Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Multimedia Services in Intelligent Environments: Advances in Recommender Systems -- Abstract -- 1…Introduction -- 2…Recommender Systems -- 3…Conclusions -- References -- 2 A Survey of Approaches to Designing Recommender Systems -- Abstract -- 1…Introduction to Recommender Systems -- 1.1 Formulation of the Recommendation Problem -- 1.1.1 The Input to a Recommender System -- 1.1.2 The Output of a Recommender System -- 1.2 Methods of Collecting Knowledge About User Preferences -- 1.2.1 The Implicit Approach -- 1.2.2 The Explicit Approach -- 1.2.3 The Mixing Approach -- 2…Summarization of Approaches to Recommendation -- 2.1 Content-Based Methods -- 2.2 Collaborative Methods -- 2.2.1 User-Based Collaborative Filtering Systems -- 2.2.2 Item-Based Collaborative Filtering Systems -- 2.2.3 Personality Diagnosis -- 2.3 Hybrid Methods -- 2.3.1 Adding Content-Based Characteristics to Collaborative Models -- 2.3.2 Adding Collaborative Characteristics to Content-Based Models -- 2.3.3 A Single Unifying Recommendation Model -- 2.3.4 Other Types of Recommender Systems -- 2.4 Fundamental Problems of Recommender Systems -- References -- 3 Hybrid User Model for Capturing a User's Information Seeking Intent -- Abstract -- 1…Introduction -- 2…Related Work -- 2.1 Methodologies for Building a User Model for Information Retrieval -- 2.2 Decision Theory for Information Retrieval -- 3…Capturing a User's Intent in an Information Seeking Task -- 3.1 Overview -- 3.2 Interest Set -- 3.3 Context Network -- 3.4 Preference Network -- 4…Hybrid User Model -- 4.1 Overview -- 4.2 Sub-Value Function Over Query -- 4.3 Sub-Value Function for Threshold -- 4.4 Complexity of Hybrid User Model -- 4.4.1 Implementation -- 5…Evaluation -- 5.1 Objectives -- 5.2 Testbeds -- 5.3 Vector Space Model and Ide dec-hi -- 5.4 Procedures. , 5.5 Traditional Procedure -- 5.6 Procedure to Assess Long-Term Effect -- 6…Results and Discussion -- 6.1 Results of Traditional Procedure -- 6.2 Results of New Procedure to Assess Long-Term Effect -- 7…Discussion -- 8…Application of Hybrid User Model -- 9…Conclusions and Future Work -- References -- 4 Recommender Systems: Network Approaches -- Abstract -- 1…Introduction -- 2…Recommender Systems Review -- 3…Background: Graphs and NoSQL -- 3.1 Current NoSQL Implementations -- 3.2 The Algebraic Connectivity Metric -- 3.3 Recommendation Comparison and Propagation -- 4…The Effect of Algebraic Connectivity on Recommendations -- 4.1 Application to Improve Recommendations -- 5…Recommendations Experiment and Results -- 6…Conclusion -- References -- Resource List -- 5 Toward the Next Generation of Recommender Systems: Applications and Research Challenges -- Abstract -- 1…Introduction -- 2…Recommender Systems in Software Engineering -- 3…Recommender Systems in Data and Knowledge Engineering -- 4…Recommender Systems for Configurable Items -- 5…Recommender Systems for Persuasive Technologies -- 6…Further Applications -- 7…Issues for Future Research -- 8…Conclusions -- References -- 6 Content-Based Recommendation for Stacked-Graph Navigation -- Abstract -- 1…Introduction -- 2…Related Work -- 3…Stacked Graphs -- 3.1 Views and View Properties -- 4…Content-Based Recommendation -- 4.1 View Data Set -- 4.2 User Profile -- 4.2.1 Inferring Preferences for Seen Views -- 4.2.2 Inferring Preferences for Attributes of Seen Views -- 4.3 Content-Based Recommendation -- 4.4 Usage Scenario -- 5…User Study -- 6…Results and Discussions -- 7…Conclusion and Future Work -- References -- 7 Pattern Extraction from Graphs and Beyond -- Abstract -- 1…Introduction -- 2…Foundations -- 2.1 Graphs -- 2.2 Graph Representations -- 2.3 Basic Notions of Graph Components -- 3…Explicit Models. , 3.1 Tree -- 3.2 Cohesive Subgraphs -- 3.3 Cliques -- 4…Implicit Models -- 4.1 Modularity and Its Approximation -- 4.2 Network Flow -- 5…Beyond Static Patterns -- 5.1 Sequential Pattern Mining in Data Stream -- 5.2 Explicit Approaches for Tracing Communities -- 5.3 Implicit Approaches for Tracing Communities -- 6…Conclusion -- References -- Source List -- 8 Dominant AHP as Measuring Method of Service Values -- Abstract -- 1…Introduction -- 2…Necessity of Measuring Service Values -- 2.1 Significance of Service Science -- 2.2 Scientific Approach to Service Science -- 3…AHP as a Measuring Method of Service Values -- 3.1 Saaty's AHP -- 3.2 Dominant AHP -- 4…AHP and Dominant AHP from a Perspective of Utility Function -- 4.1 Expressive form of Multi-Attribute Utility Function -- 4.2 Saaty's AHP from a perspective of utility function -- 4.3 Dominant AHP from a viewpoint of utility function -- 5…Conclusion -- 9 Applications of a Stochastic Model in Supporting Intelligent Multimedia Systems and Educational Processes -- Abstract -- 1…Introduction -- 2…Formulating a Minimum of a Random Number of Nonnegative Random Variables -- 3…Distribution Function of the Formulated Minimum -- 4…Applications in Systems and Processes -- 5…Conclusions -- References.

Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Artificial Intelligence-Based Technologies -- References -- Part I Advances in Artificial Intelligence Tools and Methodologies -- 2 Synthesizing 2D Ground Images for Maps Creation and Detecting Texture Patterns -- 2.1 Introduction -- 2.2 Synthesizing 2D Consecutive Region-Images for Space Map Generation -- 2.3 Texture Paths Detection -- 2.4 Simulated Case Study and Comparison with Other Methods -- 2.5 Discussion -- References -- 3 Affective Computing: An Introduction to the Detection, Measurement, and Current Applications -- 3.1 Introduction -- 3.2 Background -- 3.3 Detection and Measurement Devices for Affective Computing -- 3.3.1 Brain Computer Interfaces (BCIs) -- 3.3.2 Facial Expression and Eye Tracking Technologies -- 3.3.3 Galvanic Skin Response -- 3.3.4 Multimodal Input Devices -- 3.3.5 Emotional Speech Recognition and Natural Language Processing -- 3.4 Application Examples -- 3.4.1 Entertainment -- 3.4.2 Chatbots -- 3.4.3 Medical Applications -- 3.5 Conclusions -- References -- 4 A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Problem Definition -- 4.3.1 Frequent Itemset Hiding Problem -- 4.3.2 Inverse Frequent Itemset Hiding Problem -- 4.4 Hiding Approach -- 4.5 Conclusion and Future Steps -- References -- 5 A Rough Inference Software System for Computer-Assisted Reasoning -- 5.1 Introduction -- 5.2 Basic Concepts -- 5.2.1 Rough Sets -- 5.2.2 Information System -- 5.2.3 Decision System -- 5.2.4 Indiscernibility Relation -- 5.3 The Approximate Algorithms for Information Systems -- 5.3.1 The Approximate Algorithm for Attribute Reduction -- 5.3.2 The Algorithm for Approximate Rule Generation -- 5.4 Implementation of the Rough Inference System. , 5.5 An Application in Electrical Engineering-A Case Study -- 5.6 Conclusions -- References -- Part II Advances in Artificial Intelligence-based Applications and Services -- 6 Context Representation and Reasoning in Robotics-An Overview -- 6.1 Introduction -- 6.2 Context -- 6.2.1 Definitions of Context -- 6.2.2 Context Aware Systems -- 6.2.3 Context Representation -- 6.3 Context Reasoning -- 6.3.1 Reasoning Approaches and Techniques -- 6.3.2 Reasoning Tools -- 6.4 Conclusions and Future Work -- References -- 7 Smart Tourism and Artificial Intelligence: Paving the Way to the Post-COVID-19 Era -- 7.1 Introduction -- 7.2 Methodology and Research Approach -- 7.3 Artificial Intelligence and Smart Tourism -- 7.3.1 Artificial Intelligence -- 7.3.2 AI Smart Tourism Recommender Systems -- 7.3.3 Deep Learning -- 7.3.4 Augmented Reality In tourism -- 7.3.5 AI Autonomous Agents -- 7.4 Smart Tourism in COVID-19 Pandemic -- 7.5 Conclusions and Future Directions -- References -- 8 Challenges and AI-Based Solutions for Smart Energy Consumption in Smart Cities -- 8.1 Introduction -- 8.2 Smart Energy in Smart Cities -- 8.3 Energy Consumption Challenges and AI Solutions -- 8.3.1 End-User Consumers in Smart Cities -- 8.3.2 Demand Forecasting -- 8.3.3 Prosumers Management -- 8.3.4 Consumption Privacy -- 8.4 Discussion -- References -- 9 How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics -- 9.1 Introduction -- 9.2 The Development of Log File Analysis and Learning Analytics -- 9.3 Analysing Log File Data in Researching Dynamic Problem-Solving -- 9.4 Extracting, Structuring and Analysing Log File Data to Make Different Thinking Profiles Visible -- 9.4.1 Aims -- 9.4.2 Methods -- 9.5 Participants -- 9.6 Instruments -- 9.7 Procedures -- 9.8 Results -- 9.9 Discussion -- 9.10 Conclusions and Limitations. , References -- 10 AI in Consumer Behavior -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Artificial Intelligence (AI) in Consumer Behavior -- 10.3.1 Artificial Intelligence -- 10.3.2 Consumer Behavior -- 10.3.3 AI in Consumer Behavior -- 10.3.4 AI and Ethics -- 10.4 Conclusion -- References -- Part III Theoretical Advances in Computation and System Modeling -- 11 Coupled Oscillator Networks for von Neumann and Non-von Neumann Computing -- 11.1 Introduction -- 11.2 Basic Unit, Network Architecture and Computational Principle -- 11.3 Nonlinear Oscillator Networks and Phase Equation -- 11.3.1 Example -- 11.4 Oscillator Networks for Boolean Logic -- 11.4.1 Registers -- 11.4.2 Logic Gates -- 11.5 Conclusions -- References -- 12 Design and Implementation in a New Approach of Non-minimal State Space Representation of a MIMO Model Predictive Control Strategy-Case Study and Performance Analysis -- 12.1 Introduction -- 12.2 Centrifugal Chiller-System Decomposition -- 12.2.1 Centrifugal Chiller Dynamic Model Description -- 12.2.2 Centrifugal Chiller Dynamic MIMO ARMAX Model Description -- 12.2.3 Centrifugal Chiller Open Loop MIMO ARMAX Discrete-Time Model -- 12.2.4 Centrifugal Chiller Dynamic MIMO ARMAX Model Nonminimal State Space Description -- 12.3 MISO MPC Strategy Design in a Minimal State Space Realization -- 12.3.1 MIMO MPC Optimization Problem Formulation -- 12.3.2 MIMO MPC Parameters Design -- 12.3.3 MIMO MPC MATLAB SIMULINK Simulation Results -- 12.4 MIMO MPC Strategy Design in a Nonminimal State Space Realization -- 12.5 Conclusions -- References.

Anmerkung: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I Machine Learning Fundamentals -- 1 Introduction -- References -- 2 Machine Learning -- 2.1 Introduction -- 2.2 Machine Learning Categorization According to the Type of Inference -- 2.2.1 Model Identification -- 2.2.2 Shortcoming of the Model Identification Approach -- 2.2.3 Model Prediction -- 2.3 Machine Learning Categorization According to the Amount of Inference -- 2.3.1 Rote Learning -- 2.3.2 Learning from Instruction -- 2.3.3 Learning by Analogy -- 2.4 Learning from Examples -- 2.4.1 The Problem of Minimizing the Risk Functional from Empirical Data -- 2.4.2 Induction Principles for Minimizing the Risk Functional on Empirical Data -- 2.4.3 Supervised Learning -- 2.4.4 Unsupervised Learning -- 2.4.5 Reinforcement Learning -- 2.5 Theoretical Justifications of Statistical Learning Theory -- 2.5.1 Generalization and Consistency -- 2.5.2 Bias-Variance and Estimation-Approximation Trade-Off -- 2.5.3 Consistency of Empirical Minimization Process -- 2.5.4 Uniform Convergence -- 2.5.5 Capacity Concepts and Generalization Bounds -- 2.5.6 Generalization Bounds -- References -- 3 The Class Imbalance Problem -- 3.1 Nature of the Class Imbalance Problem -- 3.2 The Effect of Class Imbalance on Standard Classifiers -- 3.2.1 Cost Insensitive Bayes Classifier -- 3.2.2 Bayes Classifier Versus Majority Classifier -- 3.2.3 Cost Sensitive Bayes Classifier -- 3.2.4 Nearest Neighbor Classifier -- 3.2.5 Decision Trees -- 3.2.6 Neural Networks -- 3.2.7 Support Vector Machines -- References -- 4 Addressing the Class Imbalance Problem -- 4.1 Resampling Techniques -- 4.1.1 Natural Resampling -- 4.1.2 Random Over-Sampling and Random Under-Sampling -- 4.1.3 Under-Sampling Methods -- 4.1.4 Over-Sampling Methods -- 4.1.5 Combination Methods -- 4.2 Cost Sensitive Learning -- 4.2.1 The MetaCost Algorithm. , 4.3 One Class Learning -- 4.3.1 One Class Classifiers -- 4.3.2 Density Models -- 4.3.3 Boundary Methods -- 4.3.4 Reconstruction Methods -- 4.3.5 Principal Components Analysis -- 4.3.6 Auto-Encoders and Diabolo Networks -- References -- 5 Machine Learning Paradigms -- 5.1 Support Vector Machines -- 5.1.1 Hard Margin Support Vector Machines -- 5.1.2 Soft Margin Support Vector Machines -- 5.2 One-Class Support Vector Machines -- 5.2.1 Spherical Data Description -- 5.2.2 Flexible Descriptors -- 5.2.3 v - SVC -- References -- Part II Artificial Immune Systems -- 6 Immune System Fundamentals -- 6.1 Introduction -- 6.2 Brief History and Perspectives on Immunology -- 6.3 Fundamentals and Main Components -- 6.4 Adaptive Immune System -- 6.5 Computational Aspects of Adaptive Immune System -- 6.5.1 Pattern Recognition -- 6.5.2 Immune Network Theory -- 6.5.3 The Clonal Selection Principle -- 6.5.4 Immune Learning and Memory -- 6.5.5 Immunological Memory as a Sparse Distributed Memory -- 6.5.6 Affinity Maturation -- 6.5.7 Self/Non-self Discrimination -- References -- 7 Artificial Immune Systems -- 7.1 Definitions -- 7.2 Scope of AIS -- 7.3 A Framework for Engineering AIS -- 7.3.1 Shape-Spaces -- 7.3.2 Affinity Measures -- 7.3.3 Immune Algorithms -- 7.4 Theoretical Justification of the Machine Learning -- 7.5 AIS-Based Clustering -- 7.5.1 Background Immunological Concepts -- 7.5.2 The Artificial Immune Network (AIN) Learning Algorithm -- 7.5.3 AiNet Characterization and Complexity Analysis -- 7.6 AIS-Based Classification -- 7.6.1 Background Immunological Concepts -- 7.6.2 The Artificial Immune Recognition System (AIRS) Learning Algorithm -- 7.6.3 Source Power of AIRS Learning Algorithm and Complexity Analysis -- 7.7 AIS-Based Negative Selection -- 7.7.1 Background Immunological Concepts -- 7.7.2 Theoretical Justification of the Negative Selection Algorithm. , 7.7.3 Real-Valued Negative Selection with Variable-Sized Detectors -- 7.7.4 AIS-Based One-Class Classification -- 7.7.5 V-Detector Algorithm -- References -- 8 Experimental Evaluation of Artificial Immune System-Based Learning Algorithms -- 8.1 Experimentation -- 8.1.1 The Test Data Set -- 8.1.2 Artificial Immune System-Based Music Piece Clustering and Database Organization -- 8.1.3 Artificial Immune System-Based Customer Data Clustering in an e-Shopping Application -- 8.1.4 AIS-Based Music Genre Classification -- 8.1.5 Music Recommendation Based on Artificial Immune Systems -- References -- 9 Conclusions and Future Work.

Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-VOL.1: Novel Methodologies to Engineering Smart Software Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Survey of Recent Relevant Literature -- 2 Synergies Between Artificial Intelligence and Software Engineering: Evolution and Trends -- 2.1 Introduction -- 2.2 Methodology -- 2.3 The Evolution of AI in Software Engineering -- 2.4 Top Authors and Topics -- 2.5 Trends in AI Applications to Software Engineering -- 2.5.1 Machine Learning and Data Mining -- 2.5.2 Knowledge Representation and Reasoning -- 2.5.3 Search and Optimisation -- 2.5.4 Communication and Perception -- 2.5.5 Cross-Disciplinary Topics -- 2.6 AI-Based Tools -- 2.7 Conclusion -- References -- Part II Artificial Intelligence-Assisted Software Development -- 3 Towards Software Co-Engineering by AI and Developers -- 3.1 Introduction -- 3.2 Software Development Support and Automation Level by Machine Learning -- 3.2.1 Project Planning: Team Composition -- 3.2.2 Requirements Engineering: Data-Driven Persona -- 3.2.3 Design: Detection of Design Patterns -- 3.2.4 Categorization of Initiative and Level of Automation -- 3.3 Quality of AI Application Systems and Software -- 3.3.1 Metamorphic Testing -- 3.3.2 Improving Explainability -- 3.3.3 Systems and Software Architecture -- 3.3.4 Integration of Goals, Strategies, and Data -- 3.4 Towards Software Co-Engineering by AI and Developers -- 3.5 Conclusion -- References -- 4 Generalizing Software Defect Estimation Using Size and Two Interaction Variables -- 4.1 Introduction -- 4.2 Background -- 4.3 A Proposed Approach -- 4.3.1 Selection of Sample Projects -- 4.3.2 Data Collection -- 4.3.3 The Scope and Decision to Go with 'Interaction' Variables. , 4.3.4 Data Analysis and Results Discussion -- 4.3.5 The Turning Point -- 4.3.6 Models Performance-Outside Sample -- 4.4 Conclusion and Limitations -- 4.5 Future Research Directions -- 4.6 Annexure-Model Work/Details -- References -- 5 Building of an Application Reviews Classifier by BERT and Its Evaluation -- 5.1 Background -- 5.2 The Process of Building a Machine Learning Model -- 5.3 Dataset -- 5.4 Preprocessing -- 5.5 Feature Engineering -- 5.5.1 Bag of Words (BoW) [4] -- 5.5.2 FastText [5, 6] -- 5.5.3 Bidirectional Encoder Representations from Transformers (BERT) Embedding [7] -- 5.6 Machine-Learning Algorithms -- 5.6.1 Naive Bayes -- 5.6.2 Logistic Regression -- 5.6.3 BERT -- 5.7 Training and Evaluation Methods -- 5.8 Results -- 5.9 Discussion -- 5.9.1 Comparison of Classifier Performances -- 5.9.2 Performance of the Naive Bayes Classifiers -- 5.9.3 Performance of the Logistic Regression Classifiers -- 5.9.4 Visualization of Classifier Attention Using the BERT -- 5.10 Threats to Validity -- 5.10.1 Labeling Dataset -- 5.10.2 Parameter Tuning -- 5.11 Summary -- References -- 6 Harmony Search-Enhanced Software Architecture Reconstruction -- 6.1 Introduction -- 6.2 Related Work -- 6.3 HS Enhanced SAR -- 6.3.1 SAR Problem -- 6.3.2 HS Algorithm -- 6.3.3 Proposed Approach -- 6.4 Experimentation -- 6.4.1 Test Problems -- 6.4.2 Competitor approaches -- 6.5 Results and Discussion -- 6.6 Conclusion and Future Work -- References -- 7 Enterprise Architecture-Based Project Model for AI Service System Development -- 7.1 Introduction -- 7.2 Related Work -- 7.3 AI Servie System and Enterprise Architecture -- 7.3.1 AI Service System -- 7.3.2 Enterprise Architecture and AI Service System -- 7.4 Modeling Business IT Alignment for AI Service System -- 7.4.1 Generic Business-AI Alignment Model -- 7.4.2 Comparison with Project Canvas Model. , 7.5 Business Analysis Method for Constructing Domain Specific Business-AI Alignment Model -- 7.5.1 Business Analysis Tables -- 7.5.2 Model Construction Method -- 7.6 Practice -- 7.6.1 Subject Project -- 7.6.2 Result -- 7.7 Discussion -- 7.8 Conclusion -- References -- Part III Software Engineering Tools to Develop Artificial Intelligence Applications -- 8 Requirements Engineering Processes for Multi-agent Systems -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Agents, Multiagent Systems, and the BDI Model -- 8.2.2 Requirements Engineering -- 8.3 Techniques and Process of Requirements Engineering for Multiagent Systems -- 8.3.1 Elicitation Requirements Techniques for Multiagent Systems -- 8.3.2 Requirements Engineering Processes for Multiagent Systems -- 8.3.3 Requirements Validation -- 8.4 Conclusion -- References -- 9 Specific UML-Derived Languages for Modeling Multi-agent Systems -- 9.1 Introduction -- 9.2 Backgroud -- 9.2.1 UML -- 9.2.2 Agents, Multiagent Systems, and the BDI Model -- 9.2.3 BDI Models -- 9.3 AUML-Agent UML -- 9.4 AORML-Agent-Object-Relationship Modeling Language -- 9.4.1 Considerations About AORML -- 9.5 AML-Agent Modeling Language -- 9.5.1 Considerations About AML -- 9.6 MAS-ML-Multiagent System Modeling Language -- 9.6.1 Considerations About MAS-ML -- 9.7 SEA-ML-Semantic Web Enabled Agent Modeling Language -- 9.7.1 Considerations -- 9.8 MASRML-A Domain-Specific Modeling Language for Multi-agent Systems Requirements -- 9.8.1 Considerations -- References -- 10 Methods for Ensuring the Overall Safety of Machine Learning Systems -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Safety of Machine Learning Systems -- 10.2.2 Conventional Safety Model -- 10.2.3 STAMP and Its Related Methods -- 10.2.4 Standards for Software Lifecycle Processes and System Lifecycle Processes -- 10.2.5 Social Technology Systems and Software Engineering. , 10.2.6 Software Layer Architecture -- 10.2.7 Assurance Case -- 10.2.8 Autonomous Driving -- 10.3 Safety Issues in Machine Learning Systems -- 10.3.1 Eleven Reasons Why We Cannot Release Autonomous Driving Cars -- 10.3.2 Elicitation Method -- 10.3.3 Eleven Problems on Safety Assessment for Autonomous Driving Car Products -- 10.3.4 Validity to Threats -- 10.3.5 Safety Issues of Automatic Operation -- 10.3.6 Task Classification -- 10.3.7 Unclear Assurance Scope -- 10.3.8 Safety Assurance of the Entire System -- 10.3.9 Machine Learning and Systems -- 10.4 STAMP S& -- S Method -- 10.4.1 Significance of Layered Modeling of Complex Systems -- 10.4.2 STAMP S& -- S and Five Layers -- 10.4.3 Scenario -- 10.4.4 Specification and Standard -- 10.5 CC-Case -- 10.5.1 Definition of CC-Case -- 10.5.2 Technical Elements of CC-Case -- 10.6 Measures for Autonomous Driving -- 10.6.1 Relationship Between Issues and Measures Shown in This Section -- 10.6.2 Measure 1: Analyze Various Quality Attributes in Control Action Units -- 10.6.3 Measure 2: Modeling the Entire System -- 10.6.4 Measure 3: Scenario Analysis and Specification -- 10.6.5 Measure 4: Socio-Technical System -- 10.7 Considerations in Level 3 Autonomous Driving -- 10.7.1 Example of Autonomous Driving with the 5-layered Model of STAMP S& -- S -- 10.8 Conclusion -- References -- 11 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.1 Introduction -- 11.2 Machine Learning and Online Unintelligence: Improvisation or Programming? -- 11.3 The New Paradigm of Information from Digital Media and Social Networks -- 11.4 Numbers, Images and Texts: Sources of Errors, Misinformation and Unintelligence -- 11.5 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.6 Results -- 11.7 Lessons Learned -- 11.8 Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4. , References -- 12 Quantum Computing Meets Artificial Intelligence: Innovations and Challenges -- 12.1 Introduction -- 12.1.1 Benefits of Quantum Computing for AI -- 12.2 Quantum Computing Motivations -- 12.2.1 What Does ``Quantum'' Mean? -- 12.2.2 The Wave-Particle Duality -- 12.2.3 Qubit Definition -- 12.2.4 The Schrödinger Equation -- 12.2.5 Superposition -- 12.2.6 Interference -- 12.2.7 Entanglement -- 12.2.8 Gate-Based Quantum Computing -- 12.3 Quantum Machine Learning -- 12.3.1 Variational Quantum Algorithms -- 12.3.2 Data Encoding -- 12.3.3 Quantum Neural Networks -- 12.3.4 Quantum Support Vector Machine -- 12.3.5 Variational Quantum Generator -- 12.4 Quantum Computing Limitations and Challenges -- 12.4.1 Scalability and Connectivity -- 12.4.2 Decoherence -- 12.4.3 Error Correction -- 12.4.4 Qubit Control -- 12.5 Quantum AI Software Engineering -- 12.5.1 Hybrid Quantum-Classical Frameworks -- 12.5.2 Friction-Less Development Environment -- 12.5.3 Quantum AI Software Life Cycle -- 12.6 A new Problem Solving Approach -- 12.6.1 Use Case 1: Automation and Transportation Sector -- 12.6.2 Use Case 2: Food for the Future World -- 12.6.3 Use Case 3: Cheaper Reliable Batteries -- 12.6.4 Use Case 4: Cleaner Air to Breathe -- 12.6.5 Use Case 5: AI-Driven Financial Solutions -- 12.7 Summary and Conclusion -- References.

Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Machine Learning Paradigms -- References -- Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation -- 2 Using a Multi Module Model for Learning Analytics to Predict Learners' Cognitive States and Provide Tailored Learning Pathways and Assessment -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Multi Module Model and Logical Architecture of the System -- 2.4 Learners Clustering, Using the K-Means Algorithm, Supporting System's Modules -- 2.5 Evaluation and Discussion of Experimental Results -- 2.6 Ethics and Privacy for Learning Analytics -- 2.7 Conclusions and Future Work -- References -- 3 Analytics for Student Engagement -- 3.1 Effects of Student Engagement -- 3.2 Conceptualizing Student Engagement -- 3.3 Measuring Student Engagement -- 3.4 Analytics for Student Engagement -- 3.4.1 Early Alert Analytics -- 3.4.2 Dashboard Visualization Analytics -- 3.5 Dashboard Visualizations of Student Engagement -- 3.6 Comparative Reference Frame -- 3.7 Challenges and Potential Solutions for Analytics of Student Engagement: -- 3.7.1 Challenge 1: Connecting Engagement Analytics to Recommendations for Improvement -- 3.7.2 Potential Solutions: Using Diverse Metrics of Engagement to Improve Feedback Provided -- 3.7.3 Challenge 2: Quantifying Meaningful Engagement -- 3.7.4 Potential Solutions: Analytics Reflecting Quantity and Quality of Student Engagement -- 3.7.5 Challenge 3: Purposeful Engagement Reflection -- 3.7.6 Potential Solutions: Options for Purposeful Engagement Reflection -- 3.7.7 Challenge 4: Finding an Appropriate Reference Norm -- 3.7.8 Potential Solutions: Alternative Reference Frames -- 3.8 Conclusion -- References -- 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification. , 4.1 Introduction -- 4.2 Math Learning Process -- 4.3 Analyzing Empirical Evidence -- 4.3.1 Observations on a Learning Episode -- 4.3.2 Setting the Task -- 4.3.3 Students and Knowing Math -- 4.4 Math Meaningfulness and Three Modes of Manipulating the Blue Graph -- 4.4.1 The Adaptation Process: Dragging Points and Using Sliders -- 4.4.2 Typing the Parameters Values -- 4.4.3 Perceiving the 'a' Parameter and Its Properties -- 4.4.4 Typing Values Without Immediate Feedback -- 4.5 Discussion -- 4.5.1 Metacognitive Enactivism -- 4.6 As a Conclusion -- 4.6.1 Objectification as a Condition for Academic Knowing -- References -- Learning Analytics to Predict Student Performance -- 5 Learning Feedback Based on Dispositional Learning Analytics -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Educational Context -- 5.2.2 The Crucial Predictive Power of Cognitive Data -- 5.2.3 An Unexpected Source of Variation: National Cultural Values -- 5.2.4 LA, Formative Assessment, Assessment of Learning and Feedback Preferences -- 5.2.5 LA and Learning Emotions -- 5.3 The Current Study -- 5.3.1 Participants -- 5.3.2 E-tutorial Trace Data -- 5.3.3 Performance Data -- 5.3.4 Disposition Data -- 5.3.5 Analyses -- 5.4 Results -- 5.4.1 Performance -- 5.4.2 National Cultural Values -- 5.4.3 Cognitive Learning Processing Strategies -- 5.4.4 Metacognitive Learning Regulation Strategies -- 5.4.5 Attitudes and Beliefs Towards Learning Quantitative Methods -- 5.4.6 Epistemic Learning Emotions -- 5.4.7 Activity Learning Emotions -- 5.4.8 Adaptive Motivation and Engagement -- 5.4.9 Maladaptive Motivation and Engagement -- 5.5 Discussion and Conclusion -- References -- 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 HOU Distance Learning Methodology and Data Description. , 6.4 Interview Based Survey Results -- 6.5 Machine Learning Techniques, Experiments and Results -- 6.5.1 Machine Learning Techniques, Experiments and Results -- 6.5.2 The Experiments -- 6.5.3 Results -- 6.5.4 Student Behavior Tool -- 6.6 Discussion -- 6.7 Conclusion -- Appendix -- References -- Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses -- 7 An Architectural Perspective of Learning Analytics -- 7.1 Introduction -- 7.2 What is an Architectural Perspective? -- 7.3 Functional Viewpoints -- 7.3.1 Knowledge Discovery Functions -- 7.3.2 Analytical Functions -- 7.3.3 Predictive Functions -- 7.3.4 Generative Functions -- 7.4 Quality Attributes -- 7.5 Information Viewpoint -- 7.6 Architectural Patterns and Styles -- 7.6.1 Model-View-Control (MVC) -- 7.6.2 Publisher-Subscriber -- 7.6.3 Microservices -- 7.6.4 An Architecture for Learning Analytics -- 7.7 Discussion -- References -- 8 Multimodal Learning Analytics in a Laboratory Classroom -- 8.1 Introduction -- 8.2 Classroom Research -- 8.3 The Science of Learning Research Classroom -- 8.4 The Social Unit of Learning Project -- 8.5 Conceptualization(s) of Engagement -- 8.6 Multimodal Learning Analytics of Engagement in Classrooms -- 8.7 Observation Data -- 8.8 Features Selection, Extraction and Evaluation -- 8.8.1 Multimodal Behavioral Features -- 8.8.2 Feature Visualization -- 8.8.3 Feature Extraction Conclusions -- 8.9 Illustration of High Level Construct Based on Features Extracted -- 8.9.1 Attention to Teacher Speech -- 8.9.2 Teacher Attention -- 8.9.3 Student Concentration During Individual Task -- 8.9.4 Engagement During Pair and Group Work -- 8.10 Implications -- 8.11 Conclusion -- References -- 9 Dashboards for Computer-Supported Collaborative Learning -- 9.1 The Emergence of Learning Analytics and Dashboards -- 9.2 Collaborative Learning Theories. , 9.2.1 Group Cognition (GC) -- 9.2.2 Shared Mental Models (SMMs) -- 9.2.3 Situational Awareness (SA) -- 9.2.4 Socially Shared Regulation of Learning (SSRL) -- 9.3 Tools for CSCL -- 9.3.1 Group Awareness Tools (GATs) -- 9.3.2 Shared Mirroring Systems -- 9.3.3 Ambient Displays -- 9.4 Learning Dashboards for CSCL -- 9.5 How Can Collaborative Learning Dashboards Be Improved? -- 9.5.1 Principle 1: Adopt Iterative, User-Centred Design -- 9.5.2 Principle 2: Navigate the Theoretical Space -- 9.5.3 Principle 3: Visualize to Support Decision-Making -- References -- Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning -- 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software -- 10.1 Introduction -- 10.2 Distance Learning -- 10.3 Mobile Learning and Mobile Learning Analytics -- 10.4 Personalised Learning Software -- 10.5 Data Collection -- 10.5.1 Modalities of Interaction in PCs -- 10.5.2 Modalities of Interaction in Smartphones -- 10.6 Multi-criteria Analysis -- 10.6.1 Combining Modalities of Interaction in HCI -- 10.6.2 Combining Modalities of Interaction in Smartphones -- 10.7 Conclusions -- References -- 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Description of the Student Model -- 11.3.1 Analyzing Data That Have Been Gathered by the Implementation of ELaC -- 11.3.2 The Improved Student Model of ELaCv2 -- 11.4 Description of the Operation of the Student Model -- 11.4.1 Examples of Operation -- 11.5 Evaluation-Results -- 11.6 Conclusion -- References.

Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Multimedia Services in Intelligent Environments: Recommendation Services -- Abstract -- 1…Introduction -- 2…Recommendation Services -- 3…Conclusions -- References and Further Readings -- 2 User Modeling in Mobile Learning Environments for Learners with Special Needs -- Abstract -- 1…Introduction -- 2…Overview of a Mobile Educational System for Students with Special Needs -- 2.1 Students with Moving Difficulties -- 2.2 Students with Sight Problems -- 2.3 Dyslexic Students -- 3…Mobile Coordination of People Who Support Children with Special Needs -- 4…Conclusions -- References -- 3 Intelligent Mobile Recommendations for Exhibitions Using Indoor Location Services -- Abstract -- 1…Introduction and Motivation -- 2…Related Experience -- 3…Design and Storyboard -- 4…Architectural Issues -- 4.1 Mobile Devices Subsystem at Visitor Level -- 4.1.1 Operational Specifications of Subsystem of Mobile Devices at Visitor Level -- 4.1.2 Underlying Infrastructure for the Smartphone App: Cell Application -- 4.1.3 Presentation Multimedia Data -- 4.1.4 Instant Messaging Application -- 4.1.5 Positioning Application -- 4.2 Mobile Devices Management and Disposal Subsystem -- 4.2.1 Operational Specifications of Mobile Devices Management and Disposal Subsystem -- 4.2.2 Interaction with Other Subsystems -- 4.2.3 Correct Operation Issues -- 4.3 Networking Services Subsystem -- 4.3.1 Operational Specifications of Networking Services Subsystem -- 4.3.2 Interaction with Other Subsystems -- 4.3.3 Communication Server -- 4.3.4 Monitoring and Management Visitors Administration Console -- 4.4 Data Management System -- 4.5 Wireless Network Infrastructure Subsystem -- 5…Indoor Positioning and Technologies -- 5.1 Microsoft .NET Compact Framework -- 5.2 XMPP Protocol -- 5.3 Openfire -- 5.4 Windows Media Player Mobile. , 5.5 Macromedia Flash Player -- 5.6 Microsoft Visual Studio -- 6…Case Study: The Digital Exhibition of the History of Ancient Olympic Games and Application Visual Evaluation -- 7…Conclusions and Future Steps -- References -- 4 Smart Recommendation Services in Support of Patient Empowerment and Personalized Medicine -- Abstract -- 1…Introduction -- 2…Review and Methods -- 2.1 What Information Constitutes a User Profile? -- 2.2 How Profiling Information is Represented? -- 2.3 How Profiling Information is Obtained? -- 3…Profiling Mechanisms for Patient Empowerment -- 3.1 Challenges and Opportunities -- 3.2 Patient Profiling Server -- 3.3 ALGA-C -- 3.3.1 Quality of Life and Perceived Health State -- 3.3.2 Quality of Life and Psychological Aspects -- 3.3.3 Quality of Life and Psychosocial Aspects -- 3.3.4 Quality of Life and Cognitive Aspects -- 4…p-Medicine Interactive Empowerment Services -- 4.1 p-Medicine Portal -- 4.2 PHR -- 4.3 HDOT Components -- 4.4 Recommendation Service -- 4.5 e-Consent -- 5…Conclusion -- Acknowledgments -- A.x(118). 6…Appendix -- References -- 5 Ontologies and Cooperation of Distributed Heterogeneous Information Systems for Tracking Multiple Chronic Diseases -- Abstract -- 1…Introduction -- 2…Home Telemonitoring for Patients with Chronic Diseases -- 3…Ontology-Based Knowledge Representation -- 3.1 Knowledge Representation Formalisms -- 4…Heterogeneous Medical Knowledge -- 4.1 Techniques for Achieving Semantic Interoperability -- 5…Cooperation of Distributed Heterogeneous Information Systems -- 5.1 Ontologies and Cooperation -- 6…Ontology Construction for Monitoring Patients with Chronic Diseases -- 6.1 Ontology Development Methodologies -- 6.2 Steps for Creating an Ontology -- 6.3 Languages and Tools for Ontologies Reasoning -- 7…Summary -- References -- 6 Interpreting the Omics 'era' Data -- Abstract -- 1…Molecular Structures. , 2…Tree Hierarchies -- 3…Next Generation Sequencing -- 4…Network Biology -- 5…Visualization in Biology---the Present and the Future -- Acknowledgments -- References -- 7 Personalisation Systems for Cultural Tourism -- Abstract -- 1…Introduction -- 2…Personalising City Tours -- 3…Personalising Museum Tours -- 4…Technology -- 5…User Modeling -- 6…Discussion -- References -- Resource List -- 8 Educational Recommender Systems: A Pedagogical-Focused Perspective -- Abstract -- 1…Introduction -- 2…Recommender Systems and Educational Recommender Systems -- 3…Advantages of Introducing Recommender Systems in the Classroom -- 3.1 Student Performance -- 3.2 Social Learning Enhancement -- 3.3 Increased Motivation -- 4…Challenges -- 5…Conclusions -- A.x(118). Appendix A: Selected Resources -- References -- 9 Melody-Based Approaches in Music Retrieval and Recommendation Systems -- Abstract -- 1…Introduction -- 2…Music Similarity and Classification -- 2.1 Train/Test Tasks -- 2.2 Methods and Tools -- 2.3 Audio Tag Classification -- 2.4 Cover Song Classification -- 2.4.1 Methods and Tools -- 2.5 Audio Music Similarity and Retrieval -- 2.5.1 Methods and Tools -- 3…Rhythmical Analysis -- 3.1 Tempo Estimation -- 3.2 Beat Tracking -- 3.2.1 Methods and Tools -- 4…Structural Analysis -- 4.1 Key Detection -- 4.2 Chord Estimation -- 4.3 Audio Melody Extraction -- 4.4 Multiple F_0 Estimation -- 4.4.1 Overlapping Partials -- 4.4.2 Diverse Spectral Characteristics -- 4.4.3 Other Noise Sources -- 4.4.4 Evaluation -- 4.5 Structural Segmentation -- 4.5.1 Methods and Tools -- 5…Query-Based Music Identification -- 5.1 Query by Tapping -- 5.2 Query by Humming (QbH) and Melody Similarity -- 5.2.1 Note-Based Approaches -- 5.2.2 Direct Matching -- 5.2.3 Melody Matching -- 6…Summary -- 7…Resource List -- 7.1 Software -- 7.1.1 Weka -- 7.1.2 Torch -- 7.1.3 Marsyas -- 7.1.4 Praat. , 7.1.5 Audacity -- 7.2 Resources -- 7.2.1 EdaBoard -- 7.2.2 comp.dsp -- 7.2.3 iMIRSEL -- References -- 10 Composition Support of Presentation Slides Based on Transformation of Semantic Relationships into Layout Structure -- Abstract -- 1…Introduction -- 2…Related Work -- 2.1 Authoring Support of Semantic Contents -- 2.2 User Interfaces for Presentation Composition -- 2.3 Automatic Generation of Presentation Slides -- 3…Framework -- 3.1 Process of Presentation Preparation -- 3.2 Representation of Presentation Scenario -- 3.3 Generating Presentation Slides Using Layout Templates -- 4…Composition of Presentation Scenario -- 4.1 Data Structure of Presentation Scenario -- 4.2 Operations for Scenario Composition -- 5…Automatic Generation of Presentation Slides -- 5.1 Processing Flow -- 5.2 Grouping Slide Components -- 5.3 Allocating Slide Components on Slides -- 6…Prototype System -- 6.1 Scenario Composition Interface -- 6.2 Examples of Generated Slides -- 7…Comparison with Existing Systems -- 7.1 Systems Handling Semantic Contents -- 7.2 Systems Supporting Slide Composition -- 8…Conclusion -- References.

Anmerkung: Intro -- Series Editor's Foreword -- Preface -- Contents -- 1 Introduction -- 1.1 Subject of the Book -- 1.2 Purpose of the Book and Contribution to Science -- 1.3 Structure of the Book -- Further Reading -- 2 e-Government: The Concept, the Environment and Critical Issues for the Back-Office Systems -- 2.1 Introduction -- 2.2 The Environment of Public Administration -- 2.3 Developing e-Government-Models and Levels of Development -- 2.3.1 The Three Rings Model -- 2.3.2 The Model of Focus and Centrality -- 2.3.3 The 5 Levels of e-Government Development -- 2.3.4 The Model of 13 Levels of Digital Service Integration -- 2.4 Towards an Electronic Public Administration: The Roadmap -- 2.5 Critical Issues for e-Government -- 2.6 Back-Office Systems Development-Critical Issues for the Greek Case -- 2.6.1 e-Administration-The Current Back-Office in Greece -- 2.6.2 The Current Situation for the Personnel of the Public Administration in Greece -- 2.6.3 Description of the Support System -- 2.6.4 Benefits of the System-Perspectives -- 2.6.5 Critical Issues for the System Development -- References -- 3 Semantic Web: The Evolution of the Web and the Opportunities for the e-Government -- 3.1 The Internet as the Foundation for Service Providing -- 3.2 The Web as an Internet Service-Historical and Technological References -- 3.3 From Web to Web 2.0 -- 3.4 The Road to Web 3.0 and Web X.0 -- 3.5 Web 2.0 Versus Web 3.0: A Comparative Analysis -- 3.5.1 Web 2.0 as a Collaborative Interactive Platform -- 3.5.2 Web 2.0 Technologies -- 3.5.3 The Semantic Web: Making Data Meaningful -- 3.5.4 The Levels of the Semantic Web -- 3.5.5 The Basic Technologies Used in the Semantic Web -- 3.5.6 Web 1.0-2.0-3.0: A Comparative Presentation -- 3.6 Semantic Web Applications -- 3.7 Advantages and Challenges for the Semantic Web -- 3.8 Benefits from the Application of the Semantic Web. , 3.9 Opportunities for e-Government from the Implementation of Semantic Web Solutions -- References -- 4 Representation and Knowledge Management for the Benefit of e-Government-Opportunities Through the Tools of the Semantic Web -- 4.1 Knowledge and e-Government-Competitive Advantage for the Public Sector -- 4.1.1 Knowledge as a Concept -- 4.1.2 Knowledge Creation -- 4.1.3 Knowledge Coding -- 4.2 Knowledge Management in Terms of Semantic Web-Critical Issues for Their Application in e-Government -- 4.2.1 The Concept "Knowledge Management" -- 4.2.2 Critical Issues for the Implementation of Knowledge Management in the Public Sector -- 4.2.3 Knowledge Management Procedures -- 4.2.4 Knowledge Management Systems -- 4.3 Semantic Tools for Knowledge Management in the Domain of Public Administration -- 4.3.1 The RDF Data Model -- 4.3.2 RDF Schema Specification Language -- 4.3.3 The URI and URI's Use -- 4.3.4 Web Ontology Language-OWL -- 4.3.5 Reasoning Tools -- 4.4 Modelling and Extraction of Knowledge in the Field of e-Government-Our Proposal as "The e-Government Ontology" -- 4.4.1 The e-Government Ontology Motivation -- 4.4.2 The Ontology Development in Protégé 4.3 -- 4.5 Knowledge Acquisition from "The e-Government Ontology" -- 4.5.1 SPARQL -- 4.5.2 SPARQL-DL in OWL2 Query Tab of Protégé -- 4.5.3 DL Query Tool of Protégé -- 4.6 Evaluation of Ontology -- 4.6.1 Categorization of the Ontology -- 4.6.2 Basic Principles of Design -- 4.6.3 Methodology of the Ontology Development -- 4.7 Semantic Modelling in the Domain of Official Statistics -- 4.7.1 The Official Statistics Domain -- 4.7.2 Developing an Ontology for the Modelling of Knowledge in the Field of Official Statistics of the ELS -- 4.7.3 Results -- 4.7.4 Assessment and Evaluation of Ontology -- 4.8 Knowledge Representation in the Internal Audit Field -- 4.8.1 Introduction and Motivation. , 4.8.2 Presentation of the Audit Field -- 4.8.3 Modelling of Knowledge Within the Auditing Sector -- 4.8.4 Examples of the Application of Restrictions, Rules and Queries in Ontology -- 4.8.5 Evaluation-Assessment of Ontology -- 4.8.6 Conclusions -- References -- 5 Towards Open Data and Open Governance-Representation of Knowledge and Triplification of Data in the Field of the Greek Open Government Data -- 5.1 From e-Government Towards Open Government -- 5.2 Benefits-Perspectives from the Opening of Government Data -- 5.3 The Case of Greek Open Government Data -- 5.4 Critical Issues for Opening Government Data -- 5.4.1 Basic Rules -- 5.4.2 Basic Steps for Opening Government Data -- 5.4.3 A Proposal for Linked Open Government Data -- 5.5 The Open Data Ontology-Our Proposal for the Case of the Greek Open Data Repository -- 5.5.1 Introduction and Motivation -- 5.5.2 The Ontology Implementation in Protége -- 5.5.3 Ontology Evaluation -- 5.6 Open Data Triplification-The Case of the Greek Open Data from the "Diavgeia Program" -- 5.6.1 Introduction -- 5.6.2 Relevant Tools -- 5.6.3 Triplification-The Case of Open Data by the "Diavgeia Program -- 5.6.4 Conclusions -- 5.7 Open Data Repositories-The Case of the e-Government Ontology Publishing as an Open Ontology in CKAN -- 5.7.1 Methodology-Steps for the Development of the CKAN Repository -- 5.7.2 Opening Data Through CKAN: The Case of Publishing-Opening e-Government Ontology -- References -- 6 Production and Publication of Linked Open Data: The Case of Open Ontologies -- 6.1 Linked Open Data -- 6.1.1 Semantic Web, Linked Data and Linked Open Data-The Fundamental Rules -- 6.1.2 The Way That Linked Data Work -- 6.1.3 Linking Information Resources to Other Resources Through Redirection -- 6.1.4 Basic Steps for Publishing Linked Open Data -- 6.2 Linked Open Data Tools -- 6.2.1 Apache Jena and Apache Fuseki Server. , 6.2.2 Eclipse RDF4J Framework -- 6.2.3 Pubby -- 6.2.4 Apache Tomcat -- 6.2.5 D2R Server and D2RQ Mapping Language -- 6.2.6 OpenLink Virtuoso Server and Virtuoso Open Source Server -- 6.2.7 Comparison of the Basic Tools -- 6.3 Triplification-The Case of Production of RDF Triples from Data in Relational Databases in National Municipal Registry -- 6.3.1 Motivation -- 6.3.2 The Case of the National Municipal Registry -- 6.3.3 Triplification-Steps and Methodology -- 6.3.4 Conclusions and Future Work -- 6.4 RDF Serialisation from JSON Data-The Case of JSON Data in Diavgeia.gov.gr -- 6.4.1 Introduction -- 6.4.2 JSON Versus JSON-LD -- 6.4.3 Producing RDF Triples out of JSON Data -- 6.4.4 Conclusion-Future Work -- 6.5 Publication of Linked Data: The Case of the Open Ontology for Open Government -- 6.5.1 Create SparqL EndPoint and Publish Linked Open Data Using Fuseki and Pubby Server -- 6.5.2 Ontology Publish as Linked Open Data -- 6.6 Publication of Open Ontology in the LOD Cloud -- 6.6.1 Create a SparqL Endpoint Through OpenLink Virtuoso -- 6.6.2 Publication of the e-Government Ontology in the LOD Cloud -- 6.6.3 Publication of the Ontology in Linked Open Vocabularies -- 6.7 Conclusions -- 6.7.1 Pubby Operation -- 6.7.2 Benefits of Publishing the Ontology for Open Government as Open Data -- References -- 7 Education and e-Government-The Case of a Moodle Based Platform for the Education and Evaluation of Civil Servants -- 7.1 Introduction -- 7.2 Current Situation in the Education of Civil Servants in Greece -- 7.3 e-Training in e-Government -- 7.4 The Wiki as a Means of Education and Knowledge Management in Public Institutions -- 7.5 Suggested Wiki Implementation Through Moodle -- References -- 8 Conclusions-Future Work -- Appendix -- A.1 Customization of the Configuration CKAN File (production.ini). , A.2 Configuration File of Pubby Server (Access in a SparqL Endpoint in the Same Machine) -- A.3 Configuration File of Pubby Server (Access in a Remote SparqL Endpoint) -- A.4 Summary of Key Open Government Licence Sites.

Anmerkung: Intro -- Foreword -- References -- Preface -- Contents -- 1 Machine Learning Paradigms: Advances in Data Analytics -- Bibliography -- Data Analytics in the Medical, Biological and Signal Sciences -- 2 A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- 2.1 Introduction -- 2.2 State of the Art -- 2.2.1 Biomedical Information Retrieval -- 2.2.2 Biomedical Classification -- 2.2.3 Biomedical Clustering -- 2.2.4 Biomedical Recommendation -- 2.2.5 Cognitive Computing and IBM Watson -- 2.2.6 Frame Semantics and Framester -- 2.3 Architecture of Our System -- 2.3.1 Content Analyzer Module -- 2.3.2 Machine Learning Module -- 2.3.3 Recommendation Module -- 2.4 Experiments -- 2.4.1 The Test Dataset -- 2.4.2 Experiment Setup -- 2.4.3 Recommendation Module Setup -- 2.4.4 Results -- 2.5 Conclusion and Future Trends -- References -- 3 Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- 3.1 Introduction -- 3.2 Background -- 3.3 Feature Representation for Image Classification -- 3.3.1 Global Features -- 3.3.2 Local Features -- 3.3.3 Bag of Visual Words -- 3.3.4 Pixel-Level Features -- 3.4 Security and Biometrics -- 3.4.1 Supervised Classification -- 3.5 Aerospace and Satellite Monitoring -- 3.5.1 Supervised Classification -- 3.5.2 Unsupervised Classification -- 3.6 Document Analysis and Language Understanding -- 3.6.1 Supervised Classification -- 3.6.2 Unsupervised Classification -- 3.7 Information Retrieval -- 3.7.1 Supervised Classification -- 3.7.2 Unsupervised Classification -- 3.8 Classification in Image-Based Medical Analytics -- 3.8.1 Diagnostic Inspective Acquisition Imaging -- 3.8.2 Nuclear Medicine Imaging -- 3.8.3 Clinical Radiology Imaging -- 3.8.4 Horizon of the Research and Future Challenges -- 3.9 Conclusions -- 3.10 Further Readings -- References. , 4 Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- 4.1 Introduction -- 4.2 Classical Data Mining Technics and Heart Diseases -- 4.2.1 Popular Data Mining Algorithms -- 4.2.2 Data Mining and Heart Diseases -- 4.3 Sequential Mining in Medical Domain -- 4.4 Sequential Mining -- 4.4.1 Important Terms and Notations -- 4.4.2 Sequential Patterns Mining -- 4.4.3 General and Specific Techniques Used by SPM Algorithms -- 4.4.4 Extensions of Sequential Pattern Mining Algorithms -- 4.5 Discussion -- 4.6 Conclusion -- References -- 5 Machine Learning Methods for the Protein Fold Recognition Problem -- 5.1 Introduction -- 5.2 Supervised Learning -- 5.3 Deep Learning Methods in Pattern Recognition -- 5.4 Features of the Amino Acid Sequence -- 5.5 Protein Fold Machine Learning-Based Classification Methods -- 5.5.1 Datasets Used in the Described Experiments -- 5.5.2 Methods -- 5.6 Discussion, Conclusions and Future Work -- References -- 6 Speech Analytics Based on Machine Learning -- 6.1 Introduction -- 6.2 Speech Phoneme Signal Analysis -- 6.3 Speech Signal Pre-processing -- 6.4 Speech Information Retrieval Scheme -- 6.5 Feature Extraction -- 6.5.1 Time Domain Features -- 6.5.2 Frequency Domain