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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

    • The Master Adaptive Learner

      • 1st Edition
      • September 29, 2019
      • William Cutrer + 4 more
      • English
      • Paperback
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      • eBook
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      Tomorrow’s best physicians will be those who continually learn, adjust, and innovate as new information and best practices evolve, reflecting adaptive expertise in response to practice challenges. As the first volume in the American Medical Association’s MedEd Innovation Series, The Master Adaptive Learner is an instructor-focused guide covering models for how to train and teach future clinicians who need to develop these adaptive skills and utilize them throughout their careers.

    • Model Management and Analytics for Large Scale Systems

      • 1st Edition
      • September 14, 2019
      • Bedir Tekinerdogan + 4 more
      • English
      • Paperback
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      • eBook
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      Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

    • Riemannian Geometric Statistics in Medical Image Analysis

      • 1st Edition
      • September 2, 2019
      • Xavier Pennec + 2 more
      • English
      • Paperback
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      Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology Content includes: The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs Applications of statistics on manifolds and shape spaces in medical image computing Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science.

    • Advances in Domain Adaptation Theory

      • 1st Edition
      • August 14, 2019
      • Ievgen Redko + 4 more
      • English
      • Hardback
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      • eBook
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      Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version. Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm.

    • Introduction to Modeling in Physiology and Medicine

      • 2nd Edition
      • August 1, 2019
      • Claudio Cobelli + 1 more
      • English
      • Paperback
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      • eBook
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      Introduction to Modeling in Physiology and Medicine, Second Edition, develops a clear understanding of the fundamental principles of good modeling methodology. Sections show how to create valid mathematical models that are fit for a range of purposes. These models are supported by detailed explanation, extensive case studies, examples and applications. This updated edition includes clearer guidance on the mathematical prerequisites needed to achieve the maximum benefit from the material, a greater detail regarding basic approaches to modeling, and discussions on non-linear and stochastic modeling. The range of case study material has been substantially extended, with examples drawn from recent research experience. Key examples include a cellular model of insulin secretion and its extension to the whole-body level, a model of insulin action during a meal/oral glucose tolerance test, a large-scale simulation model of type 1 diabetes and its use in in silico clinical trials and drug trials.

    • Metadata for Transmedia Resources

      • 1st Edition
      • July 13, 2019
      • Ana Vukadin
      • English
      • Paperback
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      Transmedia is a technique of delivering a single piece of content in individual parts via different media and communication platforms (books, films, TV shows, games, live performances, etc.). In the book transmedia is considered as a case-in-point for the need to rethink library cataloguing and metadata practices in a new, heterogeneous information environment where the ability to bring together information from various sources into a meaningful whole becomes a critical information skill. Transmedia sheds new light on some of the long-existing questions of bibliographic information organisation (the definition of work, modelling of bibliographic relationships, subject analysis of fiction, etc.) and introduces libraries to new, transient and interactive media forms such as interactive fiction, gaming events, or performances. The book investigates how various theories and practices of bibliographic information organisation can be applied to transmedia, focusing on the solutions provided by the new bibliographic conceptual model IFLA LRM, as well as linked open data models and standards. It strongly advocates collaborative practices and reuse of knowledge that underpin an emerging vision of the library catalogue as a 'mediation tool' that assembles, links and integrates information across a variety of communication contexts.

    • Untangling Smart Cities

      • 1st Edition
      • June 27, 2019
      • Luca Mora + 1 more
      • English
      • Paperback
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      • eBook
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      Untangling Smart Cities: From Utopian Dreams to Innovation Systems for a Technology-Enabled Urban Sustainability helps all key stakeholders understand the complex and often conflicting nature of smart city research, offering valuable insights for designing and implementing strategies to improve the smart city decision-making processes. The book drives the reader to a better theoretical and practical comprehension of smart city development, beginning with a thorough and systematic analysis of the research literature published to date. It addition, it provides an in-depth understanding of the entire smart city knowledge domain, revealing a deeply rooted division in its cognitive-epistemolo... structure as identified by bibliometric insights. Users will find a book that fills the knowledge gap between theory and practice using case study research and empirical evidence drawn from cities considered leaders in innovative smart city practices.

    • 5G NR

      • 1st Edition
      • June 15, 2019
      • Sassan Ahmadi
      • English
      • Hardback
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      • eBook
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      5G NR: Architecture, Technology, Implementation, and Operation of 3GPP New Radio Standards is an in-depth, systematic, technical reference on 3GPP’s New Radio standards (Release 15 and beyond), covering the underlying theory, functional descriptions, practical considerations, and implementation of the 5G new radio access technology. The book describes the design and operation of individual components and shows how they are integrated into the overall system and operate from a system’s perspective. Uniquely, this book gives detailed information on RAN protocol layers, transports, network architectures, and services, as well as practical implementation and deployment issues, making it suitable for researchers and engineers who are designing and developing 5G systems.Reflecting on the author's 30 plus years of experience in signal processing, microelectronics, and wireless communication system design, this book is ideal for professional engineers, researchers, and graduate students who are working and researching in cellular communication systems and protocols as well as mobile broadband wireless standards.

    • Analyzing Social Media Networks with NodeXL

      • 2nd Edition
      • May 8, 2019
      • Derek Hansen + 3 more
      • English
      • Paperback
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      • eBook
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      Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Second Edition, provides readers with a thorough, practical and updated guide to NodeXL, the open-source social network analysis (SNA) plug-in for use with Excel. The book analyzes social media, provides a NodeXL tutorial, and presents network analysis case studies, all of which are revised to reflect the latest developments. Sections cover history and concepts, mapping and modeling, the detailed operation of NodeXL, and case studies, including e-mail, Twitter, Facebook, Flickr and YouTube. In addition, there are descriptions of each system and types of analysis for identifying people, documents, groups and events. This book is perfect for use as a course text in social network analysis or as a guide for practicing NodeXL users.

    • Data Architecture: A Primer for the Data Scientist

      • 2nd Edition
      • April 30, 2019
      • W.H. Inmon + 2 more
      • English
      • Paperback
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      Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics