الصفحة 27
الصفحة 27
Defense against Bioterror : Detection Technologies, Implementation Strategies and Commercial Opportunities ; Proceedings of the NATO Advanced Research Workshop on Defense against Bioterror: Detection Technologies, Implementation Strategies and Commercial Opportunities, held in Madrid, Spain from 8 to 11 April 2004
A critical assessment of state-of-the-art of emerging ("breakthrough") biosensor technologies that will allow for the rapid identification of biological threat agents in the environment and human population, Identification of directions for future research, and to promote close working relationships between scientists from different countries and with different professional experience. The volume is devoted to a comprehensive overview of the current state of biological weapons threat; challenges confronting biodetection technologies and systems; ongoing research and development; and, future requirements. Biosensor technologies including detection platforms, networked alarm-type biodetector systems, implementation strategies, electro-optical and electrochemical biosensors.
Defect-Oriented Testing for Nano-Metric CMOS VLSI Circuits
Failures of nano-metric technologies owing to defects and shrinking process tolerances give rise to significant challenges for IC testing. As the variation of fundamental parameters such as channel length, threshold voltage, thin oxide thickness and interconnect dimensions goes well beyond acceptable limits, new test methodologies and a deeper insight into the physics of defect-fault mappings are needed. In Defect-Oriented Testing for Nano-Metric CMOS VLSI Circuits state of the art of defect-oriented testing is presented from both a theoretical approach as well as from a practical point of view. Step-by-step handling of defect modeling, defect-oriented testing, yield modeling and its usage in common economics practices enables deeper understanding of concepts.
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 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 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 III ; 3rd International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised Papers
The workshop on Declarative Agent Languages and Technologies is a we- established venue for researchers interested in sharing their experiences in the areas of declarative and formal aspects of agents and multi-agent systems, and in engineering and technology. Today it is still a challenge to develop techno- gies that can satisfy the requirements of complex agent systems. The design and development of multi-agent systems still calls for models and technologies that ensure predictability, enable feature discovery, allow for the veri?cation of properties, and guarantee ?exibility. Declarative approaches are potentially a valuable means for satisfying the needs of multi-agent system developers and for specifying multi-agent systems.
Decision Support for Global Enterprises
Decision Support for Global Enterprises consists of peer-reviewed and invited papers with two primary goals: (1) Stimulate creative discussion between academic researchers and the practitioner IS community to improve the research and practice in the area. (2) Increase awareness of the problems and challenges faced by global enterprises that can be met with innovative decision support systems. Limitations are also explored, covering the following topics: the emerging enterprise decision making processes and technologies; decision making in uncertain, changing conditions; the changing infrastructure in organizations and society; the expanding role of web technologies; and emerging theories and practices for managing knowledge and making decisions.
Decision Modeling and Behavior in Complex and Uncertain Environments
Devoted to examining new research at the interface of operations research, behavioral and cognitive sciences, and decision analysis. In these 14 self-contained chapters, four themes emerge, providing the reader with a variety of perspectives both theoretic and applied to meet the challenges of devising models to understand the decision-making process. The main broad topics include: the integration of decision analysis and behavioral models / innovations in behavioral models / exploring descriptive behavior models / experimental studies
Decision Making in the Manufacturing Environment : Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods
Manufacturing is the backbone of any industrialized nation. Recent worldwide advances in manufacturing technologies have brought about a metamorphosis in industry. Fast-changing technologies on the product front have created a need for an equally fast response from manufacturing industries. To meet these challenges, manufacturing industries have to select appropriate manufacturing strategies, product designs, manufacturing processes, work piece and tool materials, and machinery and equipment. The selection decisions are complex, as decision making is more challenging today. Decision makers in the manufacturing sector frequently face the problem of assessing a wide range of options and selecting one based on a set of conflicting criteria.
Decision Making for Complex Socio-Technical Systems : Robustness from Lessons Learned in Long-Term Radioactive Waste Governance
The long-term governance of radioactive waste continues to be a major complex and contentious socio-technical issue worldwide. Traditionally, it has been considered as mainly a challenge to scientists and engineers to develop technical "solutions" to specific problems. But increasingly these narrow solutions have been enlarged by wider societal considerations such as ethics, public involvement, control and retrievability – needs that have in the meanwhile been recognised by the nuclear community, at least in a general way. In this book, we analyse motives for a broad discourse as well as suggest prerequisites to launch it. The author attempts to give a novel, empirically based and technically sound treatment of fundamental issues in long-term management and governance. Written to be accessible to a wide selection of the interested public, the study proposes a combination of technical design issues, analysis methods and institutional backup in a dynamic procedure, and with involvement at all levels of political, commercial and social life.
