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Fault-Tolerant Computing Systems: Tests, Diagnosis, Fault Treatment 5th International GI/ITG/GMA Conference Nürnberg, September 25–27, 1991 Proceedings (Informatik-Fachberichte #283)
by Mario Dal Cin Wolfgang Hohl5th International GI/ITG/GMA Conference, Nürnberg, September 25-27, 1991. Proceedings
Fault-Tolerant Design
by Elena DubrovaThis textbook serves as an introduction to fault-tolerance, intended for upper-division undergraduate students, graduate-level students and practicing engineers in need of an overview of the field. Readers will develop skills in modeling and evaluating fault-tolerant architectures in terms of reliability, availability and safety. They will gain a thorough understanding of fault tolerant computers, including both the theory of how to design and evaluate them and the practical knowledge of achieving fault-tolerance in electronic, communication and software systems. Coverage includes fault-tolerance techniques through hardware, software, information and time redundancy. The content is designed to be highly accessible, including numerous examples and exercises. Solutions and powerpoint slides are available for instructors.
Fault-Tolerant Design and Control of Automated Vehicles and Processes: Insights for the Synthesis of Intelligent Systems (Studies in Systems, Decision and Control #201)
by Ralf StetterThis book summarizes strategies, methods, algorithms, frameworks and systems for the fault-tolerant design and control of automated vehicles and processes. Intelligent systems may be able to accommodate inevitable faults, but this ability requires targeted design processes and advanced control systems. This book explains the respective elements involved in automated vehicles and processes. It provides detailed descriptions of fault-tolerant design, not offered in the existent scientific literature. With regard to fault-tolerant control, the focus is on innovative methods, which can accommodate not only uncertainties, but also shared and flexible redundant elements. The book is intended to present a concise guide for researchers in the field of fault-tolerant design and control, and to provide concrete insights for design and control engineers working in the field of automated vehicles and processes.
Fault-Tolerant Distributed Transactions on Blockchain (Synthesis Lectures on Data Management)
by Suyash Gupta Jelle Hellings Mohammad SadoghiSince the introduction of Bitcoin—the first widespread application driven by blockchain—the interest of the public and private sectors in blockchain has skyrocketed. In recent years, blockchain-based fabrics have been used to address challenges in diverse fields such as trade, food production, property rights, identity-management, aid delivery, health care, and fraud prevention. This widespread interest follows from fundamental concepts on which blockchains are built that together embed the notion of trust, upon which blockchains are built. 1. Blockchains provide data transparancy. Data in a blockchain is stored in the form of a ledger, which contains an ordered history of all the transactions. This facilitates oversight and auditing. 2. Blockchains ensure data integrity by using strong cryptographic primitives. This guarantees that transactions accepted by the blockchain are authenticated by its issuer, are immutable, and cannot be repudiated by the issuer. This ensures accountability. 3. Blockchains are decentralized, democratic, and resilient. They use consensus-based replication to decentralize the ledger among many independent participants. Thus, it can operate completely decentralized and does not require trust in a single authority. Additions to the chain are performed by consensus, in which all participants have a democratic voice in maintaining the integrity of the blockchain. Due to the usage of replication and consensus, blockchains are also highly resilient to malicious attacks even when a significant portion of the participants are malicious. It further increases the opportunity for fairness and equity through democratization. These fundamental concepts and the technologies behind them—a generic ledger-based data model, cryptographically ensured data integrity, and consensus-based replication—prove to be a powerful and inspiring combination, a catalyst to promote computational trust. In this book, we present an in-depth study of blockchain, unraveling its revolutionary promise to instill computational trust in society, all carefully tailored to a broad audience including students, researchers, and practitioners. We offer a comprehensive overview of theoretical limitations and practical usability of consensus protocols while examining the diverse landscape of how blockchains are manifested in their permissioned and permissionless forms.
Fault-Tolerant Message-Passing Distributed Systems: An Algorithmic Approach
by Michel RaynalThis book presents the most important fault-tolerant distributed programming abstractions and their associated distributed algorithms, in particular in terms of reliable communication and agreement, which lie at the heart of nearly all distributed applications. These programming abstractions, distributed objects or services, allow software designers and programmers to cope with asynchrony and the most important types of failures such as process crashes, message losses, and malicious behaviors of computing entities, widely known under the term "Byzantine fault-tolerance". The author introduces these notions in an incremental manner, starting from a clear specification, followed by algorithms which are first described intuitively and then proved correct. The book also presents impossibility results in classic distributed computing models, along with strategies, mainly failure detectors and randomization, that allow us to enrich these models. In this sense, the book constitutes an introduction to the science of distributed computing, with applications in all domains of distributed systems, such as cloud computing and blockchains. Each chapter comes with exercises and bibliographic notes to help the reader approach, understand, and master the fascinating field of fault-tolerant distributed computing.
