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DOM Enlightenment: Exploring JavaScript and the Modern DOM
by Cody LindleyWith DOM Enlightenment, you'll learn how to manipulate HTML more efficiently by scripting the Document Object Model (DOM) without a DOM library. Using code examples in cookbook style, author Cody Lindley (jQuery Cookbook) walks you through modern DOM concepts to demonstrate how various node objects work. Over the past decade, developers have buried the DOM under frameworks that simplify its use. This book brings these tools back into focus, using concepts and code native to modern browsers. You'll understand the role jQuery plays in DOM scripting, and learn how to use the DOM directly in applications for mobile devices and specific browsers that require low overhead. Understand JavaScript node objects and their relationship to the DOM Learn the properties and methods of document, element, text, and DocumentFragment objects Delve into element node selecting, geometry, and inline styles Add CSS style sheets to an HTML document and use CSSStyleRule objects Set up DOM events by using different code patterns Learn the author's vision for dom.js, a jQuery-inspired DOM Library for modern browsers
Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13542)
by Konstantinos Kamnitsas Lisa Koch Mobarakol Islam Ziyue Xu Jorge Cardoso Qi Dou Nicola Rieke Sotirios TsaftarisThis book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings (Lecture Notes in Computer Science #14293)
by Lisa Koch M. Jorge Cardoso Enzo Ferrante Konstantinos Kamnitsas Mobarakol Islam Meirui Jiang Nicola Rieke Sotirios A. Tsaftaris Dong YangThis book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings (Lecture Notes in Computer Science #12968)
by M. Jorge Cardoso Shadi Albarqouni Islem Rekik Nicola Rieke Ziyue Xu Konstantinos Kamnitsas Daguang Xu Qi Dou Bishesh Khanal Debdoot Sheet Sotirios TsaftarisThis book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning.
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings (Lecture Notes in Computer Science #12444)
by Shadi Albarqouni Spyridon Bakas Konstantinos Kamnitsas M. Jorge Cardoso Bennett Landman Wenqi Li Fausto Milletari Nicola Rieke Holger Roth Daguang Xu Ziyue XuThis book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains.For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (Lecture Notes in Computer Science #11795)
by Qian Wang Fausto Milletari Hien V. Nguyen Shadi Albarqouni M. Jorge Cardoso Nicola Rieke Ziyue Xu Konstantinos Kamnitsas Vishal Patel Badri Roysam Steve Jiang Kevin Zhou Khoa Luu Ngan LeThis book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.
Domain Adaptation for Visual Understanding
by Vishal M. Patel Richa Singh Mayank Vatsa Nalini RathaThis unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition.Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods.This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.
Domain Adaptation in Computer Vision Applications
by Gabriela CsurkaThis comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes. Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning. This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
Domain Adaptation in Computer Vision with Deep Learning
by Hemanth Venkateswara Sethuraman PanchanathanThis book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation.Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Domain Decomposition Methods in Science and Engineering XXIV (Lecture Notes in Computational Science and Engineering #125)
by Petter E. Bjørstad Susanne C. Brenner Lawrence Halpern Hyea Hyun Kim Ralf Kornhuber Talal Rahman Olof B. WidlundThese are the proceedings of the 24th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in Svalbard, Norway in February 2017. Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems. The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2017.
Domain-Driven Design in PHP
by Carlos Buenosvinos Christian Soronellas Keyvan AkbaryReal examples written in PHP showcasing DDD Architectural Styles, Tactical Design, and Bounded Context Integration About This Book • Focuses on practical code rather than theory • Full of real-world examples that you can apply to your own projects • Shows how to build PHP apps using DDD principles Who This Book Is For This book is for PHP developers who want to apply a DDD mindset to their code. You should have a good understanding of PHP and some knowledge of DDD. This book doesn't dwell on the theory, but instead gives you the code that you need. What You Will Learn • Correctly design all design elements of Domain-Driven Design with PHP • Learn all tactical patterns to achieve a fully worked-out Domain-Driven Design • Apply hexagonal architecture within your application • Integrate bounded contexts in your applications • Use REST and Messaging approaches In Detail Domain-Driven Design (DDD) has arrived in the PHP community, but for all the talk, there is very little real code. Without being in a training session and with no PHP real examples, learning DDD can be challenging. This book changes all that. It details how to implement tactical DDD patterns and gives full examples of topics such as integrating Bounded Contexts with REST, and DDD messaging strategies. In this book, the authors show you, with tons of details and examples, how to properly design Entities, Value Objects, Services, Domain Events, Aggregates, Factories, Repositories, Services, and Application Services with PHP. They show how to apply Hexagonal Architecture within your application whether you use an open source framework or your own. Style and approach This highly practical book shows developers how to apply domain-driven design principles to PHP. It is full of solid code examples to work through.
Domain-Driven Design with Golang: Use Golang to create simple, maintainable systems to solve complex business problems
by Matthew BoyleUnderstand the concept of Domain-driven design and build two DDD systems from scratch that can be showcased as part of your portfolioKey FeaturesExplore Domain-driven design as a timeless concept and learn how to apply it with GoBuild a domain-driven monolithic application and a microservice from scratchLeverage patterns to make systems scalable, resilient, and maintainableBook DescriptionDomain-driven design (DDD) is one of the most sought-after skills in the industry. This book provides you with step-by-step explanations of essential concepts and practical examples that will see you introducing DDD in your Go projects in no time. Domain-Driven Design with Golang starts by helping you gain a basic understanding of DDD, and then covers all the important patterns, such as bounded context, ubiquitous language, and aggregates. The latter half of the book deals with the real-world implementation of DDD patterns and teaches you how to build two systems while applying DDD principles, which will be a valuable addition to your portfolio. Finally, you'll find out how to build a microservice, along with learning how DDD-based microservices can be part of a greater distributed system. Although the focus of this book is Golang, by the end of this book you'll be able to confidently use DDD patterns outside of Go and apply them to other languages and even distributed systems.What you will learnGet to grips with domains and the evolution of Domain-driven designWork with stakeholders to manage complex business needsGain a clear understanding of bounded context, services, and value objectsGet up and running with aggregates, factories, repositories, and servicesFind out how to apply DDD to monolithic applications and microservicesDiscover how to implement DDD patterns on distributed systemsUnderstand how Test-driven development and Behavior-driven development can work with DDDWho this book is forThis book is for intermediate-level Go developers who are looking to ensure that they not only write maintainable code, but also deliver great business value. If you have a basic understanding of Go and are interested in learning about Domain-driven design, or you've explored Domain-driven design before but never in the context of Go, then this book will be helpful.
Domain-Driven Design with Java - A Practitioner's Guide: Create simple, elegant, and valuable software solutions for complex business problems
by Premanand Chandrasekaran Karthik KrishnanAdopt a practical and modern approach to architecting and implementing DDD-inspired solutions to transform abstract business ideas into working software across the entire spectrum of the software development life cycleKey FeaturesImplement DDD principles to build simple, effective, and well-factored solutionsUse lightweight modeling techniques to arrive at a common collective understanding of the problem domainDecompose monolithic applications into loosely coupled, distributed components using modern design patternsBook DescriptionDomain-Driven Design (DDD) makes available a set of techniques and patterns that enable domain experts, architects, and developers to work together to decompose complex business problems into a set of well-factored, collaborating, and loosely coupled subsystems. This practical guide will help you as a developer and architect to put your knowledge to work in order to create elegant software designs that are enjoyable to work with and easy to reason about. You'll begin with an introduction to the concepts of domain-driven design and discover various ways to apply them in real-world scenarios. You'll also appreciate how DDD is extremely relevant when creating cloud native solutions that employ modern techniques such as event-driven microservices and fine-grained architectures. As you advance through the chapters, you'll get acquainted with core DDD's strategic design concepts such as the ubiquitous language, context maps, bounded contexts, and tactical design elements like aggregates and domain models and events. You'll understand how to apply modern, lightweight modeling techniques such as business value canvas, Wardley mapping, domain storytelling, and event storming, while also learning how to test-drive the system to create solutions that exhibit high degrees of internal quality. By the end of this software design book, you'll be able to architect, design, and implement robust, resilient, and performant distributed software solutions.What you will learnDiscover how to develop a shared understanding of the problem domainEstablish a clear demarcation between core and peripheral systemsIdentify how to evolve and decompose complex systems into well-factored componentsApply elaboration techniques like domain storytelling and event stormingImplement EDA, CQRS, event sourcing, and much moreDesign an ecosystem of cohesive, loosely coupled, and distributed microservicesTest-drive the implementation of an event-driven system in JavaGrasp how non-functional requirements influence bounded context decompositionsWho this book is forThis book is for intermediate Java programmers looking to upgrade their software engineering skills and adopt a collaborative and structured approach to designing complex software systems. Specifically, the book will assist senior developers and hands-on architects to gain a deeper understanding of domain-driven design and implement it in their organization. Familiarity with DDD techniques is not a prerequisite; however, working knowledge of Java is expected.
Domain-Driven Laravel: Learn to Implement Domain-Driven Design Using Laravel
by Jesse GriffinMap concepts and ideas in domain-driven design (DDD) and transpose them into clean, testable, and quality code that is effective alongside the Laravel framework. This book teaches you how to implement the concepts and patterns present in DDD in the real world as a complete web application. With these tactics and concepts in place, you'll engage in a variety of example applications, built from the ground up, and taken directly from real-world domains. Begin by reviewing foundational stepping stones (with small, manageable examples to show proof of concepts as well as illustrations to conceptualize the more complex topics) of both DDD and Laravel. Specifically, such topics as entities, value objects, developing an ubiquitous language, DTOs, and knowledge discovery. Next, you will dive into some more advanced topics of DDD and use these concepts as a guide to make customizations to the default Laravel installation, giving you an understanding of why these alterations are vital to the DDD and Laravel platform. Finally, you will cover the very powerful Eloquent ORM that comes stock with Laravel and understand how it can be utilized to represent entities, handle repositories, and support domain events. Although there is a basic coverage chapter and a setup tutorial for Laravel (along with a high level intro about the components used within it), Domain-Driven Laravel is best suited to readers who have been at least exposed to the framework and have had the opportunity to tinker around with it.What You'll Learn Utilize a blazing-fast rapid development pipeline built from DDD building blocks and facilitated with LaravelImplement value objects, repositories, entities, anti-corruption layers and others using Laravel as a web frameworkApply enhanced techniques for quick prototyping of complex requirements and quality results using an iterative and focused approach Create a base framework (Laravel) that can serve as a template to start off any projectGain insight on which details are important to a project’s success and how to acquire the necessary knowledge Who This Book Is ForIdeal for for frontend/backend web developers, devops engineers, Laravel framework lovers and PHP developers hoping to learn more about either Domain Driven Design or the possibilities with the Laravel framework. Those with a working knowledge of plain PHP can also gain value from reading this book.
Domain Engineering: Product Lines, Languages, and Conceptual Models
by Tony Clark Iris Reinhartz-Berger Sholom Cohen Arnon Sturm Jorn BettinDomain engineering is a set of activities intended to develop, maintain, and manage the creation and evolution of an area of knowledge suitable for processing by a range of software systems. It is of considerable practical significance, as it provides methods and techniques that help reduce time-to-market, development costs, and project risks on one hand, and helps improve system quality and performance on a consistent basis on the other. In this book, the editors present a collection of invited chapters from various fields related to domain engineering. The individual chapters present state-of-the-art research and are organized in three parts. The first part focuses on results that deal with domain engineering in software product lines. The second part describes how domain-specific languages are used to support the construction and deployment of domains. Finally, the third part presents contributions dealing with domain engineering within the field of conceptual modeling. All chapters utilize a similar terminology, which will help readers to understand and relate to the chapters content. The book will be especially rewarding for researchers and students of software engineering methodologies in general and of domain engineering and its related fields in particular, as it contains the most comprehensive and up-to-date information on this topic.
Domain Generalization with Machine Learning in the NOvA Experiment (Springer Theses)
by Andrew T.C. SuttonThis thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.
Domain Modeling Made Functional: Tackle Software Complexity with Domain-Driven Design and F#
by Scott WlaschinYou want increased customer satisfaction, faster development cycles, and less wasted work. Domain-driven design (DDD) combined with functional programming is the innovative combo that will get you there. In this pragmatic, down-to-earth guide, you'll see how applying the core principles of functional programming can result in software designs that model real-world requirements both elegantly and concisely - often more so than an object-oriented approach. Practical examples in the open-source F# functional language, and examples from familiar business domains, show you how to apply these techniques to build software that is business-focused, flexible, and high quality. Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming - all the techniques you need will be introduced and explained. Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation---ensuring that the code and design never get out of sync. Encode business rules in the design so that you have "compile-time unit tests," and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and hexagonal architectures. Finally, create a functional domain model that works with traditional databases, NoSQL, and event stores, and safely expose your domain via a website or API. Solve real problems by focusing on real-world requirements for your software. What You Need: The code in this book is designed to be run interactively on Windows, Mac and Linux.You will need a recent version of F# (4.0 or greater), and the appropriate .NET runtime for your platform.Full installation instructions for all platforms at fsharp.org.
The Domain Name Registration System: Liberalisation, Consumer Protection and Growth (Routledge Research in Information Technology and E-Commerce Law)
by Jenny NgThis book offers a comparative analysis of the domain name registration systems utililsed in Australia and the United Kingdom. Taking an international perspective, the author analyses the global trends and dynamics of the domain name registration systems and explores the advantages and disadvantages of restrictive and less restrictive systems by addressing issues of consumer protection. The book examines the regulatory frameworks in the restrictive and unrestrictive registration systems and considers recent developments in this area. Jenny Ng also examines the legal and economic implications of these regulatory frameworks, drawing upon economic theory, regulatory and systems theory as well as applying rigorous legal analysis. In doing so, this work proposes ways in which such systems could be better designed to reflect the needs of the specific circumstances in individual jurisdictions. The Domain Name Registration System will be of particular interest to academics and students of IT law and e-commerce.
Domain Oriented Systems Development: Practices and Perspectives
by Kiyoshi Itoh Satoshi Kumagai Toyohiko HirotaDomain Oriented Systems Development is the sixth volume in the Advanced Information Processing Technology series of the Information Processing Society of Japan. It draws together a collection of research papers on domain analysis and modeling written by a group of software engineers and researchers from Japan, Korea, Canada and Austria. The
Domain-Specific Computer Architectures for Emerging Applications: Machine Learning and Neural Networks
by Chao WangWith the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application.DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures.Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.
Domain Specific High-Level Synthesis for Cryptographic Workloads (Computer Architecture and Design Methodologies)
by Ayesha Khalid Goutam Paul Anupam ChattopadhyayThis book offers an in-depth study of the design and challenges addressed by a high-level synthesis tool targeting a specific class of cryptographic kernels, i.e. symmetric key cryptography. With the aid of detailed case studies, it also discusses optimization strategies that cannot be automatically undertaken by CRYKET (Cryptographic kernels toolkit. The dynamic nature of cryptography, where newer cryptographic functions and attacks frequently surface, means that such a tool can help cryptographers expedite the very large scale integration (VLSI) design cycle by rapidly exploring various design alternatives before reaching an optimal design option. Features include flexibility in cryptographic processors to support emerging cryptanalytic schemes; area-efficient multinational designs supporting various cryptographic functions; and design scalability on modern graphics processing units (GPUs). These case studies serve as a guide to cryptographers exploring the design of efficient cryptographic implementations.
Domain-Specific Knowledge Graph Construction (SpringerBriefs in Computer Science)
by Mayank KejriwalThe vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book will describe a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations. The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”.
Domain-Specific Languages in R: Advanced Statistical Programming
by Thomas MailundGain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages.What You'll LearnProgram with domain-specific languages using RDiscover the components of DSLsCarry out large matrix expressions and multiplications Implement metaprogramming with DSLsParse and manipulate expressions Who This Book Is ForThose with prior programming experience. R knowledge is helpful but not required.
Domain-Specific Processors: Systems, Architectures, Modeling, and Simulation (Signal Processing And Communications Ser. #Vol. 20)
by Shuvra S. Bhattacharyya Ed F. Deprettere Jürgen TeichRanging from low-level application and architecture optimizations to high-level modeling and exploration concerns, this authoritative reference compiles essential research on various levels of abstraction appearing in embedded systems and software design. It promotes platform-based design for improved system implementation and modeling and enhanced
Domina Las Apps: Una Guía para Principiantes para Comenzar a Ganar Dinero con Apps
by Adidas WilsonLas teconlogías de la comunicación avanzan constatemente para adaptarse a los tiempos. Las apps de mensajes son algo enorme ahora. Han superdo completamente a las redes sociales al convertirse en nuestra forma primaria de comunicación. Cuando la mayoría de los empresarios inician, les gusta leer artículos sobre "cómo arrasar con tu primer app," "contruyendo la app de mil millones de dólares" y muchos libros relcionados a este tema. Están pegados a este lado de la historia y cegados al otro. A fin de tener tu propia historia de éxito, debes descubrir por qué otras apps fallan. La dolorosa verdad es que hay más app fallidas que apps exitosas.