Search results for "Introduction to Human-Computer Interaction"
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PC DA ZERO - Guida Facile E Pratica Per Usare Il Computer
Corso per imparare ad usare il computer iniziando dalla tastiera. Conoscere il sistema operativo. Come elaborare testi con Word ed eseguire calcoli con Excel. Come navigare in internet ed inviare email. Creare presentazioni e database. Queste lezioni base le puoi leggere gratuitamente, anche collegandoti al sito www.pcdazero.it
R 883.00
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Tour of Computer Science Concepts
A Tour of Computer Science Concepts provides students with a solid foundational knowledge base within the discipline of computer science.The opening chapter offers readers a concise overview of computer history, including the development of computers and the birth of the internet. Additional chapters discuss the differences between analog and digital data, as well as techniques to map one type to another; number base systems; data storage; computer architecture and hardware components; and system software and application software. Students learn about hypertext markup language (HTML) and Cascading Style Sheets (CSS). Fundamental programming concepts, such as variable declaration, assignment statements, user input/output, conditional statements, and loop control structures and functions, are demonstrated through the use of JavaScript. Closing chapters cover computer networks, data transmission between devices, and the increased importance of cybersecurity in modern-day computing. Each chapter features a summary, review of key concepts and terms, and discussion questions to enrich the learning experience.Succinct yet highly informative, A Tour of Computer Science Concepts is an ideal resource for foundational courses in computer science.
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Computer Vision
This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation ofobjects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges.Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions.Topics and Features:• Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment.• Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems.• Gives detailed visual examples of applications of object detection in the real world.• Presents a detailed description of popular imaging modalities for object detection adopted by researchers.• Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.• Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures. • Discusses techniques for detection of objects in real-world applications.• Contains various hands-on practical examples and a tutorial for solving object detection problems using Python.• Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges.The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.
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Language and the Rise of the Algorithm
A wide-ranging history of the algorithm.Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians well before the computer age: How do you draw the line between computational rules and the complexities of making systems comprehensible to people? By attending to this question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day. Here, Jeffrey M. Binder offers a compelling tour of four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz’s calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole’s nineteenth-century logic system; and the early programming language ALGOL, short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. Machine learning, in its increasing dependence on words, erodes the line between technical and everyday language, revealing the urgent stakes underlying this boundary. The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction.
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Performance Modeling and Design of Computer Systems
Tackling the questions that systems designers care about, this book brings queueing theory decisively back to computer science. The book is written with computer scientists and engineers in mind and is full of examples from computer systems, as well as manufacturing and operations research. Fun and readable, the book is highly approachable, even for undergraduates, while still being thoroughly rigorous and also covering a much wider span of topics than many queueing books. Readers benefit from a lively mix of motivation and intuition, with illustrations, examples and more than 300 exercises – all while acquiring the skills needed to model, analyze and design large-scale systems with good performance and low cost. The exercises are an important feature, teaching research-level counterintuitive lessons in the design of computer systems. The goal is to train readers not only to customize existing analyses but also to invent their own.
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Introduction to Parallel and Vector Solution of Linear Systems
Although the origins of parallel computing go back to the last century, it was only in the 1970s that parallel and vector computers became available to the scientific community. The first of these machines-the 64 processor llliac IV and the vector computers built by Texas Instruments, Control Data Corporation, and then CRA Y Research Corporation-had a somewhat limited impact. They were few in number and available mostly to workers in a few government laboratories. By now, however, the trickle has become a flood. There are over 200 large-scale vector computers now installed, not only in government laboratories but also in universities and in an increasing diversity of industries. Moreover, the National Science Foundation's Super computing Centers have made large vector computers widely available to the academic community. In addition, smaller, very cost-effective vector computers are being manufactured by a number of companies. Parallelism in computers has also progressed rapidly. The largest super computers now consist of several vector processors working in parallel. Although the number of processors in such machines is still relatively small (up to 8), it is expected that an increasing number of processors will be added in the near future (to a total of 16 or 32). Moreover, there are a myriad of research projects to build machines with hundreds, thousands, or even more processors. Indeed, several companies are now selling parallel machines, some with as many as hundreds, or even tens of thousands, of processors.
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Introduction to Genetic Algorithms
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics-particularly in machine learning, scientific modeling, and artificial life-and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
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Computer Vision
This comprehensive text/ book presents a broad review of both traditional (i.e., conventional) and the deep learning aspects of object detection in various adversarial conditions of real-world in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of objects, applications of object detection, and challenges of real-world faced by the research community for object detection task. The book addresses various degradations and artifacts of real-world for object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the pre-eminent researchers in the field, addressing not only the background of visibility enhancement, but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative challenges of real-world.Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging conditions of real world.Topics and Features:· Offers the first truly comprehensive presentation of aspects of the object detection in degraded and non-degraded environment.· Include in depth various degradation and artifacts, and impact of those artifacts in real-world for solving the object detection problems.· Give detailed visual examples of applications of object detection in real-world.· Presents a detailed description of popular imaging modalities adopted by the researchers for object detection.· Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.· Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures.· Discusses techniques for detection of objects in real-world applications.· Contains various hands-on practical examples and tutorial for solving object detection problems using python.· Motivates readers to build vision based systems for solving the object detection problems in degraded and non-degraded challenges of real-world.The book will be of great interest to a broad audience ranging from researchers and practitioners, graduate and post graduate students involved in computer vision task with respect to object detection in degraded and non-degraded vision problems of real-world.
R 9,749.87
R 4,273.00
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