| Preface |
| Acknowledgements |
| Intelligence and Security Informatics: Research Frameworks / Chapter 1： |
| Research Methodology and Frameworks for ISI / 1.1： |
| The ACP Approach / 1.2： |
| Modeling with Artificial Societies / 1.2.1： |
| Analysis with Computational Experiments / 1.2.2： |
| Control Through Parallel Execution / 1.2.3： |
| Foundations in Philosophy and Physics / 1.2.4： |
| Outline of Chapters / 1.3： |
| Agent Modeling of Terrorist Organization Behavior / Chapter 2： |
| Modeling Organizational Behavior / 2.1： |
| Action Extraction from the Web / 2.2： |
| Action Data Collection / 2.2.1： |
| Raw Action Extraction / 2.2.2： |
| Action Elimination / 2.2.3： |
| Action Refinement / 2.2.4： |
| Extracting Causal Knowledge from the Web / 2.3： |
| Construction of Action Hierarchy / 2.4： |
| Designing, Causal Scenarios / 2.5： |
| Case Study on Terrorist Organization / 2.6： |
| Conclusion / 2.7： |
| Security Story Generation for Computational Experiments / Chapter 3： |
| Story Generation Systems / 3.1： |
| System Workflow and Narrative Structure / 3.2： |
| Story Extraction Approach / 3.3： |
| Text Processing with Domain Knowledge / 3.3.1： |
| Event Detection and Event Element Extraction / 3.3.2： |
| Design and Organization of Patterns / 3.3.3： |
| Event Element Standardization / 3.3.4： |
| Evaluation of Event Relations / 3.3.5： |
| Experiment / 3.4： |
| Forecasting Croup Behavior via Probabilistic Plan Inference / 3.5： |
| Review of Plan-Based Inference / 4.1： |
| Probabilistic Plan Representation / 4.2： |
| Probabilistic Reasoning Approach / 4.3： |
| Notation / 4.3.1： |
| Computation / 4.3.2： |
| Case Study in Security Informatics / 4.4： |
| Construction of Plan Library / 4.4.1： |
| The Test Set / 4.4.2： |
| Experimental Results / 4.4.3： |
| Forecasting Complex Croup Behavior via Multiple Plan Recognition / 4.5： |
| Multiple Plan Recognition for Behavior Prediction / 5.1： |
| The MPR Problem Definition / 5.2： |
| The Proposed MPR Approach / 5.3： |
| Constructing the Explanation Graph / 5.3.1： |
| Computing Probability of an Explanation / 5.3.2： |
| Finding the Best Explanation / 5.3.3： |
| Algorithm and Complexity Analysis / 5.3.4： |
| Discussion / 5.3.5： |
| Experimental Design / 5.4： |
| Results / 5.4.2： |
| Social Computing in ISI: A Synthetic View / 5.5： |
| Social Computing / 6.1： |
| Theoretical and Infrastructure Underpinnings / 6.1.1： |
| Major Application Areas / 6.1.2： |
| A Social Computing-Based ISI Research Framework / 6.2： |
| Control and Management Through Parallel Execution / 6.2.1： |
| Main Issues in the ACP-Based ISI Research Framework / 6.3： |
| Modeling Cyber-Physical Societies / 6.3.1： |
| Scenario-Based Computational Experiment and Evaluation / 6.3.2： |
| Interactive Co-Evolution of Artificial and Actual Systems / 6.3.3： |
| Social Media Information Processing and Standardization / 6.3.4： |
| ISI Research Platform / 6.3.5： |
| Summary / 6.4： |
| Cyber-Enabled Social Movement Organizations / Chapter 7： |
| Studies on Social Movement Organizations: A Review / 7.1： |
| A New Research Framework for CeSMOs / 7.2： |
| CeSMO Research Questions / 7.2.1： |
| A Social Computing-Based CeSMO Research Framework / 7.2.2： |
| Case Study: Wenchuan Earthquake / 7.3： |
| Discussions on CeSMO Research Issues / 7.4： |
| CeSMO Behavior Modeling / 7.4.1： |
| CeSMO Network Analysis / 7.4.2： |
| CeSMO Social and Cultural Information Modeling and Analysis / 7.4.3： |
| CeSMO Behavior Prediction / 7.4.4： |
| Cultural Modeling for Behavior Analysis and Prediction / 7.5： |
| Modeling Cultural Data in Security Informatics / 8.1： |
| Major Machine Learning Methods / 8.2： |
| Naive Bayesian (NB) / 8.2.1： |
| Support Vector Machines (SVMs) / 8.2.2： |
| Artificial Neural Networks / 8.2.3： |
| k-Nearest Neighbor (kNN) / 8.2.4： |
| Decision Trees / 8.2.5： |
| Random Forest (RF) / 8.2.6： |
| Associative Classification (AC) / 8.2.7： |
| Experiment and Analysis / 8.3： |
| Datasets / 8.3.1： |
| Evaluation Measures / 8.3.2： |
| Observations and Analysis / 8.3.3： |
| Discussions on Cultural Modeling Research Issues / 8.4： |
| Cultural Datasets Construction / 8.4.1： |
| Attribute Selection / 8.4.2： |
| Best Performance of Classifiers / 8.4.3： |
| Handling the Class Imbalance Problem / 8.4.4： |
| Model Interpretability / 8.4.5： |
| Incorporation of Domain Knowledge / 8.4.6： |
| Cultural and Social Dynamics of Behavioral Patterns / 8.4.7： |
| Index / 8.5： |
| Preface |
| Acknowledgements |
| Intelligence and Security Informatics: Research Frameworks / Chapter 1： |
ＥＢ
