Data Mining - Concepts and Techniques.

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

A robust optimal power system dispatching method based on modified affine strategy is proposed to cope with the generation reserve coordination problem considering wind power uncertainty. Firstly, focusing on power system with wind turbines, a two-stage power optimal dispatch model of generation and reserve is established. Secondly, considering the statistical information of wind power errors, the self-organizing mapping(SOM) neural network is applied to make the wind power data get clustered, and then multiple uncertainty sets of wind power output error are constructed. Then, on the basis of the robust optimization model with multiple uncertainty sets, a modified affine strategy method is proposed, and it is transformed into linear programming(LP) model by dual principle. A 6-bus system and IEEE 118-bus system are tested for case studies, then the modified affine strategy is compared with the conventional affine strategy. The results show that the modified affine strategy has higher solution efficiency and better economic benefits, improves the problem of conservative modeling of uncertainty sets, and verifies the effectiveness and advancement of the proposed strategy.

Modified Affine Strategy for Two-Stage Robust Optimization of Generation and Reserve with Correlated Wind Power

A method is disclosed, comprising obtaining, by a first apparatus, a first identifier associated with the first apparatus, wherein the first identifier is different from a second identifier associated with the first apparatus, providing, by the first apparatus, at least one message to at least one further apparatus of at least one further apparatus, wherein each of the at least one message comprises a key information of the first apparatus and is associated with the first identifier associated with the first apparatus, and determining, by the first apparatus, a session key of a communication session associated with the first apparatus and the at least one further apparatus, based on at least one message received, by the first apparatus, provided by at least one further apparatus of the at least one further apparatus, wherein each of the at least one message comprises a key information of the respective further apparatus and is associated with a first identifier associated with the respective further apparatus.

Privacy preserving authentication and key agreement protocol for apparatus-to-apparatus communication

Learning-based line impedance estimation for partially observable distribution systems

Voiceprint authentication is a promising approach for user authentication due to its low acquisition cost and non-contact characteristics. However, most of existing voiceprint authentication systems directly store user voiceprint templates in a database, without taking any effective measures to protect user privacy. Once the voiceprint templates in the database are leaked, user voice information becomes public, which brings severe security threats to the users and impact practical applications of a voiceprint authentication system. Few literature works study privacy-preserving voiceprint authentication although it is particularly needed. Most of existing solutions based on cryptography suffer from high computational costs. But usability of some efficient systems is poor due to additional interactions with users. To tackle the above issues, this paper proposes VoiceSketch, a privacy-preserving voiceprint authentication system based on a secure sketch to protect the privacy of user voiceprint templates with high usability. VoiceSketch does not store the voiceprint templates directly. Instead, it uses a secure sketch to correct errors between multiple voiceprint features of the same user and use cryptographic hash function to extract an authentication key from a user’s voiceprint template. We analyze the security of VoiceSketch based on secure sketch and hash function. We also implement VoiceSketch and design a series of experiments to verify its accuracy and effectiveness.

VoiceSketch: a Privacy-Preserving Voiceprint Authentication System

The integrated energy system is an important part in the development of the energy field. It has attracted much attention because it can promote energy consumption and improve energy utilization efficiency. At present, the steady-state analysis of integrated energy systems has been deeply studied in economic dispatch and optimal operation, but the dynamic simulation of integrated energy systems still needs to be explored. In order to better study the influence between the various subsystems of the integrated energy system, this paper establishes a typical electric-gas integrated energy system simulation platform with wind uncertainty. The system is two-way coupled, and at the same time, uncertainty factors are introduced into the system through wind turbines, and the comprehensive energy system simulation is carried out through the multi-rate model established in this paper. The simulation results show that the proposed electric-gas energy system with wind uncertainty The simulation model can effectively reflect the interaction between sub-systems, and can reflect the impact of uncertainty on the integrated energy system, and can also improve the simulation efficiency to a certain extent.

Zheng Yan

Papers

Modified Affine Strategy for Two-Stage Robust Optimization of Generation and Reserve with Correlated Wind Power

Privacy preserving authentication and key agreement protocol for apparatus-to-apparatus communication

Learning-based line impedance estimation for partially observable distribution systems

VoiceSketch: a Privacy-Preserving Voiceprint Authentication System

Dynamic Simulation of Electric-gas Integrated Energy System with Variable Wind Power