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The book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2022. Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real world and industrial problems arising in engineering, economics, medicine, and other domains can be formulated as optimization tasks. This book presents recent advances in computational optimization. The book includes important real problems like modeling of physical processes, parameter settings for controlling different processes, agricultural modeling transportation problems, energy management, machine scheduling, air pollution modeling, optimization of fast-food restaurant chain, and solving engineering and financial problems. It shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming Monte Carlo method, and others. This book demonstrates how some real-world problems arising in engineering, economics, and other domains can be formulated as optimization problems.

This book constitutes the refereed proceedings of the 31st International Symposium on String Processing and Information Retrieval, SPIRE 2024, held in Puerto Vallarta, Mexico, during September 23–25, 2024. The 22 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 41 submissions. The papers reflect the continuation of the long and well-established tradition of encouraging high-quality research at the broad nexus of string processing, information retrieval, and computational biology.

This book constitutes the proceedings of the 18th Annual Conference on Theory and Applications of Models of Computation, TAMC 2024, which was held in Hong Kong, China, during May 13–15, 2024. The 30 full papers presented in this book were carefully reviewed and selected from 69 submissions. The main themes of the selected papers are computability, complexity, algorithms, information theory, as well as their integration with machine learning theory and the foundations of artificial intelligence.

​This book constitutes the refereed proceedings of CVM 2024, the 12th International Conference on Computational Visual Media, held in Wellington, New Zealand, in April 2024.The 34 full papers were carefully reviewed and selected from 212 submissions. The papers are organized in topical sections as follows:Part I: Reconstruction and Modelling, Point Cloud, Rendering and Animation, User Interations.Part II: Facial Images, Image Generation and Enhancement, Image Understanding, Stylization, Vision Meets Graphics.

This book constitutes the refereed proceedings of CVM 2024, the 12th International Conference on Computational Visual Media, held in Wellington, New Zealand, in April 2024.The 34 full papers were carefully reviewed and selected from 212 submissions. The papers are organized in topical sections as follows:Part I: Reconstruction and Modelling, Point Cloud, Rendering and Animation, User Interations.Part II: Facial Images, Image Generation and Enhancement, Image Understanding, Stylization, Vision Meets Graphics.

This book constitutes the proceedings of the 28th International Conference on Implementation and Application of Automata, CIAA 2024, held in Akita, Japan, during September 3-6, 2024. The 24 full papers and one invited paper presented here were carefully reviewed and selected from 38 submissions. These papers have been covering various fields in the application, implementation, and theory of automata and related structures.

This book constitutes the refereed proceedings of the 20th Conference on Computabilitty in Europe, CiE 2024, held in Amsterdam, The Netherlands, during July 8–12, 2024 The 26 full papers and 2 short papers included in this book were carefully reviewed and selected from 43 submissions. This CIE 2024 focus on development of computability-related science, ranging over mathematics, computer science and applications in various natural and engineering sciences.

Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three parts. It provides a comprehensive review and summary of federated learning technology, as well as presenting numerous novel federated learning algorithms which no other books have summarized. The work also references the most recent papers, articles and reviews from the past several years to keep pace with the academic and industrial state of the art of federated learning.The first part lays a foundational understanding of federated learning by going through its definition and characteristics, and also possible application scenarios and related privacy protection technologies. The second part elaborates on some of the federated learning algorithms innovated by JD Technology which encompass both vertical and horizontal scenarios, including vertical federated tree models, linear regression, kernel learning, asynchronous methods, deep learning, homomorphic encryption, and reinforcement learning. The third and final part shifts in scope to federated learning systems — namely JD Technology's own FedLearn system — by discussing its design and implementation using gRPC, in addition to specific performance optimization techniques plus integration with blockchain technology.This book will serve as a great reference for readers who are experienced in federated learning algorithms, building privacy-preserving machine learning applications or solving real-world problems with privacy-restricted scenarios.

