This book covers ideas, methods, algorithms, and tools for the in-depth study of the performance and reliability of dependable fault-tolerant systems. The chapters identify the current challenges that designers and practitioners must confront to ensure the reliability, availability, and performance of systems, with special focus on their dynamic behaviors and dependencies. Topics include network calculus, workload and scheduling; simulation, sensitivity analysis and applications; queuing networks analysis; clouds, federations and big data; and tools. This collection of recent research exposes system researchers, performance analysts, and practitioners to a spectrum of issues so that they can address these challenges in their work.

This book introduces social manufacturing, the next generation manufacturing paradigm that covers product life cycle activities that deal with Internet-based organizational and interactive mechanisms under the context of socio-technical systems in the fields of industrial and production engineering. Like its subject, the book's approach is multi-disciplinary, including manufacturing systems, operations management, computational social sciences and information systems applications. It reports on the latest research findings regarding the social manufacturing paradigm, the architecture, configuration and execution of social manufacturing systems and more. Further, it describes the individual technologies enabled by social manufacturing for each topic, supported by case studies. The technologies discussed include manufacturing resource minimalization and their socialized reorganizations, blockchain models in cybersecurity, computing and decision-making, social business relationships and organizational networks, open product design, social sensors and extended cyber-physical systems, and social factory and inter-connections. This book helps engineers and managers in industry to practice social manufacturing, as well as offering a systematic reference resource for researchers in manufacturing. Students also benefit from the detailed discussions of the latest research and technologies that will have been put into practice by the time they graduate.

Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection has never been more important, as the research this book presents an alternative to conventional surveillance and risk assessment. This book is a multidisciplinary excursion comprised of data mining, early warning systems, information technologies and risk management and explores the intersection of these components in problematic domains. It offers the ability to apply the most modern techniques to age old problems allowing for increased effectiveness in the response to future, eminent, and present risk.

In a world increasingly awash in information, the field of information science has become an umbrella stretched so broadly as to threaten its own integrity. However, while traditional information science seeks to make sense of information systems against a social, cultural, and political backdrop, there exists a lack of current literature exploring how such transactions can exert force in the other direction-that is, how information systems mold the individuals who utilize them and society as a whole. The Handbook of Research on Innovations in Information Retrieval, Analysis, and Management explores new developments in the field of information and communication technologies and explores how complex information systems interact with and affect one another, woven into the fabric of an information-rich world. Touching on such topics as machine learning, research methodologies, and mobile data aggregation, this book targets an audience of researchers, developers, managers, strategic planners, and advanced-level students. This handbook contains chapters on topics including, but not limited to, customer experience management, information systems planning, cellular networking, public policy development, and knowledge governance.

This book uses motivating examples and real-life attack scenarios to introduce readers to the general concept of fault attacks in cryptography. It offers insights into how the fault tolerance theories developed in the book can actually be implemented, with a particular focus on a wide spectrum of fault models and practical fault injection techniques, ranging from simple, low-cost techniques to high-end equipment-based methods. It then individually examines fault attack vulnerabilities in symmetric, asymmetric and authenticated encryption systems. This is followed by extensive coverage of countermeasure techniques and fault tolerant architectures that attempt to thwart such vulnerabilities. Lastly, it presents a case study of a comprehensive FPGA-based fault tolerant architecture for AES-128, which brings together of a number of the fault tolerance techniques presented. It concludes with a discussion on how fault tolerance can be combined with side channel security to achieve protection against implementation-based attacks. The text is supported by illustrative diagrams, algorithms, tables and diagrams presenting real-world experimental results.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Online education and social interaction is on the rise. This new vehicle for human learning and communication opens the door for the latest exploration in emerging cyber fields.Evolving Psychological and Educational Perspectives on Cyber Behavior identifies learners' online behavior based on the theories in human psychology, defines online education phenomena as explained by the social and cognitive learning theories and principles, and interprets the complexity of cyber learning. This title offers a multi-disciplinary approach that incorporates the findings from brain research, biology, psychology, human cognition, developmental theory, sociology, motivation theory, and social behavior perfect for school teachers, counselors, researchers, and online designers.

