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الصفحة 10
Distributed and Parallel Systems : Cluster and Grid Computing
DAPSY (Austrian-Hungarian Workshop on Distributed and Parallel Systems) is an international conference series with biannual events dedicated to all aspects of distributed and parallel computing. DAPSY started under a different name in 1992 (Sopron, Hungary) as regional meeting of Austrian and Hungarian researchers focusing on transputer-related parallel computing; a hot research topic of that time. A second workshop followed in 1994 (Budapest, Hungary). As transputers became history, the scope of the workshop widened to include parallel and distributed systems in general and the 1st DAPSYS in 1996 (Miskolc, Hungary) reflected the results of these changes.
Distributed and parallel computing ; 6th International conference on algorithms and architectures for parallel processing, ICA3PP, Melbourne, Australia, October 2-3, 2005, Proceedings
There are many applications that require parallel and distributed processing to allow complicated engineering, business and research problems to be solved in a reasonable time. Parallel and distributed processing is able to improve company profit, lower costs of design, production, and deployment of new technologies, and create better business environments. The major lesson learned by car and aircraft engineers, drug manufacturers, genome researchers and other specialist is that a computer system is a very powerful tool that is able to help them solving even more complicated problems. That has led computing specialists to new computer system architecture and exploiting parallel computers, clusters of clusters, and distributed systems in the form of grids.
Discovery science ; Vol. 3735 ; 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings
This book constitutes the refereed proceedings of the 8th International Conference on Discovery Science, DS 2005, held in Singapore in October 2005, co-located with the International Conference on Algorithmic Learning Theory (ALT 2005). The 21 revised long papers and the 6 revised regular papers presented together with 9 project reports and 5 invited papers were carefully reviewed and selected from 112 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science.
Discovering Knowledge in Data : An Introduction to Data Mining
Provides the tools needed to thrive in today’s big data world. Demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”.
Digital Enterprise Technology : Perspectives and Future Challenges
Digital engineering methods and systems are vitally important for performing key technical and business functions of global enterprises in a distributed and collaborative manner. The product design and engineering systems are gradually being developed to include a variety of tools for DfX, as well as incorporate aspects of digital manufacturing.The chapters presented in this book are contributed by world class leaders in the field. This volume includes relevant examples of current state-of-art in the development and use of systems and methods for the digital modelling of global development and realization processes in the context of life cycle management.
Digital Design and Implementation with Field Programmable Devices
The focus of this book is on a practical knowledge of digital system design for programmable devices. The book covers all necessary topics under one cover, and covers each topic just enough that is actually used by an advanced digital designer. In the three parts of the book, we cover digital system design concepts, use of tools, and systematic design of digital systems
DevOps Tools for Java Developers : Best Practices from Source Code to Production Containers
Explore software lifecycle best practices Use DevSecOps methodologies to facilitate software development and delivery Understand the business value of DevSecOps best practices Manage and secure software dependencies Develop and deploy applications using containers and cloud native technologies Manage and administrate source control repositories and development processes Use automation to set up and administer build pipelines Identify common deployment patterns and antipatterns Maintain and monitor software after deployment
Designing big data platforms : How to use, deploy, and maintain big data systems
Provides expert guidance and valuable insights on getting the most out of Big Data systems. Helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management / Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems / Highlights and explains how data is processed at scale / Includes an introduction to the foundation of a modern data platform
Designing and evaluating e-management decision tools : The integration of decision and negotiation models into internet-multimedia technologies
Presents the most relevant concepts for designing intelligent decision tools in an Internet-based multimedia environment and assessing the tools using concepts of statistical design of experiments. The book covers : Decision modeling paradigms , Visual interactive decision modeling , Online preference elicitation , collaborative decision making , negotiation and conflict resolution , marketing decision optimization , and guidelines for designing and evaluating decision support tools. This book is designed for the following uses: 1) for researchers and engineers, who are seeking recent advances and who are developing e-management systems; 2) for practitioners and managers, who seek insights about ICT potential and using ICT for business intelligence management; and 3) for students, who seek theoretical and practical concepts of building and evaluating prototype decision tools.
Design, user experience, and usability interaction design ; 9th International Conference, DUXU 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I
This book constitutes the refereed proceedings of the 9th International Conference on Design, User Experience, and Usability, DUXU 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic. From a total of 6326 submissions, a total of 1439 papers and 238 posters has been accepted for publication in the HCII 2020 proceedings. The 40 papers included in this volume were organized in topical sections on UX design methods, tools and guidelines, interaction design and information visualization, and emotional design.