Computer Arithmetic in Practice: Exercises and Programming is a simple, brief introductory volume for undergraduate and graduate students at university courses interested in understanding the foundation of computers. It is focused on numeric data formats and capabilities of computers to perform basic arithmetic operations. It discusses mainly such topics as: Basic concepts of computer architecture Assembly language programming skills Data formats used to express integer and real numbers Algorithms of basic arithmetic operations Short overview of nonlinear functions evaluation Discussion on limited number representation and computer arithmetic Exercises and programming tasks This book provides an accessible overview of common data formats used to write numbers in programming languages and how the computer performs four basic arithmetic operations from the point of view of the processor instruction set. The book is primarily didactic in nature, therefore the theoretical information is enriched with many numerical examples and exercises to be solved using a ‘sheet of paper and a pencil'. Answers are provided for most of the tasks. The theoretical discussed issues are illustrated by listings of algorithms presenting the way to implement arithmetic operations in low-level language. It allows development of the skills of optimal programming, taking into consideration the computer architecture and limitations. Creating software using low-level language programming, despite the initial difficulties, gives the ability to control the code and create efficient applications. This allows for effective consolidation of knowledge and acquisition of practical skills required at this stage of education, mainly a specialist in the field of information technology, electronics, telecommunications, other related disciplines, or at the level of general education with introduction to information technology. It may be also useful for engineers interested in their own professional development and teachers as well.

This book provides an insight into 12th International Conference on Soft Computing for Problem Solving (SocProS 2023), organized by The Department of Applied Mathematics and Scientific Computing, Saharanpur Campus of Indian Institute of Technology, Roorkee, India, in conjunction with Continuing Education Center during 11–13 August 2023. This book presents the latest achievements and innovations in the interdisciplinary areas of soft computing, machine learning, and data science. It covers original research papers in the areas of algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and health care, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

This book provides an insight into 12th International Conference on Soft Computing for Problem Solving (SocProS 2023), organized by The Department of Applied Mathematics and Scientific Computing, Saharanpur Campus of Indian Institute of Technology, Roorkee, India, in conjunction with Continuing Education Center during 11–13 August 2023. This book presents the latest achievements and innovations in the interdisciplinary areas of soft computing, machine learning, and data science. It covers original research papers in the areas of algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and health care, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Spatial gems are computational techniques for processing spatial data. This book, a follow-up to the first Spatial Gems volume, is a further collection of techniques contributed by leading research experts. Although these approaches were developed by their authors as part of larger research projects, the gems represent fundamental solutions that are generically applicable to many different problems. Our goal is to expose these useful techniques that are not yet in textbooks and often buried inside technical research papers to share them with software developers, graduate students, professors, and professional researchers.

Spatial gems are computational techniques for processing spatial data. This book, a follow-up to the first Spatial Gems volume, is a further collection of techniques contributed by leading research experts. Although these approaches were developed by their authors as part of larger research projects, the gems represent fundamental solutions that are generically applicable to many different problems. Our goal is to expose these useful techniques that are not yet in textbooks and often buried inside technical research papers to share them with software developers, graduate students, professors, and professional researchers.

This book presents algorithms and tools that are designed to model and extract information from personal contact networks, which represent which individuals in a population are physically in contact with one another. The authors developed these tools based on research they conducted during the COVID-19 pandemic, with the goal of improving responses to epidemics in the future. The book provides methods for modelling the transmission of infection across a population. The authors explain how an epidemic model can be used to strategically distribute vaccines and minimize the spread of a virus. The book shows how evolutionary computation, graph compression, and network induction can be utilized to manage issues that arise from an epidemic.

Crypto can be cryptic. Serious Cryptography, 2nd Edition arms you with the tools you need to pave the way to understanding modern crypto.This thoroughly revised and updated edition of the bestselling introduction to modern cryptography breaks down fundamental mathematical concepts without shying away from meaty discussions of how they work. In this practical guide, you'll gain immeasurable insight into topics like authenticated encryption, secure randomness, hash functions, block ciphers, and public-key techniques such as RSA and elliptic curve cryptography.You'll find coverage of topics like:The basics of computational security, attacker models, and forward secrecyThe strengths and limitations of the TLS protocol behind HTTPS secure websitesQuantum computation and post-quantum cryptographyHow algorithms like AES, ECDSA, Ed25519, Salsa20, and SHA-3 workAdvanced techniques like multisignatures, threshold signing, and zero-knowledge proofsEach chapter includes a discussion of common implementation mistakes using real-world examples and details what could go wrong and how to avoid these pitfalls. And, true to form, you'll get just enough math to show you how the algorithms work so that you can understand what makes a particular solution effective—and how they break. NEW TO THIS EDITION: This second edition has been thoroughly updated to reflect the latest developments in cryptography. You'll also find a completely new chapter covering the cryptographic protocols in cryptocurrency and blockchain systems.Whether you're a seasoned practitioner or a beginner looking to dive into the field, Serious Cryptography will demystify this often intimidating topic. You'll grow to understand modern encryption and its applications so that you can make better decisions about what to implement, when, and how.

