Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more.

Security in IoT Social Networks takes a deep dive into security threats and risks, focusing on real-world social and financial effects. Mining and analyzing enormously vast networks is a vital part of exploiting Big Data. This book provides insight into the technological aspects of modeling, searching, and mining for corresponding research issues, as well as designing and analyzing models for resolving such challenges. The book will help start-ups grow, providing research directions concerning security mechanisms and protocols for social information networks. The book covers structural analysis of large social information networks, elucidating models and algorithms and their fundamental properties. Moreover, this book includes smart solutions based on artificial intelligence, machine learning, and deep learning for enhancing the performance of social information network security protocols and models. This book is a detailed reference for academicians, professionals, and young researchers. The wide range of topics provides extensive information and data for future research challenges in present-day social information networks.

Cybersecurity has been gaining serious attention and recently has become an important topic of concern for organizations, government institutions, and largely for people interacting with digital online systems. As many individual and organizational activities continue to grow and are conducted in the digital environment, new vulnerabilities have arisen which have led to cybersecurity threats. The nature, source, reasons, and sophistication for cyberattacks are not clearly known or understood, and many times invisible cyber attackers are never traced or can never be found. Cyberattacks can only be known once the attack and the destruction have already taken place long after the attackers have left. Cybersecurity for computer systems has increasingly become important because the government, military, corporate, financial, critical infrastructure, and medical organizations rely heavily on digital network systems, which process and store large volumes of data on computer devices that are exchanged on the internet, and they are vulnerable to ""continuous"" cyberattacks. As cybersecurity has become a global concern, it needs to be clearly understood, and innovative solutions are required. Advancing Cybersecurity for Digital Transformation: Opportunities and Challenges looks deeper into issues, problems, and innovative solutions and strategies that are linked to cybersecurity. This book will provide important knowledge that can impact the improvement of cybersecurity, which can add value in terms of innovation to solving cybersecurity threats. The chapters cover cybersecurity challenges, technologies, and solutions in the context of different industries and different types of threats. This book is ideal for cybersecurity researchers, professionals, scientists, scholars, and managers, as well as practitioners, stakeholders, researchers, academicians, and students interested in the latest advancements in cybersecurity for digital transformation.

Parallel Programming with OpenACC is a modern, practical guide to implementing dependable computing systems. The book explains how anyone can use OpenACC to quickly ramp-up application performance using high-level code directives called pragmas. The OpenACC directive-based programming model is designed to provide a simple, yet powerful, approach to accelerators without significant programming effort. Author Rob Farber, working with a team of expert contributors, demonstrates how to turn existing applications into portable GPU accelerated programs that demonstrate immediate speedups. The book also helps users get the most from the latest NVIDIA and AMD GPU plus multicore CPU architectures (and soon for Intel (R) Xeon Phi (TM) as well). Downloadable example codes provide hands-on OpenACC experience for common problems in scientific, commercial, big-data, and real-time systems. Topics include writing reusable code, asynchronous capabilities, using libraries, multicore clusters, and much more. Each chapter explains how a specific aspect of OpenACC technology fits, how it works, and the pitfalls to avoid. Throughout, the book demonstrates how the use of simple working examples that can be adapted to solve application needs.

This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.

Edsger Wybe Dijkstra (1930-2002) was one of the most influential researchers in the history of computer science, making fundamental contributions to both the theory and practice of computing. Early in his career, he proposed the single-source shortest path algorithm, now commonly referred to as Dijkstra's algorithm. He wrote (with Jaap Zonneveld) the first ALGOL 60 compiler, and designed and implemented with his colleagues the influential THE operating system. Dijkstra invented the field of concurrent algorithms, with concepts such as mutual exclusion, deadlock detection, and synchronization. A prolific writer and forceful proponent of the concept of structured programming, he convincingly argued against the use of the Go To statement. In 1972 he was awarded the ACM Turing Award for "fundamental contributions to programming as a high, intellectual challenge; for eloquent insistence and practical demonstration that programs should be composed correctly, not just debugged into correctness; for illuminating perception of problems at the foundations of program design." Subsequently he invented the concept of self-stabilization relevant to fault-tolerant computing. He also devised an elegant language for nondeterministic programming and its weakest precondition semantics, featured in his influential 1976 book A Discipline of Programming in which he advocated the development of programs in concert with their correctness proofs. In the later stages of his life, he devoted much attention to the development and presentation of mathematical proofs, providing further support to his long-held view that the programming process should be viewed as a mathematical activity. In this unique new book, 31 computer scientists, including five recipients of the Turing Award, present and discuss Dijkstra's numerous contributions to computing science and assess their impact. Several authors knew Dijkstra as a friend, teacher, lecturer, or colleague. Their biographical essays and tributes provide a fascinating multi-author picture of Dijkstra, from the early days of his career up to the end of his life.

