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Cognitive Informatics for Revealing Human Cognition

Book Description

With the developments and intersection of science and engineering, cognitive informatics has emerged as a new and intriguing field of study which investigates the natural intelligence and internal information processing mechanisms of the brain as well as the methods involved in perception and cognition. Cognitive Informatics for Revealing Human Cognition: Knowledge Manipulations in Natural Intelligence presents a comprehensive collection of research that builds a link between natural and life sciences with informatics and computer science. This book is practical for researchers, practitioners, and graduate students interested in investigating cognitive mechanisms and the human information processes.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Preface
    1. SECTION 1: COGNITIVE INFORMATICS
    2. SECTION 2: COGNITIVE COMPUTING
    3. SECTION 3: DENOTATIONAL MATHEMATICS
    4. SECTION 4: COMPUTATIONAL INTELLIGENCE
    5. SECTION 5: APPLICATIONS OF COGNITIVE INFORMATICS AND COGNITIVE COMPUTING
  6. Acknowledgment
  7. Section 1: Cognitive Informatics
    1. Chapter 1: Towards the Synergy of Cognitive Informatics, Neural Informatics, Brain Informatics, and Cognitive Computing
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. COGNITIVE INFORMATICS: THE SCIENCE OF ABSTRACT INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE
      4. 3. NEURAL INFORMATICS: HOW INTELLIGENCE AND KNOWLEDGE ARE REPRESENTED IN THE BRAIN
      5. 4. DENOTATIONAL MATHEMATICS: A GENERAL METAMETHODOLOGY FOR COGNITIVE INFORMATICS, BRAIN INFORMATICS, COGNITIVE COMPUTING, AND COMPUTATIONAL INTELLIGENCE
      6. 5. BRAIN INFORMATICS: EXPLORING THE IMAGES AND MECHANISMS OF THE BRAIN
      7. 6. COGNITIVE COMPUTING: TOWARD THE NEXT GENERATION OF COGNITIVE COMPUTERS
      8. 7. CONCLUSION
    2. Chapter 2: Perspectives on the Field of Cognitive Informatics and its Future Development
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. COGNITIVE INFORMATICS (CI): THE SCIENCE OF ABSTRACT INTELLIGENCE AND COGNITIVE COMPUTING
      4. 3. CI/CC AND COGNITIVE MEMORY
      5. 4. THE MULTIDISCIPLINARY RESEARCH FOR COGNITIVE COMPUTATION
      6. 5. EVOLUTION OF COGNITIVE DYNAMICAL SYSTEMS AND COGNITIVE INFORMATICS
      7. 6. COGNITIVE COMPUTING AND SYMBIOTIC COMPUTING
      8. 7. ROBOTICS AND COGNITIVE INFORMATION PROCESSING
      9. 8. CI/CC AND EVOLUTIONARY COMPUTATION
      10. 9. INCONSISTENCY, MEMORY, AND INCONSISTENCY-INDUCED LEARNING
      11. 10. CONCLUSION
    3. Chapter 3: Role-Based Human-Computer Interactions
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. NATURAL INTELLIGENCE AND ARTIFICIAL INTELLIGENCE
      4. 3. INTERACTION
      5. 4. THE DIFFERENCES BETWEEN HCI AND AI
      6. 5. SHARED MODELS FOR INTERACTIONS
      7. 6. ROLE-BASED INTERACTION
      8. 7. CASE STUDY: RESTRAIN MENTAL WORKLOAD WITH ROLES
      9. 8. RELATED WORK
      10. 9. CONCLUSION
    4. Chapter 4: Main Retina Information Processing Pathways Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. DESIGN AND IMPLEMENTATION OF THE RETINA MODEL
      4. EXPERIMENT DESIGN AND RESULTS ANALYSIS
      5. RETINA PERFORMANCE
      6. CONCLUSION
    5. Chapter 5: Songs to Syntax
      1. ABSTRACT
      2. INTRODUCTION
