Computational Intelligence and Its Applications

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Table of Contents
Preface to the USTC Alumnis Series
1 Adaptive Particle Filters
1.1 Bayesian Filtering for Dynamic State Estimation
1.1.1 State and Observation Models
1.1.2 Bayesian Filtering Method
1.2 Fundamentals of Particle Filters
1.2.1 Sequential Monte Carlo Method
1.2.2 Basic Particle Filtering Algorithms
1.3 Challenging Issues in Particle Filtering
1.3.1 Unknown or Varying State Model
1.3.2 Construction of Proposal Density
1.3.3 Determination of Sample Size
1.3.4 Curse of Dimensionality
1.4 Adaptive Particle Filtering Algorithms
1.4.1 Algorithms with Adaptive Sample Size.
1.4.2 Algorithms with Adaptive Proposal:Density
1.4.3 Other Related Algorithms
1.5 Summary
Brief Introduction of Authors

2 Feature Localization and Shape Indexing for ContentBased Image Retrieval
2.1 Introduction
2.2 Locales for Feature Localization
2.3 Search by Object Model
2.4 Shape Indexing and Recognition
2.5 Experimental Results
2.5.1 Search Using Locale-based Models
2.5.2 Video Locales
2.5.3 Shape Indexing and Recognition
2.6 Conclusion
Brief Introduction of Authors

3 BlueGene/L Failure Analysis and Prediction Models
3.1 Introduction
3.2 BlueGene/L Architecture, RAS Event Logs, and Job Logs
3.2.1 BlueGene/L Architecture
3.2.2 RAS Event Logs
3.2.3 Job Logs
3.3 Impact of Failures on Job Executions
3.4 Failure Prediction Based on Failure Characteristics
3.4.1 Temporal Characteristics
3.4.2 Spatial Characteristics
3.5 Predicting Failures Using the Occurrence of Non-Fatal Events
3.6 Related Work
3.7 Concluding Remarks and Future Directions
Brief Introduction of Authors

4 A Neuro-Fuzzy Approach towards Adaptive IntrusionTolerant Database Systems
4.1 Overview
4.2 ITDB architecture
4.3 The Need for Adaptivity
4.4 Intelligent Techniques Solutions in AdaptiveITDB
4.5 Intelligent Techniques Solutions in AdaptiveITDB
4.6 The Design of Reconfiguration Components
4.7 Performance Metrics for Adaptive ITDB
4.8 Adaptation Criteria
4.9 The Rule-Based Adaptive Controller
4.10 The Neuro-Fuzzy Adaptive Controller
4.11 The collection of training data
4.12 Evaluation Methodology
4.12.1 Transaction Simulation
4.12.2 Evaluation Criteria
4.13 Evaluation of NFAC and RBAC Performance
4.14 Conclusion
4.15 Future Work
Brief Introduction of Authors

5 Artificial Neural Network Applications in Software Re-liability
5.1 Introduction
5.2 Analytical Software Reliability Models
5.3 ANN Models
5.3.1 Model I-Traditional ANN Modeling
5.3.2 Model II- FDP&FCP Modeling
5.3.3 Models III- Early Prediction Modeling
5.4 Numerical Applications
5.4.1 Applications of Traditional ANN Models
5.4.2 Applications of FDP&FCP ANN Models
5.4.3 Applications of Early Prediction ANN Models
5.5 Conclusions and Discussions
Brief Introduction of Authors

6 A New Computational Intelligent Approach to Protein Tertiary Structure Prediction
6.1 Introduction
6.2 New Fragment Retrieval Methods
6.2.1 Fragment Retrieval Using BLAST
6.2.2 Information Content of Retrieved Fragments
6.2.3 Whole Template Retrieval Using Secondary Structures
6.3 New Protein 3-D Structure Prediction Methods
6.3.1 Multidimensional Scaling (MDS) Methods
6.3.2 MDS-based Structure Prediction
6.3.3 Refinement Using Local Optimization
6.3.4 Non-Harmonic and Non-Local Objective Functions
6.4 Identifying Near-Native Structures from Predicted Candidates.
6.4.1 A New Clustering-Based Selection Method
6.4.2 Combined Ranking Method
6.5 Experimental Results
6.6 Summary
Brief Introduction of Authors

7 Recursive Nonparametric Discriminant Analysis for Object Detection
7.1 Introduction
7.2 Related Work
7.3 Discriminant Feature Extraction for Object Detection
7.3.1 Fisher Discriminant Analysis and Nonparametric Dis-criminant Analysis
7.3.2 Recursive Nonparametric Discriminant Analysis
7.4 Constructing Classifiers using RNDA Features and AdaBoost
7.4.1 AdaBoost Algorithm
7.4.2 Applying AdaBoost to Combine RNDA Features
7.5 Experiments
7.5.1 Training
7.5.2 Frontal and Profile Face Detection
7.5.3 Eye Detection
7.5.4 Face Recognition Experiments
7.5.5 Discussion on Computational Complexity
7.6 Conclusion
Brief Introduction of Authors

8 On the Privacy Preserving Properties of Projection-Based Data Perturbation Techniques
8.1 Introduction
8.2 Perturbation Approaches
8.2.1 Additive-Noise-Based Approach
8.2.2 Distance-Preserving-Based Projection
8.2.3 Non-Distance-Preserving-Based Projection
8.2.4 The General-Linear-Transformation-Based Perturbation
8.3 Direct Attack
8.3.1 ICA Revisited
8.3.2 Drawbacks of Direct ICA
8.4 Sample-Based Attack
8.4.1 Attacks for Distance-Preserving-Based Projection
8.4.2 Attacks for Non-Distance-Preserving-Based Projection
8.4.3 Attacks for General-Linear-Transformation-Based Per-turbation
8.5 Empirical Evaluations
8.5.1 Effect of Noise and the Transformation Matrix
8.5.2 Effect of the Sample Size
9 Bayesian Networks Modeling for Software Inspection Effectiveness
10 CI and CFD:integration through Smart Simulations
11 Intelligent Video Content Analysis and Applications
Editors of the Book
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Computational Intelligence and Its Applications