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

Data Mining Practical Machine Learning Tools and Techniques

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

Trust: The Social Virtues and the Creation of Prosperity

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

The computing and networking capacities of today's wireless portable devices allow for ubiquitous services, which are seamlessly networked. Indeed, wireless handheld devices now embed the necessary resources to act as both service clients and providers. However, the ubiquitous networking of services remains challenged by the inherent mobility and resource constraints of the devices, which make services a priori highly volatile. This paper discusses the design, implementation, and experimentation of the ubiSOAP service-oriented middleware, which leverages wireless networking capacities to effectively enable the ubiquitous networking of services. ubiSOAP specifically defines a layered communication middleware that underlies standard SOAP-based middleware, hence supporting legacy Web Services while exploiting nowadays ubiquitous connectivity.

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ubiSOAP: A Service-Oriented Middleware for Ubiquitous Networking

The Web services architecture is expected to play a prominent role in developing next generation distributed systems. This chapter discusses how to build dependable systems based on the Web services architecture. More specifically, it surveys base fault tolerance mechanisms, considering both backward and forward error recovery mechanisms, and shows how they are adapted to deal with the specifics of the Web in the light of ongoing work in the area. Existing solutions, targeting the development of dependable composite Web services, may be subdivided into two categories that are respectively related to the specification of Web services composition and to the design of dedicated distributed protocols.

https://hal.inria.fr/inria-00414794/document

Dependability in the web services architecture

One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world while relying on a large number of mobile devices. Those devices can host different types of sensors incorporated in every aspect of our lives. However, given the increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of (sensing) devices in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.

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Probabilistic registration for large-scale mobile participatory sensing

The advent of light-weight terminals (e.g., PDA) with integrated communication capabilities facilitates service access and hosting anytime, anywhere. However, effective service access requires dealing with the service's QoS, including related resource consumption. This paper introduces a comprehensive framework for QoS-aware service location in ad hoc networks. Our framework divides into: (i) QoS specification that is expressive enough to capture most significant QoS-related properties, and concise enough to have minimal computation cost associated with QoS management; and (ii) a benefit function for evaluating the overall benefit of service instances available in the network from the standpoint of QoS regarding both the user's perspective and resource consumption on hosts. Application of the proposed QoS framework is addressed in the context of the mobile ADHoc WS system, which supports QoS-aware location of Web services deployed in mobile ad hoc networks.

/pdf/qos-aware-service-location-in-mobile-ad-hoc-networks-54gdd4xkgd.pdf

QoS-aware service location in mobile ad hoc networks

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

Valérie Issarny

Papers

ubiSOAP: A Service-Oriented Middleware for Ubiquitous Networking

Dependability in the web services architecture

Probabilistic registration for large-scale mobile participatory sensing

QoS-aware service location in mobile ad hoc networks

Dynamic decision networks for decision-making in self-adaptive systems: a case study