This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.

This book explores the ways in which artists use technology to create different perceptions of time in art in order to reflect on contemporary relationships to technology. By considering the links between technology, movement and contemporary art, the book explores changing relationship between temporality in art, art history, media art theory, modernity, contemporary art, and digital art. This book challenges the dominant view that kinetic art is an antiquated artistic experiment and considers the changing perception of kinetic art by focusing on exhibitions and institutions that have recently challenged the notion of kinetic art as a marginalised and forgotten artistic experiment with mechanical media. This is achieved by deconstructing Frank Popper's argument that kinetic art is a precursor to subsequent explorations in the intersections between art, science and technology. Rather than pandering to the prevailing art historical assumption that kinetic sculpture is merely a precursor to art in a digital culture, the book proposes that perhaps kineticism succeeded too well, where movement has become a ubiquitous element of the aesthetic of contemporary art. If, as Boris Groys has recently suggested, installation has become the dominant mode of art in the contemporary age, then movement in real time with the viewer is used to aestheticise and explore the facets of our peculiar time.

This book provides a comprehensive reference in large data center networking. It first summarizes the developing trend of DCNs, and reports four novel DCNs, including a switch-centric DCN, a modular DCN, a wireless DCN, and a hybrid DCN. Furthermore another important factor in DCN targets at managing and optimizing the network activity at the level of transfers to aggregate correlated data flows and thus directly to lower down the network traffic resulting from such data transfers. In particular, the book reports the in-network aggregation of incast transfer, shuffle transfer, uncertain incast transfer, and the cooperative scheduling of uncertain multicast transfer.

Shipping is the world's oldest sharing economy and is conducted in a self-organizing manner. Shipping is capital, energy, and information intensive, and with the growing impact of digitalization and climate change, there is a need to rethink the management and operations of this critical global industry - assisted in no small way by maritime informatics. Building upon the recently published inaugural book Maritime Informatics by Springer, this book will address some of the most recent practical developments and experiences, particularly from a global perspective. The focus of the book is to address contemporary movements to tackle global concerns and to complement Maritime Informatics.

This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications. The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management. This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence.

The inspiration for this book came from the Industrial Session of the ISMIS 2017 Conference in Warsaw. It covers numerous applications of intelligent technologies in various branches of the industry. Intelligent computational methods and big data foster innovation and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research findings to create new designs and products. As such, the contributions cover solutions to the problems experienced by practitioners in the areas of artificial intelligence, complex systems, data mining, medical applications and bioinformatics, as well as multimedia- and text processing. Further, the book shows new directions for cooperation between science and industry and facilitates efficient transfer of knowledge in the area of intelligent information systems.

This handbook covers recent advances in the integration of three areas, namely, cloud computing, cyber-physical systems, and the Internet of things which is expected to have a tremendous impact on our daily lives. It contains a total of thirteen peer-reviewed and edited chapters. This book covers topics such as context-aware cyber-physical systems, sustainable cloud computing, fog computing, and cloud monitoring; both the theoretical and practical aspects belonging to these topics are discussed. All the chapters also discuss open research challenges in the areas mentioned above. Finally, the handbook presents three use cases regarding healthcare, smart buildings and disaster management to assist the audience in understanding how to develop next-generation IoT- and cloud-enabled cyber-physical systems. This timely handbook is edited for students, researchers, as well as professionals who are interested in the rapidly growing fields of cloud computing, cyber-physical systems, and the Internet of things.

SmartData empowers personal data by wrapping it in a cloak of intelligence such that it now becomes the individual's virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought. SmartData: Privacy Meets Evolutionary Robotics includes the advances made in the technology of simulating virtual worlds, together with the ideas emerging from fields of evolutionary robotics and embodied cognition within a framework of dynamical systems as an approach toward this ultimate goal. The book brings together top researchers in the field and addresses current personal data privacy challenges in the online-world.

This book tackles the latest research trends in technology acceptance models and theories. It presents high-quality empirical and review studies focusing on the main theoretical models and their applications across various technologies and contexts. It also provides insights into the theoretical and practical aspects of different technological innovations that assist decision-makers in formulating the required policies and procedures for adopting a specific technology.

This book focuses on the implementation of Artificial Intelligence in Business, Education and Healthcare, It includes research articles and expository papers on the applications of Artificial Intelligence on Decision Making, Entrepreneurship, Social Media, Healthcare, Education, Public Sector, FinTech, and RegTech. It also discusses the role of Artificial Intelligence in the current COVID-19 pandemic, in the health sector, education, and others. It also discusses the impact of Artificial Intelligence on decision-making in vital sectors of the economy.