  8. Section 2: Cognitive Computing
    1. Chapter 6: Cognitive Memory for Semantic Agents Architecture in Robotic Interaction
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. RELATED WORK
      4. 2. HUMAN ROROT MULTIMODAL INTERACTION
      5. 3. ARCHITECTURE COMPONENTS
      6. 4. COGNITIVE MEMORY FOR SEMANTIC AGENTS
      7. 5. EVENT KNOWLEDGE REPRESENTATION LANGUAGE
      8. 6. MEMORY INFERENCE ENGINE
      9. 7. FUSION AGENT
      10. 8. FISSION AGENT
      11. 9. HUMAN AID APPLICATION
      12. 10. CONCLUSION
    2. Chapter 7: Interactive Feature Visualization and Detection for 3D Face Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. RELATED WORK
      4. 2. FACE ALIGNMENT AND CORRESPONDENCE
      5. 3. INTERACTIVE FEATURE VISUALIZATION
      6. 4. EXPERIMENTAL RESULTS
      7. 5. CONCLUSION
    3. Chapter 8: A Computational Simulation of the Cognitive Process of Children Knowledge Acquisition and Memory Development
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND AND RELATED WORK
      4. 3. DESCRIBING CHILDREN MEMORY DEVELOPMENT BY CONCEPT ALGEBRA
      5. 4. DESIGN OF THE TOOL FOR CHILDREN MEMORY DEVELOPMENT SIMULATIONS
      6. 5. IMPLEMENTATION OF THE CHILDREN MEMORY SIMULATION TOOL
      7. 6. CONCLUSION
    4. Chapter 9: A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION AND FUTURE WORK
    5. Chapter 10: Cognitive Informatics and Cognitive Computing in Year 10 and Beyond
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. THE FRAMEWORK OF COGNITIVE INFORMATICS AND COGNITIVE COMPUTING
      4. 3. PSYCHOLOGICALLY REALISTIC COGNITIVE COMPUTING BEYOND 2011
      5. 4. NEW VISION FOR THE WORLD OF WIRELESS COMMUNICATIONS ENABLED WITH COGNITION
      6. 5. GRANULAR COMPUTING AND COGNITIVE INFORMATICS
      7. 6. DEALING WITH EMERGENT COGNITIVE SYSTEMS
      8. 7. COGNITIVE TEXTURE: A UNIFIED MULTI-SENSORY FEEDBACK FRAMEWORK
      9. 8. BOUNDED RATIONALITY AND INCONSISTENCY IN COGNITIVE COMPUTING SYSTEMS
      10. 9. COGNITIVE INFORMATICS TOWARDS 2031
      11. 10. COMPUTATIONAL INTELLIGENCE IN BIOMETRIC
      12. 11. CONCLUSION
    6. Chapter 11: Inference Algebra (IA)
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. THE FRAMEWORK OF FORMAL INFERENCES AND RELATED WORK
      4. 3. INFERENCE ALGEBRA AND THE MODEL OF FORMAL CAUSATIONS
      5. 7. CONCLUSION
    7. Chapter 12: Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. THE PRINCIPLE OF JUSTIFIABLE GRANULARITY
      4. 3. OPTIMAL ALLOCATION OF INFORMATION GRANULARITY
      5. 4. GRANULAR ANALYTIC HIERARCHY PROCESS (AHP)
      6. 5. GRANULAR TIME SERIES–TOWARDS INTERPRETABLE MODELS
      7. 6. THE DEVELOPMENT OF GRANULAR LOGIC: A HOLISTIC VIEW AT A COLLECTION OF LOGIC DESCRIPTORS
      8. 7. CONCLUSION
  9. Section 3: Denotational Mathematics
    1. Chapter 13: Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK: ONTOLOGY AND CONCEPT ALGEBRA
      4. 3. SEMANTIC OPERATIONS ON FORMAL CONCEPTS IN CONCEPT ALGEBRA
      5. 4. APPLICATIONS OF CONCEPT ALGEBRA IN KNOWLEDGE REPRESENTATION AND MACHINE LEARNING
      6. 5. CONCLUSION
    2. Chapter 14: Text Semantic Mining Model Based on the Algebra of Human Concept Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. THE ALGEBRA OF HUMAN CONCEPT LEARNING
      4. TEXT SEMANTIC MINING MODEL BASED ON THE ALGEBRA OF HUMAN CONCEPT LEARNING
