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Information Security Applications
This book constitutes the revised selected papers from the 23rd International Conference on Information Security Applications, WISA 2022, which took place on Jeju Island, South Korea, during August 2022. The 25 papers included in this book were carefully reviewed and selected from 76 submissions. They were organized in topical sections as follows: network security; cryptography; vulnerability analysis; privacy enhancing technique; security management; security engineering.
World Congress of Medical Physics and Biomedical Engineering 2006
These proceedings of the World Congress 2006, the fourteenth conference in this series, offer a strong scientific program covering a wide range of issues and challenges which are currently present in Medical physics and Biomedical Engineering. About 2,500 peer reviewed contributions are presented in a six volume book, comprising 25 tracks, joint conferences and symposia, and including invited contributions from well known researchers in this field.
Advances in Grid and Pervasive Computing
We are proud to present the proceedings of the First International Conference on Grid and Pervasive Computing 2006, held at Tunghai University during May 3-5.
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Multimedia and Ubiquitous Engineering
The new multimedia standards (for example, MPEG-21) facilitate the seamless integration of multiple modalities into interoperable multimedia frameworks, transforming the way people work and interact with multimedia data. These key technologies and multimedia solutions interact and collaborate with each other in increasingly effective ways, contributing to the multimedia revolution and having a significant impact across a wide spectrum of consumer, business, healthcare, education, and governmental domains. Multimedia and Ubiquitous Engineering provides an opportunity for academic and industry professionals to discuss recent progress in the area of multimedia and ubiquitous environment including models and systems, new directions, novel applications associated with the utilization and acceptance of ubiquitous computing devices and systems.
Computational Intelligence in Urban Infrastructure
Computational Intelligence in Urban Infrastructure consolidates experiences and research results in computational intelligence and its applications in urban infrastructure. It discusses various techniques and application areas of smart urban infrastructure including topics related to smart city management. Major topics covered include smart home automation, intelligent lighting, smart human care services, intelligent transportation systems, ontologies in urban development domain, and intelligent monitoring, control, and security of critical infrastructure systems supported by case studies. Features: Covers application of AI and computational intelligence techniques in urban infrastructure planning Discusses characteristics and features of smart urban management Explores relationship between smart home and smart city management Deliberates various smart home techniques Includes different case studies for supporting and analyzing various aspects of smart urban infrastructure management This book is aimed at researchers, graduate students, libraries in communication networks, urban and town planning, and civil engineering.
Handbook of Research on Big Data and the IoT
The increase in connected devices in the internet of things (IoT) is leading to an exponential increase in the data that an organization is required to manage. To successfully utilize IoT in businesses, big data analytics are necessary in order to efficiently sort through the increased data. The combination of big data and IoT can thus enable new monitoring services and powerful processing of sensory data streams. The Handbook of Research on Big Data and the IoT is a pivotal reference source that provides vital research on emerging trends and recent innovative applications of big data and IoT, challenges facing organizations and the implications of these technologies on society, and best practices for their implementation. While highlighting topics such as bootstrapping, data fusion, and graph mining, this publication is ideally designed for IT specialists, managers, policymakers, analysts, software engineers, academicians, and researchers.
ECAI 2023
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrat...
Learning Robot Vision under Insufficient Data
Machine learning is used today in a wide variety of applications, especially within computer vision, robotics, and autonomous systems. Example use cases include detecting people or other objects using cameras in autonomous vehicles, or navigating robots through collision-free paths to solve different tasks. The flexibility of machine learning is attractive as it can be applied to a wide variety of challenging tasks, without detailed prior knowledge of the problem domain. However, training machine learning models requires vast amounts of data, which leads to a significant manual effort, both for collecting the data and for annotating it. In this thesis, we study and develop methods for traini...
