i. This book will contain AI, ML, DL, big data and security never before considered ii. Innovative artificial intelligence techniques and algorithms iii. Only emerging from recent research and development, e.g. AI for big data from security perspective, which are not covered in any existing texts iv. Artificial Intelligence for big data and security Applications with advanced features v. Key new finding of machine learning and deep learning for Security Applications

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists get benefited from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field.

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

This book examines the interplay between IT solutions and specific management methods in organizations. In particular, it assesses the impact of IT reliability on factors like employees' commitment and organizational performance. After developing the necessary theoretical foundation, the book presents a framework for aligning IT solutions with a number of specific management methods in organizations. In addition, it demonstrates the extent to which IT reliability can be an indicator for this alignment, and discusses the impact on employees' commitment and organizational performance under various management methods. Case studies from organizations in Switzerland and Poland help to illustrate the findings. In closing, the book presents roadmaps for improving IT and business alignment so as to achieve higher commitment and better results.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. Exploring 12 different themes - from the Olympics to Presidents & Royals and from Movies to Myths & Legends - each pair of visualizations explores different technologies and forms, blurring the boundary between visualization as an exploratory tool and an artform in its own right. This beautiful book provides an intimate, behind-the-scenes account of all 24 projects and shares the authors' personal notes and drafts every step of the way. The book features: Detailed information on data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors' notebooks Never-before-published technical write-ups, with beginner-friendly explanations of core data visualization concepts Practical lessons based on the data and design challenges overcome during each project Full-color pages, showcasing all 24 final data visualizations This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling.

This book explores the changes in political communication in light of the development of a public opinion mediated by web 2.0 technologies. One of the most important changes in political communication is related to the process of disintermediation, i.e. the process by which digital technologies allow citizens to compete in the public space with those agents who, traditionally, co-opted public opinion. However, while disintermediation has undeniably generated a number of advances, having linked citizens to the public debate, the authors highlight some aspects where disintermediation is moving away from a rational and inclusive public space. They argue that these aspects, related to the immediacy, polarization and incivility of the communication, obscure the possibilities for democratization of digital political communication.

This book discusses applications of blockchain in healthcare sector. The security of confidential and sensitive data is of utmost importance in healthcare industry. The introduction of blockchain methods in an effective manner will bring secure transactions in a peer-to-peer network. The book also covers gaps of the current available books/literature available for use cases of Distributed Ledger Technology (DLT) in healthcare. The information and applications discussed in the book are immensely helpful for researchers, database professionals, and practitioners. The book also discusses protocols, standards, and government regulations which are very useful for policymakers.

Computer and communication networks are among society's most important infrastructures. The internet, in particular, is a giant global network of networks with central control or administration. It is a paradigm of a complex system, where complexity may arise from different sources: topological structure, network evolution, connection and node diversity, and /or dynamical evolution. This is the first book entirely devoted to the new and emerging field of nonlinear dynamics of TCP/IP networks. It addresses both scientists and engineers working in the general field of communication networks.

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: * It covers both technical and soft skills. * It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. * It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: * It covers both technical and soft skills. * It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. * It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

This book is the first publication to give a comprehensive, structured treatment to the important topic of situational awareness in cyber defense. It presents the subject in a logical, consistent, continuous discourse, covering key topics such as formation of cyber situational awareness, visualization and human factors, automated learning and inference, use of ontologies and metrics, predicting and assessing impact of cyber attacks, and achieving resilience of cyber and physical mission. Chapters include case studies, recent research results and practical insights described specifically for this book. Situational awareness is exceptionally prominent in the field of cyber defense. It involves science, technology and practice of perception, comprehension and projection of events and entities in cyber space. Chapters discuss the difficulties of achieving cyber situational awareness - along with approaches to overcoming the difficulties - in the relatively young field of cyber defense where key phenomena are so unlike the more conventional physical world. Cyber Defense and Situational Awareness is designed as a reference for practitioners of cyber security and developers of technology solutions for cyber defenders. Advanced-level students and researchers focused on security of computer networks will also find this book a valuable resource.

This book demonstrates the inevitability of a continuously growing role of data in our society and it stresses that this role does not need to be threatening: to the contrary, collection and analysis of data can help us prevent traffic jams, suppress epidemics, or produce tailor made medicine. The authors sketch the contours of a new information society, in which everything will be measured from our heartbeat during our morning run to the music we listen to and our walking patterns through department stores and they discuss the resistances within the society that have to be overcome. Sander Klous holds a PhD in High Energy Physics and contributed to the discovery of the Higgs boson at CERN (Nobel prize 2013). Klous works at KPMG and is professor in Big Data at the University of Amsterdam. Nart Wielaard is a self-employed consultant and business writer. He develops compelling and clear stories on complex topics for a broad range of clients. Wielaard specializes in the domain where technology, society and business meet.

