bluuebunny/crossref_metadata_2025_split huggingface dataset:
Designing Sociable Robots
Authors: Breazeal Cynthia 
Published: December 2018

Cynthia Breazeal here presents her vision of the sociable robot of the future, a synthetic creature and not merely a sophisticated tool. A sociable robot will be able to understand us, to communicate and interact with us, to learn from us and grow with us. It will be socially intelligent in a humanlike way. Eventually sociable robots will assist us in our daily lives, as collaborators and companions. Because the most successful sociable robots will share our social characteristics, the effort to make sociable robots is also a means for exploring human social intelligence and even what it means to be human.
Breazeal defines the key components of social intelligence for these machines and offers a framework and set of design issues for their realization. Much of the book focuses on a nascent sociable robot she designed named Kismet. Breazeal offers a concrete implementation for Kismet, incorporating insights from the scientific study of animals and people, as well as from artistic disciplines such as classical animation. This blending of science, engineering, and art creates a lifelike quality that encourages people to treat Kismet as a social creature rather than just a machine. The book includes a CD-ROM that shows Kismet in action.
Bradford Books imprint
Dynamical Systems in Neuroscience
Authors: Izhikevich Eugene M. 
Published: December 2018

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition.
In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology.
Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines.
Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience.
An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.
From Molecule to Metaphor
Authors: Feldman Jerome 
Published: December 2018

In From Molecule to Metaphor, Jerome Feldman proposes a theory of language and thought that treats language not as an abstract symbol system but as a human biological ability that can be studied as a function of the brain, as vision and motor control are studied. This theory, he writes, is a "bridging theory" that works from extensive knowledge at two ends of a causal chain to explicate the links between. Although the cognitive sciences are revealing much about how our brains produce language and thought, we do not yet know exactly how words are understood or have any methodology for finding out. Feldman develops his theory in computer simulations—formal models that suggest ways that language and thought may be realized in the brain. Combining key findings and theories from biology, computer science, linguistics, and psychology, Feldman synthesizes a theory by exhibiting programs that demonstrate the required behavior while remaining consistent with the findings from all disciplines.
After presenting the essential results on language, learning, neural computation, the biology of neurons and neural circuits, and the mind/brain, Feldman introduces specific demonstrations and formal models of such topics as how children learn their first words, words for abstract and metaphorical concepts, understanding stories, and grammar (including "hot-button" issues surrounding the innateness of human grammar). With this accessible, comprehensive book Feldman offers readers who want to understand how our brains create thought and language a theory of language that is intuitively plausible and also consistent with existing scientific data at all levels.
Bradford Books imprint
Gaussian Processes for Machine Learning
Authors: Rasmussen Carl Edward , Williams Christopher K. I. 
Published: December 2018

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
High-Level Vision
Authors: Ullman Shimon 
Published: December 2018

Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image.
In this book, Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image. In particular, he examines two major problems. The first, object recognition and classification, involves recognizing objects despite large variations in appearance caused by changes in viewing position, illumination, occlusion, and object shape. The second, visual cognition, involves the extraction of shape properties and spatial relations in the course of performing visual tasks such as object manipulation, planning movements in the environment, or interpreting graphical material such as diagrams, graphs and maps.
The book first takes up object recognition and develops a novel approach to the recognition of three-dimensional objects. It then studies a number of related issues in high-level vision, including object classification, scene segmentation, and visual cognition. Using computational considerations discussed throughout the book, along with psychophysical and biological data, the final chapter proposes a model for the general flow of information in the visual cortex.
Understanding vision is a key problem in the brain sciences, human cognition, and artificial intelligence. Because of the interdisciplinary nature of the theories developed in this work, High-Level Vision will be of interest to readers in all three of these fields.
Bradford Books imprint
The Human Semantic Potential
Authors: Regier Terry 
Published: December 2018