This comprehensive volume presents essential mathematical results devoted to topics of mathematical analysis, differential equations and their various applications. It focuses on differential operators, Wardowski maps, low-oscillation functions, Galois and Pataki connections, Hardy-type inequalities, to name just a few.Effort has been made for this unique title to have an interdisciplinary flavor and features several applications such as in tomography, elastic scattering, fluid mechanics, etc.This work could serve as a useful reference text to benefit professionals, academics and graduate students working in theoretical computer science, computer mathematics, and general applied mathematics.

Logic is a foundational mathematical discipline for Computer Science. This unique compendium provides the main ideas and techniques originating from logic. It is divided into two volumes — propositional logic and predicate logic. The volume presents some of the most important concepts starting with a variety of logic formalisms — Hilbert/Frege systems, tableaux, sequents, and natural deduction in both propositional and first-order logic, as well as transformations between these formalisms. Topics like circuit design, resolution, cutting planes, Hintikka sets, paramodulation, and program verification, which do not appear frequently in logic books are discussed in detail.The useful reference text has close to 800 exercises and supplements to deepen understanding of the subject. It emphasizes proofs and overcomes technical difficulties by providing detailed arguments. Computer scientists and mathematicians will benefit from this volume.

This book covers the basics of numerical methods. Avoiding the definition-theorem-proof style, it instead focuses on numerical examples and simple pseudo-codes. The text begins with a chapter on floating point arithmetic before moving on to discuss norms, conditions numbers, solutions of systems of equations, the least squares problem, eigenvalue problems, interpolation, numerical integration, ordinary differential equations, optimization (including a detailed case study), and practical error estimations. Exercises (partly in MATLAB) are provided at the end of each chapter. Suitable for readers with minimal mathematical knowledge, the book not only offers an elementary introduction to numerical mathematics for programmers and engineers but also provides supporting material for students and teachers of mathematics.

C++ is famous for getting all the default behaviors wrong and for sometimes making demons fly out of your nose. Through 25 puzzles, from the useful to the outright weird, we explore some of C++'s most interesting quirks. How does initialization actually work? Do temporaries even exist? Why is `+!!''` a valid expression in C++? As you work through each puzzle, you will peel off some of the layers of complexity of C++, getting a fundamental understanding of how the language works. This will help you write better code and recognize issues more easily while debugging. Each puzzle in the book is a complete, seemingly simple C++ program, but can you figure out the output for each, or will the answers stump you? Most of the programs compile and have deterministic, though sometimes surprising, output. Some might, however, have undefined behavior, where anything can happen, including making demons fly out of your nose! Yet others might have unspecified behavior with output that is not completely deterministic; we just know nothing as bad as nasal demons will happen. All puzzles are accompanied by detailed discussions, taking a look under the hood at what exactly is going on to help you understand more about C++'s unexpected behaviors, as well as fundamental concepts like initialization, overload resolution, and implicit conversions. For the non-deterministic cases, we'll explore some typical behaviors one can expect to see in practice, what causes them, and why they shouldn't be relied on. After working through the book, you'll have tools and techniques to help you write better and safer code, and a better understanding of the fundamentals of the language, the background for some of C++'s apparent weirdness, and a better feel for identifying bugs and unsafe code in your own programs. What You Need: The book assumes basic knowledge of C++. If you want to run the programs yourself, you can use either a local C++ compiler or the online compilers at https://godbolt.org.

Quantum computing and associated technologies and ideas seem to be everywhere today, even in blockbuster movies. As a field, quantum information science only goes back a few decades, but it's generating outsize excitement and investment in the 21st century. There's reason for the enthusiasm, but we're still a long way from being able to build and use these exotic machines.In this book, author Sebastian Hassinger guides you through the foundational ideas of quantum computing, providing insight into where it came from, what it is today, and what it may be in the future.Grounded in information science and quantum mechanics with straightforward explorations of the technology's crucial concepts and elements, this book demystifies the field and helps you develop your quantum intuition, making it easier to learn and use the technologies available on the cloud today.You'll learn:The origins of quantum information science, quantum physics, and relevant conceptsInformation science and the birth and evolution of the'classical'computer industryHow quantum concepts change information science and create new potential for computationThe current state of quantum computation hardware, error correction, and algorithmsQuantum computer hype: magical thinking and reasonable expectationsProblem-solving with quantum information science and quantum computation

This book addresses and disseminates state-of-the-art research and development in the applications of soft computing techniques for renewable energy systems. It covers topics such as solar energy, wind energy, and solar concentrator technologies, as well as building systems and power generation systems. In all these areas, applications of soft computing methods such as artificial neural networks, genetic algorithms, particle swarm optimization, cuckoo search, fuzzy logic, and a combination of these, called hybrid systems, are included. This book is a source for students interested in the fields of renewable energy and the application of the soft computing. In addition, our book can be considered as a reference for researchers and academics since it will include applications of soft computing in different renewable energy systems.