Fault-Tolerant Parallel and Distributed Systems
by Dimiter R. Avresky David R. KaeliThe most important use of computing in the future will be in the context of the global "digital convergence" where everything becomes digital and every thing is inter-networked. The application will be dominated by storage, search, retrieval, analysis, exchange and updating of information in a wide variety of forms. Heavy demands will be placed on systems by many simultaneous re quests. And, fundamentally, all this shall be delivered at much higher levels of dependability, integrity and security. Increasingly, large parallel computing systems and networks are providing unique challenges to industry and academia in dependable computing, espe cially because of the higher failure rates intrinsic to these systems. The chal lenge in the last part of this decade is to build a systems that is both inexpensive and highly available. A machine cluster built of commodity hardware parts, with each node run ning an OS instance and a set of applications extended to be fault resilient can satisfy the new stringent high-availability requirements. The focus of this book is to present recent techniques and methods for im plementing fault-tolerant parallel and distributed computing systems. Section I, Fault-Tolerant Protocols, considers basic techniques for achieving fault-tolerance in communication protocols for distributed systems, including synchronous and asynchronous group communication, static total causal order ing protocols, and fail-aware datagram service that supports communications by time.
Fault-Tolerant Parallel Computation (The Springer International Series in Engineering and Computer Science #401)
by Paris Christos Kanellakis Alex Allister ShvartsmanFault-Tolerant Parallel Computation presents recent advances in algorithmic ways of introducing fault-tolerance in multiprocessors under the constraint of preserving efficiency. The difficulty associated with combining fault-tolerance and efficiency is that the two have conflicting means: fault-tolerance is achieved by introducing redundancy, while efficiency is achieved by removing redundancy. This monograph demonstrates how in certain models of parallel computation it is possible to combine efficiency and fault-tolerance and shows how it is possible to develop efficient algorithms without concern for fault-tolerance, and then correctly and efficiently execute these algorithms on parallel machines whose processors are subject to arbitrary dynamic fail-stop errors. The efficient algorithmic approaches to multiprocessor fault-tolerance presented in this monograph make a contribution towards bridging the gap between the abstract models of parallel computation and realizable parallel architectures. Fault-Tolerant Parallel Computation presents the state of the art in algorithmic approaches to fault-tolerance in efficient parallel algorithms. The monograph synthesizes work that was presented in recent symposia and published in refereed journals by the authors and other leading researchers. This is the first text that takes the reader on the grand tour of this new field summarizing major results and identifying hard open problems. This monograph will be of interest to academic and industrial researchers and graduate students working in the areas of fault-tolerance, algorithms and parallel computation and may also be used as a text in a graduate course on parallel algorithmic techniques and fault-tolerance.
Fault-Tolerant Real-Time Systems: The Problem of Replica Determinism (The Springer International Series in Engineering and Computer Science #345)
by Stefan PolednaReal-time computer systems are very often subject to dependability requirements because of their application areas. Fly-by-wire airplane control systems, control of power plants, industrial process control systems and others are required to continue their function despite faults. Fault-tolerance and real-time requirements thus constitute a kind of natural combination in process control applications. Systematic fault-tolerance is based on redundancy, which is used to mask failures of individual components. The problem of replica determinism is thereby to ensure that replicated components show consistent behavior in the absence of faults. It might seem trivial that, given an identical sequence of inputs, replicated computer systems will produce consistent outputs. Unfortunately, this is not the case. The problem of replica non-determinism and the presentation of its possible solutions is the subject of Fault-Tolerant Real-Time Systems: The Problem of Replica Determinism. The field of automotive electronics is an important application area of fault-tolerant real-time systems. Systems like anti-lock braking, engine control, active suspension or vehicle dynamics control have demanding real-time and fault-tolerance requirements. These requirements have to be met even in the presence of very limited resources since cost is extremely important. Because of its interesting properties Fault-Tolerant Real-Time Systems gives an introduction to the application area of automotive electronics. The requirements of automotive electronics are a topic of discussion in the remainder of this work and are used as a benchmark to evaluate solutions to the problem of replica determinism.
