Technology holds the key for bridging the gap between access to quality education and the need for enhanced learning experiences. Cases on Technological Adaptability and Transnational Learning: Issues and Challenges contains case studies on divergent themes of personalized learning environments, inclusive learning for social change, innovative learning and assessment techniques, technology and international partnership and transnational collaboration for enhanced access under the core domain of technological adaptability and transnational learning.

Wireless Sensor Networks (WSNs) and the Internet of Things are facing tremendous advances both in terms of energy-efficiency as well as in the number of available applications. Consequently, there are challenges that need to be tackled for the future generation of WSNs. After giving an overview of the WSN protocols and IEEE 802.15.4 standard, this book proposes IEEE 802.15.4 Medium Access Control (MAC) sub-layer performance enhancements by employing not only RTS/CTS combined with packet concatenation but also scheduled channel poling (MC-SCP). Results have shown that the use of the RTS/CTS mechanism improves channel efficiency by decreasing the deferral time before transmitting a data packet. Furthermore, the Sensor Block Acknowledgment MAC (SBACK-MAC) protocol enables more efficiency as it allows the aggregation of several acknowledgement responses in one special Block Acknowledgment (BACK) Response packet. The throughput and delay performance have been mathematically derived under both ideal conditions (a channel environment with no transmission errors) and non-ideal conditions (with transmission errors). Simulation results successfully validate the proposed analytical models. This research reveals the importance of an appropriate design for the MAC sub-layer protocol for the desired WSN application. Depending on the mission of the WSN application, different protocols are required. Therefore, the overall performance of a WSN application certainly depends on the development and application of suitable e.g., MAC, network layer protocols.

Going Virtual: Distributed Communities in Practice contributes to the understanding of how more subtle kinds of knowledge can be managed in a distributed international environment. It describes academic work in the field of Knowledge Management, with a specific focus on the management of knowledge which cannot be managed by the normal capture-codify-store approach and hopes to answer the question, "what is the nature of the more 'subtle' kind of knowledge and how can it be managed in the distributed environment?"

Trojans, Worms, and Spyware provides practical, easy to understand, and readily usable advice to help organizations to improve their security and reduce the possible risks of malicious code attacks. Despite the global downturn, information systems security remains one of the more in-demand professions in the world today. With the widespread use of the Internet as a business tool, more emphasis is being placed on information security than ever before. To successfully deal with this increase in dependence and the ever growing threat of virus and worm attacks, Information security and information assurance (IA) professionals need a jargon-free book that addresses the practical aspects of meeting new security requirements.
This book provides a comprehensive list of threats, an explanation of what they are and how they wreak havoc with systems, as well as a set of rules-to-live-by along with a system to develop procedures and implement security training. It is a daunting task to combat the new generation of computer security threats - new and advanced variants of Trojans, as well as spyware (both hardware and software) and "bombs" - and Trojans, Worms, and Spyware will be a handy must-have reference for the computer security professional to battle and prevent financial and operational harm from system attacks.
*Provides step-by-step instructions to follow in the event of an attack
*Case studies illustrate the "do's," "don'ts," and lessons learned from infamous attacks
*Illustrates to managers and their staffs the importance of having protocols and a response plan in place

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

Smart homes, smart cities, and wearable technologies are the growing applications of the internet of things (IoT). Ranging from healthcare tracking applications to smart watches and smart bands for personal safety, the IoT has turned out to be one of the most indispensable parts of our lives even as it is becoming more interconnected to better serve people. With the exponential growth of the IoT and its applications, building the next-generation smart world becomes much more feasible. The IoT and the Net Revolutions Automating the World covers a spectrum of intelligent applications of the IoT in parking, traffic management, waste management, lighting, air pollution controlling, healthcare, weather tracking, retail, and other areas calling for automation. Highlighting a wide range of topics such as e-commerce, security management, and web infrastructure, this book is ideal for academicians, students, researchers, industry professionals, IT consultants, engineers, and scientists.

