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Connected Code
Coding, once considered an arcane craft practiced by solitary techies, is now recognized by educators and theorists as a crucial skill, even a new literacy, for all children. Programming is often promoted in K-12 schools as a way to encourage \"computational thinking\" -- which has now become the umbrella term for understanding what computer science has to contribute to reasoning and communicating in an ever-increasingly digital world.InConnected Code,Yasmin Kafai and Quinn Burke argue that although computational thinking represents an excellent starting point, the broader conception of \"computational participation\" better captures the twenty-first-century reality. Computational participation moves beyond the individual to focus on wider social networks and a DIY culture of digital \"making.\" Kafai and Burke describe contemporary examples of computational participation: students who code not for the sake of coding but to create games, stories, and animations to share; the emergence of youth programming communities; the practices and ethical challenges of remixing (rather than starting from scratch); and the move beyond stationary screens to programmable toys, tools, and textiles.
eBook
The computing universe : a journey through a revolution
\"Computers now impact almost every aspect of our lives, from our social interactions to the safety and performance of our cars. How did this happen in such a short time? And this is just the beginning. In this book, Tony Hey and Gyuri Pâapay lead us on a journey from the early days of computers in the 1930s to the cutting-edge research of the present day that will shape computing in the coming decades. Along the way, they explain the ideas behind hardware, software, algorithms, Moore's Law, the birth of the personal computer, the Internet and the Web, the Turing Test, Jeopardy's Watson, World of Warcraft, spyware, Google, Facebook, and quantum computing. This book also introduces the fascinating cast of dreamers and inventors who brought these great technological developments into every corner of the modern world. This exciting and accessible introduction will open up the universe of computing to anyone who has ever wondered where his or her smartphone came from\"-- Provided by publisher.
Book
Big Data, Little Data, No Data
\"Big Data\" is on the covers of Science, Nature , the Economist , and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
eBook
Careers in computer science
Careers in computer science are among the hottest, most in-demand occupations in the United States today. Comments from people in the industry, current statistics and forecasts, and realistic descriptions provide a useful look at computer science jobs ranging from software developers to information security analysts to database administrators.
Book
Research methods in human-computer interaction
A comprehensive guide for both quantitative and qualitative research methods, this book on the discipline of human-computer interaction (HCI) is essential reading for researchers of all types.
eBook
The computer : a very short introduction
Computers have changed so much since the room-filling, bulky magnetic tape running monsters of the mid 20th century. They now form a vital part of most people's lives. And they are more ubiquitous than might be thought - you may have more than 30 computers in your home: not just the desktop and laptop but think of the television, the fridge, the microwave. But what is the basic nature of the modern computer? How does it work? How has it been possible to squeeze so much power into increasingly small machines? And what will the next generations of computers look like? In this Very Short Introduction, Darrel Ince looks at the basic concepts behind all computers; the changes in hardware and software that allowed computers to become so small and commonplace; the challenges produced by the computer revolution - especially whole new modes of cybercrime and security issues; the Internet and the advent of 'cloud computing'; and the promise of whole new horizons opening up with quantum computing, and even computing using DNA-- Source other than Library of Congress.
Book
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations
Artificial neural networks (ANNs) have very successfully been used in numerical simulations for a series of computational problems
ranging from image classification/image recognition, speech recognition, time series analysis, game intelligence, and computational
advertising to numerical approximations of partial differential equations (PDEs). Such numerical simulations suggest that ANNs have the
capacity to very efficiently approximate high-dimensional functions and, especially, indicate that ANNs seem to admit the fundamental
power to overcome the curse of dimensionality when approximating the high-dimensional functions appearing in the above named
computational problems. There are a series of rigorous mathematical approximation results for ANNs in the scientific literature. Some of
them prove convergence without convergence rates and some of these mathematical results even rigorously establish convergence rates but
there are only a few special cases where mathematical results can rigorously explain the empirical success of ANNs when approximating
high-dimensional functions. The key contribution of this article is to disclose that ANNs can efficiently approximate high-dimensional
functions in the case of numerical approximations of Black-Scholes PDEs. More precisely, this work reveals that the number of required
parameters of an ANN to approximate the solution of the Black-Scholes PDE grows at most polynomially in both the reciprocal of the
prescribed approximation accuracy
eBook
Computing tomorrow : future research directions in computer science
The book's purpose is to show that long-term research in computer science is crucial and that it must not be driven solely by commercial considerations. The authors don't shirk difficult aspects of their topics, but try to expose them in the simplest terms possible, in order that the reader can understand the issues involved.
Book
Object detection using YOLO: challenges, architectural successors, datasets and applications
Object detection is one of the predominant and challenging problems in computer vision. Over the decade, with the expeditious evolution of deep learning, researchers have extensively experimented and contributed in the performance enhancement of object detection and related tasks such as object classification, localization, and segmentation using underlying deep models. Broadly, object detectors are classified into two categories viz. two stage and single stage object detectors. Two stage detectors mainly focus on selective region proposals strategy via complex architecture; however, single stage detectors focus on all the spatial region proposals for the possible detection of objects via relatively simpler architecture in one shot. Performance of any object detector is evaluated through detection accuracy and inference time. Generally, the detection accuracy of two stage detectors outperforms single stage object detectors. However, the inference time of single stage detectors is better compared to its counterparts. Moreover, with the advent of YOLO (You Only Look Once) and its architectural successors, the detection accuracy is improving significantly and sometime it is better than two stage detectors. YOLOs are adopted in various applications majorly due to their faster inferences rather than considering detection accuracy. As an example, detection accuracies are 63.4 and 70 for YOLO and Fast-RCNN respectively, however, inference time is around 300 times faster in case of YOLO. In this paper, we present a comprehensive review of single stage object detectors specially YOLOs, regression formulation, their architecture advancements, and performance statistics. Moreover, we summarize the comparative illustration between two stage and single stage object detectors, among different versions of YOLOs, applications based on two stage detectors, and different versions of YOLOs along with the future research directions.
Journal Article