Handbook on Artificial Intelligence-Empowered Applied Software Engineering: VOL.2: Smart Software Applications in Cyber-Physical Systems

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