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

Cryptographic Boolean Functions and Applications, Second Edition is designed to be a comprehensive reference for the use of Boolean functions in modern cryptography. While the vast majority of research on cryptographic Boolean functions has been achieved since the 1970s, when cryptography began to be widely used in everyday transactions, in particular banking, relevant material is scattered over hundreds of journal articles, conference proceedings, books, reports and notes, some of them only available online. This book follows the previous edition in sifting through this compendium and gathering the most significant information in one concise reference book. The work therefore encompasses over 600 citations, covering every aspect of the applications of cryptographic Boolean functions. Since 2008, the subject has seen a very large number of new results, and in response, the authors have prepared a new chapter on special functions. The new edition brings 100 completely new references and an expansion of 50 new pages, along with heavy revision throughout the text.

This volume is focused on the emerging concept of Collaborative Innovation Networks (COINs). COINs are at the core of collaborative knowledge networks, distributed communities taking advantage of the wide connectivity and the support of communication technologies, spanning beyond the organizational perimeter of companies on a global scale. It includes the refereed conference papers from the 6th International Conference on COINs, June 8-11, 2016, in Rome, Italy. It includes papers for both application areas of COINs, (1) optimizing organizational creativity and performance, and (2) discovering and predicting new trends by identifying COINs on the Web through online social media analysis. Papers at COINs16 combine a wide range of interdisciplinary fields such as social network analysis, group dynamics, design and visualization, information systems and the psychology and sociality of collaboration, and intercultural analysis through the lens of online social media. They will cover most recent advances in areas from leadership and collaboration, trend prediction and data mining, to social competence and Internet communication.

This book provides theoretical perspectives and practical experiences on smart governance for smart cities. It presents a balanced linkage between research, policies and practices on this area. The authors discuss the sustainability challenges raised by rapid urbanization, challenges with smart governance models in various countries, and a new governance paradigm seen as a capable approach able to overcome social, economic and environmental sustainability problems. The authors include case studies on transformation, adaption and transfers; and country, regional, municipal contextualization. Also included are best practices on monitoring and evaluating smart governance and impact assessment. The book features contributions from researchers, academics, and practitioners in the field. Analyzes smart governance for cities from a variety of perspectives and a variety of sectors - both in theory and in practice Features information on the linkage between United Nations Sustainable Development Goals and smart governance Covers the connection between research, policies and practice in smart governance for smart cities

This book contributes a basic framework for and specific insights into interdisciplinary connections between production, logistics, and traffic subsystems. The book is divided into two parts, the first of which presents an overview of interdisciplinarity in value-added networks and freight traffic. This includes an introduction to the topic and a description of an integrated framework of production, logistics, and traffic. Furthermore, it describes the barriers and challenges of interdisciplinary decision-making and project management. In turn, the second part presents domain-specific perspectives on interdisciplinary decision support, exploring domain-specific challenges of interdisciplinary interfaces and requirements for management methods and instruments from the standpoint of production management, logistics management, traffic management, and information technologies.

Technological innovation and evolution continues to improve personal and professional lifestyles, as well as general organizational and business practices; however, these advancements also create potential issues in the security and privacy of the user's information. Innovative Solutions for Access Control Management features a comprehensive discussion on the trending topics and emergent research in IT security and governance. Highlighting theoretical frameworks and best practices, as well as challenges and solutions within the topic of access control and management, this publication is a pivotal reference source for researchers, practitioners, students, database vendors, and organizations within the information technology and computer science fields.

