This book focuses on privacy and security concerns in big data and differentiates between privacy and security and privacy requirements in big data. It focuses on the results obtained after applying a systematic mapping study and implementation of security in the big data for utilizing in business under the establishment of "Business Intelligence". The chapters start with the definition of big data, discussions why security is used in business infrastructure and how the security can be improved. In this book, some of the data security and data protection techniques are focused and it presents the challenges and suggestions to meet the requirements of computing, communication and storage capabilities for data mining and analytics applications with large aggregate data in business.

This book is one of the first books that deal with the COVID-19 pandemic. COVID-19 pandemic has affected countries all over the world and has made a significant impact on daily life and healthcare facilities and treatment systems. The book covers the main recent emerging technologies that are related to the COVID-19 crisis. The technologies that are included in this book play a significant role in tackling COVID-19 in the future. The scope of this book is to cover all advanced emerging technologies and artificial intelligence techniques to fight against COVID-19 pandemic.

The book examines patterns of participation in human rights treaties. International relations theory is divided on what motivates states to participate in treaties, specifically human rights treaties. Instead of examining the specific motivations, this dissertation examines patterns of participation. In doing so, it attempts to match theoretical expectations of state behavior with participation. The conclusion of this study is that the data suggests there are multiple motivations that lead states to participate in human rights treaties. The book is divided into five substantive chapters. After an introduction, the second chapter examines the literature on why states join treaties in general, and human rights treaties in particular. The third chapter reviews the obligations states commit to under the fifteen treaties under consideration. The fourth chapter uses basic quantitative methods to examine any differences in the participation rates between democratic and non-democratic states. The fifth chapter examines reservations, declarations, and objections made in conjuncture with the fifteen treaties. The chapter employs both quantitative and qualitative methods to determine if there are substantial differences between democratic and non-democratic states. Finally, the sixth chapter examines those states that participate in the most human rights treaties to determine if there are characteristics that help to identify these states. Additionally, the chapter examines and evaluates theoretical predictions about participation.

This book aims to bring together Researchers, Scientists, Engineers, Scholars and Students in the areas of computer engineering and information technology, and provides a forum for the dissemination of original research results, new ideas, Research and development, practical experiments, which concentrate on both theory and practices, for the benefit of the society. The book also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Computer Science and Information Technology in the context of Distributed computing, Big data, High performance computing, Internet-of-Things, and digital pedagogy. It is becoming increasingly important to develop adaptive, intelligent computing-centric, energy-aware, secure and privacy-aware mechanisms in high performance computing and IoT applications. This book aspires to convey researchers' experiences, to present excellent result analysis, future scopes, and challenges facing the field of computer science, information technology, telecommunication, and digital pedagogy. This book aims to attract researchers and practitioners who are working in Information Technology and Computer Science. This book is about basics and high level concepts regarding intelligent computing paradigm, communications, and digital learning process. The book serves as a useful guide for Undergraduates, Postgraduates and Research Scholar in the field of Computer Science, Information Technology, and Electronics Engineering. We believe that this volume not only presents novel and interesting ideas but also will stimulate interesting discussions from the participants and inspire new ideas.

This book represents the combined peer-reviewed proceedings of the ninth International Symposium on Intelligent Distributed Computing - IDC'2015, of the Workshop on Cyber Security and Resilience of Large-Scale Systems - WSRL'2015, and of the International Workshop on Future Internet and Smart Networks - FI&SN'2015. All the events were held in Guimaraes, Portugal during October 7th-9th, 2015. The 46 contributions published in this book address many topics related to theory and applications of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.

This book presents a foray into the fascinating process of risk management, beginning from classical methods and approaches to understanding risk all the way into cutting-age thinking. Risk management by necessity must lie at the heart of governing our ever more complex digital societies. New phenomena and activities necessitate a new look at how individuals, firms, and states manage the uncertainty they must operate in. Initial chapters provide an introduction to traditional methods and show how they can be built upon to better understand the workings of the modern economy. Later chapters review digital activities and assets like cryptocurrencies showing how such emergent risks can be conceptualized better. Network theory figures prominently and the book demonstrates how it can be used to gauge the risk in the digital sectors of the economy. Predicting the unpredictable black swan events is also discussed in view of a wider adoption of economic simulations. The journey concludes by looking at how individuals perceive risk and make decisions as they operate in a virtual social network. This book interests the academic audience, but it also features insights and novel research results that are relevant for practitioners and policymakers.

Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.

Geographic information systems have developed rapidly in the past decade, and are now a major class of software, with applications that include infrastructure maintenance, resource management, agriculture, Earth science, and planning. But a lack of standards has led to a general inability for one GIS to interoperate with another. It is difficult for one GIS to share data with another, or for people trained on one system to adapt easily to the commands and user interface of another. Failure to interoperate is a problem at many levels, ranging from the purely technical to the semantic and the institutional. Interoperating Geographic Information Systems is about efforts to improve the ability of GISs to interoperate, and has been assembled through a collaboration between academic researchers and the software vendor community under the auspices of the US National Center for Geographic Information and Analysis and the Open GIS Consortium Inc. It includes chapters on the basic principles and the various conceptual frameworks that the research community has developed to think about the problem. Other chapters review a wide range of applications and the experiences of the authors in trying to achieve interoperability at a practical level. Interoperability opens enormous potential for new ways of using GIS and new mechanisms for exchanging data, and these are covered in chapters on information marketplaces, with special reference to geographic information. Institutional arrangements are also likely to be profoundly affected by the trend towards interoperable systems, and nowhere is the impact of interoperability more likely to cause fundamental change than in education, as educators address the needs of a new generation of GIS users with access to a new generation of tools. The book concludes with a series of chapters on education and institutional change. Interoperating Geographic Information Systems is suitable as a secondary text for graduate level courses in computer science, geography, spatial databases, and interoperability and as a reference for researchers and practitioners in industry, commerce and government.

Enterprise Information Systems Assurance and System Security: Managerial and Technical Issues brings together authoritative authors to address one of the most pressing challenges in the IT field - how to create secure environments for the application of technology to serve future needs. This book bridges the gap between theory and practice, academia and industry, computer science and MIS. The chapters provide an integrated, holistic perspective on this complex set of challenges, supported with practical experiences of leading figures from all realms. ""Enterprise Information Systems Assurance and System Security: Managerial and Technical Issues"" provides an excellent collection for corporate executives who are charged with securing their systems and data, students studying the topic of business information security, and those who simply have an interest in this exciting topic.

This practically-focused text presents a hands-on guide to making biometric technology work in real-life scenarios. Extensively revised and updated, this new edition takes a fresh look at what it takes to integrate biometrics into wider applications. An emphasis is placed on the importance of a complete understanding of the broader scenario, covering technical, human and implementation factors. This understanding may then be exercised through interactive chapters dealing with educational software utilities and the BANTAM Program Manager. Features: provides a concise introduction to biometrics; examines both technical issues and human factors; highlights the importance of a broad understanding of biometric technology implementation from both a technical and operational perspective; reviews a selection of freely available utilities including the BANTAM Program Manager; considers the logical next steps on the path from aspiration to implementation, and looks towards the future use of biometrics in context.

Data warehousing and mining technologies are key assets today in many areas of human knowledge, from scientific to commercial and industrial settings, and the last decades have seen tremendous advances in those fields. ""Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions"" provides insight into the latest findings concerning data warehousing, data mining, and their applications in everyday human activities. Comprising a valuable resource for researchers, practitioners, and academicians, this advanced publication offers insight into recent field trends, techniques on how these technologies operate, and analysis of their effects.

Video segmentation is the most fundamental process for appropriate index ing and retrieval of video intervals. In general, video streams are composed 1 of shots delimited by physical shot boundaries. Substantial work has been done on how to detect such shot boundaries automatically (Arman et aI. , 1993) (Zhang et aI. , 1993) (Zhang et aI. , 1995) (Kobla et aI. , 1997). Through the inte gration of technologies such as image processing, speech/character recognition and natural language understanding, keywords can be extracted and associated with these shots for indexing (Wactlar et aI. , 1996). A single shot, however, rarely carries enough amount of information to be meaningful by itself. Usu ally, it is a semantically meaningful interval that most users are interested in re trieving. Generally, such meaningful intervals span several consecutive shots. There hardly exists any efficient and reliable technique, either automatic or manual, to identify all semantically meaningful intervals within a video stream. Works by (Smith and Davenport, 1992) (Oomoto and Tanaka, 1993) (Weiss et aI. , 1995) (Hjelsvold et aI. , 1996) suggest manually defining all such inter vals in the database in advance. However, even an hour long video may have an indefinite number of meaningful intervals. Moreover, video data is multi interpretative. Therefore, given a query, what is a meaningful interval to an annotator may not be meaningful to the user who issues the query. In practice, manual indexing of meaningful intervals is labour intensive and inadequate.

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research.In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for the reader for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.

Distributed and Parallel Database Object Management brings together in one place important contributions and state-of-the-art research results in this rapidly advancing area of computer science. Distributed and Parallel Database Object Management serves as an excellent reference, providing insights into some of the most important issues in the field.

This book is an outcome of the second national conference on Communication, Cloud and Big Data (CCB) held during November 10-11, 2016 at Sikkim Manipal Institute of Technology. The nineteen chapters of the book are some of the accepted papers of CCB 2016. These chapters have undergone review process and then subsequent series of improvements. The book contains chapters on various aspects of communication, computation, cloud and big data. Routing in wireless sensor networks, modulation techniques, spectrum hole sensing in cognitive radio networks, antenna design, network security, Quality of Service issues in routing, medium access control protocol for Internet of Things, and TCP performance over different routing protocols used in mobile ad-hoc networks are some of the topics discussed in different chapters of this book which fall under the domain of communication. Moreover, there are chapters in this book discussing topics like applications of geographic information systems, use of radar for road safety, image segmentation and digital media processing, web content management system, human computer interaction, and natural language processing in the context of Bodo language. These chapters may fall under broader domain of computation. Issues like robot navigation exploring cloud technology, and application of big data analytics in higher education are also discussed in two different chapters. These chapters fall under the domains of cloud and big data, respectively.

The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no," "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.

Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.

Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrialcontrol.

This book surveys recent advances in Conversational Information Retrieval (CIR), focusing on neural approaches that have been developed in the last few years. Progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. The book contains nine chapters. Chapter 1 motivates the research of CIR by reviewing the studies on how people search and subsequently defines a CIR system and a reference architecture which is described in detail in the rest of the book. Chapter 2 provides a detailed discussion of techniques for evaluating a CIR system – a goal-oriented conversational AI system with a human in the loop. Then Chapters 3 to 7 describe the algorithms and methods for developing the main CIR modules (or sub-systems). In Chapter 3, conversational document search is discussed, which can be viewed as a sub-system of the CIR system. Chapter 4 is about algorithms and methods for query-focused multi-document summarization. Chapter 5 describes various neural models for conversational machine comprehension, which generate a direct answer to a user query based on retrieved query-relevant documents, while Chapter 6 details neural approaches to conversational question answering over knowledge bases, which is fundamental to the knowledge base search module of a CIR system. Chapter 7 elaborates various techniques and models that aim to equip a CIR system with the capability of proactively leading a human-machine conversation. Chapter 8 reviews a variety of commercial systems for CIR and related tasks. It first presents an overview of research platforms and toolkits which enable scientists and practitioners to build conversational experiences, and continues with historical highlights and recent trends in a range of application areas. Chapter 9 eventually concludes the book with a brief discussion of research trends and areas for future work. The primary target audience of the book are the IR and NLP research communities. However, audiences with another background, such as machine learning or human-computer interaction, will also find it an accessible introduction to CIR.

Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies. The challenge of data intensive computing is to provide the hardware architectures and related software systems and techniques which are capable of transforming ultra-large data into valuable knowledge. "Handbook of Data Intensive Computing" is written by leading international experts in the field. Experts from academia, research laboratories and private industry address both theory and application. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Data-intensive applications typically are well suited for large-scale parallelism over the data and also require an extremely high degree of fault-tolerance, reliability, and availability. Real-world examples are provided throughout the book.

