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This book is a collection of selected high-quality research papers presented at the International Conference on Computing in Engineering and Technology (ICCET 2021), organized by Dr. Babasaheb Ambedkar Technological University, Lonere, India, during January 30–31, 2021. Focusing on frontier topics and next-generation technologies, it presents original and innovative research from academics, scientists, students and engineers alike. The theme of the conference is Applied Information Processing System.
Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.
The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference.The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference.
This interdisciplinary graduate text gives a full, explicit, coherent and up-to-date account of the modern theory of neural information processing systems and is aimed at student with an undergraduate degree in any quantitative discipline (e.g. computer science, physics, engineering, biology, or mathematics). The book covers all the major theoretical developments from the 1940s tot he present day, using a uniform and rigorous style of presentation and of mathematical notation. The text starts with simple model neurons and moves gradually to the latest advances in neural processing. An ideal textbook for postgraduate courses in artificial neural networks, the material has been class-tested. It is fully self contained and includes introductions to the various discipline-specific mathematical tools as well as multiple exercises on each topic.
The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes computer science, neuroscience, statistics, physics, cognitive science, and many branches of engineering, including signal processing and control theory. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented.
How to apply data quality management techniques to marketing, sales, and other specific business units Author and information quality management expert Larry English returns with a sequel to his much-acclaimed book, Improving Data Warehouse and Business Information Quality. In this new book he takes a hands-on approach, showing how to apply the concepts outlined in the first book to specific business areas like marketing, sales, finance, and human resources. The book presents real-world scenarios so you can see how to meld data quality concepts to specific business areas such as supply chain management, product and service development, customer care, and others. Step-by-step instruction, practical techniques, and helpful templates from the author help you immediately apply best practices and start modeling your own quality initiatives. Maintaining the quality and accuracy of business data is crucial; database managers are in need of specific guidance for data quality management in all key business areas Information Quality Applied offers IT, database, and business managers step-by-step instruction in setting up methodical and effective procedures The book provides specifics if you have to manage data quality in marketing, sales, customer care, supply chain management, product and service management, human resources, or finance The author includes templates that readers can put to immedate use for modeling their own quality initiatives A Companion Web site provides templates, updates to the book, and links to related sites
The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
This book explores fundamental principles for securing IT systems and illustrates them with hands-on experiments that may be carried out by the reader using accompanying software. The experiments highlight key information security problems that arise in modern operating systems, networks, and web applications. The authors explain how to identify and exploit such problems and they show different countermeasures and their implementation. The reader thus gains a detailed understanding of how vulnerabilities arise and practical experience tackling them. After presenting the basics of security principles, virtual environments, and network services, the authors explain the core security principles of authentication and access control, logging and log analysis, web application security, certificates and public-key cryptography, and risk management. The book concludes with appendices on the design of related courses, report templates, and the basics of Linux as needed for the assignments. The authors have successfully taught IT security to students and professionals using the content of this book and the laboratory setting it describes. The book can be used in undergraduate or graduate laboratory courses, complementing more theoretically oriented courses, and it can also be used for self-study by IT professionals who want hands-on experience in applied information security. The authors' supporting software is freely available online and the text is supported throughout with exercises.