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Software and Intelligent Sciences

Book Description

The junction of software development and engineering combined with the study of intelligence has created a bustling intersection of theory, design, engineering, and conceptual thought. Software and Intelligent Sciences: New Transdisciplinary Findings sits at a crossroads and informs advanced researchers, students, and practitioners on the developments in computer science, theoretical software engineering, cognitive science, cognitive informatics, and intelligence science. The crystallization of accumulated knowledge by the fertilization of these areas, have led to the emergence of a transdisciplinary field known as software and intelligence sciences, to which this book is an important contribution and a resource for both fields alike.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Associate Editors
    2. List of Reviewers
  5. Preface
  6. Acknowledgment
  7. Section 1: Computational Intelligence
    1. Chapter 1: Convergence of Software Science and Computational Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. THE EMERGENCE OF SOFTWARE SCIENCE
      4. FROM ARTIFICIAL INTELLIGENCE TO COMPUTATIONAL INTELLIGENCE
      5. A NEW TRANSDISCIPLINARY RESEARCH FIELD OF SOFTWARE AND INTELLIGENCE SCIENCES
      6. HIGHLIGHTS OF THE INAUGURAL ISSUE
      7. CONCLUSION
    2. Chapter 2: On Abstract Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. THE COGNITIVE INFORMATICS FOUNDATIONS OF ABSTRACT INTELLIGENCE
      4. A FORMAL MODEL OF ABSTRACT INTELLIGENCE
      5. MEASUREMENT OF INTELLIGENCE
      6. A UNIFIED FRAMEWORK OF ABSTRACT INTELLIGENCE AND ITS PARADIGMS
      7. CONCLUSION
    3. Chapter 3: Hierarchies of Architectures of Collaborative Computational Intelligence
      1. ABSTRACT
      2. INTRODUCTION AND MOTIVATING INSIGHTS
      3. GRANULATION AND DEGRANULATION: A CONCEPT AND A QUANTIFICATION OF THE PROCESS
      4. THE PRINCIPLE OF JUSTIFIABLE GRANULARITY
      5. THE DESIGN OF METASTRUCTURE AND ITS COMBINATORIAL OPTIMIZATION
      6. PERFORMANCE EVALUATION OF THE METASTRUCTURE
      7. METASTRUCTURE CONSTRUCTION THROUGH A HIERARCHY OF CLUSTERS OF CLUSTERS
      8. THE DESIGN OF METASTRUCTURES FOR RULE-BASED GRANULAR MODELS
      9. CONCLUDING COMMENTS
    4. Chapter 4: Challenges in the Design of Adoptive, Intelligent and Cognitive Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. ENGINEERING DESIGN
      4. AREA-SPECIFIC EDP
      5. EVOLUTION OF SYSTEMS
      6. CHALLENGES IN THE DESIGN OF COMPLEX SYSTEMS
      7. CONCLUDING REMARKS
    5. Chapter 5: On Visual Semantic Algebra (VSA)
      1. ABSTRACT
      2. INTRODUCTION
      3. COGNITIVE INFORMATICS THEORIES FOR PATTERN RECOGNITION
      4. VISUAL SEMANTIC ALGEBRA
      5. APPLICATIONS OF VSA
      6. CONCLUSION
  8. Section 2: Cognitive Computing
    1. Chapter 6: On Cognitive Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. THEORETICAL FOUNDATIONS FOR COGNITIVE COMPUTING
      4. MODELS OF COGNITIVE COMPUTING
      5. APPLICATIONS OF COGNITIVE COMPUTING
      6. CONCLUSION
    2. Chapter 7: On the System Algebra Foundations for Granular Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. THE ABSTRACT SYSTEM MODEL OF GRANULES
      4. MANIPULATION OF GRANULAR SYSTEMS BY SYSTEM ALGEBRA
      5. PROPERTIES OF GRANULAR SYSTEMS
      6. REPRESENTATION OF GRANULAR SYSTEMS
      7. CONCLUSION
    3. Chapter 8: Semantic Matching, Propagation and Transformation for Composition in Component-Based Systems
      1. ABSTRACT
