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From leading industrial/research experts, here is an insider's look at today's best practices for software reliability engineering. Using this guide, software developers, designers, and project managers, high-level applications programmers and designers, and students will be able to tap into an unparalleled repository of accumulated experience and expertise.
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications. Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering department of the Chinese University of Hong Kong.
Quality-of-Service (QoS) is normally used to describe the non-functional characteristics of Web services and as a criterion for evaluating different Web services. QoS Management of Web Services presents a new distributed QoS evaluation framework for these services. Moreover, three QoS prediction methods and two methods for creating fault-tolerant Web services are also proposed in this book. It not only provides the latest research results, but also presents an excellent overview of QoS management of Web sciences, making it a valuable resource for researchers and graduate students in service computing. Zibin Zheng is an associate research fellow at the Shenzhen Research Institute, The Chinese University of Hong Kong, China. Professor Michael R. Lyu also works at the same institute.
This book offers a systematic and practical overview of Quality of Service prediction in cloud and service computing. Intended to thoroughly prepare the reader for research in cloud performance, the book first identifies common problems in QoS prediction and proposes three QoS prediction models to address them. Then it demonstrates the benefits of QoS prediction in two QoS-aware research areas. Lastly, it collects large-scale real-world temporal QoS data and publicly releases the datasets, making it a valuable resource for the research community. The book will appeal to professionals involved in cloud computing and graduate students working on QoS-related problems.
Software fault tolerance techniques involve error detection, exception handling, monitoring mechanisms, and error recovery. This issue of Trends in Software focuses on identification, formulation, application, and evaluation of current software fault tolerance techniques.
This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.
This text presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning" or "global learning."
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
System reliability, availability and robustness are often not well understood by system architects, engineers and developers. They often don't understand what drives customer's availability expectations, how to frame verifiable availability/robustness requirements, how to manage and budget availability/robustness, how to methodically architect and design systems that meet robustness requirements, and so on. The book takes a very pragmatic approach of framing reliability and robustness as a functional aspect of a system so that architects, designers, developers and testers can address it as a concrete, functional attribute of a system, rather than an abstract, non-functional notion.
Welcome to the 2nd International Conference on Image and Video Retrieval, CIVR2003. The goal of CIVR is to illuminate the state of the art in visual information retrieval and to stimulate collaboration between researchers and practitioners. This year we received 110 submissions from 26 countries. Based upon the reviews of at least 3 members of the program committee, 43 papers were accepted for the research track of the conference. First, we would like to thank all of the members of the Program Committee and the additional referees listed below. Their reviews of the submissions played a pivotal role in the quality of the conference. Moreover,we are grateful to Nicu Sebe and Xiang Zhou for hel...