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This book provides an introduction to the basic ideas involved in cybersecurity, whose principal aim is protection of IT systems against unwanted behaviour mediated by the networks which connect them. Due to the widespread use of the Internet in modern society for activities ranging from social networking and entertainment to distribution of utilities and public administration, failures of cybersecurity can threaten almost all aspects of life today.Cybersecurity is a necessity in the modern world, where computers and other electronic devices communicate via networks, and breakdowns in cybersecurity cost society many resources.The aims of cybersecurity are quite simple: data must not be read, modified, deleted or made unavailable by persons who are not allowed to. To meet this major challenge successfully in the digitally interconnected world, one needs to master numerous disciplines because modern IT systems contain software, cryptographic modules, computing units, networks, and human users?all of which can influence the success or failure in the effort.

Introduction to Cybersecurity : A Multidisciplinary Challenge

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.&nbsp;Deep learning methods for various data domains, such as text, images, and graphs are presented in detail.

Neural Networks and Deep Learning : 2nd Edition

This book looks at the growing segment of Internet of Things technology (IoT) known as Internet of Medical Things (IoMT), an automated system that aids in bridging the gap between isolated and rural communities and the critical healthcare services that are available in more populated and urban areas. Many technological aspects of IoMT are still being researched and developed, with the objective of minimizing the cost and improving the performance of the overall healthcare system. This book focuses on innovative IoMT methods and solutions being developed for use in the application of healthcare services, including post-surgery care, virtual home assistance, smart real-time patient monitoring, implantable sensors and cameras, and diagnosis and treatment planning. It also examines critical issues around the technology, such as security vulnerabilities, IoMT machine learning approaches, and medical data compression for lossless data transmission and archiving.&nbsp;Internet of Medical Things&nbsp;is a valuable reference for researchers, students, and postgraduates working in biomedical, electronics, and communications engineering, as well as practicing healthcare professionals.

Internet of Medical Things : Remote Healthcare Systems and Applications

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Pattern Recognition And Machine Learning

The research areas as well as the knowledge gained for the practical use of robots are growing and expanding beyond manufacturing and industrial automation, making inroads in sectors such as health care and terrain sensing, as well as general assistive systems working in close interaction with humans. In a situation like this, it is necessary for future robot systems to become less stiff and more specialized by taking inspiration from the mechanical compliance and versatility found in natural materials and organisms.At present, a new discipline is emerging in this area, called »Soft Robotics«. It particularly challenges the traditional thinking of engineers, as the confluence of technologies, ranging from new materials, sensors, actuators and production techniques to new design tools, will make it possible to create new systems whose structures are almost completely made of soft materials, which bring about entirely new functions and behaviors, similar in many ways to natural systems.These Proceedings focus on four main topics:• Soft Actuators and Control• Soft Interactions• Soft Robot Assistants: Potential and Challenges• Human-centered »Soft Robotics«.

Soft Robotics : Transferring Theory to Application

This book discusses major issues in the field of agriculture: crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and their effective resolution via automation, including IoT, wireless communications, machine learning, artificial intelligence, and deep learning. It further discusses the sterile insect technique, which is a replacement of conventional pesticide and fertilizer techniques. Hydroponics and vertical farming, two of the top-ranked agricultural engineering accomplishments of the past century, are also discussed.Features:<ul><li>Offers in-depth insights regarding the fundamentals of technologies associated with the agriculture sector</li><li>Synthesizes earlier works of researchers and inventors in this field</li><li>Sheds light on the challenges and problems of supply and demand worldwide</li><li>Encourages the reader to innovate and ideate upon those issues</li><li>Analyses the wide array of services provided by companies worldwide and discusses recent breakthroughs in agriculture automation</li></ul>This book is aimed at the work of many researchers to obtain a concise overview of the current implementation of automation in agriculture and derive important insight into its upcoming challenges.

Transforming Agricultural Technology by Artificial Intelligence and Robotics

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.&nbsp;Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models.&nbsp;After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media

Industry 4.0 is revolutionizing chemical manufacturing. Today's chemical companies are swiftly embracing the digital era, recognizing the significant benefits of interconnected products, production equipment, and personnel. As technology advances and production volumes grow, there is an increasing need for new computational tools and innovative solutions to address everyday challenges.&nbsp;AI in Chemical Engineering: Unlocking the Power Within Data&nbsp;introduces readers to the essential concepts of machine learning and their application in the chemical and process industries, aiming to enhance efficiency, adaptability, and profitability. This work delves into the transformation of traditional plant operations into integrated and intelligent systems, providing readers with a foundation for developing and understanding the tools necessary for data collection and analysis, thereby gaining valuable insights and practical applications.<ul><li>Introduces the principles and applications of unsupervised learning and discusses the role of machine learning in extracting information from plant data and transforming it into knowledge</li><li>Conveys the concepts, principles, and applications of supervised learning, setting the stage for developing advanced monitoring systems, complex predictive models, and advanced computer vision applications</li><li>Explores implementation of reinforced learning ideas for chemical process control and optimization, investigating various model structures and discussing their practical implementation in both simulation and experimental units</li><li>Incorporates sample code examples in Python to illustrate key concepts</li><li>Includes real-life case studies in the context of chemical engineering and covers a wide variety of chemical engineering applications from oil and gas to bioengineering and electrochemistry</li><li>Clearly defines types of problems in chemical engineering subject to AI solutions and relates them to subfields of AI</li></ul>This practical text, designed for advanced chemical engineering students and industry practitioners, introduces concepts and theories in a logical and sequential manner. It serves as an essential resource, helping readers understand both current and emerging developments in this important and evolving field.

