al. [20] in their studies. The aim of the review was to analyze literature on the factors that influence rural school learners’ intention to adopt ICT technologies. Figure 1 presents the protocol which was undertaken in three phases. The phases include the identification of articles, developing a classification and coding framework, and the actual coding and classifying of themes.

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Fig. 1 A schematic representation of the adopted methodology and results framework for this study [19]

2.1 Identification of the Research Articles Identification of research articles involved the construction of search terms, identification of data sources, and the selection of articles based on the inclusion and exclusion criteria [19]. The search terms were constructed to identify factors that influence rural school learners’ intention to adopt ICT technologies for learning. The terms used for searching the articles were ICT adoption factors in learning; E-learning adoption at schools; M-learning adoption at schools; ICT adoption by learners; ICT technology adoption at schools in rural areas; ICT benefits in learning; ICT challenges in learning. During searching, the terms were sometimes merged with terms such as “and”, “or” to come up with search phrases. The articles were retrieved from five electronic multidisciplinary databases, which are Scopus, ScienceDirect, IEEE, ACM, and CiteSeerX. Google Scholar was also used as a search engine for general searching of articles. The initial search yielded 291 articles, which were downloaded and reviewed by scheming through the abstracts and the discussion sections. Articles were selected for further analysis if they were peer reviewed journal or conference papers and published between 2005 and May 2020. After the first round of analysis, 218 articles were excluded. The remaining 73 articles were analyzed and 44 articles were excluded, allowing 29 articles to be used for the systematic literature analysis.

2.2 Classification and Coding Framework The classification and coding framework developed by Amui et al. [20] was adapted in this study. Table 1 presents the classification, description of the classification and the assigned codes. Six classifications and twenty codes were developed for this study.

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Table 1 Classification and code framework Classification

Description

Codes

Context

Developed countries

1A

Developing countries

1B

Focus

Method

Field/Area

Theory/Model

Factors that affect adoption

Not applicable

1C

Factors affecting adoption of ICT as a main theme

2A

ICT adoption in secondary schools as main theme

2B

m-learning used interchangeably

2C

e-learning used interchangeably

2D

Qualitative

3A

Quantitative

3B

Theoretical

3C

Empirical

3D

Case studies/interviews

3E

Survey

3F

Mixed-method

3G

Education sector

4A

Other sectors

4B

Not applicable

4C

Single

5A

Combined

5B

Not applicable

5C

Adoption factors (perceived usefulness, ease to use, anxiety, and self-efficacy), infrastructure, support, social influence, resources, active learning and personalized learning

3 Analysis and Discussion of Findings After analyzing the 29 selected articles in this systematic literature review, a classification and coded framework was utilized (Table 1). The results of the classification and coded framework are presented in Table 2.

3.1 Context of Research The context of a research is a valuable parameter that exposes the background of research [22, 23]. The analysis found that fifty-nine per cent (59%) of the studies were conducted in developing countries (1B) as compared to thirty-four per cent (34%) in developed countries (1A); see Table 2. However, three per cent (3%) of the studies compared developed and developing countries (1A and 1B), and the other

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Table 2 Classification and coding framework No

Authors

Context

Focus

Method

Sector

1

Manduku et al. (2012)

1B

2A

3F

4A

2

Moghaddam et al. (2013)

1A

2A

3G

4B

3

Mehra et al. (2011)

1A

2D

3F

4A

4

Sun et al. (2008)

1A

2D

3G

4A

5

Prasad et al. (2015)

1B

2A

3F

4A

6

Baki et al. (2018)

1A

2D

3B

4A

7

Nikou et al. (2017)

1A

2C

3B

4A

8

Rajesh et al. (2018)

1A

2C

3B

4B

9

Miller et al. (2006)

1B

2A

3A

4A

10

Bakhsh et al. (2017)

1B

2C

3B

4A

11

Hamzah et al. (2018)

1B

2C

3A

4C

12

Adel Ali et al. (2018)

1A

2C

3D

4A

13

Gakenga et al. (2015)

1B

2A, 2B

3G

4A

14

Qteishat et al. (2013)