This book recapitulates the major developments in Decision Support Systems (DSS) over the last 30 years in order to evaluate the research areas of decision making and in which direction the field should proceed. As it attempts to find a consensus about the next steps for the future of DSS research, the book also enforces the trends and new technologies currently in use. The book examines topics such as decision analysis for enterprise systems and non-hierarchical networks, integrated solutions for decision support and knowledge management in distributed environments, decision support system evaluation and analysis through social networks, and e-learning and its application to real environments. It clearly presents the evidence to support their cases and attempts to promote an extensive and objective discussion. In addition, the book also reflects on approaches to dead-end ideas and failures in DSS to better understand the lessons learned. The contributions for this book have been written by thought leaders and influential researchers from the EURO Working Group of Decision Support Systems (EWG-DSS).

The essays in this open access volume identify the key ingredients for success in capitalizing on public investments in scientific projects and the development of large-scale research infrastructures. Investment in science - whether in education and training or through public funding for developing new research tools and technologies - is a crucial priority. Authors from big research laboratories/organizations, funding agencies and academia discuss how investing in science can produce societal benefits as well as identifying future challenges for scientists and policy makers. The volume cites different ways to assess the socio-economic impact of Research Infrastructures and their role as hubs of global collaboration, creativity and innovation. It highlights the different benefits stemming from fundamental research at the local, national and global level, while also inviting us to rethink the notion of "benefit" in the 21st century. Public investment is required to maintain the pace of technological and scientific advancements over the next decades. Far from advocating a radical transformation and massive expansion in funding, the authors suggest ways for maintaining a strong foundation of science and research to ensure that we continue to benefit from the outputs. The volume draws inspiration from the first "Economics of Big Science" workshop, held in Brussels in 2019 with the aim of creating a new space for dialogue and interaction between representatives of Big Science organizations, policy makers and academia. It aspires to provide useful reading for policy makers, scientists and students of science, who are increasingly called upon to explain the value of fundamental research and adopt the language and logic of economics when engaging in policy discussions.

The proliferation of Internet of Things (IoT) has enabled rapid enhancements for applications, not only in home and environment scenarios, but also in factory automation. Now, Industrial Internet of Things (IIoT) offers all the advantages of IoT to industry, with applications ranging from remote sensing and actuating, to de-centralization and autonomy. In this book, the editor presents the IIoT and its place during the new industrial revolution (Industry 4.0) as it takes us to a better, sustainable, automated, and safer world. The book covers the cross relations and implications of IIoT with existing wired/wireless communication/networking and safety technologies of the Industrial Networks. Moreover, the book includes practical use-case scenarios from the industry for the application of IIoT on smart factories, smart cities, and smart grids. IoT-driven advances in commercial and industrial building lighting and in street lighting are presented as an example to shed light on the application domain of IIoT. The state of the art in Industrial Automation is also presented to give a better understanding of the enabling technologies, potential advantages, and challenges of the Industry 4.0 and IIoT. Finally, yet importantly, the security section of the book covers the cyber-security related needs of the IIoT users and the services that might address these needs. User privacy, data ownership, and proprietary information handling related to IIoT networks are all investigated. Intrusion prevention, detection, and mitigation are all covered at the conclusion of the book.

This book presents best selected papers presented at the 4th International Conference on Smart Computing and Informatics (SCI 2020), held at the Department of Computer Science and Engineering, Vasavi College of Engineering (Autonomous), Hyderabad, Telangana, India. It presents advanced and multi-disciplinary research towards the design of smart computing and informatics. The theme is on a broader front which focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solutions to varied problems in society, environment and industries. The scope is also extended towards the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.

This book features a collection of high-quality, peer-reviewed papers presented at International Conference on Ubiquitous Intelligent Systems (ICUIS 2021) organized by Shree Venkateshwara Hi-Tech Engineering College, Tamil Nadu, India, during April 16-17, 2021. The book covers topics such as cloud computing, mobile computing and networks, embedded computing frameworks, modeling and analysis of ubiquitous information systems, communication networking models, big data models and applications, ubiquitous information processing systems, next-generation ubiquitous networks and protocols, advanced intelligent systems, Internet of things, wireless communication and storage networks, intelligent information retrieval techniques, AI-based intelligent information visualization techniques, cognitive informatics, smart automation systems, healthcare informatics and bioinformatics models, security and privacy of intelligent information systems, and smart distributed information systems.

