الصفحة 80
الصفحة 80
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.
Deontic Logic in Computer Science ; 9th International Conference, DEON 2008, Luxembourg, Luxembourg, July 15-18, 2008. Proceedings
This volume presents the refereed proceedings of the 9th International Conference on Deontic Logic in Computer Science, DEON 2008, held in Luxembourg in July 2008.
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.
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.
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%.
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.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deepfake
The technology used to create such digital content has quickly become accessible to the masses, such as “DEEPFAKE.” Deep fakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that yields to synthesize a sequence of face images and voices of characters corresponding to their identities, such as voice tone, facial expression, with a good lip synchronization. Therefore, this study is about developing real-time video generation software, which generates a target video from a single input image. Several methods and algorithms have been applied to detect, analyze personalize facial expression, voice and natural head poses to present a life-like image instead of a low quality one.
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 space probes : To the outer solar system and beyond
This second edition includes an entirely new chapter on holographic message plaques for future interstellar probes – a NASA-funded project.
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, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
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.
