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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.

Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions.By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers.

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.

\"Big Data\" is on the covers ofScience, Nature, theEconomist, andWiredmagazines, on the front pages of theWall Street Journaland theNew 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.

Network Routing: Algorithms, Protocols, and Architectures, Second Edition explores network routing and how it can be broadly categorized into Internet routing, PSTN routing, and telecommunication transport network routing. The book systematically considers these routing paradigms, as well as their interoperability, discussing how algorithms, protocols, analysis, and operational deployment impact these approaches and addressing both macro-state and micro-state in routing. Readers will learn about the evolution of network routing, the role of IP and E.164 addressing and traffic engineering in routing, the impact on router and switching architectures and their design, deployment of network routing protocols, and lessons learned from implementation and operational experience. Numerous real-world examples bring the material alive. Bridges the gap between theory and practice in network routing, including the fine points of implementation and operational experienceRouting in a multitude of technologies discussed in practical detail, including, IP/MPLS, PSTN, and optical networkingPresents routing protocols such as OSPF, IS-IS, BGP in detailDetails various router and switch architecturesDiscusses algorithms on IP-lookup and packet classificationAccessible to a wide audience with a vendor-neutral approach

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

</P> <b>A comprehensive guide to Fog and Edge applications, architectures, and technologies</b> <p>Recent years have seen the explosive growth of the Internet of Things &#40;IoT&#41;: the internet&#45; connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever&#45;increasing bandwidth demands and privacy concerns of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data closer to devices and more efficiently than traditional cloud architecture. <p><i>Fog and Edge Computing: Principles and Paradigms</i>provides a comprehensive overview of the state&#45;of&#45;the&#45;art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. <p>Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: <ul> <li>Discusses IoT and new computing paradigms in the domain such as Fog, Edge and Mist</li> <li>Provides insights on transitioning from current Cloud&#45;centric and 4G/5G wireless environments to Fog computing</li> <li>Examines methods to optimize virtualized, pooled, and shared resources</li> <li>Identifies potential technical challenges and offers suggestions for possible solutions</li> <li>Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management</li> <li>Includes access to a website portal for advanced online resources</li> </ul> <p><i>Fog and Edge Computing: Principles and Paradigms</i>is an essential source of up&#45;to&#45;date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

In a unique and systematic way, this book discusses the security and privacy aspects of the cloud, and the relevant cloud forensics.Cloud computing is an emerging yet revolutionary technology that has been changing the way people live and work. However, with the continuous growth of cloud computing and related services, security and privacy has become a critical issue. Written by some of the top experts in the field, this book specifically discusses security and privacy of the cloud, as well as the digital forensics of cloud data, applications, and services. The first half of the book enables readers to have a comprehensive understanding and background of cloud security, which will help them through the digital investigation guidance and recommendations found in the second half of the book.Part One of Security, Privacy and Digital Forensics in the Cloud covers cloud infrastructure security; confidentiality of data; access control in cloud IaaS; cloud security and privacy management; hacking and countermeasures; risk management and disaster recovery; auditing and compliance; and security as a service (SaaS). Part Two addresses cloud forensics – model, challenges, and approaches; cyberterrorism in the cloud; digital forensic process and model in the cloud; data acquisition; digital evidence management, presentation, and court preparation; analysis of digital evidence; and forensics as a service (FaaS).Thoroughly covers both security and privacy of cloud and digital forensicsContributions by top researchers from the U.S., the European and other countries, and professionals active in the field of information and network security, digital and computer forensics, and cloud and big dataOf interest to those focused upon security and implementation, and incident managementLogical, well-structured, and organized to facilitate comprehensionSecurity, Privacy and Digital Forensics in the Cloud is an ideal book for advanced undergraduate and master's-level students in information systems, information technology, computer and network forensics, as well as computer science. It can also serve as a good reference book for security professionals, digital forensics practitioners and cloud service providers.

&#8220;Ultimately, this is a remarkable book, a practical testimonial, and a comprehensive bibliography rolled into one. It is a single, bright sword cut across the various murky green IT topics. And if my mistakes and lessons learned through the green IT journey are any indication, this book will be used every day by folks interested in greening IT.&#8221;<br /> &#8212; <i>Simon Y. Liu, Ph.D. &amp; Ed.D., Editor-in-Chief,</i> IT Professional <i>Magazine, IEEE Computer Society, Director, U.S. National Agricultural Library</i> <p><b>This book presents a holistic perspective onGreen IT by discussing its various facets and showing how to strategically embrace it</b></p> <p><i>Harnessing Green IT: Principles and Practices</i> examines various ways of making computing and information systems greener &#8211; environmentally sustainable -, as well as several means of using Information Technology (IT) as a tool and an enabler to improve the environmental sustainability. The book focuses on both greening of IT and greening by IT &#8211; complimentary approaches to attaining environmental sustainability. &#160; In a single volume, it &#160; comprehensively covers several key aspects of Green IT - green technologies, design, standards, maturity models, strategies and adoption -, and presents a clear approach to greening IT encompassing green use, green disposal, green design, and green manufacturing. It also illustrates how to stratgically apply green IT in practice in several areas.</p> <p>Key Features:</p> <ul> <li>Presents a comprehensive coverage of key topics of imprortance and practical relevance&#160; - green technologies, design, standards, maturity models, strategies and adoption</li> <li>Highlights several useful approaches to embracing green IT in several areas</li> <li>Features chapters written by accomplished experts from industry and academia who have first-hand knowledge and expertise in specific areas of green IT</li> <li>Presents a set of review and discussion questions for each chapter that will help the readers to examine and explore the green IT domain further</li> <li>Includes a companion website providing&#160; resources for further information and presentation slides</li> </ul> <p>This book will be an invaluable resource for IT Professionals, academics, students, researchers, project leaders/managers, IT business executives, CIOs, CTOs and anyone interested in Green IT and harnessing it to enhance our environment.</p>

Machine Learning for Healthcare: Handling and Managing Data will provide in-depth information about handling and managing healthcare data through machine learning methods. This book will express the long-standing challenges in healthcare informatics and provide rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. This book may appeal to Computer Science/Information Technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.