This book reports the developments of the Total Least Square (TLS) algorithms for parameter estimation and adaptive filtering. Specifically, it introduces the authors'latest achievements in the past 20 years, including the recursive TLS algorithms, the approximate inverse power iteration TLS algorithm, the neural based MCA algorithm, the neural based SVD algorithm, the neural based TLS algorithm, the TLS algorithms under non-Gaussian noises, performance analysis methods of TLS algorithms, etc. In order to faster the understanding and mastering of the new methods provided in this book for readers, before presenting each new method in each chapter, a specialized section is provided to review the closely related several basis models. Throughout the book, large of procedure of new methods are provided, and all new algorithms or methods proposed by us are tested and verified by numerical simulations or actual engineering applications. Readers will find illustrative demonstration examples on a range of industrial processes to study. Readers will find out the present deficiency and recent developments of the TLS parameter estimation fields, and learn from the the authors'latest achievements or new methods around the practical industrial needs. In my opinion, this book can be assimilated by advanced undergraduates and graduate students, as well as statisticians, because of the new tools in data analysis, applied mathematics experts, because of the novel theories and techniques that we propose, engineers, above all for the applications in control, system identification, computer vision, and signal processing.

This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare. Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature's ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice. In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care. By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms. In this book, the reader will discover Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges; How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making; How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis; Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios; Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories. Audience This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts.

If you've ever felt the exhilaration of cracking a thorny problem or the joy of witnessing elegant code that challenges your mind, then this book is tailor-made for you. Prepare yourself to dive into a diverse array of mind-bending JavaScript brain teasers. Each puzzle is more than just a code exercise; it's a gateway to unlocking new insights and honing your abilities. As you work through the challenges, you'll learn to think pragmatically, optimize your code for efficiency, and avoid hidden pitfalls that can catch you off guard. What's more - the puzzle format will help you remember what you've learned! Over the years, JavaScript has gained a bit of a reputation for its quirky and unique characteristics. It's important for developers to be aware of these quirks to write more robust and predictable JavaScript code. In this book, we invite you on a journey designed to unveil the intricate nuances and peculiarities that define the language's essence. Learn how to simulate function overloading, embed hidden information within objects, develop self-contained functions capable of maintaining their own local state, generate number sequences using functional programming, handle exotic forms of IIFEs, and prevent loss of precision when dealing with large numbers. Grasp concepts like closures, hoisting, symbols, execution context, event queue, text normalization, and more, one puzzle at a time. Discover how to create your own custom objects that function as shortcuts, accurately calculate the length of emojis and Unicode characters, master the usage of the'this'keyword, leverage bitwise operators, and convert color names to hex notation for universal use across various platforms. Plus, steer clear of those usual traps and blunders that trip up JavaScript developers. Forget passive learning. With JavaScript Brain Teasers, you'll actively engage with code puzzles that require creative thinking and problem-solving, which help you memorize difficult concepts.What You Need:Certain code examples take advantage of recently introduced JavaScript features. Therefore, it's important to use a JavaScript environment that supports ECMAScript 2020 or a newer version. The simplest way to experiment with the examples is use your browser's console. Just make sure your browser is up-to-date.

Programmers love Go because it is lightweight, easy to work with, and easy to read. Go gives you the benefits of dynamically typed languages (speed of development) while keeping the upsides of strongly typed languages (security and performance). Go is a simple language, but programming in Go is about more than just mastering syntax. There's an art to using Go effectively. Squeeze out the full use of advanced networking and multi-core power for which Go was designed. Save precious coding hours with recipes that help you manage objects, collect garbage, and safely use memory. Tackle Unicode, concurrency, and serialization with ease. All the clean, reusable solutions you need for a wide variety of problems common to Go development. Outfitted with these recipes, your next apps will be more polished and more maintainable than ever. Start out by tackling time and see how the Go time packager provides types that will do most of the heavy lifting for you. Next, work on recipes tailored to the nuances of processing text, like normalizing strings to avoid bugs. From there, whip up some functions on-the-fly and store functions in variables and data structures. Ever wondered why Go seems to be peppered with error handling? Working through the next recipes, you'll discover the benefits, which include more robust code. In the section on HTTP, you'll learn tricks like running multiple HTTP servers on the same port and setting timeouts. With concurrency recipes, you'll limit the number of goroutines to improve performance, give your code awareness of timeouts, and decide when to keep your code sequential instead of making it concurrent. Throughout the book, you'll make Go sizzle following simple recipes and tweaking them to fit your own apps. Using tools like strong typing and concurrency primitives, build a Go codebase that stays maintainable at scale.What You Need:You should know the Go language syntax and have some experience in programming.You will need a Go SDK, a Git client, and for some of the chapters, a C compiler.