"Handbook of Data Intensive Computing" is designed as a reference for practitioners and researchers, including programmers, computer and system infrastructure designers, and developers. This book can also be beneficial for business managers, entrepreneurs, and investors.

This book constitutes the refereed post-conference proceedings of the 17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020, held in Rapperswil, Switzerland, in July 2020. The conference was held virtually due to the COVID-19 crisis. The 60 revised full papers presented together with 2 technical industrial papers were carefully reviewed and selected from 80 submissions. The papers are organized in the following topical sections: smart factory; digital twins; Internet of Things (IoT, IIoT); analytics in the order fulfillment process; ontologies for interoperability; tools to support early design phases; new product development; business models; circular economy; maturity implementation and adoption; model based systems engineering; artificial intelligence in CAx, MBE, and PLM; building information modelling; and industrial technical contributions.

The book throws light on the ongoing trends in international business, integration of information technology with global businesses, its role in value co-creation, resource integration, and service for service exchange. While discussing the issues of these areas, chapters of this book also delve into prevalent problematic areas which are closely related like employment, ethical aspects, power creation, and so on. Recognizing the role digitization and new technologies play in enabling global managers to communicate with outside world directly via digital channels irrespective of their location (which is especially true in time of COVID-19), the book takes an emerging economy perspective and throws light on new theories, perceptions, employment opportunities, and innovative ideas through its content. The book not only discusses effects of information technology but also the latest emerging technology in global business like use of artificial intelligence, robotics, machine learning, big data, and their integration with the global business 4.0. Since emergence of these new technologies requires proper infrastructural development, the book also throws light on government initiatives and CSR in this respect. It contains takeaways for both undergraduate and graduate students, researchers and academicians, industry watchers, practitioners, start-ups, and entrepreneurs

This book provides an insight into IoT intelligence in terms of applications and algorithmic challenges. The book is dedicated to addressing the major challenges in realizing the artificial intelligence in IoT-based applications including challenges that vary from cost and energy efficiency to availability to service quality in multidisciplinary fashion. The aim of this book is hence to focus on both the algorithmic and practical parts of the artificial intelligence approaches in IoT applications that are enabled and supported by wireless sensor networks and cellular networks. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent wireless/wired enabling technologies. Includes the most up-to-date research and applications related to IoT artificial intelligence (AI); Provides new and innovative operational ideas regarding the IoT artificial intelligence that help advance the telecommunications industry; Presents AI challenges facing the IoT scientists and provides potential ways to solve them in critical daily life issues.

This book presents the main theoretical foundations behind smart services as well as specific guidelines and practically proven methods on how to design them. Furthermore, it gives an overview of the possible implementation architectures and shows how the designed smart services can be realized with specific technologies. Finally, it provides four specific use cases that show how smart services have been realized in practice and what impact they have within the businesses. The first part of the book defines the basic concepts and aims to establish a shared understanding of terms, such as smart services, service systems, smart service systems or cyber-physical systems. On this basis, it provides an analysis of existing work and includes insights on how an organization incorporating smart services could enhance and adjust their management and business processes. The second part on the design of smart services elaborates on what constitutes a successful smart service and describes experiences in the area of interdisciplinary teams, strategic partnerships, the overall service systems and the common data basis. In the third part, technical reference architectures are presented in detail, encompassing topics on the design of digital twins in cyber physical systems, the communication between entities and sensors in the age of Industry 4.0 as well as data management and integration. The fourth part then highlights a number of analytical possibilities that can be realized and that can constitute or be part of smart services, including machine learning and artificial intelligence methods. Finally, the applicability of the introduced design and development method is demonstrated by considering specific real-world use cases. These include services in the industrial and mobility sector, which were developed in direct cooperation with industry partners. The main target audience of this book is industry-focused readers, especially practitioners from industry, who are involved in supporting and managing digital business. These include professionals working in business development, product management, strategy, and development, ranging from middle management to Chief Digital Officers. It conveys all the basics needed for developing smart services and successfully placing them on the market by explaining technical aspects as well as showcasing practical use cases.