Applying mechanisms and principles of human intelligence and converging the brain and artificial intelligence (AI) is currently a research trend. The applications of AI in brain simulation are countless. Brain-inspired intelligent systems will improve next-generation information processing by applying theories, techniques, and applications inspired by the information processing principles from the brain. Exploring Future Opportunities of Brain-Inspired Artificial Intelligence focuses on the convergence of AI with brain-inspired intelligence. It presents research on brain-inspired cognitive machines with vision, audition, language processing, and thinking capabilities. Covering topics such as data analysis tools, knowledge representation, and super-resolution, this premier reference source is an essential resource for engineers, developers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

Zeroing Neural Networks Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip applications for robots. Building on the original ZNN model, finite-time zeroing neural networks (FTZNN) enable efficient, accurate, and predictive real-time computations. Setting up discretized FTZNN algorithms for different time-varying matrix problems requires distinct steps. Zeroing Neural Networks provides in-depth information on the finite-time convergence of ZNN models in solving computational problems. Divided into eight parts, this comprehensive resource covers modeling methods, theoretical analysis, computer simulations, nonlinear activation functions, and more. Each part focuses on a specific type of time-varying computational problem, such as the application of FTZNN to the Lyapunov equation, linear matrix equation, and matrix inversion. Throughout the book, tables explain the performance of different models, while numerous illustrative examples clarify the advantages of each FTZNN method. In addition, the book: Describes how to design, analyze, and apply FTZNN models for solving computational problems Presents multiple FTZNN models for solving time-varying computational problems Details the noise-tolerance of FTZNN models to maximize the adaptability of FTZNN models to complex environments Includes an introduction, problem description, design scheme, theoretical analysis, illustrative verification, application, and summary in every chapter Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications is an essential resource for scientists, researchers, academic lecturers, and postgraduates in the field, as well as a valuable reference for engineers and other practitioners working in neurocomputing and intelligent control.

Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators deals with the modeling and control of parallel robots. The book's content will benefit students, researchers and engineers in robotics by providing a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. Moreover, this methodology is compatible with the real-time implementation of model-based and robust control schemes. And, it can easily extend the proposed robust control solutions to other robotic architectures.

DNA or Deoxyribonucleic Acid computing is an emerging branch of computing that uses DNA sequence, biochemistry, and hardware for encoding genetic information in computers. Here, information is represented by using the four genetic alphabets or DNA bases, namely A (Adenine), G (Guanine), C (Cytosine), and T (Thymine), instead of the binary representation (1 and 0) used by traditional computers. This is achieved because short DNA molecules of any arbitrary sequence of A, G, C, and T can be synthesized to order. DNA computing is mainly popular for three reasons: (i) speed (ii) minimal storage requirements, and (iii) minimal power requirements. There are many applications of DNA computing in the field of computer science. Nowadays, DNA computing is widely used in cryptography for achieving a strong security technique, so that unauthorized users are unable to retrieve the original data content. In DNA-based encryption, data are encrypted by using DNA bases (A, T, G, and C) instead of 0 and 1. As four DNA bases are used in the encryption process, DNA computing supports more randomness and makes it more complex for attackers or malicious users to hack the data. DNA computing is also used for data storage because a large number of data items can be stored inside the condensed volume. One gram of DNA holds approx DNA bases or approx 700 TB. However, it takes approx 233 hard disks to store the same data on 3 TB hard disks, and the weight of all these hard disks can be approx 151 kilos. In a cloud environment, the Data Owner (DO) stores their confidential encrypted data outside of their own domain, which attracts many attackers and hackers. DNA computing can be one of the best solutions to protect the data of a cloud server. Here, the DO can use DNA bases to encrypt the data by generating a long DNA sequence. Another application of DNA computing is in Wireless Sensor Network (WSN). Many researchers are trying to improve the security of WSN by using DNA computing. Here, DNA cryptography is used along with Secure Socket Layer (SSL) that supports a secure medium to exchange information. However, recent research shows some limitations of DNA computing. One of the critical issues is that DNA cryptography does not have a strong mathematical background like other cryptographic systems. This edited book is being planned to bring forth all the information of DNA computing. Along with the research gaps in the currently available books/literature, this edited book presents many applications of DNA computing in the fields of computer science. Moreover, research challenges and future work directions in DNA computing are also provided in this edited book.

Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation. Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.

AI is the greatest threat to our existence that we have ever faced.

The scramble to create superhuman AI has put us on the path to extinction – but it’s not too late to change course. Two pioneering researchers in the field, Eliezer Yudkowsky and Nate Soares, explain why artificial superintelligence would be a global suicide bomb and call for an immediate halt to its development.

The technology may be complex but the facts are simple: companies and countries are in a race to build machines that will be smarter than any person, and the world is devastatingly unprepared for what will come next.

Could a machine superintelligence wipe out our entire species? Would it want to? Would it want anything at all? In this urgent book, Yudkowsky and Soares explore the theory and the evidence, present one possible extinction scenario and explain what it would take for humanity to survive.

The world is racing to build something truly new – and if anyone builds it, everyone dies.

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

Coulomb Interactions in Particle Beams, Volume 223 in the Advances in Imaging and Electron Physics series, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features 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 computing methods used in all these domains, with this release exploring Coulomb Interactions in Particle Beams.

Algebraic Theory for True Concurrency presents readers with the algebraic laws for true concurrency. Parallelism and concurrency are two of the core concepts within computer science. This book covers the different realms of concurrency, which enables programs, algorithms or problems to be broken out into order-independent or partially ordered components to improve computation and execution speed. There are two primary approaches for executing concurrency: interleaving concurrency and true concurrency. The main representative of interleaving concurrency is bisimulation/rooted branching bisimulation equivalences which is also readily explored. This work eventually founded the comprehensive axiomatization modulo bisimulation equivalence -- ACP (Algebra of Communicating Processes).The other approach to concurrency is true concurrency. Research on true concurrency is active and includes many emerging applications. First, there are several truly concurrent bisimulation equivalences, including: pomset bisimulation equivalence, step bisimulation equivalence, history-preserving (hp-) bisimulation equivalence, and hereditary history-preserving (hhp-) bisimulation equivalence, the most well-known truly concurrent bisimulation equivalence.

Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introductory approaches with the latest advanced techniques. Beginning with an introduction of univariate and multivariate statistical analysis, the book then explores multivariate calibration and validation methods. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical chemistry are then discussed, providing useful examples of the techniques in chemistry applications. Drawing on the knowledge of a global team of researchers, this book will be a helpful guide for chemists interested in developing their skills in multivariate data and error analysis.

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach - putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

A groundbreaking treatise by one of the great mathematicians of our age, who outlines a style of thinking by which great ideas are conceived. What inspires and spurs on a great idea? Can we train ourselves to think in a way that will enable world-changing understandings and insights to emerge? Richard Hamming said we can. He first inspired a generation of engineers, scientists, and researchers in 1986 with "You and Your Research," an electrifying sermon on why some scientists do great work, why most don't, why he did, and why you can-and should-too. The Art of Doing Science and Engineering is the full expression of what "You and Your Research" outlined. It's a book about thinking; more specifically, a style of thinking by which great ideas are conceived. The book is filled with stories of great people performing mighty deeds-but they are not meant simply to be admired. Instead, they are to be aspired to, learned from, and surpassed. Hamming consistently returns to Shannon's information theory, Einstein's theory of relativity, Grace Hopper's work on high-level programming, Kaiser's work on digital fillers, and his own work on error-correcting codes. He also recounts a number of his spectacular failures as clear examples of what to avoid. Originally published in 1996 and adapted from a course that Hamming taught at the US Naval Postgraduate School, this edition includes an all-new foreword by designer, engineer, and founder of Dynamicland Bret Victor, plus more than 70 redrawn graphs and charts. The Art of Doing Science and Engineering is a reminder that a capacity for learning and creativity are accessible to everyone. Hamming was as much a teacher as a scientist, and having spent a lifetime forming and confirming a theory of great people and great ideas, he prepares the next generation for even greater distinction.

