الصفحة 8
الصفحة 8
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
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 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 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 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.
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 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.
Datatype-Generic Programming ; International Spring School, SSDGP 2006, Nottingham, UK, April 24-27, 2006, Revised Lectures
A leitmotif in the evolution of programming paradigms has been the level and extent of parametrisation that is facilitated — the so-called genericity of the paradigm. The sorts of parameters that can be envisaged in a programming language range from simple values, like integers and fioating-point numbers, through structured values, types and classes, to kinds (the type of types and/or classes).Datatype-generic programming is about parametrising programsby the structure of the data that they manipulate. To appreciate the importance of data type genericity,one need look no further than the internet. The internet is a massive repository of structured data, but the structure is rarely exploited. For example, compression of data can be much more efiective if its structure is known, but most compression algorithms regard the input data as simply a string of bits, and take no account of its internal organisation. Datatype-generic programming is about exploiting the structure of data when it is relevant and ignoring it when it is not. Programming languages most c- monly used at the present time do not provide efiective mechanisms for do- menting and implementing datatype genericity.
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.
Data mining and machine learning applications
Elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.
Data Algorithms with Spark
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns
Cryptology and Network Security ; 6th International Conference, CANS 2007, Singapore, December 8-10, 2007, Proceedings
This book presented signatures, network security, secure keyword search and private information retrieval, public key encryption, intrusion detection, email security, denial of service attacks, and authentication.
Cryptographics : Exploiting Graphics Cards For Security
CryptoGraphics: Exploiting Graphics Cards for Security explores the potential for implementing ciphers within graphics processing units (GPUs), and describes the relevance of GPU-based encryption and decryption to the security of applications involving remote displays.
Creative applications of artificial intelligence in education
Explores the synergy between AI and education, highlighting its potential impact on pedagogical practices. It navigates the evolving landscape of AI-powered educational technologies and suggests practical ways to personalise instruction, nurture human-AI co-creativity, and transform the learning experience. Spanning from primary to higher education, this short and engaging volume proposes concrete examples of how educational stakeholders can be empowered in their AI literacy to foster creativity, inspire critical thinking, and promote problem-solving by embracing AI as a tool for expansive learning. Structured in three parts, the book starts developing the creative engagement perspective for learning and teaching to then present practical applications of AI in K-12 and higher education, covering different fields (teacher education, professional education, business education) as well as different types of AI supported tools (games, chatbots, and AI assisted assessment).
Creating Web-based Laboratories
Remote web-based experimentation, enabling students and researchers to access the laboratory anytime via the Internet, is becoming an increasingly attractive way to complement or even replace traditional laboratory sessions. Placing a video camera & microphone before the equipment and apparatus to capture what is actually happening in the laboratory allows the images and audio data to be streamed to the client side. Researchers in different countries can share equipment and conduct research cooperatively and remotely. The authors summarise their research and discuss the development of the 5 web-based laboratories launched from the National University of Singapore. The principles, structure, and technologies required for the creation of Internet remote experimentation systems are discussed with particular emphasis on the integration of hardware and software systems. Also highlighted is the design and development of interfaces and components for use in typical web-based laboratories or similar web-control applications.
Creating Flash Widgets with Flash CS4 and ActionScript 3.0
Creating Flash Widgets with Flash CS4 and ActionScript 3.0 is an introduction to developing widgets for the Internet using the features of Flash CS4 and ActionScript 3.0. Many social-networking sites, blogs, and personal home pages have adopted the use of widgets and Flash developers can create and distribute their own widgets for others to use. A step-by-step example demonstrates how to design and develop your own Flash widgets and integrate them with XML. In addition, publishing, promoting, and capitalizing on your Flash widgets is discussed.
Cooperative Design, Visualization, and Engineering ; 17th International Conference, CDVE 2020, Bangkok, Thailand, October 25–28, 2020, Proceedings
This book constitutes the proceedings of the 17th International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2020, held in Bangkok, Thailand, in October 2020.* The 33 full papers and 7 short papers presented were carefully reviewed and selected from 74 submissions. The achievement, progress and future challenges are reported in areas such as health care, industrial design, banking IT systems, cultural activities support, operational maritime cybersecurity assurance, emotion communication, and social network data analytics.
Control of Single Wheel Robots
This monograph presents a novel concept of a mobile robot, which is a single-wheel, gyroscopically stabilized robot. The robot is balanced by a spinning wheel attached through a two-link manipulator at the wheel bearing, and actuated by a drive motor.
Content based image retrieval systems
With an advent of technology, huge collection of digital images is formed as repositories on crime prevention, medical diagnosis, military, face finding, satellites and remote sensing. The task of searching for similar images in the repository is difficult. The data is growing enormously which makes it difficult to store and manage. The traditional image retrieval technique is inefficient in retrieving these images. Content-based image retrieval is an approach from data mining community, which provides the solution of managing this huge quantity of data. In this research, a Content-Based Image Retrieval (CBIR) system has been developed using color and texture as retrieval features from the image repository. The system allows the user to search for an image based on any of the two features alone or in combination by assigning weights to the features. The histogram and color moments approach is used to extract the color feature, texture feature is extracted using statistical moments and co-occurrence matrix method and the shape feature is extracted using the morphological operations. The images and the extracted feature vectors are stored in the Pickle file. The system is robust as it provides search based on the multiple features. The performance of the system was evaluated by analyzing the retrieval results using precision and recall.
CONCUR 2005 - Concurrency Theory
This volume contains the papers presented at CONCUR 2005, the 16th - ternational Conference on Concurrency Theory. The purpose of the CONCUR series of conferences is to bring together researchers,developers, and students in order to advance the theory of concurrency and to promote its applications. The Program Committee selected 38 papers for presentation. Because of the format of the conference and the high number of submissions, many good papers could not be included. Although submissions werereadand evaluated, the papers that appear in this volume may di?er in form and contents from the corresponding submissions.