Databases are designed to support data storage, processing, and retrieval activities related to data management. The use of databases in various applications has resulted in an enormous wealth of data, which populates many types of databases around the world. Advanced Database Query Systems: Techniques, Applications and Technologies focuses on technologies and methodologies of database queries, XML and metadata queries, and applications of database query systems, aiming at providing a single account of technologies and practices in advanced database query systems. This book provides the state of the art information for academics, researchers and industry practitioners who are interested in the study, use, design and development of advanced and emerging database queries with ultimate aim of building competencies for exploiting the opportunities of the data and knowledge society.

Corporations accumulate a lot of valuable data and knowledge over time, but storing and maintaining this data can be a logistic and financial headache for business leaders and IT specialists. Uncovering Essential Software Artifacts through Business Process Archaeology introduces an emerging method of software modernisation used to effectively manage legacy systems and company operations supported by such systems. This book presents methods, techniques, and new trends on business process archaeology as well as some industrial success stories. Business experts, professionals, and researchers working in the field of information and knowledge management will use this reference source to efficiently and effectively implement and utilise business knowledge.

Medical internet of things (IoT)-based applications are being utilized in several industries and have been shown to provide significant advantages to users in critical health applications. Artificial intelligence (AI) plays a key role in the growth and success of medical IoT applications and IoT devices in the medical sector. To enhance revenue, improve competitive advantage, and increase consumer engagement, the use of AI with medical IoT should be encouraged in the healthcare and medical arena. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications provides greater knowledge of how AI affects healthcare and medical efficacy in order to improve outputs. It focuses on a thorough and comprehensive introduction to machine learning. Covering topics such as patient treatment, cyber-physical systems, and telemedicine, this premier reference source is a dynamic resource for hospital administrators, medical professionals, government officials, students and faculty of higher education, librarians, researchers, and academicians.

Within a given enterprise, database management involves the monitoring, administration, and maintenance of the databases, which constantly change with new technologies and new forms of data. Cross-Disciplinary Models and Applications of Database Management: Advancing Approaches is an updated look at the latest tools and technology within the burgeoning field of database management. Perfect for the network administrator, technician, information technology specialist or consultant, or for academics and students, this volume presents the latest the field has to offer by way of cases and new research. As database languages, models, and systems change, it s vital for practitioners within the field to stay abreast of the latest research and methods being used around the world, and this book offers the most current advances available.

The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.

This book is a collection of essays exploring adaptive systems from many perspectives, ranging from computational applications to models of adaptation in living and social systems. The essays on computation discuss history, theory, applications, and possible threats of adaptive and evolving computations systems. The modeling chapters cover topics such as evolution in microbial populations, the evolution of cooperation, and how ideas about evolution relate to economics.
The title Perspectives on Adaptation in Natural and Artificial Systems honors John Holland, whose 1975 Book, Adaptation in Natural and Artificial Systems has become a classic text for many disciplines in which adaptation play a central role. The essays brought together here were originally written to honor John Holland, and span most of the different areas touched by his wide-ranging and influential research career. The authors include some of the most prominent scientists in the fields of artificial intelligence evolutionary computation, and complex adaptive systems. Taken together, these essays present a broad modern picture of current research on adaptation as it relates to computers, living systems, society, and their complex interactions.

Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management focuses on the challenges of distributed systems imposed by data intensive applications and on the different state-of-the-art solutions proposed to overcome such challenges. Providing hints on how to manage low-level data handling issues when performing data intensive distributed computing, this publication is ideal for scientists, researchers, engineers, and application developers, alike. With the knowledge of the correct data management techniques for their applications, readers will be able to focus on their primary goal, assured that their data management needs are handled reliably and efficiently.

The past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.

This book explores the main elements of e-Democracy, the term normally used to describe the implementation of democratic government processes by electronic means. It provides insights into the main technological and human issues regarding governance, government, participation, inclusion, empowerment, procurement and, last but not least, ethical and privacy issues. Its main aim is to bridge the gap between technological solutions, their successful implementation, and the fruitful utilization of the main set of e-Services totally or partially delivered by governments or non-government organizations. Today, various parameters actively influence e-Services' success or failure: cultural aspects, organisational issues, bureaucracy and workflows, infrastructure and technology in general, user habits, literacy, capacity or merely interaction design. This includes having a significant population of citizens who are willing and able to adopt and use online services; as well as developing the managerial and technical capability to implement applications that meet citizens' needs. This book helps readers understand the mutual dependencies involved; further, a selection of success stories and failures, duly commented on, enables readers to identify the right approach to innovation in governmental e-Services. With its balanced humanistic and technological approach, the book mainly targets public authorities, decision-makers, stakeholders, solution developers, and graduate students.

Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

The concept of virtual worlds is strongly related to the current innovations of new media communication. As such, it is increasingly imperative to understand the criteria for creating virtual worlds as well as the evolution in system architecture, information visualization and human interaction. Meta-plasticity in Virtual Worlds: Aesthetics and Semantics Concepts provides in-depth coverage of the state-of-the-art among the best international research experiences of virtual world concept creations from a wide range of media culture fields, at the edge of artistic and scientific inquiry and emerging technologies. Written for professionals, researchers, artists and designers, this text is a perfect companion for those who want to improve their understanding of the strategic role of virtual worlds within the development of digital communication.

The World Wide Web is changing the way we use technology, bringing e-learning and teaching to a whole new dimension of collaboration and communication. Looking Toward the Future of Technology-Enhanced Education: Ubiquitous Learning and the Digital Native bridges the gap between technology and education by presenting innovative research on the future of education. An essential reference on e-learning, this scholarly publication examines current research in technology enhanced learning, provides new didactic models for education, and discusses the newest technologies and their impact on education.

This volume presents several machine intelligence technologies, developed over recent decades, and illustrates how they can be combined in application. One application, the detection of dementia from patterns in speech, is used throughout to illustrate these combinations. This application is a classic stationary pattern detection task, so readers may easily see how these combinations can be applied to other similar tasks. The expositions of the methods are supported by the basic theory they rest upon, and their application is clearly illustrated. The book's goal is to allow readers to select one or more of these methods to quickly apply to their own tasks. Includes a variety of machine intelligent technologies and illustrates how they can work together Shows evolutionary feature subset selection combined with support vector machines and multiple classifiers combined Includes a running case study on intelligent processing relating to Alzheimer's / dementia detection, in addition to several applications of the machine hybrid algorithms

The general focus of this book is on multimodal communication, which captures the temporal patterns of behavior in various dialogue settings. After an overview of current theoretical models of verbal and nonverbal communication cues, it presents studies on a range of related topics: paraverbal behavior patterns in the classroom setting; a proposed optimal methodology for conversational analysis; a study of time and mood at work; an experiment on the dynamics of multimodal interaction from the observer's perspective; formal cues of uncertainty in conversation; how machines can know we understand them; and detecting topic changes using neural network techniques. A joint work bringing together psychologists, communication scientists, information scientists and linguists, the book will be of interest to those working on a wide range of applications from industry to home, and from health to security, with the main goals of revealing, embedding and implementing a rich spectrum of information on human behavior.

A key focus in recent years has been on sustainable development and promoting environmentally conscious practices. In today's rapidly evolving technological world, it is important to consider how technology can be applied to solve problems across disciplines and fields in these areas. Further study is needed in order to understand how technology can be applied to sustainability and the best practices, considerations, and challenges that follow. Futuristic Trends for Sustainable Development and Sustainable Ecosystems discusses recent advances and innovative research in the area of information and communication technology for sustainable development and covers practices in several artificial intelligence fields such as knowledge representation and reasoning, natural language processing, machine learning, and the semantic web. Covering topics such as blockchain, deep learning, and renewable energy, this reference work is ideal for computer scientists, industry professionals, researchers, academicians, scholars, instructors, and students.

Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.

The discovery and development of new computational methods have expanded the capabilities and uses of simulations. With agent-based models, the applications of computer simulations are significantly enhanced. Multi-Agent-Based Simulations Applied to Biological and Environmental Systems is a pivotal reference source for the latest research on the implementation of autonomous agents in computer simulation paradigms. Featuring extensive coverage on relevant applications, such as biodiversity conservation, pollution reduction, and environmental risk assessment, this publication is an ideal source for researchers, academics, engineers, practitioners, and professionals seeking material on various issues surrounding the use of agent-based simulations.