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Intro; Preface; Contents; Improved Logical Passing Strategy and Gameplay Algorithm for Humanoid Soccer Robots Using Colored Petri Nets; Abstract; 1 Introduction; 2 Background; 2.1 Petri Nets; 2.2 Colored Petri Nets; 3 Inception and Development of Proposed Algorithm; 3.1 Previous Work; 3.2 PaLS Algorithm; 3.3 Improved PaLS (iPaLS) Algorithm; 4 Modeling and Testing of Algorithms; 4.1 Modeling of PaLS; 4.2 PaLS and BDD Simulation Results; 4.3 Modeling of iPaLS; 4.4 iPaLS Simulation Results; 5 Conclusions; References; Analyzing Cleaning Robots Using Probabilistic Model Checking; 1 Introduction
2 An iRobot Usage Scenario3 Abstracting the Real-World Scenario; 4 A DSL for Robot Motion and Analysis; 4.1 Syntax of the DSL; 4.2 Probabilistic Model Checking; 4.3 Modelling and Analysing the Abstract Scenario in PRISM; 4.4 A DSL Semantics in PRISM; 4.5 Tool support; 5 Validating the DSL; 6 Evaluation; 6.1 Model's Scalability; 6.2 Comparing algorithms; 6.3 A More Realistic Environment; 7 Related Work; 8 Conclusion; References; From Petri Nets to UML: A New Approach for Model Analysis; 1 Introduction; 2 Previous Work; 3 Background and Case Study; 3.1 Colored Petri Nets
3.2 Semantics of UML SMs3.3 Case Study; 3.4 From OPN to HCPN; 3.5 Initialization; 4 Translating Petri Nets Back to UML; 4.1 Sequence Diagram Construction; 4.2 Object Diagram Construction; 5 Model Analysis; 5.1 Generic Property Analysis; 6 Conclusion; References; Using Belief Propagation-Based Proposal Preparation for Automated Negotiation over Environmental Issues; 1 Introduction; 1.1 Related Work; 1.2 Objectives and Contributions; 2 Automated Negotiation; 2.1 Agent-Based Model; 2.2 Problem Statement; 2.3 Belief Propagation-Based Proposal Preparation (BPPP)
2.4 Z-Scoring Approach for Selecting Among the Prepared Proposals2.5 Argument Handling Model; 3 Experiments; 3.1 Case Study A: Energy-System Planning in Alberta; 3.2 Data Preparation and Implementation; 3.3 Results and Discussions; 3.4 Case Study B: King County House Sales; 4 Conclusion and Future Work; References; SAIL: A Scalable Wind Turbine Fault Diagnosis Platform; 1 Introduction; 1.1 Motivation; 1.2 Key Challenges; 1.3 Contributions; 2 Background; 2.1 Industrial Applications; 2.2 Big Data Analytics; 3 SAIL Architecture; 3.1 Scalable Monitoring System; 3.2 Bottom-Up Architecture
4 Mathematical Framework4.1 ARMA Representation of Vibration Data; 4.2 Harmonic Decomposition; 4.3 Model Order Selection; 4.4 Dictionary Learning and Compression; 4.5 Sparse Decomposition; 4.6 Dictionary Learning Using K-SVD; 5 Fault Diagnosis Framework; 5.1 Sensor Layer; 5.2 Fog Layer; 5.3 Cloud Layer; 6 Experimental Results; 6.1 Algorithm Accuracy Test; 6.2 Platform Scalability Test; 7 Conclusion and Future Work; References; Efficient Authentication of Approximate Record Matching for Outsourced Databases; 1 Introduction; 2 Preliminaries; 2.1 Record Similarity Measurement; 2.2 Mapping Strings to Euclidean Space
2 An iRobot Usage Scenario3 Abstracting the Real-World Scenario; 4 A DSL for Robot Motion and Analysis; 4.1 Syntax of the DSL; 4.2 Probabilistic Model Checking; 4.3 Modelling and Analysing the Abstract Scenario in PRISM; 4.4 A DSL Semantics in PRISM; 4.5 Tool support; 5 Validating the DSL; 6 Evaluation; 6.1 Model's Scalability; 6.2 Comparing algorithms; 6.3 A More Realistic Environment; 7 Related Work; 8 Conclusion; References; From Petri Nets to UML: A New Approach for Model Analysis; 1 Introduction; 2 Previous Work; 3 Background and Case Study; 3.1 Colored Petri Nets
3.2 Semantics of UML SMs3.3 Case Study; 3.4 From OPN to HCPN; 3.5 Initialization; 4 Translating Petri Nets Back to UML; 4.1 Sequence Diagram Construction; 4.2 Object Diagram Construction; 5 Model Analysis; 5.1 Generic Property Analysis; 6 Conclusion; References; Using Belief Propagation-Based Proposal Preparation for Automated Negotiation over Environmental Issues; 1 Introduction; 1.1 Related Work; 1.2 Objectives and Contributions; 2 Automated Negotiation; 2.1 Agent-Based Model; 2.2 Problem Statement; 2.3 Belief Propagation-Based Proposal Preparation (BPPP)
2.4 Z-Scoring Approach for Selecting Among the Prepared Proposals2.5 Argument Handling Model; 3 Experiments; 3.1 Case Study A: Energy-System Planning in Alberta; 3.2 Data Preparation and Implementation; 3.3 Results and Discussions; 3.4 Case Study B: King County House Sales; 4 Conclusion and Future Work; References; SAIL: A Scalable Wind Turbine Fault Diagnosis Platform; 1 Introduction; 1.1 Motivation; 1.2 Key Challenges; 1.3 Contributions; 2 Background; 2.1 Industrial Applications; 2.2 Big Data Analytics; 3 SAIL Architecture; 3.1 Scalable Monitoring System; 3.2 Bottom-Up Architecture
4 Mathematical Framework4.1 ARMA Representation of Vibration Data; 4.2 Harmonic Decomposition; 4.3 Model Order Selection; 4.4 Dictionary Learning and Compression; 4.5 Sparse Decomposition; 4.6 Dictionary Learning Using K-SVD; 5 Fault Diagnosis Framework; 5.1 Sensor Layer; 5.2 Fog Layer; 5.3 Cloud Layer; 6 Experimental Results; 6.1 Algorithm Accuracy Test; 6.2 Platform Scalability Test; 7 Conclusion and Future Work; References; Efficient Authentication of Approximate Record Matching for Outsourced Databases; 1 Introduction; 2 Preliminaries; 2.1 Record Similarity Measurement; 2.2 Mapping Strings to Euclidean Space