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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

BooksJournals

    • Constraints, Language and Computation

      • 1st Edition
      • June 28, 2014
      • M. A. Rosner + 2 more
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 1 8 3
      • Hardback
        9 7 8 0 1 2 5 9 7 9 3 0 6
      • eBook
        9 7 8 0 0 8 0 5 0 2 9 6 0
      Constraint-based linguistics is intersected by three fields: logic, linguistics, and computer sciences. The central theme that ties these different disciplines together is the notion of a linguistic formalism or metalanguage. This metalanguage has good mathematical properties, is designed to express descriptions of language, and has a semantics that can be implemented on a computer. Constraints, Language and Computation discusses the theory and practice of constraint-based computational linguistics. The book captures both the maturity of the field and some of its more interesting future prospects during a particulary important moment of development in this field.

    • Readings in Human-Computer Interaction

      • 1st Edition
      • June 28, 2014
      • Ronald M. Baecker
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 4 6 5
      • eBook
        9 7 8 0 0 8 0 5 1 5 7 4 8
      The effectiveness of the user-computer interface has become increasingly important as computer systems have become useful tools for persons not trained in computer science. In fact, the interface is often the most important factor in the success or failure of any computer system. Dealing with the numerous subtly interrelated issues and technical, behavioral, and aesthetic considerations consumes a large and increasing share of development time and a corresponding percentage of the total code for any given application. A revision of one of the most successful books on human-computer interaction, this compilation gives students, researchers, and practitioners an overview of the significant concepts and results in the field and a comprehensive guide to the research literature. Like the first edition, this book combines reprints of key research papers and case studies with synthesizing survey material and analysis by the editors. It is significantly reorganized, updated, and enhanced; over 90% of the papers are new. An invaluable resource for systems designers, cognitive scientists, computer scientists, managers, and anyone concerned with the effectiveness of user-computer interfaces, it is also designed for use as a primary or supplementary text for graduate and advanced undergraduate courses in human-computer interaction and interface design.

    • Alpha Architecture Reference Manual

      • 1st Edition
      • June 28, 2014
      • Alpha Architecture Committee
      • English
      • Paperback
        9 7 8 1 5 5 5 5 8 0 9 8 8
      • eBook
        9 7 8 1 4 8 3 2 9 4 3 3 9
      This is the authoritative reference on Digital Equipment Corporation's new 64-bit RISC Alpha architecture. Written by the designers of the internal Digital specifications, this book contains complete descriptions of the common architecture required for all implementations and the interfaces required to support the OSF/1 and OpenVMS operating systems.

    • The Digital Technical Documentation Handbook

      • 1st Edition
      • June 28, 2014
      • Susan K. Schultz + 3 more
      • English
      • Paperback
        9 7 8 1 5 5 5 5 8 1 0 3 9
      • eBook
        9 7 8 1 4 8 3 2 9 6 2 7 2
      The Digital Technical Documentation Handbook describes the process of developing and producing technical user information at Digital Equipment Corporation. * Discusses techniques for making user information _more effective * Covers the draft and reviewprocess, the production and distribution of printed and electronic media, archiving, indexing, testing for usability, and many other topics * Provides quality assurance checklists, contains a glossary and a bibliography of resources for technicalcommunicato...

    • Information System Development Process

      • 1st Edition
      • Volume 30
      • June 28, 2014
      • N. Prakash + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 4 8 1 5 9 4 1
      • eBook
        9 7 8 1 4 8 3 2 9 8 4 8 1
      This volume aims to pave the way to a greater understanding of the information system development process. Traditionally, information systems have been perceived as a slice of real world history. This has led to a strong emphasis on the development of conceptual models, the requirements specifications of which can readily be expressed. However, the route to such an expression, or the process of development, has not received any substantial attention.It is now agreed that a study of the development process affords notable benefits. Firstly, it helps to create an understanding of what a realistic development process is and how it proceeds from an initial specification to its acceptable representation. Secondly, the nature of guidance that can be provided by the next generation of CASE tools can be substantially improved. It can be expected that these tools will cease to be mere drafting aids and consistency checking programs. Instead it is likely that they will provide a procreative environment in which the development engineer will play an important role. This tool/user symbiosis should have a beneficial impact on both the productivity of the developer and on the quality of the product.In bringing together researchers and practitioners from such diverse areas as AI, Software Engineering, Decision Support and Information Systems, it is hoped this publication will take the quest to comprehend information system development processes a significant step forwards.

    • Machine Learning Proceedings 1994

      • 1st Edition
      • June 28, 2014
      • William W. Cohen
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 3 5 6
      • eBook
        9 7 8 1 4 8 3 2 9 8 1 8 4
      Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

    • Machine Learning Proceedings 1991

      • 1st Edition
      • June 28, 2014
      • Lawrence A. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 0 0 7
      • eBook
        9 7 8 1 4 8 3 2 9 8 1 7 7
      Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at Northwestern University, Evanston, Illinois, USA, in June 1991. The book focuses on constructive induction, learning from theory and data, automated knowledge acquisition, learning in intelligent information retrieval, machine learning in engineering automation, computational models of human learning, and learning reaction strategies. The selection first offers information on design rationale capture as knowledge acquisition, a domain-independent framework for effective experimentation in planning, and knowledge refinement using a high-level, non-technical vocabulary. The text then elaborates on improving the performance of inconsistent knowledge bases via combined optimization method, flexibility of speculative refinement, and a prototype based symbolic concept learning system. Topics include using task descriptions to generate error candidates, functional descriptions of knowledge-based systems, combined optimization method, and inconsistency and related work. The book ponders on learning words from context, modeling the acquisition and improvement of motor skills, a computational model of acquisition for children's addition strategies, and computer modeling of acquisition orders in child language. The manuscript also takes a look at knowledge acquisition combining analytical and empirical techniques; designing integrated learning systems for engineering design; and machine learning for nondestructive evaluation. The selection is highly recommended for researchers interested in machine learning.

    • Machine Learning Proceedings 1989

      • 1st Edition
      • June 28, 2014
      • Alberto Maria Segre
      • English
      • Paperback
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      • eBook
        9 7 8 1 4 8 3 2 9 7 4 0 8
      Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

    • Uncertainty in Artificial Intelligence

      • 1st Edition
      • June 28, 2014
      • MKP
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 3 2 5
      • eBook
        9 7 8 1 4 8 3 2 9 8 6 0 3
      Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.

    • Computer Hardware Description Languages and their Applications

      • 1st Edition
      • June 28, 2014
      • D. Borrione + 1 more
      • English
      • Paperback
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      • eBook
        9 7 8 1 4 8 3 2 9 8 4 5 0
      The topic areas presented within this volume focus on design environments and the applications of hardware description and modelling – including simulation, verification by correctness proofs, synthesis and test. The strong relationship between the topics of CHDL'91 and the work around the use and re-standardization of the VHDL language is also explored. The quality of this proceedings, and its significance to the academic and professional worlds is assured by the excellent technical programme here compiled.

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