物件導向軟體之架構(Object-Oriented Software Construction)探討

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

National Institute of Standards and Technology における超伝導研究及び生活

Linked: The New Science of Networks

Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges

Statistical results have predicted that 70% of world's population will reside in cities by the year 2050. With the advancement in the field of information and communication technology, the concept of smart cities is gaining more prominence. Smart cities use the ICT technology to improve the functioning of the city and enhance the community services provided. To keep the data in smart city secure, different existing security mechanisms have been deployed directly in smart or have been modified to smart city scale. Most of the security mechanisms today need cryptographic keys, hence, key generation plays a pivotal role in the security of any smart city. In this paper, we proposes a key generation methodology especially designed for smart cities using a random number generator which collects entropy from different user end devices and sensors present in the city and generate secure random keys at a central node. We have also tested our method against NSIT random number test suite and have got acceptable results.

Smart key generation for smart cities

A greenhouse is a structure generally made of glass, designed to provide protection and controlled environment to raise plants indoors. In order to achieve high quality and quantity of produce, proper management and data collection of the greenhouse environment is required. Manual practice of plant monitoring is laborious and time consuming. The proposed greenhouse system is an application which demonstrates the concept of Internet of Things and involves ubiquitous monitoring and controlling of environmental parameters within the greenhouse, which directly or indirectly control the plant growth and so their production. The system thus designed, mainly aims to remotely monitor and control the greenhouse using temperature sensor, light sensor, soil moisture, water level sensor and actuators through a customized webpage. Moreover, real-time representation of the sensed data is graphically plotted as well as stored for improved monitoring and further analysis, illustrating the concept of cloud instrumentation.

Ubiquitous greenhouse monitoring system

In spite of presence of many classical and modified data analysis techniques, data analysis in the field of software engineering still remains a challenge because of the presence of large number of both continuous and discreet explanatory variables judging the outcome of one and more than one dependant variables. Requirement for an efficient multivariate data analysis technique which fulfils the constraints associated with software data led to the design of OSR (optimized set reduction) which uses a greedy algorithm for data analysis using both the principles of machine learning and conventional statistics. With the incoming of big data and other increasing dimensions of data set, we, through this paper, try to propose a new algorithm, based on the similar lines of optimised set reduction, using its strength to extract subsets. As the current trend of programming demands an algorithm to execute in parallel, we also propose a modification to our algorithm for it to run in a multicore platform with good efficiency.

A parallel dynamic programming approach for data analysis

In today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios.

/pdf/cognitive-spam-recognition-using-hadoop-and-multicast-update-32ia1a0fn2.pdf

Cognitive Spam Recognition Using Hadoop and Multicast-Update

A major problem in virtualized cloud datacenters today is the inefficiency of communication between virtual machines, i.e. inter-VM communication. Efforts have gone into optimizing communication between VMs present on the same physical server in the cloud, i.e. co-resident or co-located VMs; however, optimizing communication between VMs present on different physical servers is a separate issue. As these communicate via the TCP/IP network stack, improvements in performance or efficiency are normally proposed by customizations to the communication protocols requisite to the datacenter. We propose a mechanism to increase the average speed of inter-VM file distribution in the cloud for VMs on separate physical servers by implementing a BitTorrent-like peer-to-peer (P2P) system. The proposal, named VirtTorrent, uses direct communication between VMs in the cloud to implement a protocol similar to BitTorrent, allowing files to be distributed swiftly and with minimal network congestion. As inter-VM communication and network congestion are both important issues in the datacenter, we believe that VirtTorrent can make a marked improvement in the scenarios laid out in this paper.

https://dl.acm.org/doi/pdf/10.1145/2896387.2896405

K. Chandrasekaran

Papers

Smart key generation for smart cities

Ubiquitous greenhouse monitoring system

A parallel dynamic programming approach for data analysis

Cognitive Spam Recognition Using Hadoop and Multicast-Update

VirtTorrent: BitTorrent for Inter-VM File Distribution