As information technology is rapidly progressing, an enormous amount of media can be easily exchanged through Internet and other communication networks. Increasing amounts of digital image, video, and music have created numerous information security issues and is now taken as one of the top research and development agendas for researchers, organizations, and governments worldwide. ""Multimedia Forensics and Security"" provides an in-depth treatment of advancements in the emerging field of multimedia forensics and security by tackling challenging issues such as digital watermarking for copyright protection, digital fingerprinting for transaction tracking, and digital camera source identification.

This book presents a comprehensive framework for IoT, including its architectures, security, privacy, network communications, and protocols. The book starts by providing an overview of the aforementioned research topics, future directions and open challenges that face the IoT development. The authors then discuss the main architectures in the field, which include Three- and Five-Layer Architectures, Cloud and Fog Based Architectures, a Social IoT Application Architecture. In the security chapter, the authors outline threats and attacks, privacy preservation, trust and authentication, IoT data security, and social awareness. The final chapter presents case studies including smart home, wearables, connected cars, industrial Internet, smart cities, IoT in agriculture, smart retail, energy engagement, IoT in healthcare, and IoT in poultry and farming. Discusses ongoing research into the connection of the physical and virtual worlds; Includes the architecture, security, privacy, communications, and protocols of IoT; Presents a variety of case studies in IoT including wearables, smart cities, and energy management.

This book offers a systematic introduction to an understanding-oriented approach to multimedia content analysis. It integrates the visual understanding and learning models into a unified framework, within which the visual understanding guides the model learning while the learned models improve the visual understanding. More specifically, it discusses multimedia content representations and analysis including feature selection, feature extraction, image tagging, user-oriented tag recommendation and understanding-oriented multimedia applications. The book was nominated by the University of Chinese Academy of Sciences and China Computer Federation as an outstanding PhD thesis. By providing the fundamental technologies and state-of-the-art methods, it is a valuable resource for graduate students and researchers working in the field computer vision and machine learning.

Information technology is a discipline under constant evolution. This collection, entitled Breakthrough Discoveries in Information Technology Research: Advancing Trends, aims to inform researchers and practitioners of novel and emerging research in information science and technology, allowing for the discussion and dissemination of critical concepts that will promote further study and innovation. Selections explore all facets of the discipline, with specific contributions focusing on outsourcing, ethical concerns in research, biometrics, and information technology's role in disaster prediction and prevention.

This book provides an in-depth insight into the emerging paradigm of End-User Development (EUD), discussing the diversity and potential for creating effective environments for end users. Containing a unique set of contributions from a number of international researchers and institutes, many relevant issues are discussed and solutions proposed, making important aspects of end-user development research available to a broader audience. Most people are familiar with the basic functionality and interfaces of computers. However, developing new or modified applications that can effectively support users' goals still requires considerable programming expertise that cannot be expected of everyone. One of the fundamental challenges that lie ahead is the development of environments that enable users with little or no programming experience to develop and modify their own applications. The ultimate goal is to empower people to flexibly employ and personalise advanced inform ation and communication technologies.

Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. ""Research and Trends in Data Mining Technologies and Applications"" focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.

The Semantic Web has been around for some years with recent advances in mature technologies and applications. Lately, its development has been demonstrated in its contribution to businesses through the enhancement of e-commerce. Semantic Web for Business: Cases and Applications delivers real-life cases that illustrate the benefits of Semantic Web technologies as applied to e-business and e-commerce scenarios. Covering topics such as business integration, organizational knowledge management, and Semantic Web services, this book provides academic research libraries with a comprehensive reference to the commercial capabilities of Semantic Web technologies, as well as practical applications for the benefit of IT professionals, business executives, consultants, and students.

This book puts in one place and in accessible form Richard Berk's most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than "predictive policing" for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often "messy" and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.
You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.
* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.
* Helps you to understand the trade-offs implicit in various models and model architectures.
* Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.
* Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model.
* In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem.
* Presents examples in C, C++, Java, and easy-to-understand pseudo-code.
* Extensive online component, including sample code and a complete data mining workbench.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.