      5. ESTABLISHMENT OF TEXT SEMANTIC MINING MODEL BASED ON THE ALGEBRA OF HUMAN CONCEPT LEARNING
      6. REASONING BASED ON TEXT SEMANTIC MINING MODEL
      7. COMPARIONS AND EXPERIMENTS
      8. CONCLUSION
    3. Chapter 15: In Search of Effective Granulization with DTRS for Ternary Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. BRIEF INTRODUCTION TO DECISION-THEORETIC ROUGH SET MODEL
      4. INTERPRETATIONS OF CONCEPTS WITH GRANULAR COMPUTING
      5. AN ADAPTIVE LEARNING ALGORITHM FOR CLASSIFICATION
      6. AN ILLUSTRATIVE EXAMPLE
      7. CONCLUSION AND FUTURE WORK
  10. Section 4: Computational Intelligence
    1. Chapter 16: Cognitive Dynamic Systems
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. DISTINCTION BETWEEN ADAPTATION AND COGNITION
      4. 3. THE FIVE PRINCIPLES OF COGNITIVE DYNAMIC SYSTEMS
      5. 4. COGNITIVE RADAR
      6. 5. OTHER DYNAMIC SYSTEM APPLICATIONS OF COGNITION
    2. Chapter 17: A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MODULAR DYNAMICAL CRYPTOSYSTEM (MDC)
      5. 4. EXPERIMENTAL RESULTS
      6. 5. DISCUSSION OF EXPERIMENTAL RESULTS
      7. 6. CONCLUSION
    3. Chapter 18: Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. EVOLUTIONARY OPTIMIZATION
      4. 3. REQUIREMENTS FOR OPTIMIZATION IN COGNITIVE PROCESSES
      5. 4. DYNAMICS OF EVOLUTIONARY ALGORITHMS
      6. 5. ANALYSIS OF DYNAMICS OF PARTICLE SWARM
      7. 6. CONCLUSION
    4. Chapter 19: On Machine Symbol Grounding and Optimization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. INTERFACE DESIGN AS OPTIMIZATION PROBLEM
      4. 3. MACHINE LEARNING INTERFACES
      5. 4. PERSPECTIVE OF THE SYMBOL GROUNDING PROBLEM
      6. 5. A CASE STUDY
      7. 6. SUMMARY AND CONCLUSION
    5. Chapter 20: Image Dimensionality Reduction Based on the Intrinsic Dimension and Parallel Genetic Algorithm
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. COMMONLY USED METHODS FOR IMAGE DIMENSION REDUCTION
      4. 3. INTRINSIC DIMENSION ESTIMATION BASED ON THE HSV IMAGE FEATURES
      5. 4. PARALLEL GENETIC ALGORITHM
      6. 5. ADAPTIVE GENETIC OPERATORS
      7. 6. TEST ANALYSIS
      8. 7. CONCLUSION
  11. Section 5: Applications of Cognitive Informatics and Cognitive Computing
    1. Chapter 21: Equivalence between LDA/QR and Direct LDA
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORKS
      4. 3. EQUIVALENCE BETWEEN LDA/QR AND DLDA
      5. 4. EXPERIMENT SETUP
      6. 5. RESULTS AND DISCUSSIONS
      7. 6. CONCLUSION
    2. Chapter 22: A Novel Algorithm for Block Encryption of Digital Image Based on Chaos
      1. ABSTRACT
      2. INTRODUCTION
      3. CHAOTIC SYSTEMS
      4. DESIGN OF THE IMAGE ENCRYPTION ALGORITHM
      5. CONCLUSION
    3. Chapter 23: Cognitive MIMO Radio
      1. ABSTRACT
      2. INTRODUCTION
      3. DISTRIBUTED SYSTEM MODEL AND FORMULATION
      4. CU SELECTION ALGORITHMS AND AVERAGE DEPARTURE RATE ANALYSIS
      5. NUMERICAL RESULTS
      6. CENTRALLY-CONTROLLED SYSTEM MODEL AND FORMULATION
      7. SIMULATION RESULTS
      8. CONCLUSION
      9. APPENDIX
    4. Chapter 24: Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
      1. ABSTRACT
      2. INTRODUCTION
      3. ROBOT MANIPULATOR
      4. FNN-BASED TORQUE CONTROL
      5. TRAJECTORY TRACKING CONTROL
      6. SIMULATED RESULTS
      7. CONCLUSION
  12. Compilation of References
  13. About the Contributors