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Blockchain, artificial intelligence, and the internet of things : Possibilities and opportunities
Provides basic concepts and deep knowledge about various security mechanisms that can be implemented in IoT through Blockchain technology. This book aids readers in gaining insight and knowledge about providing security and solutions to different challenges in IoT using Blockchain technology. This book primarily focuses on challenges to addressing the integration of the IoT with Blockchain with respect to potential benefits for IoT. This book gives descriptive analysis of Blockchain integrated with IoT applications and platforms for the development of IoT solutions along with possible topologies to that integration. Several application examples are included in a variety of industries / Provides the latest on secure IIoT communication using Blockchain technology for Industry 4.0 / Includes updates on using Blockchain technology in secure vehicular communication (VANET), healthcare, retail, and gaming / Provides the latest on secure IIoT communication using Blockchain technology for Industry 4.0
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020
Presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.
Machine learning approach for cloud data analytics in IoT
Covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications. Elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Machine Ethics : From Machine Morals to the Machinery of Morality
Offers the first systematic guide to machine ethics, bridging between computer science, social sciences and philosophy. Based on a dialogue between an AI scientist and a novelist philosopher, the book discusses important findings on which moral values machines can be taught and how. In turn, it investigates what kind of artificial intelligence (AI) people do actually want.
Logical Aspects of Computational Linguistics ; 5th International Conference, LACL 2005, Bordeaux, France, April 28-30, 2005, Proceedings
Inaugurates the new FoLLI LNAI subline. It constitutes the refereed proceedings of the 5th International Conference on Logical Aspects of Computational Linguistics, LACL 2005, held in Bordeaux, France in April 2005. The 25 revised full papers presented were carefully reviewed and selected from over 40 submissions. The papers address a wide range of logical and formal methods in computational linguistics with studies of particular grammar formalisms and their computational properties, language engineering, and traditional topics about the syntax/semantics interface.
Logical and Relational Learning
This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.
List decoding of error-correcting codes : Winning thesis of the 2002 ACM doctoral dissertation competition
Presents some spectacular new results in the area of decoding algorithms for error-correcting codes. Specifically, it shows how the notion of “list-decoding” can be applied to recover from far more errors, for a wide variety of err- correcting codes, than achievable before. A brief bit of background : error-correcting codes are combinatorial str- tures that show how to represent (or “encode”) information so that it is - silient to a moderate number of errors. Speci?cally, an error-correcting code takes a short binary string, called the message, and shows how to transform it into a longer binary string, called the codeword, so that if a small number of bits of the codewordare ?ipped, the resulting string does not look like any other codeword. The maximum number of errorsthat the code is guaranteed to detect, denoted d, is a central parameter in its design. A basic property of such a code is that if the number of errors that occur is known to be smaller than d/2, the message is determined uniquely. This poses a computational problem, called the decoding problem : compute the message from a corrupted codeword, when the number of errors is less than d/2.
Linguistics for the age of AI
One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language.
Knowledge Representation and the Semantics of Natural Language
This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: Includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. / The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. / Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. / Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. / Considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Computational analysis and deep learning for medical care : Principles, methods, and applications
Focuses on the sophisticated methods for improving dye extraction and dyeing properties which will minimize the use of bioresource products. This book also brings out the innovative ways of wet chemical processing to alleviate the environmental impacts arising from this sector.
Comprehensive mathematics for computer scientists 2 : Calculus and ODEs, splines, probability, fourier and wavelet theory, fractals and neural networks, categories and lambda calculus
This second volume of a comprehensive tour through mathematical core subjects for computer scientists completes the ?rst volume in two - gards: Part III ?rst adds topology, di?erential, and integral calculus to the t- ics of sets, graphs, algebra, formal logic, machines, and linear geometry, of volume 1. With this spectrum of fundamentals in mathematical e- cation, young professionals should be able to successfully attack more involved subjects, which may be relevant to the computational sciences. In a second regard, the end of part III and part IV add a selection of more advanced topics. In view of the overwhelming variety of mathematical approaches in the computational sciences, any selection, even the most empirical, requires a methodological justi?cation. Our primary criterion has been the search for harmonization and optimization of thematic - versity and logical coherence. This is why we have, for instance, bundled such seemingly distant subjects as recursive constructions, ordinary d- ferential equations, and fractals under the unifying perspective of c- traction theory.
Complexity Theory and Cryptology : An Introduction to Cryptocomplexity
Modern cryptology employs mathematically rigorous concepts and methods from complexity theory. Conversely, current research in complexity theory often is motivated by questions and problems arising in cryptology. This book takes account of this trend, and therefore its subject is what may be dubbed "cryptocomplexity,'' some sort of symbiosis of these two areas. This textbook is suitable for undergraduate and graduate students of computer science, mathematics, and engineering, and can be used for courses on complexity theory and cryptology, preferably by stressing their interrelation. Starting from scratch, it is an accessible introduction to cryptocomplexity and works its way to the frontiers of current research. It provides the necessary mathematical background, has numerous figures, exercises, and examples, and presents some central, up-to-date research topics and challenges. Due to its comprehensive bibliography and subject index, it is also a valuable source for researchers, teachers, and practitioners working in these fields.
Complexity of Constraints : An Overview of Current Research Themes
This state-of-the-art survey contains the papers that were invited by the organizers after conclusion of an International Dagstuhl-Seminar on Complexity of Constraints, held in Dagstuhl Castle, Germany, in October 2006.
Competitive Programming in Python : 128 Algorithms to Develop your Coding Skills
Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. What to expect: * Master 128 algorithms in Python. * Discover the right way to tackle a problem and quickly implement a solution of low complexity.
Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining
Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and modeling, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Chaos and fractals : New frontiers of science
Covers the central ideas and concepts of chaos and fractals as well as many related topics including: the Mandelbrot set, Julia sets, cellular automata, L-systems, percolation and strange attractors.
Categories for software engineering
This book provides a gentle, software engineering oriented introduction to category theory. Assuming only a minimum of mathematical preparation, this book explores the use of categorical constructions from the point of view of the methods and techniques that have been proposed for the engineering of complex software systems: object-oriented development, software architectures, logical and algebraic specification techniques, models of concurrency, inter alia. After two parts in which basic and more advanced categorical concepts and techniques are introduced, the book illustrates their application to the semantics of CommUnity – a language for the architectural design of interactive systems. "For computer scientists, this unique book presents Category Theory in a manner tailored to their interests and with examples to which they can relate." Ira Forman, IBM "This book applies little-known yet quite powerful formal tools from category theory to software structures: designs, architectures, patterns, and styles. Rather than focus on issues at the level of computational models and semantics, it instead applies these tools to some of the problems facing the sophisticated software architect.
Brain and Human Body Modeling : Computational Human Modeling at EMBC 2018
This book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner.
