Network
Network

Author: Clay Spinuzzi

Publisher: Cambridge University Press

Published: 2015-12-17

Total Pages: 0

ISBN-13: 9781107564862

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How does a telecommunications company function when its right hand often doesn't know what its left hand is doing? How do rapidly expanding, interdisciplinary organizations hold together and perform their knowledge work? In this book, Clay Spinuzzi draws on two warring theories of work activity - activity theory and actor-network theory - to examine the networks of activity that make a telecommunications company work and thrive. In doing so, Spinuzzi calls a truce between the two theories, bringing them to the negotiating table to parley about work. Specifically, about net work: the coordinative work that connects, coordinates, and stabilizes polycontextual work activities. To develop this uneasy dialogue, Spinuzzi examines the texts, trades, and technologies at play at Telecorp, both historically and empirically. Drawing on both theories, Spinuzzi provides new insights into how net work actually works and how our theories and research methods can be extended to better understand it.

Building the Knowledge Management Network
Building the Knowledge Management Network

Author: Cliff Figallo

Publisher: John Wiley & Sons

Published: 2002-10-15

Total Pages: 370

ISBN-13: 0471427578

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A complete set of best practices, tools, and techniques for turning conversations into a rich source of business information Many organizations are now recognizing that the untapped knowledge of their members can be used to benefit every aspect of their business, from making smarter and faster decisions to improving products and efficiency. This book offers a clear-cut road map for building a successful knowledge management system to capture and fully exploit the knowledge exchanged in conversations. Written by two of the foremost experts in online communities, this book covers a set of best practices, tools, and techniques for using conversation and online interaction to provide affordable and effective knowledge-based benefits and solutions. With a unique and invaluable perspective, the authors offer guidance for collecting, capturing, and cataloging knowledge so that it can be used to improve efficiency and reduce costs in areas ranging from internal procedures through customer relations and product development. This book provides step-by-step solutions for developing an effective knowledge network, including how to: * Formulate strategies and create action plans * Select the right tools for peer-to-peer networks, interactive communities, and events * Work with legacy systems * Train staff and stimulate participation * Improve productivity and measurement criteria The companion Web site contains templates, checklists, a discussion board, and links to software.

Toward Precision Medicine
Toward Precision Medicine

Author: National Research Council

Publisher: National Academies Press

Published: 2012-01-16

Total Pages: 142

ISBN-13: 0309222222

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Motivated by the explosion of molecular data on humans-particularly data associated with individual patients-and the sense that there are large, as-yet-untapped opportunities to use this data to improve health outcomes, Toward Precision Medicine explores the feasibility and need for "a new taxonomy of human disease based on molecular biology" and develops a potential framework for creating one. The book says that a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of diseases and ultimately enhance diagnosis and treatment. The "new taxonomy" that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. The book adds that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights. This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia. Toward Precision Medicine notes that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health- and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Intelligent Internet Knowledge Networks
Intelligent Internet Knowledge Networks

Author: Syed V. Ahamed

Publisher: John Wiley & Sons

Published: 2006-12-13

Total Pages: 549

ISBN-13: 0470055987

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Introducing the basic concepts in total program control of the intelligent agents and machines, Intelligent Internet Knowledge Networks explores the design and architecture of information systems that include and emphasize the interactive role of modern computer/communication systems and human beings. Here, you’ll discover specific network configurations that sense environments, presented through case studies of IT platforms, electrical governments, medical networks, and educational networks.

Knowledge Networks
Knowledge Networks

Author: Denise Bedford

Publisher: Emerald Group Publishing

Published: 2021-10-26

Total Pages: 238

ISBN-13: 1839829508

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Knowledge Networks describes the role of networks in the knowledge economy, explains network structures and behaviors, walks the reader through the design and setup of knowledge network analyses, and offers a step by step methodology for conducting a knowledge network analysis.

Knowledge, Networks and Power
Knowledge, Networks and Power

Author: U. Holm

Publisher: Springer

Published: 2015-05-12

Total Pages: 472

ISBN-13: 1137508825

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This book presents more than four decades of research in international business at the Department of Business Studies, Uppsala University. Gradually, this research has been recognized as 'The Uppsala School'. The work in Uppsala over the years reflects a broad palette of issues and approaches.

Technology and Knowledge Flow
Technology and Knowledge Flow

Author: Guglielmo Trentin

Publisher: Elsevier

Published: 2011-08-05

Total Pages: 197

ISBN-13: 1780632673

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This book outlines how network technology can support, foster and enhance the Knowledge Management, Sharing and Development (KMSD) processes in professional environments through the activation of both formal and informal knowledge flows. Understanding how ICT can be made available to such flows in the knowledge society is a factor that cannot be disregarded and is confirmed by the increasing interest of companies in new forms of software-mediated social interaction. The latter factor is in relation both to the possibility of accelerating internal communication and problem solving processes, and/or in relation to dynamics of endogenous knowledge growth of human resources.The book will focus specifically on knowledge flow (KF) processes occurring within networked communities of professionals (NCP) and the associated virtual community environments (VCE) that foster horizontal dynamics in the management, sharing and development of fresh knowledge. Along this line a further key issue will concern the analysis and evaluation techniques of the impact of Network Technology use on both community KF and NCP performance. - The proposal of a taxonomy of Network Technology uses to support formal and informal knowledge flows - Analyses how Web 2.0 and Web 3.0 technology is deeply modifying the dynamics connected to KF and KM - Discusses dynamics underlying horizontal KF sharing processes within NCP

Handbook of Research on Computational Intelligence Applications in Bioinformatics
Handbook of Research on Computational Intelligence Applications in Bioinformatics

Author: Dash, Sujata

Publisher: IGI Global

Published: 2016-06-20

Total Pages: 543

ISBN-13: 1522504281

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Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

Graph Representation Learning
Graph Representation Learning

Author: William L. William L. Hamilton

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 141

ISBN-13: 3031015886

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Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.