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: Title -- Foreword -- Editors -- Preface -- Table of Contents -- Advances in Multimedia Services in Intelligent Environments - Software Development Challenges and Solutions -- Introduction -- Conclusions -- References and Further Reading -- Evaluating the Generality of a Life-Cycle Framework for Incorporating Clustering Algorithms in Adaptive Systems -- Introduction -- The Software Life Cycle -- Inception -- Defining Requirements for the Prototype System and Analysis and Design of the Prototype Adaptive Recommender System -- Building and Evaluating the Prototype Adaptive Recommender System -- Elaboration -- Computing the Resemblance Coefficients for the Data Set and Developing the Clustering Algorithm -- Execute the Clustering Method for the Prototype and Evaluating the Results of the Clustering Algorithm used in the Prototype -- Construction -- The Most Efficient Algorithm and Designing Stereotypes Based on This Algorithm -- Building the User Modeling Component Based on the Stereotypes and Incorporating Them into the System -- Transition-Dynamically Improving System Performance while Used by Real Users -- Conclusion about the Life Cycle Process -- References -- A Distributed Multimedia Data Management over the Grid -- Introduction -- Related Work -- Overall Framework -- Replicated Multidimensional Index Structure -- Distributed Query Processing -- Automatic Load Balancing -- Empirical Study -- Conclusion and Future Works -- References -- A View of Monitoring and Tracing Techniques and Their Application to Service-Based Environments -- Introduction -- Execution Monitoring and Tracing -- Categories of Execution Monitoring -- What Can Be Traced? -- Monitoring Techniques -- Comparison -- Monitoring Challenges for Multimedia Services -- Behavioral Change -- Information Access -- Timing -- Synchronization of Event Traces -- Distributed Monitoring. , Trace Transmission -- Conclusion -- References -- An Ontological SW Architecture SupportingContribution and Retrieval of Service and Process Models -- Introduction -- An Application in the Healthcare Domain -- Ontological SW Architecture -- Three Tier with Ontologies -- Encoding Procedures through Ontologies -- Enabling Direct Contribution through Ontologies -- Enabling Semantic Queries -- Enabling Adapted Presentation through Ontologies -- Conclusions -- References -- Model-Driven Quality Engineering of Service-Based Systems -- Introduction -- Related Work -- QoS-Enabled WSDL (Q-WSDL) -- QoS Characteristics of Web Services -- Q-WSDL Metamodel -- Composite Web Services -- QoS Prediction of Composite Web Services -- Model-Driven Reliability Prediction -- Example Application -- Conclusions -- References -- Application of Logic Models for Pervasive Computing Environments and Context-Aware Services Support -- Introduction -- Contextual Models Requirements for Pervasive Computing -- The LogiPerSe First Order Logic Model -- Partial Validation and Ambiguity -- Scalability -- Composability -- Pervasive Service Scenario -- Logic-Based System Architecture -- Logic Model - Object-Oriented Model - Ontology Integration -- Conclusions -- References -- Intelligent Host Discovery from Malicious Software -- Introduction -- Instant Message Clients -- Search Engines -- Social Networks -- Torrent Trackers -- Conclusions -- References -- Swarm Intelligence: The Ant Paradigm -- Introduction -- Swarm Intelligence -- Principles of Natural Swarms -- The Biological Origins of Ant Algorithms -- Ant Algorithms of the Ant Colony Optimization Framework -- Ant System -- MAX MIN Ant System -- Ant Colony System -- Conclusion -- References -- Formulating Discrete Geometric Random Sums for Facilitating Intelligent Behaviour of a Complex System under a Major Risk -- Introduction. , Formulation of a Discrete Geometric Random Sum -- Stochastic Derivation of a Discrete Geometric Random Sum -- Risk and Crisis Management Applications and Computational Intelligence Interpretation of a Discrete Geometric Random Sum -- Conclusions -- References -- Incorporating a Discrete Renewal Random Sum in Computational Intelligence and Cindynics -- Introduction -- Formulation of a Discrete Renewal Random Sum -- Stochastic Derivation of a Discrete Renewal Random Sum -- Application in Cindynics and Interpretation in Computational Intelligence of a Discrete Renewal Random Sum -- Conclusions -- References -- Author Index.

Anmerkung: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Introduction to Recommender Systems -- 1.2 Formulation of the Recommendation Problem -- 1.2.1 The Input to a Recommender System -- 1.2.2 The Output of a Recommender System -- 1.3 Methods of Collecting Knowledge About User Preferences -- 1.3.1 The Implicit Approach -- 1.3.2 The Explicit Approach -- 1.3.3 The Mixing Approach -- 1.4 Motivation of the Book -- 1.5 Contribution of the Book -- 1.6 Outline of the Book -- References -- 2 Review of Previous Work Related to Recommender Systems -- 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 The Learning Problem -- 3.1 Introduction -- 3.2 Types of Learning -- 3.3 Statistical Learning -- 3.3.1 Classical Parametric Paradigm -- 3.3.2 General Nonparametric---Predictive Paradigm -- 3.3.3 Transductive Inference Paradigm -- 3.4 Formulation of the Learning Problem -- 3.5 The Problem of Classification -- 3.5.1 Empirical Risk Minimization -- 3.5.2 Structural Risk Minimization -- 3.6 Support Vector Machines -- 3.6.1 Basics of Support Vector Machines -- 3.6.2 Multi-class Classification Based on SVM -- 3.7 One-Class Classification -- 3.7.1 One-Class SVM Classification -- 3.7.2 Recommendation as a One-Class Classification Problem -- References -- 4 Content Description of Multimedia Data -- 4.1 Introduction -- 4.2 MPEG-7 -- 4.2.1 Visual Content Descriptors. , 4.2.2 Audio Content Descriptors -- 4.3 MARSYAS: Audio Content Features -- 4.3.1 Music Surface Features -- 4.3.2 Rhythm Features and Tempo -- 4.3.3 Pitch Features -- References -- 5 Similarity Measures for Recommendations Based on Objective Feature Subset Selection -- 5.1 Introduction -- 5.2 Objective Feature-Based Similarity Measures -- 5.3 Architecture of MUSIPER -- 5.4 Incremental Learning -- 5.5 Realization of MUSIPER -- 5.5.1 Computational Realization of Incremental Learning -- 5.6 MUSIPER Operation Demonstration -- 5.7 MUSIPER Evaluation Process -- 5.8 System Evaluation Results -- References -- 6 Cascade Recommendation Methods -- 6.1 Introduction -- 6.2 Cascade Content-Based Recommendation -- 6.3 Cascade Hybrid Recommendation -- 6.4 Measuring the Efficiency of the Cascade Classification Scheme -- References -- 7 Evaluation of Cascade Recommendation Methods -- 7.1 Introduction -- 7.2 Comparative Study of Recommendation Methods -- 7.3 One-Class SVM---Fraction: Analysis -- 8 Conclusions and Future Work -- 8.1 Summary and Conclusions -- 8.2 Current and Future Work.