List of Figures xi List of Tables xv Foreword xvii Introduction xix Preface xxiii Acknowledgments xxv Acronyms xxvii Part I Extended Reality Education 1 Mixed Reality Use in Higher Education: Results from an International Survey 3J. Riman, N. Winters, J. Zelenak, I. Yucel, J. G. Tromp 1.1 Introduction 4 1.2 Organizational Framework 4 1.3 Online Survey About MR Usage 5 1.4 Results 6 1.4.1 Use in Classrooms 8 1.4.2 Challenges 9 1.4.3 Examples of Research in Action 10 1.4.4 Hardware and Software for Use in Classrooms and Research 10 1.4.5 Challenges Described by Researcher Respondents 12 1.4.6 Anecdotal Responses about Challenges 12 1.5 Conclusion 13 References 15 2 Applying 3D VR Technology for Human Body Simulation to Teaching, Learning and Studying 17Le Van Chung, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, Dac-Nhuong Le 2.1 Introduction 18 2.2 Related Works 18 2.3 3D Human Body Simulation System 19 2.3.1 The Simulated Human Anatomy Systems 19 2.3.2 Simulated Activities and Movements 20 2.3.3 Evaluation of the System 23 2.4 Discussion of Future Work 25 2.5 Conclusion 26 References 26 Part II Internet of Things 3 A Safety Tracking and Sensor System for School Buses in Saudi Arabia 31Samah Abbas, Hajar Mohammed, Laila Almalki Maryam Hassan, Maram Meccawy 3.1 Introduction 32 3.2 Related Work 32 3.3 Data Gathering Phase 33 3.3.1 Questionnaire 34 3.3.2 Driver Interviews 35 3.4 The Proposed Safety Tracking and Sensor School Bus System 36 3.4.1 System Analysis and Design 37 3.4.2 User Interface Design 38 3.5 Testing and Results 41 3.6 Discussion and Limitation 42 3.7 Conclusions and Future Work 42 References 42 4 A Lightweight Encryption Algorithm Applied to a Quantized Speech Image for Secure IoT 45Mourad Talbi 4.1 Introduction 46 4.2 Applications of IoT 46 4.3 Security Challenges in IoT 47 4.4 Cryptographic Algorithms for IoT 47 4.5 The Proposed Algorithm 48 4.6 Experimental Setup 50 4.7 Results and Discussion 52 4.8 Conclusion 57 References 58 Part III Mobile Technology 5 The Impact of Social Media Adoption on Entrepreneurial Ecosystem 63Bodor Almotairy, Manal Abdullah, Rabeeh Abbasi 5.1 Introduction 64 5.2 Background 65 5.2.1 Small and Medium-Sized Enterprises (SMEs) 65 5.2.2 Social Media 65 5.2.3 Social Networks and Entrepreneurial Activities 66 5.3 Analysis Methodology 66 5.4 Understanding the Entrepreneurial Ecosystem 67 5.5 Social Media and Entrepreneurial Ecosystem 69 5.5.1 Social Media Platforms and Entrepreneurship 71 5.5.2 The Drivers of Social Media Adoption 71 5.5.3 The Motivations and Benefits for Entrepreneurs to Use Social Media 71 5.5.4 Entrepreneurship Activities Analysis Techniques in Social Media Networks 71 5.6 Research Gap and Recommended Solution 73 5.6.1 Research Gap 73 5.6.2 Recommended Solution 74 5.7 Conclusion 74 References 75 6 Human Factors for E-Health Training System: UX Testing for XR Anatomy Training App 81Zhushun Timothy Cai, Oliver Medonza, Kristen Ray, Chung Van Le, Damian Schofield, Jolanda Tromp 6.1 Introduction 82 6.2 Mobile Learning Applications 82 6.3 Ease of Use and Usability 82 6.3.1 Effectiveness 83 6.3.2 Efficiency 83 6.3.3 Satisfaction 83 6.4 Methods and Materials 86 6.5 Results 89 6.5.1 Task Completion Rate (TCR) 89 6.5.2 Time-on-Task (TOT) 90 6.5.3 After-Scenario Questionnaire (ASQ) 91 6.5.4 Post-Study System Usability Questionnaire (PSSUQ) 93 6.6 Conclusion 93 References 94 Part IV Towards Digital Twins and Robotics 7 Augmented Reality at Heritage Sites: Technological Advances and Embodied Spatially Minded Interactions 101Lesley Johnston, Romy Galloway, Jordan John Trench, Matthieu Poyade, Jolanda Tromp, Hoang Thi My 7.1 Introduction 102 7.2 Augmented Reality Devices 103 7.3 Detection and Tracking 105 7.4 Environmental Variation 106 7.5 Experiential and Embodied Interactions 109 7.6 User Experience and Presence in AR 114 7.7 Conclusion 115 References 116 8 TELECI Architecture for Machine Learning Algorithms Integration in an Existing LMS 121V. Zagorskis, A. Gorbunovs, A. Kapenieks 8.1 Introduction 122 8.2 TELECI Architecture 