Multi-Paradigm Modelling for Cyber-Physical Systems explores modeling and analysis as crucial activities in the development of Cyber-Physical Systems, which are inherently cross-disciplinary in nature and require distinct modeling techniques related to different disciplines, as well as a common background knowledge. This book will serve as a reference for anyone starting in the field of CPS who needs a solid foundation of modeling, including a comprehensive introduction to existing techniques and a clear explanation of their advantages and limitations. This book is aimed at both researchers and practitioners who are interested in various modeling paradigms across computer science and engineering.

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

Advances in Imaging and Electron Physics, Volume 216, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains.

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence-or soft computing-is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information.

Innovation is the key to maintain competitive advantage. Innovation in products, processes, and business models help companies to provide economic value to their customers. Identifying the innovative ideas, implementing those ideas, and absorbing them in the market requires investing many resources that could incur large costs. Technology encourages companies to foster innovation to remain competitive in the marketplace. Emerging Technologies for Innovation Management in the Software Industry serves as a resource for technology absorption in companies supporting innovation. It highlights the role of technology to assist software companies-especially small start-ups-to innovate their products, processes, and business models. This book provides the necessary guidelines of which tools to use and under what situations. Covering topics such as risk management, prioritization approaches, and digitally-enabled innovation processes, this premier reference source is an ideal resource for entrepreneurs, software developers, software managers, business leaders, engineers, students and faculty of higher education, researchers, and academicians.

Developing new approaches and reliable enabling technologies in the healthcare industry is needed to enhance our overall quality of life and lead to a healthier, innovative, and secure society. Further study is required to ensure these current technologies, such as big data analytics and artificial intelligence, are utilized to their utmost potential and are appropriately applied to advance society. Big Data Analytics and Artificial Intelligence in the Healthcare Industry discusses technologies and emerging topics regarding reliable and innovative solutions applied to the healthcare industry and considers various applications, challenges, and issues of big data and artificial intelligence for enhancing our quality of life. Covering a range of topics such as electronic health records, machine learning, and e-health, this reference work is ideal for healthcare professionals, computer scientists, data analysts, researchers, practitioners, scholars, academicians, instructors, and students.

Virtual Reality (VR) is the use of computer technology to construct an environment that is simulated. VR places the user inside and in the center of the experience, unlike conventional user interfaces. Users are immersed and able to connect with 3D environments instead of seeing a screen in front of them. The computer has to role to provide the experiences of the user in this artificial environment by simulating as many senses as possible, such as sight, hearing, touch and smell. In Augmented Reality (AR) we have an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. It can be seen as VR imposed into real life. In both VR and AR the experience is composed of a virtual or extended world, an immersion technology, sensory feedback and interactivity. These elements use a multitude of technologies that must work together and presented to the user seamlessly integrated and synchronized. This book is dedicated to applications, new technologies and emerging trends in the fields of virtual reality and augmented reality in healthcare. It is intended to cover technical areas as well as areas of applied intervention. It is expected to cover hardware and software technologies while encompassing all components of the virtual experience. The main goal of this book is to show how to put Virtual Reality in action by linking academic and informatics researchers with professionals who use and need VR in their day-a-day work, with a special focus on healthcare professionals and related areas. The idea is to disseminate and exchange the knowledge, information and technology provided by the international communities in the area of VR, AR and XR throughout the 21st century. Another important goal is to synthesize all the trends, best practices, methodologies, languages and tools which are used to implement VR. In order to shape the future of VR, new paradigms and technologies should be discussed, not forgetting aspects related to regulation and certification of VR technologies, especially in the healthcare area. These last topics are crucial for the standardization of VR. This book will present important achievements and will show how to use VR technologies in a full range of settings able to provide decision support anywhere and anytime using this new approach.