Drawing on ideas from cognitive linguistics, connectionism, and perception, The Human Semantic Potential describes a connectionist model that learns perceptually grounded semantics for natural language in spatial terms. Languages differ in the ways in which they structure space, and Regier's aim is to have the model perform its learning task for terms from any natural language. The system has so far succeeded in learning spatial terms from English, German, Russian, Japanese, and Mixtec.
The model views simple movies of two-dimensional objects moving relative to one another and learns to classify them linguistically in accordance with the spatial system of some natural language. The overall goal is to determine which sorts of spatial configurations and events are learnable as the semantics for spatial terms and which are not. Ultimately, the model and its theoretical underpinnings are a step in the direction of articulating biologically based constraints on the nature of human semantic systems.
Along the way Regier takes up such substantial issues as the attraction and the liabilities of PDP and structured connectionist modeling, the problem of learning without direct negative evidence, and the area of linguistic universals, which is addressed in the model itself. Trained on spatial terms from different languages, the model permits observations about the possible bases of linguistic universals and interlanguage variation.
Bradford Books imprint
Improvisational Design
Authors: Ishizaki Suguru 
Published: December 2018

A theoretical framework for the design of digital communication.
Traditional visual design expresses information in fixed forms, such as print or film, so the message can be stored or distributed. With interactive media and continuously updated information, communication entails a new, more dynamic set of design problems. In this book Suguru Ishizaki offers a theoretical framework for dealing with the challenges and opportunities of what he calls "dynamic design." His approach incorporates a community of collaborating agents that control design solutions in response to a changing context. He illustrates his ideas with several examples, such as expressive e-mails and responsive maps. The book will be of particular interest to interaction designers, visual designers, software engineers, and human-computer interaction experts.
Information Politics on the Web
Authors: Rogers Richard 
Published: December 2018

Does the information on the Web offer many alternative accounts of reality, or does it subtly align with an official version? In Information Politics on the Web, Richard Rogers identifies the cultures, techniques, and devices that rank and recommend information on the Web, analyzing not only the political content of Web sites but the politics built into the Web's infrastructure. Addressing the larger question of what the Web is for, Rogers argues that the Web is still the best arena for unsettling the official and challenging the familiar.
Rogers describes the politics at work on the Web as either back-end—the politics of search engine technology—or front-end—the diversity, inclusivity, and relative prominence of sites publicly accessible on the Web. To analyze this, he developed four "political instruments," or software tools that gather information about the Web by capturing dynamic linking practices, attention cycles for issues, and changing political party commitments. On the basis of his findings on how information politics works, Rogers argues that the Web should be, and can be, a "collision space" for official and unofficial accounts of reality. (One chapter, "The Viagra Files" offers an entertaining analysis of official and unofficial claims for the health benefits of Viagra.) The distinctiveness of the Web as a medium lies partly in the peculiar practices that grant different statuses to information sources. The tools developed by Rogers capture these practices and contribute to the development of a new information politics that takes into account and draws from the competition between the official, the non-governmental, and the underground.
An Introduction to Computational Learning Theory
Authors: Kearns Michael J. , Vazirani Umesh 
Published: December 2018

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Journey to Data Quality
Authors: Lee Yang W. , Pipino Leo L. , Wang Richard Y. , Funk James D. 
Published: December 2018

A guide for assessing an organization's data quality practice and a roadmap for implementing a viable data and information quality management program, based on rigorous research and drawing on real-world examples.
All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems leve—installing the latest software or developing an expensive data warehouse—solve the basic problem of bad data quality practices. Journey to Data Quality offers a roadmap that can be used by practitioners, executives, and students for planning and implementing a viable data and information quality management program. This practical guide, based on rigorous research and informed by real-world examples, describes the challenges of data management and provides the principles, strategies, tools, and techniques necessary to meet them. The authors, all leaders in the data quality field for many years, discuss how to make the economic case for data quality and the importance of getting an organization's leaders on board. They outline different approaches for assessing data, both subjectively (by users) and objectively (using sampling and other techniques). They describe real problems and solutions, including efforts to find the root causes of data quality problems at a healthcare organization and data quality initiatives taken by a large teaching hospital. They address setting company policy on data quality and, finally, they consider future challenges on the journey to data quality.
Language Development and Learning to Read
Authors: McGuinness Diane 
Published: December 2018