Learn to use Excel dashboards and reports to better conceptualize data

Updated for all the?latest features and capabilities of Excel 2013, this go-to resource provides you with in-depth coverage of the individual functions and tools that can be used to create?compelling Excel reports. Veteran author Michael Alexander walks you through the most effective ways to present and report data. Featuring a comprehensive review of a wide array of technical and analytical concepts, this essential guide helps you go from reporting data with simple tables full of dull numbers to presenting key information through the use of high-impact, meaningful reports and dashboards that will wow management both visually and substantively.Details how to analyze large amounts of data and report the results in a way that is both visually attractive and effectiveDescribes how to use different perspectives to achieve better visibility into data, as well as how to slice data into various views on the flyShows how to automate redundant reporting and analysis processesWalks you through creating impressive dashboards, eye-catching visualizations, and real-world What-If analyses

"Excel Dashboards and Reports, Second Edition" is part technical manual, part analytical guidebook, and exactly what you need to become your organization's dashboard?dynamo

Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains. The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.

Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines. These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms. This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed related to the best practices for managing water data.

There has been an increased use of technology in educational settings since the start of the COVID-19 pandemic. Despite the benefits of including such technologies to support education, there is still the need for vigilance to counter the inherent risk that comes with the use of such technologies as the protection of students and their information is paramount to the effective deployment of any technology in education. Current Trends in Cybersecurity and Educational Technology explores the full spectrum of cybersecurity and educational technology today and brings awareness to the recent developments and use cases for emergent educational technology. Covering key topics such as artificial intelligence, gamification, robotics, and online learning, this premier reference source is ideal for computer scientists, industry professionals, policymakers, administrators, researchers, academicians, scholars, practitioners, instructors, and students.

Blockchain has potential to revolutionize how manufacturers design, engineer, make and scale their products. Blockchain is gradually proving to be an effective "middleware" solution for enabling seamless interoperability within complex supply chains. Due to its technological nature, blockchain enables secure, transparent and fast data exchanges as well as allowing for the creation of immutable records databases The main advantage of Blockchain in Manufacturing Industries is product traceability, supply chain transparency, compliance monitoring, and auditability. Moreover, leveraging blockchain technology into a manufacturing enterprise can enhance its security and reduce the rates of systematic failures. So, blockchain is now used in various sectors of the manufacturing industry, such as automotive, aerospace, defense, pharmaceutical, consumer electronics, textile, food and beverages, and more. Hence, Blockchain should be seen as an investment in future-readiness and customer-centricity, not as an experimental technology - because, the evidence is overwhelming. This book will explore the strengths of Blockchain adaptation in Manufacturing Industries and Logistics Management, cover different use cases of Blockchain Technology for Manufacturing Industries and Logistics Management, and will discuss the role, impact and challenges of adopting Blockchain in Manufacturing industries and Logistics Management. The chapters will also provide the current open issues and future research trends of Blockchain, especially for Manufacturing Industries and Logistics, and will encapsulate quantitative and qualitative research for a wide spectrum of readers of the book.

Digital Manufacturing: The Industrialization of "Art to Part" 3D Additive Printing explains everything needed to understand how recent advances in materials science, manufacturing engineering and digital design have integrated to create exciting new capabilities. Sections discuss relevant fundamentals in mechanical engineering and materials science and complex and practical topics in additive manufacturing, such as part manufacturing, all in the context of the modern digital design environment. Being successful in today's "art to part" cyber-physical manufacturing age requires a strong grounding in science and engineering fundamentals as well as knowledge of the latest techniques, all of which readers will find here. Every chapter is developed by leading specialists and based on first-hand experiences, capturing the essential knowledge readers need to solve problems related to digital manufacturing.

Present the computer concepts and Microsoft (R) Office 2016 skills perfect for your Introduction to Computing course with ILLUSTRATED COMPUTER CONCEPTS AND MICROSOFT (R) OFFICE 365 & OFFICE 2016. This all-in-one book makes the computer concepts and skills your students need to know easily accessible. The user-friendly two-page spread found throughout this and other popular Illustrated Microsoft (R) Office 2016 books clearly demonstrates key application skills. Today's most up-to-date technology developments and concepts are clarified using the distinctive step-by-step approach and the latest content from COMPUTER CONCEPTS ILLUSTRATED BRIEF. This edition highlights new Office features with a new module covering Productivity Apps. You'll find a wealth of instructional support and resources, including MindTap customized learning paths to reinforce the important skills and theories found in ILLUSTRATED COMPUTER CONCEPTS AND MICROSOFT (R) OFFICE 365 & OFFICE 2016.