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Preface to the USTC Alumni's SeriesPreface1 Adaptive Particle Filters1.1 Bayesian Filtering for Dynamic State Estimation 1.1.1 State and Observation Models1.1.2 Bayesian Filtering Method1.2 Fundamentals of Particle Filters 1.2.1 Sequential Monte Carlo Method1.2.2 Basic Particle Filtering Algorithms1.3 Challenging Issues in Particle Filtering1.3.1 Unknown or Varying State Model1.3.2 Construction of Proposal Density1.3.3 Determination of Sample Size 1.3.4 Curse of Dimensionality 1.4 Adaptive Particle Filtering Algorithms1.4.1 Algorithms with Adaptive Sample Size.1.4.2 Algorithms with Adaptive Proposal:Density 1.4.3 Other Related Algorithms1.5 SummaryReferences Brief Introduction of Authors2 Feature Localization and Shape Indexing for ContentBased Image Retrieval2.1 Introduction 2.2 Locales for Feature Localization 2.3 Search by Object Model2.4 Shape Indexing and Recognition2.5 Experimental Results 2.5.1 Search Using Locale-based Models 2.5.2 Video Locales2.5.3 Shape Indexing and Recognition2.6 ConclusionReferencesBrief Introduction of Authors3 BlueGene/L Failure Analysis and Prediction Models3.1 Introduction3.2 BlueGene/L Architecture, RAS Event Logs, and Job Logs3.2.1 BlueGene/L Architecture3.2.2 RAS Event Logs3.2.3 Job Logs 3.3 Impact of Failures on Job Executions3.4 Failure Prediction Based on Failure Characteristics3.4.1 Temporal Characteristics3.4.2 Spatial Characteristics 3.5 Predicting Failures Using the Occurrence of Non-Fatal Events3.6 Related Work3.7 Concluding Remarks and Future DirectionsReferencesBrief Introduction of Authors4 A Neuro-Fuzzy Approach towards Adaptive IntrusionTolerant Database Systems4.1 Overview4.2 ITDB architecture4.3 The Need for Adaptivity 4.4 Intelligent Techniques Solutions in AdaptiveITDB4.5 Intelligent Techniques Solutions in AdaptiveITDB4.6 The Design of Reconfiguration Components4.7 Performance Metrics for Adaptive ITDB4.8 Adaptation Criteria4.9 The Rule-Based Adaptive Controller4.10 The Neuro-Fuzzy Adaptive Controller4.11 The collection of training data4.12 Evaluation Methodology4.12.1 Transaction Simulation4.12.2 Evaluation Criteria4.13 Evaluation of NFAC and RBAC Performance4.14 Conclusion4.15 Future WorkReferencesBrief Introduction of Authors 5 Artificial Neural Network Applications in Software Re-liability5.1 Introduction5.2 Analytical Software Reliability Models 5.3 ANN Models5.3.1 Model I-Traditional ANN Modeling5.3.2 Model II- FDP&FCP Modeling5.3.3 Models III- Early Prediction Modeling5.4 Numerical Applications5.4.1 Applications of Traditional ANN Models5.4.2 Applications of FDP&FCP ANN Models5.4.3 Applications of Early Prediction ANN Models5.5 Conclusions and DiscussionsReferencesBrief Introduction of Authors6 A New Computational Intelligent Approach to Protein Tertiary Structure Prediction6.1 Introduction6.2 New Fragment Retrieval Methods6.2.1 Fragment Retrieval Using BLAST6.2.2 Information Content of Retrieved Fragments6.2.3 Whole Template Retrieval Using Secondary Structures6.3 New Protein 3-D Structure Prediction Methods6.3.1 Multidimensional Scaling (MDS) Methods6.3.2 MDS-based Structure Prediction6.3.3 Refinement Using Local Optimization6.3.4 Non-Harmonic and Non-Local Objective Functions6.4 Identifying Near-Native Structures from Predicted Candidates.6.4.1 A New Clustering-Based Selection Method 6.4.2 Combined Ranking Method6.5 Experimental Results6.6 SummaryReferencesBrief Introduction of Authors7 Recursive Nonparametric Discriminant Analysis for Object Detection7.1 Introduction7.2 Related Work7.3 Discriminant Feature Extraction for Object Detection7.3.1 Fisher Discriminant Analysis and Nonparametric Dis-criminant Analysis7.3.2 Recursive Nonparametric Discriminant Analysis7.4 Constructing Classifiers using RNDA Features and AdaBoost7.4.1 AdaBoost Algorithm7.4.2 Applying AdaBoost to Combine RNDA Features7.5 Experiments7.5.1 Training7.5.2 Frontal and Profile Face Detection7.5.3 Eye Detection7.5.4 Face Recognition Experiments7.5.5 Discussion on Computational Complexity7.6 ConclusionReferencesBrief Introduction of Authors8 On the Privacy Preserving Properties of Projection-Based Data Perturbation Techniques8.1 Introduction8.2 Perturbation Approaches8.2.1 Additive-Noise-Based Approach8.2.2 Distance-Preserving-Based Projection8.2.3 Non-Distance-Preserving-Based Projection8.2.4 The General-Linear-Transformation-Based Perturbation8.3 Direct Attack8.3.1 ICA Revisited8.3.2 Drawbacks of Direct ICA8.4 Sample-Based Attack8.4.1 Attacks for Distance-Preserving-Based Projection8.4.2 Attacks for Non-Distance-Preserving-Based Projection8.4.3 Attacks for General-Linear-Transformation-Based Per-turbation8.5 Empirical Evaluations8.5.1 Effect of Noise and the Transformation Matrix8.5.2 Effect of the Sample Size