      2. OVERVIEW: COMPONENTS AND COMPOSITION
      3. SEMANTIC DESCRIPTIONS
      4. ASSEMBLING FLOW COMPOSITIONS: MATCHING OUTPUTS TO INPUTS
      5. RELATED WORK
      6. CONCLUSION
    4. Chapter 9: Adaptive Computation Paradigm in Knowledge Representation
      1. ABSTRACT
      2. INTRODUCTION
      3. ADAPTIVE METHODS IN TERRAIN MODELING
      4. ADAPTIVE METHODOLOGY IN TRADITIONAL APPLICATIONS
      5. ADAPTIVE METHODS IN EMERGING AREAS
      6. CONCLUSION
    5. Chapter 10: Protoforms of Linguistic Database Summaries as a Human Consistent Tool for Using Natural Language in Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. LINGUISTIC SUMMARIES USING FUZZY LOGIC WITH LINGUISTIC QUANTIFIERS
      4. FUZZY QUERYING, LINGUISTIC SUMMARIES, AND THEIR PROTOFORMS
      5. IMPLEMENTATION
      6. CONCLUDING REMARKS
    6. Chapter 11: Measuring Textual Context Based on Cognitive Principles
      1. ABSTRACT
      2. INTRODUCTION
      3. FORMULIZATION OF TEXTUAL CONTEXT
      4. COMPLEXITY OF TEXTUAL CONTEXT
      5. INFORMATION OF TEXTUAL CONTEXT
      6. EXPERIMENTS IN COMPLEXITY OF TEXTUAL CONTEXT
      7. EXPERIMENTS IN INFORMATION OF TEXTUAL CONTEXT
      8. DISCUSSION
      9. CONCLUSION
    7. Chapter 12: A Lexical Knowledge Representation Model for Natural Language Understanding
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. SenseNet: A KNOWLEDGE REPRESENTATION MODEL
      5. NATURAL LANGUAGE UNDERSTANDING WITH SenseNet
      6. ANALYZING COMMUNICATION PROCESS USING SenseNet
      7. A CASE STUDY
      8. CONCLUSION AND FUTURE WORK
    8. Chapter 13: A Dualism Based Semantics Formalization Mechanism for Model Driven Engineering
      1. ABSTRACT
      2. INTRODUCTION
      3. MDE SEMANTICS PROBLEM ANALYSIS
      4. PREPARATION
      5. EID-SCE STRATEGY
      6. CASE STUDY
      7. RELATED WORK
      8. SUMMARY AND FUTURE WORK
  9. Section 3: Software Science
    1. Chapter 14: Exploring the Cognitive Foundations of Software Engineering
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC PROPERTIES OF SOFTWARE AND SOFTWARE ENGINEERING
      4. FUNDAMENTAL COGNITIVE CONSTRAINTS OF SOFTWARE ENGINEERING
      5. COGNITIVE INFORMATICS PRINCIPLES FOR SOFTWARE ENGINEERING
      6. CONCLUSION
    2. Chapter 15: Positive and Negative Innovations in Software Engineering
      1. ABSTRACT
      2. INTRODUCTION
      3. SUMMARY AND CONCLUSION
    3. Chapter 16: On the Cognitive Complexity of Software and its Quantification and Formal Measurement
      1. ABSTRACT
      2. INTRODUCTION
      3. TAXONOMY OF SOFTWARE COMPLEXITIES IN COMPUTING AND SOFTWARE ENGINEERING
      4. THE COGNITIVE WEIGHTS OF FUNDAMENTAL SOFTWARE STRUCTURES BASED ON THE GENERIC MATHEMATICAL MODEL OF PROGRAMS
      5. THE COGNITIVE COMPLEXITY OF SOFTWARE SYSTEMS
      6. COMPARATIVE STUDIES ON THE COGNITIVE COMPLEXITY OF SOFTWARE SYSTEMS
      7. CONCLUSION
    4. Chapter 17: Machine Learning and Value-Based Software Engineering
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. VBSE
      5. RESEARCH AGENDA FOR ML IN VBSE
      6. CONCLUSION
    5. Chapter 18: The Formal Design Model of a Telephone Switching System (TSS)
      1. ABSTRACT
      2. INTRODUCTION
      3. THE CONCEPTUAL MODEL OF THE TSS SYSTEM
      4. THE ARCHITECTURAL MODEL OF THE TSS SYSTEM
      5. THE STATIC BEHAVIOR MODELS OF THE TSS SYSTEM
      6. THE DYNAMIC BEHAVIOR MODEL OF THE TSS SYSTEM
      7. CONCLUSION
    6. Chapter 19: The Formal Design Model of a Lift Dispatching System (LDS)
      1. ABSTRACT
      2. INTRODUCTION
      3. THE CONCEPTUAL MODEL OF THE LDS SYSTEM
