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.

Currently there exists a gap between deep learning and the techniques required to quantify uncertainty. This paper uses the weather forecasting as an application background to illustrate the technique of deep uncertainty learning (DUL). Weather forecasting has great significance throughout human history and is traditionally approached through numerical weather prediction (NWP) in which the atmosphere is modelled as differential equations. However, due to the instability of these differential equations in the presence of uncertainties, weather forecasting through numerical simulations may not be reliable. This paper explores weather forecasting as a data mining problem. We build a deep prediction interval (DPI) model based on sequence-to-sequence (seq2seq) that predicts spatio-temporal patterns of meteorological variables in the future 37 hours, which incorporates the informative knowledge of NWP. A big contribution and surprising finding in the training process of DPI is that training by mean variance error (MVE) loss instead of mean square error loss can significantly improve the generalization of point estimation, which has never been reported in previous researches. We think this phenomenon can be regarded as a new kind of regularization which can not only be on a par with the famous Dropout but also provide more uncertainty information, and hence comes into win-win situation and we name it MV-R (Mean Variance Regularization). Based on single DPI, we then build deep ensemble. We evaluate our method on dataset from 10 realistic weather stations in Beijing of China. Experimental results shown DPI has better generalization than traditional point estimation and deep ensemble can further improve the performance. The deep ensemble method also achieved top-2 online score ranking in the competition of AI Challenger 2018. It can dramatically decrease up to 56% error compared with NWP.

Deep Uncertainty Learning: A Machine Learning Approach for Weather Forecasting.

With the rapid growth of mobile computing and social networking, social networks have extended their popularity from the Internet to mobile domain. Pervasive Social Networking (PSN) ensures social communications at any time and in any place with a universal manner. It supports online and instant (i.e., pervasive) social activities based on heterogeneous networks, e.g., the Internet, mobile cellular networks or self-organized networks or other networking technologies. It is treated as one of the killer applications in the next generation mobile networks and wireless systems (i.e., 5G). There are various applications over PSN. Typical examples include social chatting, gaming, rescuing, recommending and information sharing. Because group mobility is very common in modern life, PSN has become valuable for mobile users, especially when they are familiar strangers and often appear in the same vicinity. PSN greatly extends our experiences of social communications.

/pdf/ieee-access-special-section-editorial-trust-management-in-dtd3b6ke7u.pdf

IEEE Access Special Section Editorial: Trust Management in Pervasive Social Networking (TruPSN)

Online Fault Diagnosis of Pv Array Considering Label Errors Based on Distributionally Robust Logistic Regression

Application-layer tunnels are often used to construct covert channels in order to transmit secret data, which is often applied to raise network threats in recent years. Detection of application-layer tunnels can assist identifying a variety of network threats, thus has high research significance. In this paper, we explore application-layer tunnel detection and propose a generic detection method by applying both rules and machine learning. Our detection method mainly consists of two parts: rule-based domain name filtering for Domain Generation Algorithm (DGA) based on a trigram model and a machine learning model based on our proposed generic feature extraction framework for tunnel detection. The rule-based DGA domain name filtering can eliminate some obvious tunnels in order to reduce the amount of data processed by machine learning-based detection, thereby, the detection efficiency can be improved. The generic feature extraction framework comprehensively integrates previous research results by combining multiple detection methods, supporting multiple layers and performing multiple feature extraction. We take the three most common application-layer tunnels, i.e., DNS tunnel, HTTP tunnel and HTTPS tunnel as examples to analyze and test our detection method. The experimental results show that the proposed method is generic and efficient, compared with other existing approaches.

/pdf/detection-of-application-layer-tunnels-with-rules-and-2xzcrut2nd.pdf

Detection of Application-Layer Tunnels with Rules and Machine Learning

Backscatter communication (BC) enables ultra-low-power communications and allows devices to harvest energy simultaneously. But its practical deployment faces severe security threats caused by its nature of openness and broadcast. Authenticating backscatter devices (BDs) is treated as the first line of defense. However, complex cryptographic approaches are not desirable due to the limited computation capability of BDs. Existing physical layer authentication schemes cannot effectively support BD mobility, multiple attacker identification and attacker location tracing in an integrated way. To tackle these problems, this paper proposes BCAuth, a multi-stage authentication and attack tracing scheme based on the physical spatial information of BDs to realize enhanced BD authentication security for both static and mobile BDs. After initial authentication based on BD identity with its position information registration, preemptive authentication and re-authentication are performed according to spatial correlation of backscattered signal source locations associated with the BD. By exploiting clustering-based analysis on spacial information, BCAuth is capable of determining the number of attackers and localizing their positions. In addition, we propose a reciprocal channel-based method for BD re-authentication with better authentication performance than the clustering-based method for mobile BDs when the BDs is able to measure received signal strength (RSS), which also enables mutual authentication. We theoretically analyze BCAuth security and conduct extensive numerical simulations with various settings to show its desirable performance.

Zheng Yan

Papers

Deep Uncertainty Learning: A Machine Learning Approach for Weather Forecasting.

IEEE Access Special Section Editorial: Trust Management in Pervasive Social Networking (TruPSN)

Online Fault Diagnosis of Pv Array Considering Label Errors Based on Distributionally Robust Logistic Regression

Detection of Application-Layer Tunnels with Rules and Machine Learning

BCAuth: Physical Layer Enhanced Authentication and Attack Tracing for Backscatter Communications