Note: 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.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-Vol. 2: Smart Software Applications in Cyber-Physical Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Smart Software Applications in Scientific Document Processing -- 2 Detection, Extraction and SPN Representation of Pseudo-Algorithms in Scientific Documents -- 2.1 Introduction -- 2.2 Visual Detection of Pseudo-Codes in Documents -- 2.2.1 Extraction of Different Text Blocks in Documents -- 2.2.2 Pyramidal Image Representation -- 2.2.3 Decomposition and Classification of the Pseudo Code Sections -- 2.3 Learning -- 2.3.1 The Dataset -- 2.3.2 Evaluation of Learning Process -- 2.4 Translation of Algorithms to Graphs and SPNs -- 2.4.1 Generation of a Graph -- 2.4.2 Stochastic Petri Net Representation -- 2.5 Conclusion and Future Work -- References -- 3 A Recommender Engine for Scientific Paper Peer-Reviewing System -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Dataset and Feature -- 3.4 Methodology -- 3.4.1 Create Training Dataset -- 3.4.2 The Architecture of Recommender Engine -- 3.4.3 Final Recommendation Section -- 3.5 Result and Analysis -- 3.6 Conclusion(s) -- References -- Part II Smart Software Applications in Enterprise Modeling -- 4 Visualization of Digital-Enhanced Enterprise Modeling -- 4.1 Introduction -- 4.2 A Meta Model for Describing Value of Digital Service -- 4.3 Visualization Patterns -- 4.3.1 Service Definition Pattern -- 4.3.2 Value Proposition Pattern -- 4.3.3 Use Process Refinement pattern -- 4.4 Application Example -- 4.4.1 Healthcare Example -- 4.4.2 Application of Visualization Patterns -- 4.5 Discussions -- 4.5.1 Effectiveness -- 4.5.2 Novelty -- 4.5.3 Mapping to BMC -- 4.5.4 Limitation -- 4.6 Related Work -- 4.7 Conclusion. , References -- 5 Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies -- 5.1 Introduction -- 5.2 State of the Art -- 5.2.1 Profiling -- 5.2.2 Organizational Tools Analysis -- 5.2.3 Indexing -- 5.3 Related Works -- 5.4 Know-linking Approach -- 5.4.1 Presentation -- 5.5 Environment Study -- 5.6 Know-linking in Aerospace Manufacturer -- 5.6.1 Technical Audit -- 5.6.2 Extracting Profiles -- 5.6.3 Generating Semantic Models for Each Profile -- 5.6.4 Hidden Semantic Links Between Profiles -- 5.6.5 Indexing Based Profiles -- 5.7 Conclusion and Future Work -- References -- 6 Changes in Human Resources Management with Artificial Intelligence -- 6.1 Introduction -- 6.2 The Effect of AI in Human Resources Management -- 6.2.1 Recruitment Process with AI -- 6.2.2 Training Process with AI -- 6.2.3 Performance Assessment Process with AI -- 6.2.4 Talent Management Process with AI -- 6.2.5 Salary Management Process with AI -- 6.3 Conclusion -- References -- Part III Smart Software Applications in Education -- 7 Promoting Reading Among Teens: Analyzing the Emotional Preferences of Teenage Readers -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Our Emotion Trait Analysis Approach -- 7.3.1 Processing a Book Description -- 7.3.2 Calculating an Emotion Vector -- 7.3.3 Emotion Trait -- 7.3.4 Partitioning Books by Average Ratings -- 7.3.5 Reducing Objective Values by Comparing Synonyms -- 7.3.6 Implementation -- 7.4 Conclusions and Future Works -- References -- 8 A Multi-institutional Analysis of CS1 Students' Common Misconceptions of Key Programming Concepts -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Study Design -- 8.3.1 Research Objective -- 8.3.2 Research Questions -- 8.3.3 Data Collection -- 8.3.4 Reliability of Pre-post-test Instrument -- 8.3.5 Study Procedure -- 8.4 Experimental Results. , 8.5 Discussion of Results -- 8.6 Conclusion -- References -- Part IV Smart Software Applications in Healthcare and Medicine -- 9 Clustering-Based Scaling for Healthcare Data -- 9.1 Introduction -- 9.2 Fuzzy Clustering -- 9.3 Fuzzy Clustering for 3-Way Data -- 9.4 Fuzzy Cluster-Scaled Regression Analysis -- 9.5 Numerical Examples -- 9.6 Conclusions -- References -- 10 Normative and Fuzzy Components of Medical AI Applications -- 10.1 Preliminaries -- 10.2 Normative Issues -- 10.3 Fuzziness and Norms -- 10.4 Conclusions -- References -- Part V Smart Software Applications in Infrastructure Monitoring -- 11 Adaptive Structural Learning of Deep Belief Network and Its Application to Real Time Crack Detection of Concrete Structure Using Drone -- 11.1 Introduction -- 11.2 Adaptive Learning Method of Deep Belief Network -- 11.2.1 Restricted Boltzmann Machine and Deep Belief Network -- 11.2.2 Neuron Generation and Annihilation Algorithm of RBM -- 11.2.3 Layer Generation Algorithm of DBN -- 11.3 SDNET 2018 -- 11.3.1 Data Description -- 11.3.2 The Classification Results -- 11.4 Crack Detection for Japanese Concrete Structure -- 11.4.1 Data Collection -- 11.4.2 Detection Results -- 11.5 Real-Time Detection and Visualization System Using Drone -- 11.5.1 Embedded System -- 11.5.2 Demonstration Experiment -- 11.6 Conclusion -- References.

Note: 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.