This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book's most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example applications of stochastic programming, decomposition algorithms, detailed illustrative numerical examples of the models and algorithms, and an emphasis on computational experimentation. With a focus on both theory and implementation of the models and algorithms for solving practical optimization problems, this monograph is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and some computer programming background. Several examples of stochastic programming applications areincluded, providing numerical examples to illustrate the models and algorithms for both stochastic linear and mixed-integer programming, and showing the reader how to implement the models and algorithms using computer software.

The book explains basic ideas behind several kinds of applied multi-objective optimization and shows how it will be applied in practical contexts in the domain of healthcare, engineering design, and manufacturing. The book discusses how meta-heuristic algorithms are successful in resolving challenging, multi-objective optimization issues in various disciplines, including engineering, economics, medical and environmental management. The topic is useful for graduates, researchers and lecturers in optimization, engineering, management science and computer science.

Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, x-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. These large and highly diverse amounts of information need to be organized and mined in an appropriate way so that meaningful information can be extracted. New multimodal data fusion techniques are able to combine salient information into one single source to ensure better diagnostic accuracy and assessment. Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. This book can be used as a reference for practicing engineers, scientists, and researchers. It will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications. Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical dataInvestigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formatsFocuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare

This textbook offers an introduction to topics in algorithms and programming with python. It is originally intended for mathematical students not sufficiently aware about these computer science fields seeking a deeper understanding. It addresses fundamental questions on how to analyze the performance of an algorithm and equips readers with the skills to implement them using python. The textbook is organized in two parts. Part I introduces Python Programming offering a solid foundation to python essentials. Topics covered include first steps in python programming, programs, functions and recursion, data structures. Part II shifts focus to Algorithms and covers topics such as algorithm performance, recursion, the sorting problem, trees as data structures, etc. This book has its origins from several different courses given in the context of thematic schools to diverse audiences in different countries over the years. These countries include Cambodia, Kenya, and Madagascar.

Here is your in-depth guide to cryptography and cryptanalysis in Java. This book includes challenging cryptographic solutions that are implemented in Java 21 and Jakarta EE 11. It provides a robust introduction to Java 21's new features and updates, a roadmap for Jakarta EE 11 security mechanisms, a unique presentation of the'hot points'(advantages and disadvantages) from the Java Cryptography Architecture (JCA), a new chapter on Quantum cryptography, and more. The book dives into the classical simple cryptosystems that form the basis of modern cryptography, with fully working solutions (encryption/decryption operations). Pseudo-random generators are discussed as well as real-life implementations. Hash functions are covered along with practical cryptanalysis methods and attacks, asymmetric and symmetric encryption systems, signature and identification schemes. The book wraps up with a presentation of lattice-based cryptography and the NTRU framework library. Modern encryption schemes for cloud and big data environments (homomorphic encryption and searchable encryption) also are included. After reading and using this book, you will be proficient with crypto algorithms and know how to apply them to problems you may encounter. New to This Edition: The modernized Second Edition is updated to reflect the latest language features in Java 21 and Jakarta 11, along with the introduction of a new chapter on Quantum Cryptography (Chapter 6). What You Will Learn Develop programming skills for writing cryptography algorithms in Java Dive into security schemes and modules using Java Explore “good” vs “bad” cryptography based on processing execution times and reliability Play with pseudo-random generators, hash functions, etc. Leverage lattice-based cryptography methods, the NTRU framework library, and more Who This Book Is For Those who want to learn and leverage cryptography and cryptanalysis using Java. Some prior Java and/or algorithm programming exposure is highly recommended.

Python's simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python's versatility is evident in its applications, spanning web development, data analysis, Machine Learning and automation.The language's interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python's wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. Python: A Practical Learning Approach exemplifies Python's simplicity and versatility with numerous examples, ensuring a seamless learning journey. Beyond theory, the language's practicality allows learners to actively apply their knowledge in real-world scenarios, establishing Python as an asset in education.

