الصفحة 39
الصفحة 39
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.
Dependable Embedded Systems
This book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years.
Dependable computing ; 2nd Latin-American symposium, LADC 2005, Salvador, Brazil, October 25-28, 2005 : proceedings
Contains the proceedings of the 'Second Latin-American Symposium' on Dependable Computing, LADC 2005. This book comprises 16 papers presented together with 3 invited talks. The papers are organized in topical sections on evaluation, certification, modelling, embedded systems, time, and distributed systems algorithms.
Dependable computing - EDCC 2005 ; 5th European dependable computing Conference, Budapest, Hungary, April 20-22, 2005, Proceedings
It is always a special honor to chair the European Dependable Computing C- ference (EDCC). EDCC has become one of the well-established conferences in the ?eld of dependability in the European research area. Budapest was selected as the host of this conference due to its traditions in organizing international scienti?c events and its traditional role of serving as a meeting point between East and West. EDCC-5 was the ?fth in the series of these high-quality scienti?c conf- ences. In addition to the overall signi?cance of such a pan-European event, this year’s conference was a special one due to historic reasons. The roots of EDCC date back to the moment when the Iron Curtain fell. Originally, two groups of scientists from di?erent European countries in Western and Eastern Europe – who were active in research and education related to dependability created a – joint forum in order to merge their communities as early as in 1989. This trend has continued up to today. This year’s conference was the ?rst one where the overwhelming majority of the research groups belong to the family of European nations united in the European Union. During the past 16 years we observed that the same roots in all the professional, cultural and scienti?c senses led to a seamless integration of these research communities previously separated ar- ?cially for a long time. EDCC has become one of the main European platforms to exchange new - searchideasinthe?eldofdependability.
Dependable and adaptable networks and services ; 13th Open European Summer School and IFIP TC6.6 Workshop, EUNICE 2007, Enschede, The Netherlands, July 18-20, 2007, Proceedings
Innovative Internet Applications.’ Much has changed since then: wireless network technologies have become a constantly growing part of the Internet infrastructure, and increasingly smaller and more powerful computing devices with ?exible connectivity open the possibility of new services and applications.
Dependability Metrics : Advanced Lectures
This tutorial book gives an overview of the current state of the art in measuring the different aspects of dependability of systems: reliability, security and performance.
Demystifying Internet of Things Security : Successful IoT Device/Edge and Platform Security Deployment
The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security.
Defence Industry Applications of Autonomous Agents and Multi-Agent Systems
In this book defense and security related applications are increasingly being tackled by researchers and practioners using technologies developed in the field of Intelligent Agent research.
Defence Applications of Multi-Agent Systems; International Workshop, DAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised and Invited Papers
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2005, held in Utrecht, The Netherlands in July 2005 as an associated event of AAMAS 2005, the main international conference on autonomous agents and multi-agent systems. The 10 revised full papers presented together with 1 invited article are organized in topical sections on decision support and simulation, unmanned aerial vehicles, as well as on systems and security.
Deep structure, singularities, and computer vision ; 1st international workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, revised selected papers
Constitutes the refereed post-proceedings of the First International Workshop on Deep Structure, Singularities, and Computer Vision, DSSCV 2005, held in Maastricht. This book represents in understanding the relation between structural, topological information represented by singularities and metric information of signals, shapes, and colors.
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-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep Learning to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
Deep learning for computational problems in hardware security : Modeling attacks on strong physically unclonable function circuits
Discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning approaches to cloud security
Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
Deep learning and computer vision in remote sensing-II
Computer vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing.
Decoding the city urbanism in the Age of Big Data
Shows how Big Data change reality and, hence, the way we deal with the city. They demonstrate how the Lab interprets digital data as material that can be used for the formulation of a different urban future. The publication also looks at the negative aspects of the city-related data acquisition and control.