This book discusses blockchain technology and its potential applications in digital government and the public sector. With its robust infrastructure and append-only record system, blockchain technology is being increasingly employed in the public sector, specifically where trustworthiness and security are of importance. Written by leading scholars and practitioners, this edited volume presents challenges, benefits, regulations, frameworks, taxonomies, and applications of blockchain technology in the public domain. Specifically, the book analyzes the implementation of blockchain technologies in the public sector and the potential reforms it would bring. It discusses emerging technologies and their role in the implementation of blockchain technologies in the public sector. The book details the role of blockchain in the creation of public value in the delivery of public sector services. The book analyzes effects, impacts, and outcomes from the implementation of blockchain technologies in the public sector in select case studies. Providing up-to-date information on important developments regarding blockchain in government around the world, this volume will appeal to academics, researchers, policy-makers, public managers, international organizations, and technical experts looking to understand how blockchain can enhance public service delivery.

This book contains extended and revised versions of the best papers presented at the 28th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2020, held in Salt Lake City, UT, USA, in October 2020.*The 16 full papers included in this volume were carefully reviewed and selected from the 38 papers (out of 74 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like low-power design of RF, analog and mixed-signal circuits, EDA tools for the synthesis and verification of heterogenous SoCs, accelerators for cryptography and deep learning and on-chip Interconnection system, reliability and testing, and integration of 3D-ICs. *The conference was held virtually.

This Festschrift, Unimagined Futures - ICT Opportunities and Challenges, is the first Festschrift in the IFIP AICT series. It examines key challenges facing the ICT community today. While addressing the contemporary challenges, the book provides the opportunity to look back to help understand the contemporary scene and identify appropriate future responses to them. Experts in different areas of the ICT scene have contributed to this IFIP 60th anniversary book, which will be a key input to the ICT community worldwide on setting policy priorities and agendas for the coming decade. In addition, a number of contributions look specifically at the role of professionals and of national, regional, and global organizations in disseminating the benefits of ICT to humanity worldwide.

This book focuses on the advanced soft computational and probabilistic methods that the authors have published over the past few years. It describes theoretical results and applications, and discusses how various uncertainty measures - probability, plausibility and belief measures - can be treated in a unified way. It also examines approximations of four notable probability distributions (Weibull, exponential, logistic and normal) using a unified probability distribution function, and presents a fuzzy arithmetic-based time series model that provides an easy-to-use forecasting technique. Lastly, it proposes flexible fuzzy numbers for Likert scale-based evaluations. Featuring methods that can be successfully applied in a variety of areas, including engineering, economics, biology and the medical sciences, the book offers useful guidelines for practitioners and researchers.

In this era of heterogeneous and distributed data sources, ranging from semistructured documents to knowledge about coordination processes or workflows, logic provides a rich set of tools and techniques with which to address the questions of how to represent, query and reason about complex data. This book provides a state-of-the-art overview of research on the application of logic-based methods to information systems, covering highly topical and emerging fields: XML programming and querying, intelligent agents, workflow modeling and verification, data integration, temporal and dynamic information, data mining, authorization, and security. It provides both scientists and graduate students with a wealth of material and references for their own research and education.

The Semantic Web represents a vision for how to make the huge amount of information on the Web automatically processable by machines on a large scale. For this purpose, a whole suite of standards, technologies and related tools have been specified and developed over the last couple of years and they have now become the foundation for numerous new applications. A Developer's Guide to the Semantic Web helps the reader to learn the core standards, key components and underlying concepts. It provides in-depth coverage of both the what-is and how-to aspects of the Semantic Web. From Yu's presentation, the reader will obtain not only a solid understanding about the Semantic Web, but also learn how to combine all the pieces to build new applications on the Semantic Web. The second edition of this book not only adds detailed coverage of the latest W3C standards such as SPARQL 1.1 and RDB2RDF, it also updates the readers by following recent developments. More specifically, it includes five new chapters on schema.org and semantic markup, on Semantic Web technologies used in social networks and on new applications and projects such as data.gov and Wikidata and it also provides a complete coding example of building a search engine that supports Rich Snippets. Software developers in industry and students specializing in Web development or Semantic Web technologies will find in this book the most complete guide to this exciting field available today. Based on the step-by-step presentation of real-world projects, where the technologies and standards are applied, they will acquire the knowledge needed to design and implement state-of-the-art applications.

This volume helps to address the genuine 21st century need for advances in data science and computing technology. It provides an abundance of new research and studies on progressive and innovative technologies, including artificial intelligence, communication systems, cyber security applications, data analytics, Internet of Things (IoT), machine learning, power systems, VLSI, embedded systems, and much more. The book presents a variety of interesting and important aspects of data science and computing technologies and methodologies in a wide range of applications, including deep learning, DNA cryptography, classy fuzzy MPPT controller, driving assistance, and safety systems. Novel algorithms and their applications for solving cutting-edge computational and data science problems are included also for an interdisciplinary research perspective. The book addresses recent applications of deep learning and ANN paradigms, the role and impact of big data in the e-commerce and retail sectors, algorithms for load balancing in cloud computing, advances in embedded system based applications, optimization techniques using a MATLAB platform, and techniques for improving information and network security. Advances in Data Science and Computing Technology: Methodology and Applications provides a wealth of valuable information and food for thought on many important issues for data scientists and researchers, industry professionals, and faculty and students in the data and computing sciences.

This book summarizes the research findings presented at the 13th International Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2020), which took place on August 24-26, 2020. JCKBSE 2020 was originally planned to take place in Larnaca, Cyprus. Unfortunately, the COVID-19 pandemic forced it be rescheduled as an online conference. JCKBSE is a well-established, international, biennial conference that focuses on the applications of artificial intelligence in software engineering. The 2020 edition of the conference was organized by Hiroyuki Nakagawa, Graduate School of Information Science and Technology, Osaka University, Japan, and George A. Tsihrintzis and Maria Virvou, Department of Informatics, University of Piraeus, Greece. This research book is a valuable resource for experts and researchers in the field of (knowledge-based) software engineering, as well as general readers in the fields of artificial and computational Intelligence and, more generally, computer science wanting to learn more about the field of (knowledge-based) software engineering and its applications. An extensive list of bibliographic references at the end of each paper helps readers to probe further into the application areas of interest to them.

This book provides a multidisciplinary view into how individuals and groups interact with the information environments that surround them. The book discusses how informational environments shape our daily lives, and how digital technologies can improve the ways in which people make use of informational environments. It presents the research and outcomes of a seven-year multidisciplinary research initiative, the Leibniz-WissenschaftsCampus Tubingen Informational Environments, jointly conducted by the Leibniz-Institut fur Wissensmedien (IWM) and the Eberhard Karls Universitat Tubingen. Book chapters from leading international experts in psychology, education, computer science, sociology, and medicine provide a multi-layered and multidisciplinary view on how the interplay between individuals and their informational environments unfolds. Featured topics include: Managing obesity prevention using digital media. Using digital media to assess and promote school teacher competence. Informational environments and their effect on college student dropout. Web-Platforms for game-based learning of orthography and numeracy. How to design adaptive information environments to support self-regulated learning with multimedia. Informational Environments will be of interest to advanced undergraduate students, postgraduate students, researchers and practitioners in various fields of educational psychology, social psychology, education, computer science, communication science, sociology, and medicine.

This comprehensive overview of IoT systems architecture includes in-depth treatment of all key components: edge, communications, cloud, data processing, security, management, and uses. Internet of Things: Concepts and System Design provides a reference and foundation for students and practitioners that they can build upon to design IoT systems and to understand how the specific parts they are working on fit into and interact with the rest of the system. This is especially important since IoT is a multidisciplinary area that requires diverse skills and knowledge including: sensors, embedded systems, real-time systems, control systems, communications, protocols, Internet, cloud computing, large-scale distributed processing and storage systems, AI and ML, (preferably) coupled with domain experience in the area where it is to be applied, such as building or manufacturing automation. Written in a reader-minded approach that starts by describing the problem (why should I care?), placing it in context (what does this do and where/how does it fit in the great scheme of things?) and then describing salient features of solutions (how does it work?), this book covers the existing body of knowledge and design practices, but also offers the author's insights and articulation of common attributes and salient features of solutions such as IoT information modeling and platform characteristics.

Includes several emerging required standardization and interoperability initiatives Offers various AI and Machine Learning algorithms Discusses how health technology can face the challenge of improving the quality of life regardless of social and financial consideration, gender, age, and residence Presents real-time applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools and technologies used in healthcare Focuses on many examples of successful IoT projects from various industries

This book initiates a transformation of the Web into a self-managing, autonomous information system to challenge today's all-embracing role of big search engines as centralized information managers. In the last decades, the World Wide Web became the biggest source for all kinds of information needed. After a short review of the state of the art, a Web-based system is presented for the first time, which employs all its instances equally to provide, consume, and process information uniformly and consistently. In order to build such an efficient, decentralized, and fully integrated information space with all its needed functionalities, a set of diverse algorithms is introduced. These novel mechanisms for load balancing, routing, clustering, document classification, but also time-dependent information management pertain to almost all system levels. Finally, three different approaches to decentralized Web search are discussed that represent the backbone of the new autonomous Web.