A friendly, fully-illustrated introduction to the most important computer programming algorithms.Master the most widely used algorithms and be fully prepared when you're asked about them at your next job interview. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, you'll actually enjoy learning about algorithms with this fun and friendly guide! In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP-complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You'll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book's fully annotated code samples have been updated to Python 3. Foreword by Daniel Zingaro. About the technology The algorithms you use most often have already been discovered, tested, and proven. Grokking Algorithms, Second Edition makes it a breeze to learn, understand, and use them. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, it's the perfect way to unlock the power of algorithms in your everyday work and prepare for your next coding interview—no math required! About the book Grokking Algorithms, Second Edition teaches you important algorithms to speed up your programs, simplify your code, and solve common programming problems. Start with tasks like sorting and searching, then build your skills to tackle advanced problems like data compression and artificial intelligence. You'll even learn to compare the performance tradeoffs between algorithms. Plus, this new edition includes fresh coverage of trees, NP-complete problems, and code updates to Python 3. What's inside Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP-complete and greedy algorithms Exercises and code samples in every chapter About the reader No advanced math or programming skills required. About the author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents 1 Introduction to algorithms 2 Selection sort 3 Recursion 4 Quicksort 5 Hash tables 6 Beadth-first search 7 Trees 8 Balanced trees 9 Dijkstra's algorithm 10 Greedy algorithms 11 Dynamic programming 12 k-nearest neighbors 13 where to go next

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the'big themes'of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

“I love maps. I love math. And gosh, do I love this book, which so beautifully and clearly sounds the depths of both.” —Ben Orlin, author of Math with Bad DrawingsExplore the surprising connections between math and maps—and the myriad ways they've shaped our world and us.Why are coastlines and borders so difficult to measure? How does a UPS driver deliver hundreds of packages in a single day? And where do elusive serial killers hide? The answers lie in the crucial connection between maps and math.In Mapmatics, mathematician Paulina Rowińska leads us on a riveting journey around the globe to discover how maps and math are deeply entwined, and always have been. From a sixteenth-century map, an indispensable navigation tool that exaggerates the size of northern countries, to public transport maps that both guide and confound passengers, to congressional maps that can empower or silence whole communities, she reveals how maps and math have shaped not only our sense of space but our worldview. In her hands, we learn how to read maps like a mathematician—to extract richer information and, just as importantly, to question our conclusions by asking what we don't see.Written with authority and compassion, wit and unforgettable storytelling, this is math exposition at its best. By unpacking the math behind the maps we depend on, Mapmatics illuminates how our world works and, ultimately, how we can better look after it.

Is a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book's mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts and provides in-depth surveys of neuroscience and AI research related to the book's mission, providing all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including: The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence. Diffuse decision making by ensembles of neurons. The inside-out model to give HLAI an inner'life'and the possible role for cognitive architecture implementing the scientific method through the plan-do-check-act cycle within that model (learning to learn). A neuromodulation feature such as a machine equivalent of dopamine that reinforces learning. The embodied HLAI machine, a neurorobot, that interacts with the physical world as it learns. This book concludes by explaining the hypothesis that computer simulation is sufficient to take AI research further toward HLAI and that the scientific method is our means to enable that progress. This book will be of great interest to a broad audience, particularly neuroscientists and AI researchers, investors in AI projects, and lay readers looking for an accessible introduction to the intersection of neuroscience and artificial intelligence.

This book provides a comprehensive overview of the fundamental principles, concepts, and technologies of distributed computing. It covers various topics including the architecture of distributed systems, communication protocols, distributed file systems, distributed databases, and distributed algorithms. The book is written to help readers understand the complexities of distributed systems and how to design and develop distributed applications that are reliable, scalable, and efficient. This book is an essential text for understanding the concepts and principles of distributed systems.

This book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.

