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Most of the information available on cloud computing is either highly technical, with details that are irrelevant to non-technologists, or pure marketing hype, in which the cloud is simply used as a selling point. This book, however, explains the cloud from the user's viewpoint. The author explains what the cloud is, when to use it (and when not to), how to select a cloud service, how to integrate it with other technologies, and what the best practices are for using cloud computing. A simple and basic definition of cloud computing from the National Institute of Science and Technology is considered: a model enabling ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. Thus businesses, individuals and communities can harness information technology resources usually available only to large enterprises. This, as the author demonstrates, represents a paradigm shift for businesses and individuals alike. In additon, the book considers the contractual, legal, financial, security and risk related aspects of adopting and migrating to the cloud. Cloud patterns are examined in terms of five deployment models; and a cloud computing maturity model is derived to align the use of cloud computing with best practices.A unique aspect of the book is that it provides innovative constructs that affect the way cloud computing shall be viewed and used in the future. In particular, it addresses novel concepts for cloud computing: cloud cells, or specialist clouds for specific uses; the personal cloud; the cloud of things and services; and cloud service exchanges.

Ubiquitous computing (or ubicomp) is the label for a “third wave” of computing technologies. Following the eras of the mainframe computer and the desktop PC, it is characterized by small and powerful computing devices that are worn, carried, or embedded in the world around us. The ubicomp research agenda originated at Xerox PARC in the late 1980s; these days, some form of that vision is a reality for the millions of users of Internet-enabled phones, GPS devices, wireless networks, and “smart” domestic appliances. This book explores the vision that has driven the ubiquitous computing research program and the contemporary practices which have emerged—both the motivating mythology and the everyday messiness of lived experience. Reflecting the interdisciplinary nature of the authors’ collaboration, it takes seriously the need to understand ubicomp not only technically but also culturally, socially, politically, and economically. The authors map the terrain of contemporary ubiquitous computing, in the research community and in daily life; explore dominant narratives in ubicomp around such topics as infrastructure, mobility, privacy, and domesticity; and suggest directions for future investigation, particularly with respect to methodology and conceptual foundations.

The P versus NP problem is the most important open problem in computer science, if not all of mathematics. Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. This book provides a nontechnical introduction to P versus NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. The book traces the history and development of P versus NP, giving examples from a variety of disciplines, including economics, physics, and biology. It explores problems that capture the full difficulty of the P versus NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The book explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.

Introduction to Computational Economics Using Fortran is the essential guide to conducting economic research on a computer. Aimed at students of all levels of education as well as advanced economic researchers, it facilitates the first steps into writing programs using Fortran. Introduction to Computational Economics Using Fortran assumes no prior experience as it introduces the reader to this programming language. It shows the reader how to apply the most important numerical methods conducted by computational economists using the toolbox that accompanies this text. It offers various examples from economics and finance organized in self-contained chapters that speak to a diverse range of levels and academic backgrounds. Each topic is supported by an explanation of the theoretical background, a demonstration of how to implement the problem on the computer, and a discussion of simulation results. Readers can work through various exercises that promote practical experience and deepen their economic and technical insights. This textbook is accompanied by a website from which readers can download all program codes as well as a numerical toolbox, and receive technical information on how to install Fortran on their computer.

In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lecture at ETH Zurich is an ideal practical student guide to scientific computing on parallel computers working up from a hardware instruction level, to shared memory machines and finally to distributed memory machines. Aimed at advanced undergraduate and graduate students in applied mathematics, computer science and engineering, subjects covered include linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and in some cases Fortran. This book is also ideal for practitioners and programmers.

In Max/MSP/Jitter for Music, expert author and music technologist V. J. Manzo provides a user-friendly introduction to a powerful programming language that can be used to write custom software for musical interaction. Through clear, step-by-step instructions illustrated with numerous examples of working systems, the book equips you with everything you need to know in order to design and complete meaningful music projects. The book also discusses ways to interact with software beyond the mouse and keyboard through use of camera tracking, pitch tracking, video game controllers, sensors, mobile devices, and more. This book will be of special value for everyone who teaches music at any level, from classroom instructors to ensemble directors to private studio instructors. Whether you want to create simple exercises for beginning performers or more complex programs for aspiring composers, this book will show you how to write customized software that can complement and even inspire your instructional objectives. No specialist foreknowledge is required to use this book to enliven your experience with music technology. Even musicians with no prior programming skills can learn to supplement their lessons with interactive instructional tools, to develop adaptive instruments to aid in composition and performance activities, and to create measurement tools with which to conduct research. This book allows you to: -Learn how to design meaningful projects for composition, performance, music therapy, instruction, and research -Understand powerful software through this accessible introduction, written for beginners -Follow along through step-by-step tutorials -Grasp the principles by downloading the extensive software examples from the companion website This book is ideal for: -Music educators at all levels looking to integrate software in instruction -Musicians interested in how software can improve their practice and performance -Music composers with an interest in designing interactive music -Music therapists looking to tailor programs to the needs of specific groups or individuals And all who are interested in music technology. Visit the companion website at www.oup.com/us/maxmspjitter