Fault-Tolerant Search Algorithms: Reliable Computation with Unreliable Information (Monographs in Theoretical Computer Science. An EATCS Series)
by Ferdinando CicaleseWhy a book on fault-tolerant search algorithms? Searching is one of the fundamental problems in computer science. Time and again algorithmic and combinatorial issues originally studied in the context of search find application in the most diverse areas of computer science and discrete mathematics. On the other hand, fault-tolerance is a necessary ingredient of computing. Due to their inherent complexity, information systems are naturally prone to errors, which may appear at any level – as imprecisions in the data, bugs in the software, or transient or permanent hardware failures. This book provides a concise, rigorous and up-to-date account of different approaches to fault-tolerance in the context of algorithmic search theory. Thanks to their basic structure, search problems offer insights into how fault-tolerant techniques may be applied in various scenarios. In the first part of the book, a paradigmatic model for fault-tolerant search is presented, the Ulam—Rényi problem. Following a didactic approach, the author takes the reader on a tour of Ulam—Rényi problem variants of increasing complexity. In the context of this basic model, fundamental combinatorial and algorithmic issues in the design of fault-tolerant search procedures are discussed. The algorithmic efficiency achievable is analyzed with respect to the statistical nature of the error sources, and the amount of information on which the search algorithm bases its decisions. In the second part of the book, more general models of faults and fault-tolerance are considered. Special attention is given to the application of fault-tolerant search procedures to specific problems in distributed computing, bioinformatics and computational learning. This book will be of special value to researchers from the areas of combinatorial search and fault-tolerant computation, but also to researchers in learning and coding theory, databases, and artificial intelligence. Only basic training in discrete mathematics is assumed. Parts of the book can be used as the basis for specialized graduate courses on combinatorial search, or as supporting material for a graduate or undergraduate course on error-correcting codes.
Fault-Tolerant Systems
by Israel Koren C. Mani KrishnaFault-Tolerant Systems, Second Edition, is the first book on fault tolerance design utilizing a systems approach to both hardware and software. No other text takes this approach or offers the comprehensive and up-to-date treatment that Koren and Krishna provide. The book comprehensively covers the design of fault-tolerant hardware and software, use of fault-tolerance techniques to improve manufacturing yields, and design and analysis of networks. Incorporating case studies that highlight more than ten different computer systems with fault-tolerance techniques implemented in their design, the book includes critical material on methods to protect against threats to encryption subsystems used for security purposes. The text’s updated content will help students and practitioners in electrical and computer engineering and computer science learn how to design reliable computing systems, and how to analyze fault-tolerant computing systems.Delivers the first book on fault tolerance design with a systems approach Offers comprehensive coverage of both hardware and software fault tolerance, as well as information and time redundancy Features fully updated content plus new chapters on failure mechanisms and fault-tolerance in cyber-physical systems Provides a complete ancillary package, including an on-line solutions manual for instructors and PowerPoint slides
Fault-Tolerant Systems
by Israel Koren C. Mani KrishnaFault-Tolerant Systems is the first book on fault tolerance design with a systems approach to both hardware and software. No other text on the market takes this approach, nor offers the comprehensive and up-to-date treatment that Koren and Krishna provide. This book incorporates case studies that highlight six different computer systems with fault-tolerance techniques implemented in their design. A complete ancillary package is available to lecturers, including online solutions manual for instructors and PowerPoint slides. Students, designers, and architects of high performance processors will value this comprehensive overview of the field.The first book on fault tolerance design with a systems approachComprehensive coverage of both hardware and software fault tolerance, as well as information and time redundancyIncorporated case studies highlight six different computer systems with fault-tolerance techniques implemented in their designAvailable to lecturers is a complete ancillary package including online solutions manual for instructors and PowerPoint slides
FE Computation on Accuracy Fabrication of Ship and Offshore Structure Based on Processing Mechanics
by Hong ZHOU Jiangchao WANGThis book provides insight on processing mechanics during ship and offshore structure, and researchers, scientists, and engineers in the field of manufacturing process mechanics can benefit from the book. This book is written by subject experts based on the recent research results in FE computation on accuracy fabrication of ship and offshore structures based on processing mechanics. In order to deal with actual engineering problems during construction of ship and offshore structure, it proposes advanced computational approaches such as thermal elastic–plastic and elastic FE computations and employed to examine physical behavior and clarifies generation mechanism of mechanical response. As such, this book provides valuable knowledge, useful methods, and practical algorithms that can be considered in manufacturing process mechanics.
Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques (SpringerBriefs in Energy)
by Mrinmoy Majumder Apu K. SahaThis Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available energy for producing optimal amount of electricity which will satisfy the demand from the market. According to the results of the case studies further applications are encouraged.