      4. THE ARCHITECTURAL MODEL OF THE LDS SYSTEM
      5. THE STATIC BEHAVIOR MODELS OF THE LDS SYSTEM
      6. THE DYNAMIC BEHAVIOR MODEL OF THE LDS SYSTEM
      7. CONCLUSION
    7. Chapter 20: A Theory of Program Comprehension
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. RATIONALE OF THE THEORY
      5. VISION–COMPREHENSION THEORY
      6. DEFINITION
      7. EXPLANATIONS OF KNOWN FACTS
      8. DISCUSSIONS
      9. CONCLUSION AND FUTURE WORK
    8. Chapter 21: Requirements Elicitation by Defect Elimination
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RELATED WORK
      5. MOTIVATION
      6. ONTOLOGY IN KNOWLEDGE SHARING
      7. METHODOLOGY OF REQUIREMENTS GENERATION
      8. ARCHITECTURE OF ONTOLOGY BASED REQUIREMENTS ELICITATION
      9. ISSUES AND LIMITATIONS
      10. RESULTS
      11. CONCLUSION
    9. Chapter 22: Measurement of Cognitive Functional Sizes of Software
      1. ABSTRACT
      2. INTRODUCTION
      3. COGNITIVE COMPLEXITY
      4. WEAK MEASUREMENT THEORY AND COGNITIVE COMPLEXITY
      5. WEAK EXTENSIVE STRUCTURE
      6. PRACTICAL EVALUATION OF COGNITIVE COMPLEXITY
      7. CONCLUSION
    10. Chapter 23: Motivational Gratification
      1. ABSTRACT
      2. INTRODUCTION
      3. THE THEORETICAL FOUNDATION FOR THE PROPOSED MOTIVATIONAL GRATIFICATION MODEL
      4. INFORMATION FLOW BETWEEN SUBSYSTEMS IN THE MOTIVATIONAL GRATIFICATION MODEL
      5. IMPLICATIONS
      6. PRACTICAL IMPLICATIONS OF MGM FOR MANAGEMENT
      7. CONCLUSION
  10. Section 4: Applications of Computational Intelligence and Cognitive Computing
    1. Chapter 24: Supporting CSCW and CSCL with Intelligent Social Grouping Services
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. EXPERIMENTS AND DISCUSSIONS
      5. CONCLUSION
    2. Chapter 25: An Enhanced Petri Net Model to Verify and Validate a Neural-Symbolic Hybrid System
      1. ABSTRACT
      2. INTRODUCTION
      3. NEURAL-SYMBOLIC HYBRID SYSTEMS
      4. KNOWLEDGE BASE VERIFICATION AND VALIDATION
      5. PETRI NET AS A MODELING TOOL
      6. ENHANCED PETRI NET MODEL
      7. RULE VERIFICATION PROCESS USING PETRI NETS
      8. RULE VALIDATION PROCESS USING PETRI NETS
      9. CONCLUSION AND FUTURE WORKS
    3. Chapter 26: System Uncertainty Based Data-Driven Knowledge Acquisition
      1. ABSTRACT
      2. INTRODUCTION
      3. PRELIMINARY KNOWLEDGE
      4. SYSTEM UNCERTAINTY MEASURES BASED ON ROUGH SET THEORY
      5. ALGEBRAIC CHARACTERISTICS OF SYSTEM UNCERTAINTY MEASURES
      6. PERFORMANCE OF SYSTEM UNCERTAINTY MEASURES
      7. DKAABSU: A NEW DATA-DRIVEN KNOWLEDGE ACQUIRING ALGORITHM
      8. EXPERIMENT TESTS ON DKAABSU AND ITS CONGENERIC ALGORITHMS
      9. CONCLUSION AND FUTURE WORK
    4. Chapter 27: Hierarchical Function Approximation with a Neural Network Model
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM DESCRIPTION
      4. NEURAL NETWORKS MODEL
      5. HIERARCHICAL MODEL
      6. NUMERIC REPRESENTATION
      7. A SYMBOLIC PRACTICAL CASE OF USE
      8. CONCLUSION AND FUTURE REMARKS
    5. Chapter 28: Application of Artificial Neural Computation in Topex Waveform Data
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODOLOGY
      4. RESULTS AND DISCUSSION
      5. CONCLUSION
    6. Chapter 29: A Generic Framework for Feature Representations in Image Categorization Tasks
      1. ABSTRACT
      2. THE VISUAL FEATURE ARRAY
      3. USING VFA-GENERATED INPUT FOR IMAGE CLASSIFICATION
      4. VFA-BASED ROBOT GUIDING IN INDUSTRIAL ENVIRONMENTS
      5. CONCLUSION
  11. Compilation of References
  12. About the Contributors