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Designing and Evaluating Value Added Services in Manufacturing E-Market Places
Presents the results of a research developed under a two years program titled “ “Distributed process and production planning in manufacturing enterprise networks” and funded by the Italian Ministry of Education, University and Research (MIUR) under the program PRIN2001.
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.
Designing & Implementing an IDS in SDN
Solving the problem of the various type of unknown attacks that are hitting not only companies but also high level business individuals, of course we know that there is no way to stop the attacks permanently but this project is attempting to reduce these attacks to the possible minimum where it can detect the attack and declare its type so that the hostile can at least know what is the type of attacks on him and what to do in response and build a higher security. This system is implemented using the SDN environment and IDS technology for monitoring the traffic on the network and for detecting the attack and its type. Also the SDN technology has a built-in OpenFlow protocol. To work in an OF environment, any device that wants to communicate to an SDN controller must support the OpenFlow protocol.
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 by Evolution : Advances in Evolutionary Design
This book showcases the state of the art in evolutionary algorithms for design. The chapters are organized by experts in the following fields: evolutionary design and "intelligent design" in biology, art, computational embryogeny, and engineering.
Deploying .NET Applications : Learning MSBuild and ClickOnce
Whether building a console application, a web service, or a smart client, you eventually need to distribute your finished work. Deploying .NET Applications is a complete guide to delivering applications built with .NET. Packed with hands-on guidance, practical examples, and war stories from the authors many experiences with deployment scenarios, this book provides everything you need to know. The book begins by introducing the deployment problem, then examines why deployment is an engineering problem for organizations. Subsequent chapters provide detail about deploying each type of application, then discuss automated deployments.
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 computing ; 3rd Latin-American Symposium, LADC 2007, Morelia, Mexico, September 26-28, 2007, Proceedings
This book presented fault-tolerant algorithms, software engineering of dependable systems, networking and mobile computing, experimental dependability evaluation, as well as intrusion tolerance and security.
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 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 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.