1B

2D

3B

4A

15

Cakir et al. (2014)

1A

2D

3F

4A

16

Senaratne et al. (2019)

1B

2C

3F

4B

17

Ogundile et al. (2019)

1B

2A, 2B

3B

4A

18

Mutua et al. (2016)

1B

2D

3B

4A

19

Mtebe et al. (2014)

1B, 1A

2C

3F

4A

20

Friedrich et al. (2010)

1A

2D

3B

4C

21

Buabeng-Andoh (2015)

1A

2A, 2B

3B

4B

22

Farinkia (2018)

1B

2A, 2B

3F

4A

23

Momani et al. (2012)

1B

2A, 2B

3B

4A

24

Grandon et al. (2005)

1A, 1A

2A

3B

4A

25

Nasser et al. (2016)

1B

2C

3B

4A

26

Shraim et al. (2010)

1B

2D

3F

4A

27

Langat (2015)

1B

2A, 2B

3G

4A

28

Barakabitze et al. (2015)

1B

2A, 2B

3G

4A

29

Wei-Han Tan et al. (2012)

1B

2C

3F

4A

three per cent (3%) compared countries within the developed world (1A). Comparing the articles published in developing regions show that Asia had more articles (54%) than Africa (46%). As a result, African countries are lagging behind other developing countries in terms of publications [21, 22].

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3.2 Focus of the Research The analysis found that forty-five per cent (45%) of articles discussed factors that influence learners’ intention to adopt ICT at secondary schools as the main theme (2B). Eight per cent (8%) of articles discussed ICT adoption (2A) without mentioning secondary schools (2B). Mobile learning (m-learning) (2C) and ICT for learning were used interchangeably, which was the most preferred way for narrating ICTs as a tool of learning. Twenty per cent (20%) of the selected articles discussed factors that influence the adoption of m-learning at schools. Twenty-seven per cent (27%) of articles discussed e-learning at schools (2D). The analysis did not identify any papers that discussed the adoption or intention to adopt ICT technologies by rural school learners. Therefore, this presents a research gap that needs to be pursued.

3.3 Research Methods The analysis found that seventy per cent (70%) of the articles employed quantitative research strategies (3B); twenty per cent (20%) utilized mixed method strategies (3G); and ten per cent (10%) of articles employed qualitative research strategies (3A). The findings suggest that most articles used the quantitative method. This research will employ a mixed-method approach for data gathering because it gives a wholistic view of technology adoption by rural school learners.

3.4 Theories Adopted The literature analysis found that six theories were used to explain the adoption of ICT technologies for learning and these are: Technology Adoption Model (TAM), Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT), and Diffusion of Innovation (DoI). There were studies that adopted one theory and some combined two or more theories. For studies that adopted one theory, TAM was adopted by 35% of the studies and UTAUT was adopted by 15% of the studies. Almost 5% of the studies combined TAM, TRA, TPB, and UTAUT, and about 10% combined TAM and UTAUT. There were 25% of the papers that did not use any theory in their studies.