AI in Chemical Engineering

Praise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science, software engineering, telecommunications, and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping, change-point detection, Bayesian inference, and applications of statistics in epidemiology, clinical trials, semiconductor manufacturing, and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics from the University of Maryland. In his turn, he supervised twelve doctoral students, mostly employed on academic and research positions.

Probability and Statistics for Computer Scientists : 3rd Edition

This book covers the fundamentals of IoT and healthcare systems for carrying out system architectures, protocols, wearable devices, and interoperability. It explores major challenges in AI and smart computing in resource-constrained IoT-based applications along with cost, energy efficiency, and the availability of quality service.

Healthcare Systems and Health Informatics: Using Internet of Things

Understanding and employing cryptography has become central for securing virtually any digital application, whether user app, cloud service, or even medical implant.Heavily revised and updated, the long-awaited&nbsp;second edition&nbsp;of&nbsp;Understanding Cryptography&nbsp;follows the unique approach of making modern cryptography accessible to a broad audience, requiring only a minimum of prior knowledge. After introducing basic&nbsp;cryptography concepts, this seminal textbook&nbsp;covers nearly all symmetric, asymmetric, and post-quantum cryptographic algorithms&nbsp;currently in use in&nbsp;applications?ranging from cloud computing and smart phones all the way to industrial systems, block chains, and cryptocurrencies.Topics and features:<ul><li>Opens with a foreword by cryptography pioneer and Turing Award winner, Ron Rivest</li><li>Helps develop a comprehensive understanding of modern applied cryptography</li><li>Provides a thorough introduction to post-quantum cryptography consisting of the three standardized cipher families</li><li>Includes for every chapter a comprehensive problem set, extensive examples, and a further-reading discussion</li><li>Communicates, using a unique pedagogical approach, the essentials about foundations and use in practice, while keeping mathematics to a minimum</li><li>Supplies up-to-date security parameters for all cryptographic algorithms</li><li>Incorporates chapter reviews and discussion on such topics as historical and societal context</li></ul> This must-have book is indispensable as a textbook for graduate and advanced undergraduate courses, as well as for self-study by designers and engineers.The authors have more than 20 years’ experience teaching cryptography at various universities in the US and Europe. In addition to being renowned scientists, they have extensive experience with applying cryptography in industry, fromwhich they have drawn important lessons for their teaching.

Understanding Cryptography - From Established Symmetric and Asymmetric Ciphers to Post-Quantum Algorithms : 2nd Edition

As virtual reality expands from the imaginary worlds of science fiction and pervades every corner of everyday life, it is becoming increasingly important for students and professionals alike to understand the diverse aspects of this technology. This book aims to provide a comprehensive guide to the theoretical and practical elements of virtual reality, from the mathematical and technological foundations of virtual worlds to the human factors and the applications that enrich our lives: in the fields of medicine, entertainment, education and others.After providing a brief introduction to the topic, the book describes the kinematic and dynamic mathematical models of virtual worlds. It explores the many ways a computer can track and interpret human movement, then progresses through the modalities that make up a virtual world: visual, acoustic and haptic. It explores the interaction between the actual and virtual environments, as well as design principles of the latter. The book closes with an examination of different applications, focusing on augmented reality as a special case. Though the content is primarily VR-related, it is also relevant for many other fields.

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Computer Science

Introduction to Cybersecurity : A Multidisciplinary Challenge

Neural Networks and Deep Learning : 2nd Edition

Internet of Medical Things : Remote Healthcare Systems and Applications

Pattern Recognition And Machine Learning

Soft Robotics : Transferring Theory to Application

Transforming Agricultural Technology by Artificial Intelligence and Robotics

Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media

AI in Chemical Engineering

Probability and Statistics for Computer Scientists : 3rd Edition

Healthcare Systems and Health Informatics: Using Internet of Things

Understanding Cryptography - From Established Symmetric and Asymmetric Ciphers to Post-Quantum Algorithms : 2nd Edition

Virtual Reality Technology and Applications