This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.

In A Tour of C++, Third Edition, Bjarne Stroustrup provides an overview of ISO C++, C++20, that aims to give experienced programmers a clear understanding of what constitutes modern C++. Featuring carefully crafted examples and practical help in getting started, this revised and updated edition concisely covers most major language features and the major standard-library components needed for effective use. Stroustrup presents C++ features in the context of the programming styles they support, such as object-oriented and generic programming. His tour is remarkably comprehensive. Coverage begins with the basics, then ranges widely through more advanced topics, emphasizing newer language features. This edition covers many features that are new in C++20 as implemented by major C++ suppliers, including modules, concepts, coroutines, and ranges. It even introduces some library components in current use that are not scheduled for inclusion in the standard until C++23. This authoritative guide does not aim to teach you how to program (for that, see Stroustrup's Programming: Principles and Practice Using C++, Second Edition), nor will it be the only resource you'll need for C++ mastery (for that, see Stroustrup's The C++ Programming Language, Fourth Edition, and recommended online sources). If, however, you are a C or C++ programmer wanting greater familiarity with the current C++ language, or a programmer versed in another language wishing to gain an accurate picture of the nature and benefits of modern C++, you won't find a shorter or simpler introduction.

Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an innovative source of material on the latest advances and strategies for software quality prediction. Including a range of pivotal topics such as case-based reasoning, rate of improvement, and expert systems, this book is an ideal reference source for engineers, researchers, academics, students, professionals, and practitioners interested in novel developments in software design and analysis.

Data mapping in a data warehouse is the process of creating a link between two distinct data models' (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

The effective application of knowledge management principles has proven to be beneficial for modern organizations. When utilized in the academic community, these frameworks can enhance the value and quality of research initiatives. Enhancing Academic Research With Knowledge Management Principles is a pivotal reference source for the latest research on implementing theoretical frameworks of information management in the context of academia and universities. Featuring extensive coverage on relevant areas such as data mining, organizational and academic culture, this publication is an ideal resource for researchers, academics, practitioners, professionals, and students.

The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.

Cloud computing is rapidly expanding in its applications and capabilities through various parts of society. Utilizing different types of virtualization technologies can push this branch of computing to even greater heights. Design and Use of Virtualization Technology in Cloud Computing is a crucial resource that provides in-depth discussions on the background of virtualization, and the ways it can help shape the future of cloud computing technologies. Highlighting relevant topics including grid computing, mobile computing, open source virtualization, and virtualization in education, this scholarly reference source is ideal for computer engineers, academicians, students, and researchers that are interested in learning more about how to infuse current cloud computing technologies with virtualization advancements.

As human activities moved to the digital domain, so did all the well-known malicious behaviors including fraud, theft, and other trickery. There is no silver bullet, and each security threat calls for a specific answer. One specific threat is that applications accept malformed inputs, and in many cases it is possible to craft inputs that let an intruder take full control over the target computer system. The nature of systems programming languages lies at the heart of the problem. Rather than rewriting decades of well-tested functionality, this book examines ways to live with the (programming) sins of the past while shoring up security in the most efficient manner possible. We explore a range of different options, each making significant progress towards securing legacy programs from malicious inputs. The solutions explored include enforcement-type defenses, which excludes certain program executions because they never arise during normal operation. Another strand explores the idea of presenting adversaries with a moving target that unpredictably changes its attack surface thanks to randomization. We also cover tandem execution ideas where the compromise of one executing clone causes it to diverge from another thus revealing adversarial activities. The main purpose of this book is to provide readers with some of the most influential works on run-time exploits and defenses. We hope that the material in this book will inspire readers and generate new ideas and paradigms.

This book explores 10 unique facets of Internet health and safety, including physical safety, information security, and the responsible use of technology, offering takeaways from interviews with experts in the field and suggestions for proactively improving users' Internet safety. The Internet has become for many people—especially students and young adults—an essential and intrinsic part of their lives. It makes information available to be shared worldwide, at any time; enables learning about any topic; and allows for instantaneous communication. And it provides endless entertainment as well. But the benefits of online access are accompanied by serious potential risks. This book covers the key elements of Internet health and safety, including physical safety, information security, and the responsible use of technology. It begins with an introductory essay that gives readers the necessary conceptual framework, and then explains specific topics such as cyberbullying, file sharing, online predators, Internet fraud, and obscene and offensive content. The book also answers readers' questions in a "Q & A" section with a subject expert and includes a directory of resources that provides additional information and serves as a gateway to further study.

Model Driven Architecture (MDA) is a new approach to software development that helps companies manage large, complex software projects and save development costs while allowing new technologies that come along to be readily incorporated. Although it is based on many long-standing industry precepts and best practices, such as UML, it is enough of a departure from traditional IT approaches to require some "proof of the pudding." Real-Life MDA is composed of six case studies of real companies using MDA that will furnish that proof. The authors' approach MDA projects by describing all aspects of the project from the viewpoint of the end-usersfrom the reason for choosing an MDA approach to the results and benefits. The case studies are preceded by an introductory chapter and are followed by a wrap-up chapter summarizing lessons learned.
* Written for executives, analysts, architects, and engineers positioned to influence business-oriented software development at the highest levels.
* Filled with concrete examples and analyses of how MDA is relevant for organizations of various sizes.
* Considers a range of uses for MDA from business process analysis to full-scale software modeling and development.
* Presents results for each case study in terms of tangible, measured benefits, including automatically generated code, defect reduction, improved visibility, and ROI."

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature.