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Multi-Agent Programming:
Multi-Agent Systems are a promising technology to develop the next generation open distributed complex software systems. The main focus of the research community has been on the development of concepts (concerning both mental and social attitudes), architectures, techniques, and general approaches to the analysis and specification of multi-agent systems. This contribution has been fragmented, without any clear way of “putting it all together”, rendering it inaccessible to students and young researchers, non-experts, and practitioners. Successful multi-agent systems development is guaranteed only if we can bridge the gap from analysis and design to effective implementation.Multi-Agent Programming: Languages, Tools and Applications presents a number of mature and influential multi-agent programming languages, platforms, development tools and methodologies, and realistic applications, summarizing the state of the art in an accessible manner for professionals and computer science students at all levels.
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Integration of Constraint Programming, Artificial Intelligence, and Operations Research
This book constitutes the refereed proceedings of the 23rd International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2026, held in Rabat, Morocco, during May 26–29, 2026.The 37 full papers included in this volume were carefully reviewed and selected from 92 submissions. They focus on new ideas, techniques and applications in Constraint Programming, Artificial Intelligence, and Operations Research.
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Programming Machine Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
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Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
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Ascend AI Processor Architecture and Programming
Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei’s Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications. Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI.
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Bayesian Analysis in Natural Language Processing, Second Edition
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
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Learn Programming with C
Authored by two standout professors in the field of Computer Science and Technology with extensive experience in instructing, Learn Programming with C is a comprehensive and accessible guide to programming with one of the most popular languages. Meticulously illustrated with figures and examples, this book is a comprehensive guide to writing, editing and executing C programs on different operating systems and platforms, as well as how to embed C programs into other applications and how to create one’s own library. A variety of questions and exercises are included in each chapter to test the readers’ knowledgeWritten for the novice C programmer, especially undergraduate and graduate students, this book’s line-by-line explanation of code and succinct writing style makes it an excellent companion for classroom teaching, learning and programming labs.
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Learn Bosque Programming
Discover the benefits of regularized programming by implementing Bosque to build a variety of reliable appsKey FeaturesGet up and running with the Bosque programming language and use it to build better softwareStreamline your app development and improve productivity using Bosque programmingEliminate sources of complexity such as loops, recursion, and invariants to develop quality productsBook DescriptionBosque is a new high-level programming language inspired by the impact of structured programming in the 1970s. It adopts the TypeScript syntax and ML semantics and is designed for writing code that is easy to reason about for humans and machines. With this book, you'll understand how Bosque supports high productivity and cloud-first development by removing sources of accidental complexity and introducing novel features. This short book covers all the language features that you need to know to work with Bosque programming. You'll learn about basic data types, variables, functions, operators, statements, and expressions in Bosque and become familiar with advanced features such as typed strings, bulk algebraic data operations, namespace declarations, and concept and entity declarations. This Bosque book provides a complete language reference for learning to program with Bosque and understanding the regularized programming paradigm. You'll also explore real-world examples that will help you to reinforce the knowledge you've acquired. Additionally, you'll discover more advanced topics such as the Bosque project structure and contributing to the project. By the end of this book, you'll have learned how to configure the Bosque environment and build better and reliable software with this exciting new open-source language.What you will learnFind out what the Bosque project isIdentify accidental complexity in code and how to overcome it with BosqueUnderstand the principles of the regularized programming paradigmInstall and configure the Bosque environmentGet hands-on experience using the Bosque language and its key featuresRecognize the advantages of explicit code intermediate representation designWho this book is forThis book is for experienced developers and early adopters who are interested in learning a new, mindset-changing programming language. You’ll also find this book useful if you know TypeScript or JavaScript programming and want to understand the advantages of Bosque compared to other programming languages. Experience with any programming language and knowledge of various programming paradigms such as structured programming and functional programming are required to get started with this book.
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Network Programming and Automation Essentials
Unleash the power of automation by mastering network programming fundamentals using Python and Go best practicesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesUnderstand the fundamentals of network programming and automationLearn tips and tricks to transition from traditional networking to automated networksSolve everyday problems with automation frameworks in Python and GoBook DescriptionNetwork programming and automation, unlike traditional networking, is a modern-day skill that helps in configuring, managing, and operating networks and network devices. This book will guide you with important information, helping you set up and start working with network programming and automation.With Network Programming and Automation Essentials, you'll learn the basics of networking in brief. You'll explore the network programming and automation ecosystem, learn about the leading programmable interfaces, and go through the protocols, tools, techniques, and technologies associated with network programming. You'll also master network automation using Python and Go with hands-on labs and real network emulation in this comprehensive guide.By the end of this book, you'll be well equipped to program and automate networks efficiently.What you will learnUnderstand the foundation of network programmingExplore software-defined networks and related familiesRecognize the differences between Go and Python through comparisonLeverage the best practices of Go and PythonCreate your own network automation testing framework using network emulationAcquire skills in using automation frameworks and strategies for automationWho this book is forThis book is for network architects, network engineers, and software professionals looking to integrate programming into networks. Network engineers following traditional techniques can use this book to transition into modern-day network automation and programming. Familiarity with networking concepts is a prerequisite.
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Maya Programming with Python Cookbook
Master complex workflows and conquer the world with Python and MayaAbout This Book• Improve your modelling skills and reduce your scripting problems using Python in Maya• Learn to communicate with web applications using Python for easier team development• A quick and practical answer to every problem you can have whilst scripting in Maya with PythonWho This Book Is ForThis book is for Python developers who have just started scripting with Maya.What You Will Learn• Find out how to use Python scripting to automate tedious tasks• Create functional user interfaces to make scripts easy to share with others• Add new functionality to Maya via the power of scripting• Import and export arbitrary data into and out of Maya• Improve your workflow, and that of your team• Create custom create custom controls to make rigs that are easy to work with• Implement a system to render 3D assets for isometric games• Use script jobs to trigger actions automatically in response to user interaction• Open a command port to allow other applications to communicate with MayaIn DetailMaya is a 3D graphics and animation software, used to develop interactive 3D applications and games with stupendous visual effects. The Maya Programming with Python Cookbook is all about creating fast, powerful automation systems with minimum coding using Maya Python. With the help of insightful and essential recipes, this book will help you improve your modelling skills. Expand your development options and overcome scripting problems encountered whilst developing code in Maya. Right from the beginning, get solutions to complex development concerns faced when implementing as parts of build.Style and approachThis book is comprised of a set of practical recipes, grouped under specific topics, which can be referred to independently or in sequence. These recipes provide quick solutions to common problems, and cover most of the real-world scenarios that developers are likely to face when working with Maya.
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Distributed Programming
Distributed Programming: Theory and Practice presents a practical and rigorous method to develop distributed programs that correctly implement their specifications. The method also covers how to write specifications and how to use them. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. Larger examples include data transfer protocols, distributed shared memory, and TCP network sockets. Distributed Programming: Theory and Practice bridges the gap between books that focus on specific concurrent programming languages and books that focus on distributed algorithms. Programs are written in a "real-life" programming notation, along the lines of Java and Python with explicit instantiation of threads and programs. Students and programmers will see these as programs and not "merely" algorithms in pseudo-code. The programs implement interesting algorithms and solve problems that are large enough to serve as projects in programming classes and software engineering classes. Exercises and examples are included at the end of each chapter with on-line access to the solutions. Distributed Programming: Theory and Practice is designed as an advanced-level text book for students in computer science and electrical engineering. Programmers, software engineers and researchers working in this field will also find this book useful.
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Concurrency, Specification and Programming
This book presents novel approaches to the formal specification of concurrent and parallel systems, mathematical models for describing such systems, and programming and verification concepts for their implementation. A special emphasis is on methods based on artificial intelligence and machine learning techniques.Chapters are revised selected papers from the 29th International Workshop on Concurrency, Specification, and Programming (CS&P 2021), Berlin, Germany. Nine independent chapters cover formal approaches to topics such as requirements formalization, parsing, or granular computing, as well as their applications in recommender systems, decision making, security, optimization, and other areas. The book thus addresses both researchers and practitioners in its field.
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30 Arduino Projects for the Evil Genius, Second Edition
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.So Many Fiendishly Fun Ways to Use the Latest Arduino Boards!Fully updated throughout, this do-it-yourself guide shows you how to program and build fascinating projects with the Arduino Uno and Leonardo boards and the Arduino 1.0 development environment. 30 Arduino Projects for the Evil Genius, Second Edition, gets you started right away with the simplified C programming you need to know and demonstrates how to take advantage of the latest Arduino capabilities.You'll learn how to attach an Arduino board to your computer, program it, and connect electronics to it to create your own devious devices. A bonus chapter uses the special USB keyboard/mouse-impersonation feature exclusive to the Arduino Leonardo.30 Arduino Projects for the Evil Genius, Second Edition:Features step-by-step instructions and helpful illustrationsProvides full schematic and construction details for every projectCovers the scientific principles behind the projectsRemoves the frustration factor--all required parts are listed along with sourcesBuild these and other clever creations:High-brightness Morse code translatorSeasonal affective disorder lightKeypad security codePulse rate monitorSeven-segment LED double diceUSB message boardOscilloscopeTune playerVU meterLCD thermostatComputer-controlled fanHypnotizerServo-controlled laserLie detectorMagnetic door lockInfrared remoteLilypad clockEvil Genius countdown timerKeyboard prankAutomatic password typerAccelerometer mouse
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