La eficiencia y la optimización se erigen como principios clave de la ingeniería y la gestión. Poseer las herramientas y técnicas de optimización correctas se ha vuelto indispensable. La revolución tecnológica y el auge del análisis de datos han resaltado el inmenso valor de los algoritmos de optimización, capaces de descubrir las mejores soluciones en una amplia gama de escenarios, desde la optimización de recursos en proyectos de ingeniería hasta la estrategia de toma de decisiones en la gestión empresarial. Sin embargo, adentrarse en el aprendizaje de la optimización numérica representa un reto significativo, que demanda una sólida comprensión de las matemáticas y la programación informática. La abrumadora cantidad de nueva información y la jerga especializada pueden ser particularmente intimidantes sin la guía de un experto. Este libro se presenta como la solución a este desafío: ofrece una introducción al fascinante mundo de la optimización numérica con un estilo claro y ameno, proporcionando simultáneamente las herramientas esenciales de matemáticas y programación de manera secuencial y accesible, sin necesidad de conocimientos previos especializados. Se distingue de otras publicaciones especializadas por su enfoque eminentemente práctico. Incluye ejemplos en MATLAB, que sirven como herramienta práctica para reducir la distancia entre la teoría y su aplicación en el mundo real. La inclusión de ejemplos de código ya preparados y su explicación línea por línea no solo hace que la materia resulte más atractiva, sino que también anima a experimentar, modificar y mejorar el código con sus propias ideas. Si se acerca a la optimización numérica por primera vez como estudiante, este libro será un recurso de gran valor para sus estudios, y le brindará un entendimiento profundo de los algoritmos de optimización y su aplicación práctica. Si es un profesional que busca adentrarse en el campo de la optimización sin ser un especialista, encontrará en este libro un excelente punto de partida.

This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design.

Digital Technology in Public Health and Rehabilitation Care: COVID Era provides an in-depth examination on how digital technology has impacted public health and rehabilitation during the COVID-19 pandemic. The book showcases the range of digital technology applications utilized in healthcare, including the use of mobile phones, computers, wearable and non-wearable technologies, sensors, 3D printers, robotics, Artificial Intelligence (AI), internet technologies, websites and apps, virtual and augmented realities, and computer games. With a wealth of case studies and insights, this book is an essential resource on the impact of digital technology on public health and rehabilitation services in the COVID-19 era. The COVID-19 pandemic had a major impact on the rehabilitation services available to those in need, particularly for older adults, people with physical and cognitive impairments, children with autism and ADHD, and individuals with physical and mental health disorders. In response to these challenges, the book focuses on the potential for advanced digital technologies to revolutionize public health and rehabilitation services, highlighting the need for researchers and healthcare professionals to work together to promote digital-based interventions. Provides in-depth insights into the various digital technologies that are impacting public health and rehabilitation, including Mobile/Smart phone, Computers, wearable and non-wearable technologies, sensors, 3D printers/technologies, robotics, and moreHighlights the impact of the COVID-19 pandemic on public health and rehabilitation services, and how digital technology can help provide effective solutions during these challenging timesBrings together the expertise of multidisciplinary professionals, including researchers, doctors, nurses, physical therapists, occupational therapists, speech therapists, psychologists, and more

Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis. Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlight the relevance of related application areas for advanced as well as novice-user application. The book presents an in-depth look at core concepts, methodological aspects, and advanced feature opportunities, focusing on major real time applications in engineering science and health science. The book will appeal to researchers, data scientists, industry professionals, and graduate students in the fields of fractal graphics and its related applications.Investigates advanced fractal theories spanning neural networks, fuzzy logic, machine learning, deep learning, and hybrid intelligent systems in solving pattern recognition problemsExplores the application of fractal theories to a wide range of medical image processing modalitiesPresents case studies that illustrate the application and integration of fractal theories into intelligent computing in the resolution of important pattern recognition and machine vision problems

Governments around the world use machine learning in automated decision-making systems for a broad range of functions. However, algorithmic bias in machine learning can result in automated decisions that produce disparate impact and may compromise Charter guarantees of substantive equality. This book seeks to answer the question: what standards should be applied to machine learning to mitigate disparate impact in government use of automated decision-making? The regulatory landscape for automated decision-making, in Canada and across the world, is far from settled. Legislative and policy models are emerging, and the role of standards is evolving to support regulatory objectives. While acknowledging the contributions of leading standards development organizations, the authors argue that the rationale for standards must come from the law and that implementing such standards would help to reduce future complaints by, and would proactively enable human rights protections for, those subject to automated decision-making. The book presents a proposed standards framework for automated decision-making and provides recommendations for its implementation in the context of the government of Canada's Directive on Automated Decision-Making. As such, this book can assist public agencies around the world in developing and deploying automated decision-making systems equitably as well as being of interest to businesses that utilize automated decision-making processes.

Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithmsKey FeaturesUnderstand key data science algorithms with Python-based examplesIncrease the impact of your data science solutions by learning how to apply existing algorithmsTake your data science solutions to the next level by learning how to create new algorithmsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers. Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you'll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems. By the end of the book, you'll have the confidence to apply key mathematical concepts to your data science challenges.What you will learnMaster foundational concepts that underpin all data science applicationsUse advanced techniques to elevate your data science proficiencyApply data science concepts to solve real-world data science challengesImplement the NumPy, SciPy, and scikit-learn concepts in PythonBuild predictive machine learning models with mathematical conceptsGain expertise in Bayesian non-parametric methods for advanced probabilistic modelingAcquire mathematical skills tailored for time-series and network data typesWho this book is forThis book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you're looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists.