Feasible Mathematics: A Mathematical Sciences Institute Workshop, Ithaca, New York, June 1989 (Progress in Computer Science and Applied Logic #9)
by S.R. Buss P.J. ScottA so-called "effective" algorithm may require arbitrarily large finite amounts of time and space resources, and hence may not be practical in the real world. A "feasible" algorithm is one which only requires a limited amount of space and/or time for execution; the general idea is that a feasible algorithm is one which may be practical on today's or at least tomorrow's computers. There is no definitive analogue of Church's thesis giving a mathematical definition of feasibility; however, the most widely studied mathematical model of feasible computability is polynomial-time computability. Feasible Mathematics includes both the study of feasible computation from a mathematical and logical point of view and the reworking of traditional mathematics from the point of view of feasible computation. The diversity of Feasible Mathematics is illustrated by the. contents of this volume which includes papers on weak fragments of arithmetic, on higher type functionals, on bounded linear logic, on sub recursive definitions of complexity classes, on finite model theory, on models of feasible computation for real numbers, on vector spaces and on recursion theory. The vVorkshop on Feasible Mathematics was sponsored by the Mathematical Sciences Institute and was held at Cornell University, June 26-28, 1989.
Feasible Mathematics II (Progress in Computer Science and Applied Logic #13)
by Peter Clote Jeffrey B. RemmelPerspicuity is part of proof. If the process by means of which I get a result were not surveyable, I might indeed make a note that this number is what comes out - but what fact is this supposed to confirm for me? I don't know 'what is supposed to come out' . . . . 1 -L. Wittgenstein A feasible computation uses small resources on an abstract computa tion device, such as a 'lUring machine or boolean circuit. Feasible math ematics concerns the study of feasible computations, using combinatorics and logic, as well as the study of feasibly presented mathematical structures such as groups, algebras, and so on. This volume contains contributions to feasible mathematics in three areas: computational complexity theory, proof theory and algebra, with substantial overlap between different fields. In computational complexity theory, the polynomial time hierarchy is characterized without the introduction of runtime bounds by the closure of certain initial functions under safe composition, predicative recursion on notation, and unbounded minimization (S. Bellantoni); an alternative way of looking at NP problems is introduced which focuses on which pa rameters of the problem are the cause of its computational complexity and completeness, density and separation/collapse results are given for a struc ture theory for parametrized problems (R. Downey and M. Fellows); new characterizations of PTIME and LINEAR SPACE are given using predicative recurrence over all finite tiers of certain stratified free algebras (D.
Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)
by Frederic Ros Rabia RiadThis book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
Feature Based Product Life-Cycle Modelling: IFIP TC5 / WG5.2 & WG5.3 Conference on Feature Modelling and Advanced Design-for-the-Life-Cycle Systems (FEATS 2001) June 12–14, 2001, Valenciennes, France (IFIP Advances in Information and Communication Technology #109)
by René Soenen Gustav J. OllingFeature-based technology is the key factor towards meeting the increasingly high demands of improving and speeding up the product development process from concept to customer feedback, and is therefore expected to be able to provide for a better approach to integrate the complete product design process chain. Feature Based Product Life-Cycle Modelling is dedicated to exploring the progress towards an integrated solution for the product creation process based on feature technology. Hence, it encompasses significant phases of the product creation process, from conceptual design to recycling, including the following topics: *Life-phases modelling; *Knowledge based engineering; *Multiple-view geometric modelling; *Technological links among assemblies; *Manufacturing process cost estimation; *Manufacturing modelling; *Machining preparation; *Product deterioration prediction; *Product recovery estimation. For each topic, a state of the art, theoretic bases, tentative solutions and illustrative examples are detailed, demonstrating the successful application of feature technology to the modelling of innovative products and the efficient control of their design. The book is a selection of proceedings from the International Conference on Feature Modelling in Advanced Design-for-the-Life-Cycle Systems (FEATS 2001), which was sponsored by the International Federation for Information Processing (IFIP) and held in Valenciennes, France in June 2001.
A Feature-Centric View of Information Retrieval (The Information Retrieval Series #27)
by Donald MetzlerCommercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets.A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches.
Feature Coding for Image Representation and Recognition (SpringerBriefs in Computer Science)
by Yongzhen Huang Tieniu TanThis brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) Discuss the applications of feature coding in different visual tasks, analyze the influence of some key factors in feature coding with intensive experimental studies; (5) Provide the suggestions of how to apply different feature coding methods and forecast the potential directions for future work on the topic. It is suitable for students, researchers, practitioners interested in object recognition.
Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
by Max Kuhn Kjell JohnsonThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
by Max Kuhn Kjell JohnsonThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Guozhu Dong Huan LiuFeature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Guozhu Dong Huan LiuFeature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Feature Engineering Made Easy: Identify Unique Features From Your Dataset In Order To Build Powerful Machine Learning Systems
by Sinan OzdemirFeature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing #207)
by Isabelle Guyon Steve Gunn Masoud Nikravesh Lofti A. ZadehThis book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.