This book strikes a healthy balance between theory and applications, ensuring that it doesn't offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.

Proceedings from the 2013 LTEC conference in Kaohsiung, Taiwan. The papers examine diverse aspects of Learning Technology for Education in Cloud environments, including social, technical and infrastructure implications. Also addressed is the question of how cloud computing can be used to design applications to support real time on demand learning using technologies. The workshop proceedings provide opportunities for delegates to discuss the latest research in TEL (Technology Enhanced Learning) and its impacts for learners and institutions, using cloud technologies.

This book constitutes the refereed post-conference proceedings of the Fourth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2021, held virtually in November 2021. The 15 full papers presented were carefully reviewed and selected from 33 submissions. Also included is a summary of two panel sessions held at the conference. The papers are organized in the following topical sections: challenges in IoT Applications and Research, Modernizing Agricultural Practice Using IoT, Cyber-physical IoT systems in Wildfire Context, IoT for Smart Health, Security, Methods.

This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.

The two-volume set IFIP AICT 591 and 592 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2020, held in Novi Sad, Serbia, in August/September 2020.The 164 papers presented were carefully reviewed and selected from 199 submissions. They discuss globally pressing issues in smart manufacturing, operations management, supply chain management, and Industry 4.0. The papers are organized in the following topical sections: Part I: advanced modelling, simulation and data analytics in production and supply networks; advanced, digital and smart manufacturing; digital and virtual quality management systems; cloud-manufacturing; cyber-physical production systems and digital twins; IIOT interoperability; supply chain planning and optimization; digital and smart supply chain management; intelligent logistics networks management; artificial intelligence and blockchain technologies in logistics and DSN; novel production planning and control approaches; machine learning and artificial intelligence; connected, smart factories of the future; manufacturing systems engineering: agile, flexible, reconfigurable; digital assistance systems: augmented reality and virtual reality; circular products design and engineering; circular, green, sustainable manufacturing; environmental and social lifecycle assessments; socio-cultural aspects in production systems; data-driven manufacturing and services operations management; product-service systems in DSN; and collaborative design and engineering Part II: the Operator 4.0: new physical and cognitive evolutionary paths; digital transformation approaches in production management; digital transformation for more sustainable supply chains; data-driven applications in smart manufacturing and logistics systems; data-driven services: characteristics, trends and applications; the future of lean thinking and practice; digital lean manufacturing and its emerging practices; new reconfigurable, flexible or agile production systems in the era of industry 4.0; operations management in engineer-to-order manufacturing; production management in food supply chains; gastronomic service system design; product and asset life cycle management in the circular economy; and production ramp-up strategies for product

This book is the world's first book on 6G Mobile Wireless Networks that aims to provide a comprehensive understanding of key drivers, use cases, research requirements, challenges and open issues that are expected to drive 6G research. In this book, we have invited world-renowned experts from industry and academia to share their thoughts on different aspects of 6G research. Specifically, this book covers the following topics: 6G Use Cases, Requirements, Metrics and Enabling Technologies, PHY Technologies for 6G Wireless, Reconfigurable Intelligent Surface for 6G Wireless Networks, Millimeter-wave and Terahertz Spectrum for 6G Wireless, Challenges in Transport Layer for Tbit/s Communications, High-capacity Backhaul Connectivity for 6G Wireless, Cloud Native Approach for 6G Wireless Networks, Machine Type Communications in 6G, Edge Intelligence and Pervasive AI in 6G, Blockchain: Foundations and Role in 6G, Role of Open-source Platforms in 6G, and Quantum Computing and 6G Wireless. The overarching aim of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface and network perspective, thereby laying out a vision for 6G networks. This book not only discusses the potential 6G use cases, requirements, metrics and enabling technologies, but also discusses the emerging technologies and topics such as 6G PHY technologies, reconfigurable intelligent surface, millimeter-wave and THz communications, visible light communications, transport layer for Tbit/s communications, high-capacity backhaul connectivity, cloud native approach, machine-type communications, edge intelligence and pervasive AI, network security and blockchain, and the role of open-source platform in 6G. This book provides a systematic treatment of the state-of-the-art in these emerging topics and their role in supporting a wide variety of verticals in the future. As such, it provides a comprehensive overview of the expected applications of 6G with a detailed discussion of their requirements and possible enabling technologies. This book also outlines the possible challenges and research directions to facilitate the future research and development of 6G mobile wireless networks.

The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.*The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning. After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9. This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book's expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.

This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the communities of pure mathematicians of fuzzy sets and data scientists.