الصفحة 25
الصفحة 25
Designing Ubiquitous Information Environments : Socio-Technical Issues and Challenges; IFIP TC8 WG 8.2 International Working Conference, August 1-3, 2005, Cleveland, Ohio, U.S.A.
The book brings in diverse perspectives on ubiquitous information environments, from computer-supported collaborative work, institutional perspective, diffusion of innovation, management, sociology, individual cognition, and software engineering. It also covers a variety of technologies that make up ubiquitous information environments including RFID, wireless grid, GPS, mobile phones, and wireless local area network. The papers cover many contexts of ubiquitous computing including personal use, library, automobile, healthcare, police, professional knowledge work, remote diagnostics of machines, and marketing, attesting to the wide range of potential of ubiquitous information environments.
Designing a human future with machines
What is human flourishing in an age of machine intelligence, when many claim that the world's most complex problems can be reduced to narrow technical questions? Does more computing make us more intelligent, or simply more computationally powerful? We need not always resist reduction; our ability to simplify helps us interpret complicated situations. The trick is to know when and how to do so. Against Reduction offers a collection of provocative and illuminating essays that consider different ways of recognizing and addressing the reduction in our approach to artificial intelligence, and ultimately to ourselves.
Design Patterns in Modern C++20 : Reusable Approaches for Object-Oriented Software Design
As well as covering traditional design patterns, this book fleshes out new design patterns and approaches that will be useful to modern C++ developers. Author Dmitri Nesteruk presents concepts as a fun investigation of how problems can be solved in different ways, along the way using varying degrees of technical sophistication and explaining different sorts of trade-offs. You will: Use creational patterns such as builder, factories, prototype and singleton / Implement structural patterns such as adapter, bridge, decorator, facade and more / Work with the behavioral patterns such as chain of responsibility, command, iterator, mediator and more / Apply functional design patterns such as the Maybe Monad
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 and analysis of randomized algorithms : Introduction to design paradigms
Randomness is a powerful phenomenon that can be harnessed to solve various problems in all areas of computer science. Randomized algorithms are often more efficient, simpler and, surprisingly, also more reliable than their deterministic counterparts. Computing tasks exist that require billions of years of computer work when solved using the fastest known deterministic algorithms, but they can be solved using randomized algorithms in a few minutes with negligible error probabilities. Introducing the fascinating world of randomness, this book systematically teaches the main algorithm design paradigms – foiling an adversary, abundance of witnesses, fingerprinting, amplification, and random sampling, etc. – while also providing a deep insight into the nature of success in randomization. Taking sufficient time to present motivations and to develop the reader's intuition, while being rigorous throughout, this text is a very effective and efficient introduction to this exciting field.
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 Systems : Software, Computing, Networks : Research Results of the DICS Program
The present volume documents the results of a research program on Dependable Information and Communication Systems (DICS). The members of the project met in two workshops organized by the Hasler Foundation. This state-of-the-art survey contains 3 overview articles identifying major issues of dependability and presenting the latest solutions, as well as 10 carefully selected and revised papers depicting the research results originating from those workshops. The first workshop took place in Münchenwiler, Switzerland, in March 2004, and the second workshop, which marked the conclusion of the projects, in Löwenberg, Switzerland, in October 2005. The papers are organized in topical sections on surveys, dependable software, dependable computing, and dependable networks.
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 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.
Deontic Logic and Artificial Normative Systems ; 8th International Workshop on Deontic Logic in Computer Science, DEON 2006, Utrecht, The Netherlands, July 12-14, 2006, Proceedings
This volume presents the papers contributed to DEON 2006, the 8th Inter- tional Workshop on Deontic Logic in Computer Science, held in Utrecht, The Netherlands, July 12–14, 2006. These biennial DEON (more properly, ?EON) workshops are designed to promote international cooperation among scholars across disciplines who are interested in deontic logic and its use in computer science.
Deepfake detection = اكتشاف التزييف العميق
In the rapidly evolving era of artificial intelligence, addressing the escalating threats of deepfake technology becomes a necessity because of the increasing sophistication of AI algorithms in generating deceptive content, and since it threatens the integrity of information across diverse data. The main objective is to build a sophisticated AI-driven system to detect different types of deepfake in text, audio, and images. In English text deepfake detection, multiple pre-trained tokenizers have been used, but XLNET and BERT stand out with identifying objects outside the dataset with an accuracy of 0.9809 and both have been generalized & trained using LSTM. In Arabic text deepfake detection, Arabert has been trained using LSTM which led with an accuracy of 99.53% by generalizing the model. Both English and Arabic datasets have been generated to enhance the accuracy and effectiveness of the models. Audio deepfake detection has been generalized too, using Random Forest with an accuracy of 98.259%.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
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 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 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 and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
Declarative agent languages and technologies III ; 3rd International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised Papers
The workshop on Declarative Agent Languages and Technologies is a we- established venue for researchers interested in sharing their experiences in the areas of declarative and formal aspects of agents and multi-agent systems, and in engineering and technology. Today it is still a challenge to develop techno- gies that can satisfy the requirements of complex agent systems. The design and development of multi-agent systems still calls for models and technologies that ensure predictability, enable feature discovery, allow for the veri?cation of properties, and guarantee ?exibility. Declarative approaches are potentially a valuable means for satisfying the needs of multi-agent system developers and for specifying multi-agent systems.
Declarative agent languages and technologies II ; 2nd international workshop, DALT 2004, New York, NY, USA, July 19, 2004, revised selected papers
Nearly 40 research groups worldwide were motivated to contribute to this event by submitting their most recent research achievements, covering a wide variety of the topics listed in the call for papers. More than 30 top researchers agreed to join the Program Committee, which then collectively faced the hard task of selecting the one-day event program. The fact that research in multi-agent systems is no longer only a novel and promising research horizon at dawn is, in our opinion, the main reason behind DALT’s (still short) success story. On the one hand, agent theories and app- cations are mature enough to model complex domains and scenarios, and to successfully address a wide range of multifaceted problems, thus creating the urge to make the best use of this expressive and versatile paradigm, and also pro?t from all the important results achieved so far. On the other hand, bui- ing multi-agent systems still calls for models and technologies that could ensure system predictability, accommodate ?exibility, heterogeneity and openness, and enable system veri?cation.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
Data-Driven 3D Facial Animation
Data-Driven 3D Facial Animation: systematically describes the emerging data-driven techniques developed over the last ten years or so. Although data-driven 3D facial animation is used more and more in animation practice, to date there have been very few books that specifically address the techniques involved. Comprehensive in scope, the book covers not only traditional lip-sync (speech animation), but also expressive facial motion, facial gestures, facial modeling, editing and sketching, and facial animation transferring. It provides an up-to-date reference source for academic research and for professionals working in the facial animation field.