Design of advanced manufacturing systems : Models for capacity planning in advanced manufacturing systems
The aim of this book is to provide a framework and speci?c methods and tools for the selection and con?guration of capacity of Advanced Manufacturing Systems (AMS). In particular this book de?nes an - chitecture where the multidisciplinary aspects of the designofAMSare properly organized and addressed. The tool will support the decisi- maker in the de?nition of the con?guration of the system which is best suited for the particular competitive context where the ?rm operates or wants tooperate. Thisbookisofinterest for academic researchers in the ?eldofind- trial engineering and particularly indicated in the areas of operations and manufacturing strategy.
Design for Manufacturability and Yield for Nano-Scale CMOS
This book presented aspects of manufacturability and yield in a nano-CMOS process and how to address each aspect at the proper design step starting with the design and layout of standard cells and how to yield-grade libraries for critical area and lithography artifacts through place and route, CMP model based simulation and dummy-fill insertion, mask planning, simulation and manufacturing, and through statistical design and statistical timing closure of the design. It alerts the designer to the pitfalls to watch for and to the good practices that can enhance a design’s manufacturability and yield. This book is a must read book the serious practicing IC designer and an excellent primer for any graduate student intent on having a career in IC design or in EDA tool development.
Deployment and operation of complex software in heterogeneous execution environments : The SODALITE approach
This book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring.
Dependable software engineering : Theories, tools, and applications ; 6th International Symposium, SETTA 2020, Guangzhou, China, November 24–27, 2020, Proceedings
This book constitutes the proceedings of the 6th International Symposium on Dependable Software Engineering, SETTA 2020, held in Guangzhou, China, in November 2020. The 10 full and 1 short paper included in this volume were carefully reviewed and selected from 20 submissions. They deal with latest research results and ideas on bridging the gap between formal methods and software engineering.
Deepfake detection
Recently, various techniques of manipulating the video content have become available to everyone – online, one can find free applications e.g., for face swapping in videos. Such universal accessibility carries a notable risk of flooding online content with false information, affecting not only the greats of this world, but also the whole societies, also the rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. It is therefore necessary to develop a verification tool that will help assess the authenticity of the videos posted on the internet. This project describes the approach of using artificial intelligence solutions to detect doctored videos.
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Deep learning pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
Deep learning methods for converting speech to text = تقنيات التعلم العميق في تحويل الصوت إلى نص
Aims to design and develop a system capable of extracting audio content from films and audio recordings and converting it into text using deep learning techniques. This is done by analyzing audio patterns, extracting sounds and words from the video, and then converting them into written text. Deep learning, a branch of artificial intelligence, is used to accomplish this task. The study also includes comparing different deep learning techniques to determine their effectiveness in this context.
Datatype-Generic Programming ; International Spring School, SSDGP 2006, Nottingham, UK, April 24-27, 2006, Revised Lectures
A leitmotif in the evolution of programming paradigms has been the level and extent of parametrisation that is facilitated — the so-called genericity of the paradigm. The sorts of parameters that can be envisaged in a programming language range from simple values, like integers and fioating-point numbers, through structured values, types and classes, to kinds (the type of types and/or classes).Datatype-generic programming is about parametrising programsby the structure of the data that they manipulate. To appreciate the importance of data type genericity,one need look no further than the internet. The internet is a massive repository of structured data, but the structure is rarely exploited. For example, compression of data can be much more efiective if its structure is known, but most compression algorithms regard the input data as simply a string of bits, and take no account of its internal organisation. Datatype-generic programming is about exploiting the structure of data when it is relevant and ignoring it when it is not. Programming languages most c- monly used at the present time do not provide efiective mechanisms for do- menting and implementing datatype genericity.
Dataset Studio
Data is the new oil, which means that AI engineers can face difficulties in locating suitable datasets. Dataset Studio is a comprehensive platform designed to support AI engineers in the creation and optimization of datasets. This project offers a diverse range of services that encompass data collection, data augmentation, and data classification. As a result, this software empowers engineers by automatically generating structured data through the utilization of advanced tools and AI techniques. By automating the laborious tasks of manual data collection and extraction, Dataset Studio effectively streamlines the workflow for AI engineers, enabling them to save valuable time and focus on the more intricate aspects of dataset development and refinement.