Unlock Java's full potential for cloud computing through expert insights from real-world case studies and stay ahead with the latest trends in agile and robust Java application developmentKey FeaturesMaster concurrency and parallelism to overcome cloud computing challenges in JavaBuild scalable solutions with Big Data, ML, microservices, and serverless architecturesExplore cloud scaling, GPU utilization, and future tech innovations in Java applicationsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIf you're a software developer, architect, or systems engineer, exploring Java's concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java's concurrency and parallel processing in cloud computing. This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java's role in big data, machine learning, microservices, and serverless computing, shedding light on how Java's tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You'll navigate advanced topics, such as synchronizing Java's concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology. This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era. What you will learnUnderstand Java concurrency in cloud app developmentGet to grips with the core concepts of serverless computing in JavaBoost cloud scaling and performance using Java skillsImplement Java GPU acceleration for advanced computing tasksGain insights into Java's role in the evolving cloud and AI technologyAccess hands-on exercises for real-world Java applicationsExplore diverse Java case studies in tech and fintechImplement Java in AI-driven cloud and data workflowsAnalyze Java's application in IoT and real-time analyticsWho this book is forThis book is for Java developers, software engineers, and cloud architects with intermediate Java knowledge. It's ideal for professionals transitioning to cloud-native development or seeking to enhance their concurrent programming skills. DevOps engineers and tech leads involved in cloud migration will also find valuable insights. Basic Java proficiency, familiarity with cloud concepts, and some experience with distributed systems is expected.

This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills. The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

This book introduces the fundamentals of data structures using Java in a self-teaching format. It covers managing large databases, effective SEO, and creating web indexing services. Real-world analogies help explain technical concepts. Each chapter includes programming tasks, theoretical questions, and multiple-choice quizzes. The course begins with an introduction to data structures and Java, moving through arrays, linked lists, queues, searching and sorting, stacks, trees, multi-way search trees, hashing, files, and graphs. Each chapter builds on the previous one, ensuring a thorough understanding of data structures. Understanding these concepts is crucial for managing information and optimizing web services. This book guides readers from basic to advanced techniques, blending theory with practical skills. It is an essential resource for mastering data structures with Java, enhanced by end-of-chapter exercises and real-world examples.

Programming Language Explorations helps its readers gain proficiency in programming language practice and theory by presenting both example-focused, chapter-length explorations of fourteen important programming languages and detailed discussions of the major concepts transcending multiple languages. A language-by-language approach is sandwiched between an introductory chapter that motivates and lays out the major concepts of the field and a final chapter that brings together all that was learned in the middle chapters into a coherent and organized view of the field.Each of the featured languages in the middle chapters is introduced with a common trio of example programs and followed by a tour of its basic language features and coverage of interesting aspects from its type system, functional forms, scoping rules, concurrency patterns, and metaprogramming facilities. These chapters are followed by a brief tour of over 40 additional languages designed to enhance the reader's appreciation of the breadth of the programming language landscape and to motivate further study.Targeted to both professionals and advanced college undergraduates looking to expand the range of languages and programming patterns they can apply in their work and studies, the book pays attention to modern programming practices, keeps a focus on cutting-edge programming patterns, and provides many runnable examples, all of which are available in the book's companion GitHub repository. The combination of conceptual overviews with exploratory example-focused coverage of individual programming languages provides its readers with the foundation for more effectively authoring programs, prompting AI programming assistants, and, perhaps most importantly, learning—and creating—new languages.

本書は、全世界で標準的なアルゴリズムの教科書として位置づけられてきた'Introduction to Algorithms'の第4版の翻訳書である。 第4版ではコンピュータサイエンスの第一線を捉えるために、安定結婚問題（2 部グラフでのマッチング問題）、オンラインアルゴリズム、機械学習などの新しい章や、再帰的漸化式の解法、ハッシュアルゴリズムなど、新しい話題を豊富に取り入れている。これまでの版と同様、各節末には多様なレベルの問題が配置され、学部や大学院の講義用教科書として、また技術系専門家の手引書、あるいは事典としても活用できる。 第2巻ではPart4〜6までの「高度な設計と解析の手法」「高度なデータ構造」「グラフアルゴリズム」を収載。