Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

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

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Fog Computing is a type of distributed computing that reduces computation on the cloud by creating a new layer between the Internet of Things (IoTs) devices and the cloud. This helps to deliver the services more efficiently and effectively. The security concerns in fog computing are inherited from the cloud itself as the fog computing is derived from cloud computing. So, a major security concern in fog computing is during sharing of data, as illegitimate users could get access while the data is being shared. Attribute Based Encryption (ABE) method is proved to be beneficial for securing the data, but ABE method alone is not sufficient as there are various security concerns such as forward issue and backward issue. Various methods have been proposed by researchers to solve the problem, one such efficient solution is re-encryption. In this paper we propose a scheme that uses Ciphertext-policy Attribute based Encryption along with re-encryption for providing access control in fog and cloud communication. The analysis of the proposed scheme reveals better security and performance. We have also implemented the proposed scheme to show its effectiveness.

Securing Fog and Cloud Communication Using Attribute Based Access Control and Re-encryption

Real world applications of Wireless Sensor Networks such as border control, healthcare monitoring and target tracking require secure communications. Thus, during WSN setup, one of the first requirements is to distribute the keys to the sensor nodes which can be later used for securing the messages exchanged between sensors. The key management schemes in WSN secure the communication between a pair or a group of nodes. However, the storage capacity of the sensor nodes is limited which makes storage requirement as an important parameter for the evaluation of key management schemes. This paper classifies the existing key management schemes proposed for WSNs into three categories: storage inefficient, storage efficient and highly storage efficient key management schemes.

Storage as a parameter for classifying dynamic key management schemes proposed for WSNs

Use of Internet in today's world has been largely dominated by content dissemination but currently used IP architecture is based on connection between hosts. In order to disseminate content efficiently in cost effective manner, internet architecture needs to map "what" user wants with "where" host of network is. Content Centric Networking (CCN) decouples content from host. CCN enables in-network caching at network layer. Each node in CCN can cache content along with forwarding the content. In CCN any node can act as host and serve client as if it is actual host for requested content. Content becomes core of network not connection in CCN. Major advantages of CCN are short download time and low communication overhead. As these advantages are because CCN supports in-network caching, so it can be concluded that caching is backbone of CCN. The selection of appropriate router to cache content so that it could be able to serve future requests for longer time is very important. The aim of this paper is to explain CCN functionalities briefly. Proposed caching strategies have also been covered in this paper.

A Review on Content Centric Networking and Caching Strategies

Many current key management schemes do not effectively consider the attack probability and assume it to be same for every sensor node. However, for some applications like disaster management and military surveillance, attacks on sensor nodes may occur with different probabilities. In this paper, we have proposed a deployment based attack resistant key distribution for cellular model of wireless sensor networks. The main strength of the proposed scheme lies in incorporating the concepts such as the node density of the cell, the neighbor influence factor, gradient distance, placement of the sink node and the type of application running on the nodes to construct the attack matrix of each cell of the network. The domain key pool of the cell is further extended by the sharing of adjacent sub key pools followed by the hash computation on the keys and combining the hash computed keys with the original keys. This improves the probability of connectivity and storage overhead of the proposed scheme. To improve the resilience against the node capture attack and the communication overhead, the key pool is partitioned into three non overlapping sub key pools and the keys are hashed based on the cell and the node's identifiers. It is also shown that the proposed scheme performs better in terms of the probability of connectivity and the computation overhead when compared with existing schemes.

Deployment Based Attack Resistant Key Distribution with Non Overlapping Key Pools in WSN

In this research paper we study the problem of mutual exclusionin the context ofwireless sensor and actor network (WSAN) and propose two novel approaches tosolve it. The major requirements for any proposed approach in such context are: (1) theproposed approach must select the minimum number of actor nodes to act on the givenevent region, (2) the overlaps between acting ranges should be minimum, (3) wastage ofresources should be less, and finally, whole event region must be covered by one or morethan one actors as per their applicability. We have proposed two algorithms, centralizedprioritized h-out-of-k mutual exclusion algorithm (CPMEA), and distributed prioritizedh-out-of-k mutual exclusion algorithm (DPMEA) in this research paper. Both proposedapproaches construct an actor cover set with similar optimality. The simulation resultsshow the performance in terms of size of actor cover set, overlapped region, nonoverlappedregion and maximum actor coverage degree. We have also compared ourobtained results with previously proposed benchmark algorithms.

https://journalskuwait.org/kjs/index.php/KJS/article/download/555/109

Mayank Dave

Papers

Securing Fog and Cloud Communication Using Attribute Based Access Control and Re-encryption

Storage as a parameter for classifying dynamic key management schemes proposed for WSNs

A Review on Content Centric Networking and Caching Strategies

Deployment Based Attack Resistant Key Distribution with Non Overlapping Key Pools in WSN

Optimal nodes selection in wireless sensor and actor networks based on prioritized mutual exclusion approach