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3.5 Factors that Affect the Adoption of ICT Technologies by Learners This section discusses the factors that affect the adoption of ICT technologies by learners from rural schools in developing countries. Factors that emanate from adoption theories, and other factors such as infrastructure, support, social influence, resources, active learning, and personalized learning are also discussed. Adoption theories factors. From the point of view of technology adoption theories (TAM, UTAUT, TRA, TPB, and DOI), perceived usefulness and ease are factors that positively influence ICT technology adoption by learners [23–25]. If learners at rural schools believe that ICT technologies are useful and easy to use in learning, they would adopt the technologies. Morris and Venkatesh [26] posit that users will adopt a technology if they believe that learning to use such a technology is easy to understand. Hence, if ICT technologies for learning are difficult to learn, and demand a lot of time and effort to understand, the result is that learners will become anxious [27]. Anxiety is the fear of interacting or using a computer technology and the associated emotional fears of receiving negative outcomes. If learners find an ICT technology stressful to use in learning, they will not trust that the technology will improve their learning [28]. Furthermore, learners will find a technology which is easy to use if they have positive self-efficacy. Self-efficacy is the confidence and motivation that a user would have in learning to use a new technology [29]. Learners with positive self-efficiency will find ICT technologies easy to use and will develop competence in using the technology, which increases their chances of accepting the technology [30]. Infrastructure. ICT infrastructure is a factor that predicts the adoption and acceptance of ICT technologies by learners [31–34]. Learners will adopt ICT technologies for learning if they have access to ICT backbone infrastructure as provided by the government, ICT infrastructure as provided by schools and access to end-user devices [31]. With respect to the ICT backbone infrastructure, many developing countries have failed to provide rural areas with fixed wired access such as fiber optic or highspeed wireless access [32]. However, technologies available in rural areas such as 3G have been reported to be expensive and beyond reach for many rural people [15]. In situations where rural schools are exposed to such challenges, it follows that the schools cannot provide the learners with the required ICT infrastructure for learning. Richardson et al. [33] stated that schools should provide ICT infrastructure such as WiFi, end-user devices such as computers or mobile devices to encourage learners to adopt ICTs for learning. In circumstances where schools cannot provide end-user computing devices, the alternative is to implement the Bring Your Own Device Policy (BYOD) [31]. The implications of implementing a BYOD policy at rural schools is that learners from families living under the poverty datum do not afford to purchase end-user devices. In South Africa, Donner et al. [34] found that ICT devices were shared among members of families living under the poverty datum.

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Technical support. Technical support provided by schools is a factor that has effect on the adoption of ICT technologies by learners in rural schools [35]. Schools are encouraged to provide learners with support in terms of training and access. Training has been identified as an important initiative that entice learners to adopt technologies as witnessed in a case study carried in South Africa [16]. Social influence. Social influence was found to influence the adoption of ICT technologies in learning. Social influence is the degree to which learners believe that if important people around them use certain technology, they should also use that technology [36, 37]. Sumak et al. [38] found that social influence had a significant effect on learner behavior and attitude to adopt and use learning technologies. In this case, teachers play an important role in encouraging learners to use ICT for learning. Access resources. Adoption of ICT technologies can enable learners to access online learning management systems and open access courses. Online learning management systems provide learners with tuition and administrative resources such as content, assessment, discussion portals, communication channels, and peer tutoring resources [39, 40]. The benefit is that the learners can access archived lessons, take mock assessments, and interact with other learners on discussion forums during or after classes. As of recent, learning management systems have been moved to Cloud services to improve accessibility and efficiency [41]. The advantage of cloud-based learning management services is that learners do not need end- user software on their devices. Hence, they can access the learning management systems through any internet connected device such as a tablet, a phone or a computer. Furthermore, learning management systems provide learners with access to Massive online open courses (Moocs), a term coined to describe online courses that are offered to anyone who has interest in learning, where registration, study material, and assessment are open access [42, 43]. The benefit of Moocs to rural students is that they can undertake courses that are not offered at their schools [43], which, in turn, enable learners to pursue their passion independent of their schools. Active learning. Adopting ICT technologies in learning promotes active learning in the class or out of class. Active learning is when learners are actively participating in their learning and knowledge making process [44]. ICT technologies enable active learning because they provide a medium that stimulate learner-centered activities such as learning through gaming, collaboration, research, and debating [45]. ICT tools that facilitate active learning include discussion forums, blogs, wikis, and text messaging. Personalized learning. ICT technologies facilitate personalized learning, on which the learning pace and the resources are optimized to meet the needs and capabilities of a learner [46]. The philosophy of personalized learning is that people are different, and their learning styles are also different, hence for them to succeed, they need to be supported with personalized resources to unlock their potential [47, 48]. Fiedler [49] argued that even if learners are provided with personalized learning they will still need support from teachers and their peers to succeed. Therefore, intelligent ICT

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learning systems can be used to provide learners with personalized learning tools that enable communication and interaction. While ICT technologies provide personalized learning, they also provide learners with the flexibility of learning remotely and at any time of the day.

4 Conclusion The literature analysis uncovered a dearth on literature that focus on ICT technology adoption by rural learners in developing countries. Even though literature reported on initiatives undertaken to introduce ICT technologies for learning at rural schools, the adoption of such technologies is discussed from the point view of management and schoolteachers. In most of research papers, the aspect of learner adoption of ICT technologies was not investigated. However, the identified factors emanated from both developed and developing countries and not necessarily from literature that investigated adoption of technology from rural schools. Therefore, there is a need to conduct an empirical study in order to understand how these factors affect the rural school learner’s intention to adopt ICT technologies for learning.

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The Innovation Strategy for Citrus Crop Prediction Using Rough Set Theory Alessandro Scuderi, Giuseppe Timpanaro, Giovanni La Via, Biagio Pecorino, and Luisa Sturiale

Abstract The agri-food system of the world is undergoing a radical change in relation to the future scenario with the reconfiguration of the production factors. The future of agriculture, following the other sectors, will be that of an innovative agriculture based on digitization. The future outlook indicates that a “digital agricultural revolution” will be the change that would allow to have the quantities of food for the needs of the whole world. Predictive analysis is a tool that would provide the best use of production, reduce waste, and satisfy food needs. The process uses heterogeneous data, often large in size, in models capable of generating clear and immediately usable results to more easily achieve this goal, such as reducing material waste and inventory, and to obtain a finished product that meets specifications. The proposed theoretical model represents a first modeling to make usable the innumerable amount of data that, in the future, the agri-food system, through digital transformation, will be able to provide, to which it will be necessary to give an adequate response in methodological and operational terms. Keywords Agri-food · Big data · Digital transformation · Agriculture 4.0 · Multi-criteria decision

1 Introduction The demand for food products in the near future will be related to the growing population growth. The Food and Agriculture Organization of the United Nations (FAO) estimates that current agricultural production will have to increase by 50% more by 2050 [5]. The world population is constantly growing and will continue to grow from 7.1 billion people in 2012 to 9.8 billion in 2050 and up to 11.2 billion A. Scuderi (B) · G. Timpanaro · G. La Via · B. Pecorino Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy e-mail: [email protected] L. Sturiale Department of Civil Engineering and Architecture (DICAR), University of Catania, Catania, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 X.-S. Yang et al. (eds.), Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 236, https://doi.org/10.1007/978-981-16-2380-6_35

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in 2100. Populations in high-income countries will increase by about 10% or 111 million people [5]. In this future scenario, process and product innovations can contribute to the definition of new production and distribution models. Technological innovations take on a strategic role, as they make it possible to optimize production factors, harvest management, and distribution to consumers. Production efficiency is the main driver of technological development that leads to a reduction in the use of energy and water resources with a positive impact on the environment and climate. Finally, technological progress drives not only vertical integration, but also horizontal integration in the food chain that tends to promote large food suppliers [10]. Agriculture 4.0 will revolutionize the agro-food system, with the ability to manage seeds, fertilizers, water, and crops by providing continuous control and supporting decision-making related to consumer needs [7]. The research aims to provide an initial contribution to the perception that citrus fruit operators have of the opportunities and limitations of adopting intelligent agrifood chain. The first results will be presented, obtained from a predictive analysis approach to citrus fruit production through the application of rough sets combined with big data, to define possible future scenarios deriving from the implementation of digital transformation with econometric analysis.

2 Materials and Methods 2.1 Trend of World Citrus Production The surface dedicated globally to citrus fruits has increased in the last decade from 7.3 million hectares to 7.9 million hectares; this increase can be attributed to new geographical areas being used to cultivate citrus fruits, especially in developing countries [4]. Among the various citrus species, the cultivation of oranges is becoming increasingly important, covering an area of 3.8 million hectares in 2019, followed by small citrus fruits (tangerines, clementines, and mandarins) with 2.5 million hectares, and lemons with 1.1 million hectares, as well as other minor species. The global production of citrus fruit in 2019 is 132.7 million tons, an increase of 19.6% from 2009 [17], showing a greater dynamism in production compared product and process innovations