123 8.2.1 TELECI Interface to a Real LMS 123 8.2.2 First RS Steps in the TELECI System 124 8.2.3 Real Student Data for VS Model 125 8.2.4 TELECI Interface to VS Subsystem 126 8.2.5 TELECI Interface to AI Component 128 8.3 Implementing ML Technique 128 8.3.1 Organizational Activities 128 8.3.2 Data Processing 129 8.3.3 Computing and Networking Resources 130 8.3.4 Introduction to Algorithm 130 8.3.5 Calibration Experiment 132 8.4 Learners’ Activity Issues 133 8.5 Conclusion 136 References 137 Part V Big Data Analytics 9 Enterprise Innovation Management in Industry 4.0: Modeling Aspects 141V. Babenko 9.1 Introduction 142 9.2 Conceptual Model of Enterprise Innovation Process Management 144 9.3 Formation of Restrictions for Enterprise Innovation Management Processes 147 9.4 Formation of Quality Criteria for Assessing Implementation of Enterprise Innovation Management Processes 148 9.5 Statement of Optimization Task of Implementation of Enterprise Innovation Management Processes 148 9.6 Structural and Functional Model for Solving the Task of Dynamic 150 9.7 Formulation of the Task of Minimax Program Management of Innovation Processes at Enterprises 152 9.8 General Scheme for Solving the Task of Minimax Program Management of Innovation Processes at the Enterprises 154 9.9 Model of Multicriteria Optimization of Program Management of Innovation Processes 156 9.10 Conclusion 161 References 162 10 Using Simulation for Development of Automobile Gas Diesel Engine Systems and their Operational Control 165Mikhail G. Shatrov, Vladimir V. Sinyavski, Andrey Yu. Dunin, Ivan G. Shishlov, Sergei D. Skorodelov, Andrey L. Yakovenko 10.1 Introduction 166 10.2 Computer Modeling 167 10.3 Gas Diesel Engine Systems Developed 168 10.3.1 Electronic Engine Control System 168 10.3.2 Modular Gas Feed System 169 10.3.3 Common Rail Fuel System for Supply of the Ignition Portion of Diesel Fuel 169 10.4 Results and Discussion 172 10.4.1 Results of Diesel Fuel Supply System Simulation 172 10.4.2 Results of Engine Bed Tests 181 10.5 Conclusion 183 References 184 Part VI Towards Cognitive Computing 11 Classification of Concept Drift in Evolving Data Stream 189Mashail Althabiti and Manal Abdullah 11.1 Introduction 190 11.2 Data Mining 190 11.3 Data Stream Mining 191 11.3.1 Data Stream Challenges 191 11.3.2 Features of Data Stream Methods 193 11.4 Data Stream Sources 193 11.5 Data Stream Mining Components 193 11.5.1 Input 194 11.5.2 Estimators 194 11.6 Data Stream Classification and Concept Drift 194 11.6.1 Data Stream Classification 194 11.6.2 Concept Drift 194 11.6.3 Data Stream Classification Algorithms with Concept Drift 196 11.6.4 Single Classifier 196 11.6.5 Ensemble Classifiers 197 11.6.6 Output 200 11.7 Datasets 200 11.8 Evaluation Measures 200 11.9 Data Stream Mining Tools 201 11.10 Data Stream Mining Applications 202 11.11 Conclusion 202 References 202 12 Dynamical Mass Transfer Systems in Buslaev Contour Networks with Conflicts 207Marina Yashina, Alexander Tatashev, Ivan Kuteynikov 12.1 Introduction 208 12.2 Construction of Buslaev Contour Networks 210 12.3 Concept of Spectrum 211 12.4 One-Dimensional Contour Network Binary Chain of Contours 212 12.5 Two-Dimensional Contour Network-Chainmail 214 12.6 Random Process with Restrictions on the Contour with the Possibility of Particle Movement in Both Directions 218 12.7 Conclusion 218 References 219 13 Parallel Simulation and Visualization of Traffic Flows Using Cellular Automata Theory and QuasigasDynamic Approach 223Antonina Chechina, Natalia Churbanova, Pavel Sokolov, Marina Trapeznikova, Mikhail German, Alexey Ermakov, Obidzhon Bozorov 13.1 Introduction 224 13.2 The Original CA Model 224 13.3 The Slow-to-Start Version of the CA Model 225 13.4 Numerical Realization 225 13.5 Test Predictions for the CA Model 229 13.6 The QGD Approach to Traffic Flow Modeling 230 13.7 Parallel Implementation of the QGD Traffic Model 232 13.8 Test Predictions for the QGD Traffic Model 232 13.9 Conclusion 235 References 236
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