Features -- 6.5.3 Mel-Frequency Cepstral Coefficients -- 6.6 Data Preparation for Deep Learning -- 6.7 Experiment Results -- 6.7.1 Feature Vector Applied to Vowel Classification -- 6.7.2 Feature Vector Applied to Allophone Classification -- 6.7.3 Convolutional Neural Networks Applied to Allophone Classification -- 6.7.4 Convolutional Neural Networks Applied to Vowel Classification -- 6.8 Conclusions -- References -- Data Analytics in Social Studies and Social Interactions -- 7 Trends on Sentiment Analysis over Social Networks: Pre-processing Ramifications, Stand-Alone Classifiers and Ensemble Averaging -- 7.1 Introduction. , 7.2 Research Methodology -- 7.3 Twitter Datasets -- 7.4 Evaluation of Data Preprocessing Techniques -- 7.5 Evaluation of Stand-Alone Classifiers -- 7.6 Evaluation of Ensemble Classifiers -- 7.7 The Case of Sentiment Analysis in Social e-Learning -- 7.8 Conclusions and Future Work -- References -- 8 Finding a Healthy Equilibrium of Geo-demographic Segments for a Telecom Business: Who Are Malicious Hot-Spotters? -- 8.1 Introduction -- 8.2 Geospatial and Geo-demographic Data -- 8.3 The Combinatorial Optimization Module -- 8.4 Infrastructure-Friendly and Stressing Clients -- 8.5 Experiments -- 8.6 Conclusions -- References -- Data Analytics in Traffic, Computer and Power Networks -- 9 Advanced Parametric Methods for Short-Term Traffic Forecasting in the Era of Big Data -- 9.1 Introduction -- 9.2 Traffic Data -- 9.2.1 Traffic Network -- 9.2.2 Traffic Descriptors -- 9.2.3 Traffic Data Sources -- 9.3 Traffic Data Preprocessing -- 9.3.1 Time Series Formulation -- 9.3.2 Outlier Detection -- 9.3.3 Missing Data Imputation -- 9.3.4 Map-Matching -- 9.4 Parametric Short-Term Traffic Forecasting -- 9.4.1 Autoregressive Moving Average (ARMA) -- 9.4.2 Autoregressive Integrated Moving Average (ARIMA) -- 9.4.3 Space-Time ARIMA (STARIMA) -- 9.4.4 Lag-STARIMA -- 9.4.5 Graph-Based Lag-STARIMA (GBLS) -- References -- 10 Network Traffic Analytics for Internet Service Providers-Application in Early Prediction of DDoS Attacks -- 10.1 Introduction -- 10.2 The Procedure Adopted -- 10.2.1 Related Work -- 10.3 The Proposed Approach -- 10.3.1 Mathematical Formulation -- 10.3.2 State Space Model-Autoregressive Model-Discrete-Time Kalman Filter -- 10.4 Structure and Parameters of the MRSP Algorithm -- 10.5 Results and Performance of the MRSP Algorithm -- 10.6 Detecting Anomalies -- 10.6.1 DETECTING a DDoS ATTACK -- 10.6.2 Detecting an Anomaly -- 10.6.3 Final Remarks. , 10.7 Conclusions -- References -- 11 Intelligent Data Analysis in Electric Power Engineering Applications -- 11.1 Introduction -- 11.2 Intelligent Techniques in Ground Resistance Estimation -- 11.2.1 Grounding Systems -- 11.2.2 Application of ANN Methodologies for the Estimation of Ground Resistance -- 11.2.3 Wavelet Networks Modeling for the Estimation of Ground Resistance -- 11.2.4 Inductive Machine Learning -- 11.2.5 Genetic and Gene Expression Programming Versus Linear Regression Models -- 11.3 Estimation of Critical Flashover Voltage of Insulators -- 11.3.1 Problem Description -- 11.3.2 Genetic Algorithms -- 11.3.3 Application of ANNs -- 11.3.4 Multilayer Perceptron ANNs -- 11.3.5 Genetic Programming -- 11.3.6 Gravitational Search Algorithm Technique -- 11.4 Other Applications of Electric Power Systems -- 11.4.1 Load Forecasting -- 11.4.2 Lightning Performance Evaluation in Transmission Lines -- 11.5 Conclusions and Further Research -- References -- Data Analytics for Digital Forensics -- 12 Combining Genetic Algorithms and Neural Networks for File Forgery Detection -- 12.1 Introduction -- 12.1.1 McKemmish Predominant Model -- 12.1.2 Kent Predominant Model -- 12.1.3 Digital Evidences -- 12.1.4 File Type Identification -- 12.2 Methodology of the Proposed Method -- 12.3 Experimental Setup and Results -- 12.4 Conclusions -- References -- Theoretical Advances and Tools for Data Analytics -- 13 Deep Learning Analytics -- 13.1 Introduction -- 13.2 Preliminaries and Notation -- 13.3 Unsupervised Learning -- 13.3.1 Deep Autoencoders -- 13.3.2 Autoencoder Variants -- 13.4 Supervised Learning -- 13.4.1 Multilayer Perceptrons -- 13.4.2 Convolutional Neural Networks -- 13.4.3 Recurrent Neural Networks -- 13.5 Deep Learning Frameworks -- 13.6 Concluding Remarks -- References.

Anmerkung: Title Page -- Preface -- Organizations -- Contents -- Managing Collaborative Sessions in WSNs -- Introduction -- Related Work -- Collaboration Hierarchy in WSNs -- Types of Collaboration -- Collaboration Hierarchy -- Sessions -- WISE-MANager -- Case Study -- Advantages and Disadvantages -- Conclusions -- References -- OGRE-Coder: An Interoperable Environment for the Automatic Generation of OGRE Virtual Scenarios -- Introduction -- OGRE Markup Language (OGREML) -- OGRE-Coder: Design and Implementation Issues -- Requirements and Use Cases -- Architecture -- Implementation Tools -- OGRE-Coder Functionalities -- Authoring OGRE Virtual Environments -- Generating OGRE Code with OGRE-Coder -- Conclusions -- References -- Natural and Intuitive Video Mediated Collaboration -- Current Systems for Enabling Remote Collaboration -- Videoconferencing and Telepresence Systems -- Desktop Video Conferencing -- Interactive Tables and Smart Whiteboards -- The Importance of Usability -- Building Prototypes -- First Prototype -- Second Prototype -- Our Design Concept -- Permanent Connection, Invisible User Interface -- Natural Collaborative Tools -- Discussion -- Is Permanent Connection Too Limited? -- Where Could This Concept Be Applied? -- Challenges of Permanent Video Connections -- Future Development Challenges -- References -- Evolutionary System Supporting Music Composition -- Introduction -- Evolutionary System Supporting Music Composition -- Architecture of the System -- Genetic Algorithm -- The Process of Music Composition -- Experimental Results -- Summary and Conclusions -- References -- Usability Inspection of Informal Learning Environments: The HOU2LEARN Case -- Introduction -- Literature Review -- The HOU2LEARN Platform -- Usability Evaluation -- General -- The Method Applied -- The Experiment -- Conclusions - Future Goals -- References. , Procedural Modeling of Broad-Leaved Trees under Weather Conditions in 3D Virtual Reality -- Introduction -- Related Work -- Procedural Modeling of Broad-Leaved Trees -- Modeling of Tree under Weather Conditions -- Forest Modeling -- Experimental Program -- Conclusion -- References -- New Method for Adaptive Lossless Compression of Still Images Based on the Histogram Statistics -- Introduction -- Basic Principles of the АRL Coding Method -- Evaluation of the Lossless Coding Method Efficiency -- Experimental Results -- Conclusions -- References -- Scene Categorization with Class Extendibility and Effective Discriminative Ability -- Introduction -- Category-Specific Approach -- Whole-Construction/Whole-Representation Strategy -- Category-Specific-Construction/Whole-Representation Strategy -- Category-Specific-Construction/ Category-Specific-Representation Strategy -- Experimental Results -- Conclusions -- References -- Adaptive Navigation in a Web Educational System Using Fuzzy Techniques -- Introduction -- Adaptive Navigation Support -- The Domain Knowledge -- Student Modeling -- Discussion on the Fuzzy Cognitive Maps and Fuzzy User Modeling Used -- Conclusion -- References -- Visualization Tool for Scientific Gateway -- Introduction -- Visual Representation of Datasets -- VT as a New Discovery for Presenting Academic Research Results -- Architecture of Visualization Tool -- Directly Visual Education Form -- Conclusion -- References -- Digital Text Based Activity: Teaching Geometrical Entities at the Kindergarten -- Introduction -- Review Standards -- Method - Data Collection and Observations -- Digital Based Activities at the Kindergarten -- Using Graphical Programs (Mspaint) -- Using Slide Shows (PowerPoint) -- Using Digital Cameras -- Using Spreadsheets (EXCEL) -- Summary and Conclusions -- References. , Cross Format Embedding of Metadata in Images Using QR Codes -- Introduction -- QRCodes -- Our Proposal -- Results -- Applications -- Conclusions -- References -- An Empirical Study for Integrating Personality Characteristics in Stereotype-Based Student Modelling in a Collaborative Learning Environment for UML -- Introduction -- Personality Related Stereotypes -- Empirical Study for Defining the Triggers -- Implementation of Triggers -- Conclusion -- References -- An Efficient Parallel Architecture for H.264/AVC Fractional Motion Estimation -- Introduction -- H.264/AVC FME Observations -- Encoding with INTER8x8 Mode or above -- Statistic Charactistics of Motion Vectors -- The Proposed