This book provides a short, hands-on introduction to the science of complexity using simple computational models of natural complex systems—with models and exercises drawn from physics, chemistry, geology, and biology. By working through the models and engaging in additional computational explorations suggested at the end of each chapter, readers very quickly develop an understanding of how complex structures and behaviors can emerge in natural phenomena as diverse as avalanches, forest fires, earthquakes, chemical reactions, animal flocks, and epidemic diseases. This book provides the necessary topical background, complete source codes in Python, and detailed explanations for all computational models. Ideal for undergraduates, beginning graduate students, and researchers in the physical and natural sciences, this unique handbook requires no advanced mathematical knowledge or programming skills and is suitable for self-learners with a working knowledge of precalculus and high-school physics. The book enables readers to identify and quantify common underlying structural and dynamical patterns shared by the various systems and phenomena it examines, so that they can form their own answers to the questions of what natural complexity is and how it arises.

Pattern Theory provides a comprehensive and accessible overview of the modern challenges in signal, data, and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.

This book tells two closely-related stories: first, how the digital cloud grew out of much older networks, such as television, the railroad, and the sewer system; and second, how the cloud grafts digital technologies onto older ways of exerting power over a population (such as state violence and torture). With the latest revelations about National Security Agency surveillance, readers are increasingly aware that the cloud represents politically contested terrain. While typical responses to this debate invoke technological and legal solutions, such as do-not-track software or a new law, this book takes an alternate approach. The perspective of media studies, and, more generally, understanding the cloud as a cultural fantasy, situates these vital debates within a wider American political and social context. It allows readers to understand why discussions of threats to the ‘free’ Internet, such as spam and hackers, often invoke the specter of foreignness (e.g. China, Iran, Nigeria); why Cold War rhetoric has increasingly informed digital threats, as in the New York Times’s invention of the phrase “mutually assured cyberdestruction”; and even why the NSA’s facilities for decrypting intercepted messages are often identical to those used by archivists trying to place digital media into cold storage. By locating the materiality of the cloud within the discourses of security and participation in postwar America, A Prehistory of the Cloud offers a set of new tools for rethinking today’s digital environment.

Cloud computing has emerged as one of the most salient recent developments in information technology. Predictions about its future run the gamut, with some believing that it represents a fundamental change the nature of computing and others arguing that it is nothing more than overhyped repackaging of existing technologies. This book represents the first effort to bridge this gap by exploring cloud computing’s implications from a wide range of interdisciplinary perspectives. Individual chapters analyze the business case for cloud computing, the changing nature of reliability in the Internet, the implications of treating the Internet as critical infrastructure, architectural changes to make cloud computing more contractible, and the impact of cloud computing on copyright, privacy, and consumer protection.

The technological realm provides an unusually active laboratory not only for new ideas and products but also for the remarkable linguistic innovations that accompany and describe them. How else would words like qubit (a unit of quantum information), crowdsourcing (outsourcing to the masses), or in vitro meat (chicken and beef grown in an industrial vat) enter our language? In Virtual Words: Language on the Edge of Science and Technology, Jonathon Keats, author of Wired Magazine’s monthly Jargon Watch column, investigates the interplay between words and ideas in our fast-paced tech-driven use-it-or-lose-it society. In 28 illuminating short essays, Keats examines how such words get coined, what relationship they have to their subject matter, and why some, like blog, succeed while others, like flog, fail. Divided into broad categories--such as commentary, promotion, and slang, in addition to scientific and technological neologisms--chapters each consider one exemplary word, its definition, origin, context, and significance. Examples range from microbiome (the collective genome of all microbes hosted by the human body) and unparticle (a form of matter lacking definite mass) to gene foundry (a laboratory where artificial life forms are assembled) and singularity (a hypothetical future moment when technology transforms the whole universe into a sentient supercomputer). Together these words provide not only a survey of technological invention and its consequences, but also a fascinating glimpse of novel language as it comes into being. No one knows this emerging lexical terrain better than Jonathon Keats. In writing that is as inventive and engaging as the language it describes, Virtual Words offers endless delights for word-lovers, technophiles, and anyone intrigued by the essential human obsession with naming.

This book explores the cultural figure of the algorithm as it operates through contemporary digital culture. Drawing on sources that range from Neal Stephenson’s Snow Crash to Diderot’s Encyclopédie, from Adam Smith to the Star Trek computer, it explores the gap between theoretical ideas and pragmatic instructions. Humans have always believed that certain invocations—the marriage vow, the shaman’s curse—do not merely describe the world but make it. This book argues that the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in the mathematical concept of “effective computability” but also in cybernetics, philosophy, and magical thinking. After bringing the full history of the term into view, the book describes how the algorithm attempts to translate between the idealized space of computation and a messy reality, with unpredictable and sometimes fascinating results. Case studies of this implementation gap include the development of intelligent assistants like Siri, Google’s goal of anticipating our questions, the rise of algorithmic aesthetics at Netflix, Ian Bogost’s satiric Facebook game Cow Clicker, Uber’s cartoon maps and black box accounting, and the revolutionary economics of Bitcoin. If we want to understand the gap between abstraction and messy reality, we need to build a model of “algorithmic reading” and scholarship that attends to process as part of a new experimental humanities.