الصفحة 12
الصفحة 12
Developments in Language Theory ; 12th International Conference, DLT 2008, Kyoto, Japan, September 16-19, 2008. Proceedings
This book constitutes the refereed proceedings of the 12th International Conference on Developments in Language Theory, DLT 2008, held in Kyoto, Japan, September 2008.
Developing Ambient Intelligence ; Proceedings of the International Conference on Ambient Intelligence Developments (AmI.d’07)
The research papers included in the AmI.d proceedings are devoted to both theoretical and applied research, cover the most leading-edge research and contain contributions that have been formally reviewed and chosen by a selected International Program Committee. The contributions cover a wide range of AmI topics.
Developing Ambient Intelligence ; Proceedings of the First International Conference on Ambient Intelligence Developments (AmID'06)
As Ambient Intelligence (AmI) ecosystems are rapidly becoming a reality, they raise new research challenges. Unlike predefined static architectures as we know them today, AmI ecosystems are bound to contain a large number of heterogeneous computing, communication infrastructures and devices that will be dynamically assembled. Architectures will be sensitive, adaptive, context-aware and responsive to users‚ needs and habits.Researchers need to both enable their user-friendly application in a growing number of areas while ensuring that these applications remain reliable and secure. Held in Sophia Antipolis (France) from September the 20th to September the 22nd 2006, the first edition of the AmI.d conference tackled the latest research challenges within AmI ecosystems, presented AmI applications as well as security solutions.
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 machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
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.
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.
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
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.
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 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 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.
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 its Applications
Presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
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.
Databases in Networked Information Systems ; 5th International Workshop, DNIS 2007, Aizu-Wakamatsu, Japan, October 17-19, 2007, Proceedings
This book Is focusing on data semantics and infrastructure for information management and interchange, the papers are organized in topical sections on geospatial decision-making, Web data management systems, infrastructure of networked information systems, and Web query and web mining systems.
Database System Concepts
Presents the fundamental concepts of database management. This text is suitable for a first course in databases at the junior/senior undergraduate level or the first year graduate level
Data Warehousing and Knowledge Discovery ; 10th International Conference, DaWaK 2008 Turin, Italy, September 2-5, 2008 Proceedings
This book constitutes the refereed proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2008, held in Turin, Italy, in September 2008.
Data mining and Knowledge discovery handbook
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.
