Intro
Preface
Organization
Contents
Anomaly Classification to Enable Self-healing in Cyber Physical Systems Using Process Mining
1 Introduction
1.1 Process Mining
1.2 Anomalies in Event Logs
1.3 Ensemble Machine Learning Approaches
1.4 Models Used
2 Literature Survey
3 Problem Statement and Dataset Description
4 Methodology
4.1 Event Logs and Process Discovery
4.2 Conformance Checking
4.3 Anomaly Classification
5 Results and Discussions
5.1 Model Preparation
5.2 Dataset Generation
5.3 Conformance Checking

5.4 Bagging and Boosting Classification
6 Conclusion
References
Hyper-box Classification Model Using Mathematical Programming
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Statement
3.2 Mathematical Formulation
3.3 Testing Phase
3.4 Illustrative Example
4 Computational Results
5 Concluding Remarks
References
A Leak Localization Algorithm in Water Distribution Networks Using Probabilistic Leak Representation and Optimal Transport Distance
1 Introduction
1.1 Motivations
1.2 Related Works
1.3 Our Contributions

1.4 Content Organization
2 The Wasserstein Distance
2.1 Basic Definitions
2.2 Wasserstein Barycenter
3 Wasserstein Enabled Leak Localization
3.1 Generation of Leak Scenarios
3.2 Clustering in the Wasserstein Space
3.3 Evaluation Metrics
4 Experimental Results
4.1 Data Resources
4.2 Computational Results
5 Conclusions, Limitations, and Perspectives
References
Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming
1 Introduction and Related Work
2 Problem Formulation and Background Material
2.1 Particle Swarm Optimization

2.2 Sequential Linear Quadratic Programming
3 The Proposed UPSO-QP Approach
3.1 Local QP Problems
3.2 UPSO for Constrained Optimization
3.3 Considerations
4 Experiments
4.1 Numerical Constrained Optimization Problems
4.2 Constrained Optimization with Noisy Functions Values
4.3 Evaluation on High Dimensional Problems
5 Concluding Remarks
References
Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing
1 Introduction
1.1 Related Work
1.2 Dynamic Pricing and Learning
1.3 Contributions and Organization

2 Problem Description
2.1 BO for Function Composition
2.2 Bayesian Optimization for Dynamic Pricing
3 Proposed Method
3.1 Statistical Model and GP Regression
3.2 cEI and cUCB Acquisition Functions
4 Experiments and Results
4.1 Results on Test Functions
4.2 Results for Demand Pricing Experiments
4.3 Runtime Comparisons with State of the Art
5 Conclusion
References
A Bayesian Optimization Algorithm for Constrained Simulation Optimization Problems with Heteroscedastic Noise
1 Introduction