Research on reading has tried, and failed, to account for wide disparities in reading skill even among children taught by the same method. Why do some children learn to read easily and quickly while others, in the same classroom and taught by the same teacher, don't learn to read at all? In Language Development and Learning to Read, Diane McGuinness examines scientific research that might explain these disparities. She focuses on reading predictors, analyzing the effect individual differences in specific perceptual, linguistic, and cognitive skills may have on a child's ability to read. Because of the serious methodological problems she finds in the existing research on reading, many of the studies McGuinness cites come from other fields—developmental psychology, psycholinguistics, and the speech and hearing sciences—and provide a new perspective on which language functions matter most for reading and academic success.
McGuinness first examines the phonological development theory—the theory that phonological awareness follows a developmental path from words to syllables to phonemes—which has dominated reading research for thirty years, and finds that research evidence from other disciplines does not support the theory. McGuinness then looks at longitudinal studies on the development of general language function, and finds a "tantalizing connection" between core language functions and reading success. Finally, she analyzes mainstream reading research, which links reading ability to specific language skills, and the often flawed methodology used in these studies. McGuinness's analysis shows the urgent need for a shift in our thinking about how to achieve reading success.
Bradford Books imprint
Learning with Kernels
Authors: Schölkopf Bernhard , Smola Alexander J. 
Published: December 2018

A comprehensive introduction to Support Vector Machines and related kernel methods.
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Making Medical Decisions for the Profoundly Mentally Disabled
Authors: Cantor Norman L. 
Published: December 2018

A legal and moral analysis of medical decision making on behalf of those with such severe cognitive impairments that they cannot exercise self-determination.
In this book, Norman Cantor analyzes the legal and moral status of people with profound mental disabilities—those with extreme cognitive impairments that prevent their exercise of medical self-determination. He proposes a legal and moral framework for surrogate medical decision making on their behalf. The issues Cantor explores will be of interest to professionals in law, medicine, psychology, philosophy, and ethics, as well as to parents, guardians, and health care providers who face perplexing issues in the context of surrogate medical decision making.
The profoundly mentally disabled are thought by some moral philosophers to lack the minimum cognitive ability for personhood. Countering this position, Cantor advances both theoretical and practical arguments for according them full legal and moral status. He also argues that the concept of intrinsic human dignity should have an integral role in shaping the bounds of surrogate decision making. Thus, he claims, while profoundly mentally disabled persons are not entitled to make their own medical decisions, respect for intrinsic human dignity dictates their right to have a conscientious surrogate make medical decisions on their behalf. Cantor discusses the criteria that bind such surrogates. He asserts, contrary to popular wisdom, that the best interests of the disabled person are not always the determinative standard: the interests of family or others can sometimes be considered. Surrogates may even, consistent with the intrinsic human dignity standard, sometimes authorize tissue donation or participation in nontherapeutic medical research by profoundly disabled persons. Intrinsic human dignity limits the occasions for such decisions and dictates close attention to the preferences and feelings of the profoundly disabled persons themselves. Cantor also analyzes the underlying philosophical rationale that makes these decision-making criteria consistent with law and morals.
Making Silicon Valley
Authors: Lécuyer Christophe 
Published: December 2018

In Making Silicon Valley, Christophe Lécuyer shows that the explosive growth of the personal computer industry in Silicon Valley was the culmination of decades of growth and innovation in the San Francisco-area electronics industry. Using the tools of science and technology studies, he explores the formation of Silicon Valley as an industrial district, from its beginnings as the home of a few radio enterprises that operated in the shadow of RCA and other East Coast firms through its establishment as a center of the electronics industry and a leading producer of power grid tubes, microwave tubes, and semiconductors. He traces the emergence of the innovative practices that made this growth possible by following key groups of engineers and entrepreneurs. He examines the forces outside Silicon Valley that shaped the industry—in particular the effect of military patronage and procurement on the growth of the industry and on the development of technologies—and considers the influence of Stanford University and other local institutions of higher learning.
Lécuyer argues that Silicon Valley's emergence and its growth were made possible by the development of unique competencies in manufacturing, in product engineering, and in management. Entrepreneurs learned to integrate invention, design, manufacturing, and sales logistics, and they developed incentives to attract and retain a skilled and motivated workforce. The largest Silicon Valley firms—including Eitel-McCullough (Eimac), Litton Industries, Varian Associates, Fairchild Semiconductor, and Intel—dominated the American markets for advanced tubes and semiconductors and, because of their innovations in manufacturing, design, and management, served as models and incubators for other electronics ventures in the area.