Architecture -- Reference Pixel Array -- Integer Pixel Sampler in Reference Array -- 14-Input FME Engine -- Data Processing Order -- 3-Stages Processing -- Simulation Results -- Conclusions -- References -- Fast Two-Stage Global Motion Estimation: A Blocks and Pixels Sampling Approach -- Introduction -- Motion Models -- Global Motion Estimation -- Initial Translation Estimation -- Block Sampling and Limited Block Matching -- Initial Estimation of Perspective Model GM Parameters -- Subsampling Pixels and Levenberg-Marquardt Algorithm -- Simulation -- Conclusion -- References -- Frame Extraction Based on Displacement Amount for Automatic Comic Generation from Metaverse Museum Visit Log -- Introduction -- Comic Generation System -- Evaluation -- Implementation -- Evaluation Outline -- Results and Discussions -- Related Work -- Conclusions and Future Work -- References -- Knowledge-Based Authoring Tool for Tutoring Multiple Languages -- Introduction -- Related Work -- Architecture of Our System -- Description of the System -- Authoring Domain Knowledge -- Authoring Student Model -- Authoring of Teaching Model -- Case Study for the Instructor -- Case Study for the Student. , Student Modeling and Error Diagnosis -- Modeling the System's Authoring Process -- Conclusions -- References -- Evaluating an Affective e-Learning System Using a Fuzzy Decision Making Method -- Introduction -- Fuzzy Simple Additive Weighting -- Overall Description of the System -- Evaluation Experiment -- Results -- Conclusions -- References -- Performance Evaluation of Adaptive Content Selection in AEHS -- Introduction -- Performance Evaluation Metrics for Decision-Based AEHS -- Evaluation Methodology for Decision-Based AEHS -- Setting Up the Experiments -- Designing the Media Space -- Designing the Learner Model -- Simulating the AM of an AEHS -- Experimental Results and Discussion -- Extracting the AM of existing AEHS -- Scaling Up the Experiments -- Conclusions -- References -- AFOL: Towards a New Intelligent Interactive Programming Language for Children -- Introduction -- General Architecture of the AFOL Programming Environment -- Overview of the AFOL Programming Learning System -- AFOL Language Commands and Object Oriented Structure -- Conclusions -- References -- Multimedia Session Reconfiguration for Mobility-Aware QoS Management: Use Cases and the Functional Model -- Introduction -- Session Reconfiguration and Use Cases -- Functional Model -- Performance Evaluation -- Conclusions and Future Work -- References -- LSB Steganographic Detection Using Compressive Sensing -- Introduction -- Steganalysis -- Compressive Sensing and BM3D -- The Proposed Method -- Results -- Conclusions -- References -- Analysis of Histogram Descriptor for Image Retrieval in DCT Domain -- Introduction -- Description of the Method -- Pre-processing -- Construction of the AC-Pattern Histogram -- Construction of DC-Pattern Histogram -- Application to Image Retrieval -- Paramaters of Descriptor -- Performance Analysis -- Application to GTF Database. , Application to ORL Database -- Conclusions -- References -- A Representation Model of Images Based on Graphs and Automatic Instantiation of Its Skeletal Configuration -- Introduction -- Related Works -- A Model for Images -- Instantiating the Model -- Experiments -- Conclusion and Outlook -- References -- Advice Extraction fromWeb for Providing Prior Information Concerning Outdoor Activities -- Introduction -- Characteristics Analysis of Advices -- The Definition of Advices -- Construction of Development Data -- Characteristics of Advices -- Characteristics of Advices Suitable for Situations -- Prior Advice Acquisition -- Preprocessing -- Advice Acquisition -- Situation Classification of Advices -- Experiment -- Evaluation Data -- Experiment for Acquiring Advices -- Experiment for Classifying Situation of Advices -- Conclusion -- References -- Automatic Composition of Presentation Slides, Based on Semantic Relationships among Slide Components -- Introduction -- Approach -- Document Structure -- Processing Flow -- Slide Editing -- Semantic Relationship -- Editing Operation -- Slide Composition -- Grouping of Slide Components -- Template-Based Slide Composition -- Prototype System -- Component Editing Interface -- Display Interface -- Conclusion -- References -- Sustainable Obsolescence Management - A Conceptual Unified Framework to Form Basis of an Interactive Intelligent Multimedia System -- Introduction -- Definitions -- Sustainability / Sustainable Development -- Obsolescence -- Sustainability versus Obsolescence - Built Environment Context -- Social -- Environmental -- Economic -- Holistic Sustainable Obsolescence Management -- Obsolescence Assessment (OA) -- Obsolescence Reduction (OR) -- Concluding Remarks -- References -- Automatic Text Formatting for Social Media Based on Linefeed and Comma Insertion -- Introduction. , Text Formatting by Comma and Linefeed Insertion.

Anmerkung: Intro -- Foreword -- References -- Preface -- Contents -- 1 Introduction to Fusion of Machine Learning Paradigms -- 1.1 Editorial -- References -- Part I Recent Application Areas of Fusion of Machine Learning Paradigms -- 2 Artificial Intelligence as Dual-Use Technology -- 2.1 Introduction -- 2.2 What Is DUT -- 2.3 AI: Concepts, Models and Technology -- 2.4 Agent-Based AI and Autonomous System -- 2.4.1 Basic Model of Agent-Based AI -- 2.4.2 Conceptual Model of Autonomous Weapon System -- 2.5 Dual-Use Technology and DARPA -- 2.5.1 Historical View and Role of DARPA -- 2.5.2 DARPA's Contribution to DUT R& -- D on AI -- 2.6 DARPA-Like Organizations in Major Countries -- 2.7 Dual-Use Dilemma -- 2.8 Concluding Remarks -- References -- 3 Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques -- 3.1 Introduction -- 3.2 Background -- 3.2.1 Deep Learning for Diabetic Retinopathy -- 3.2.2 Image Preprocessing Techniques -- 3.2.3 Reinforcement Learning and Deep Learning -- 3.3 Data Augmentation Techniques -- 3.3.1 Traditional Data Augmentation -- 3.3.2 SMOTE-Based Data Augmentation -- 3.3.3 Data Augmentation Using Generative Adversarial Networks -- 3.4 Datasets of Eye Fundus Images -- 3.5 Transfer Learning Experiments -- 3.5.1 Dataset -- 3.5.2 Image Preprocessing -- 3.5.3 Image Augmentation -- 3.5.4 Deep Learning Experiments -- 3.5.5 Reinforcement Learning Experiments -- 3.6 Conclusion and Future Work -- References -- 4 A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses -- 4.1 Introduction -- 4.2 Method -- 4.2.1 Preliminaries -- 4.2.2 Hierarchical Fuzzy Structure -- 4.2.3 Stacked Deep Fuzzy Rule-Based System (SD-FRBS) -- 4.2.4 Adaptation of the First-Order TSK Structure in SD-FRBS. , 4.2.5 Concatenated Deep Fuzzy Rule-Based System (CD-FRBS) -- 4.3 Data Description and Results -- 4.3.1 MIMIC-III Dataset -- 4.3.2 SD-FRBS as a Multivariate Regressor for Granger Causality Estimation-In EEG Connectivity Index Extraction -- 4.3.3 CD-FRBS in Staging Depression Severity -- 4.4 Discussion and Conclusion -- 4.4.1 Suggested Future Works -- References -- 5 Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks -- 5.1 Introduction -- 5.2 Theoretical Background -- 5.2.1 Deep Belief Networks -- 5.2.2 Harmony Search -- 5.3 Methodology -- 5.3.1 Datasets -- 5.3.2 Experimental Setup -- 5.4 Experimental Results -- 5.5 Conclusions -- References -- 6 Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting -- 6.1 Introduction -- 6.2 Relevance Vector Regression -- 6.3 RVR Based Day Ahead Forecasting -- 6.4 Results -- 6.5 Conclusion -- References -- 7 Domain-Integrated Machine Learning for IC Image Analysis -- 7.1 Introduction -- 7.2 Hierarchical Multi-classifier System -- 7.2.1 Architecture of Hierarchical Multi-classifier System -- 7.2.2 Result and Discussion on Case Study -- 7.3 Deep Learning with Pseudo Labels -- 7.3.1 Methodology -- 7.3.2 Application to IC Image Analysis -- 7.4 Conclusions and Future Works -- References -- Part II Applications that Can Clearly Benefit from Fusion of Machine Learning Paradigms -- 8 Fleshing Out Learning Analytics and Educational Data Mining with Data and ML Pipelines -- 8.1 Introduction -- 8.2 Data and ML Pipelines -- 8.3 Related Work -- 8.4 An Automated EDM and LA Methodology -- 8.4.1 A Data Pipeline Scenario -- 8.4.2 An ML Pipeline Scenario -- 8.5 Experiments and Results -- 8.6 Conclusions and Future Work -- References -- 9 Neural Networks Based Throughput Estimation of Short Production Lines Without Intermediate Buffers -- 9.1 Introduction. , 9.2 Data Sets of i-Stage Production Line Problems -- 9.3 Deep Learning and Multilayer Perceptron -- 9.4 Experimental Process of Deep Learning Approach -- 9.5 Results of Deep Learning Approach -- 9.6 Conclusions -- References.