Databases, Information Systems, and Peer-to-Peer Computing ; International Workshops, DBISP2P 2005/2006, Trondheim, Norway, August 28-29, 2006, Revised Selected Papers
The P2P paradigm lends itself to constructing large-scale, complex, adaptive, autonomous and heterogeneous database and information systems, endowed with clearly specified and difierential capabilities to negotiate, bargain, coordinate and self-organize the information exchanges in large-scale networks. This vision will have a radical impact on the structure of complex organizations (business, sci- tific or otherwise) and on the emergence and the formation of social communities, and on how the information is organized and processed. The P2P information paradigm naturally encompasses static and wireless connectivity and static and mobile architectures. Wireless connectivity combined with the increasingly small and powerful mobile devices and sensors poses new challenges as well as opp- tunities to the database community. Information becomes ubiquitous, highly distributed and accessible anywhere and at any time over highly dynamic, - stable networks with very severe constraints on the information management and processing capabilities.
Databases, information systems, and peer-to-peer computing ; 2nd international workshop, DBISP2P 2004, Toronto, Canada, August 29-30, 2004, revised selected papers
Peer-to-peer (P2P) paradigm lends itself to constructing large-scale complex, adaptive, - tonomous and heterogeneous database and information systems, endowed with clearly speci?ed and di?erential capabilities to negotiate, bargain, coordinate, and self-organize the information exchanges in large-scale networks. This vision will have a radical impact on the structure of complex organizations (business, scienti?c, or otherwise) and on the emergence and the formation of social c- munities, and on how the information is organized and processed. The P2P information paradigm naturally encompasses static and wireless connectivity, and static and mobile architectures. Wireless connectivity c- bined with the increasingly small and powerful mobile devices and sensors pose new challenges to as well as opportunities for the database community. Inf- mation becomes ubiquitous, highly distributed and accessible anywhere and at any time over highly dynamic, unstable networks with very severe constraints on the information management and processing capabilities.
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization
This book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed.
Data science and data analytics : Opportunities and challenges
Gives the concept of data science, tools, and algorithms that exist for many useful applications / Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems / Identifies many areas and uses of data science in the smart era / Applies data science to agriculture, healthcare, graph mining, education, security, etc.
Data Monitoring in Clinical Trials : A Case Studies Approach
Randomized clinical trials are the gold standard for establishing many clinical practice guidelines and are central to evidence based medicine. Obtaining the best evidence through clinical trials must be done within the boundaries of rigorous science and ethical principles. One fundamental principle is that trials should not continue longer than necessary to reach their objectives. Therefore, trials must be monitored for recruitment progress, quality of data, adherence to patient care or prevention standards, and early evidence of benefit or harm. Frequently, a group of external experts, independent from the investigators and trial sponsor, is charged with this monitoring responsibility, especially for safety and early benefit. This group is referred to by various names, such as a data monitoring committee or a data and safety monitoring board. This book, through a series of case studies presented by many distinguished clinical trial experts, illustrates the complexity of this monitoring process.No other text has as extensive a collection of cases which provide insight into the many issues, often conflicting, that must be examined before recommendations to continue or discontinue a trial can be made. While depth in statistical methods is not required, some familiarity with statistical design and analysis issues in clinical trials is helpful. The cases cover trials which were terminated early for convincing evidence of benefit, or for harmful effects. Cases with complex issues are also included. This series of cases should provide broad background information for potential monitoring committee members and better prepare them for the challenges that may exist in the trials for which they are responsible.
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.
Data Management. Data, Data Everywhere ; 24th British National Conference on Databases, BNCOD 24, Glasgow, UK, July 3-5, 2007, Proceedings
One of the most pressing challenges is to ?nd ways of evolving database technology to cope with its new role in underpinning the massively distributed and heterogeneous applications built on top of the Internet. This has afiected both the ways in which data has been accessed and the ways in which it is represented, with XML data management becoming an important issue and, as such, heavily represented at this conference. It has also brought back issues of performance that might have been considered largely solved by the improvements in hardware, since data now has to be managed on devices of low power and small memory as well as on standard client and powerful server machines. We therefore invited papers on all aspects of data management, particularly related to how dataisused in the ubiquitous environment of the modern Internet by complex distributed and scientific applications.
Data management in a connected world : Essays dedicated to Hartmut Wedekind on the occasion of his 70th birthday
Data management systems play the most crucial role in building large application s- tems. Since modern applications are no longer single monolithic software blocks but highly flexible and configurable collections of cooperative services, the data mana- ment layer also has to adapt to these new requirements. Therefore, within recent years, data management systems have faced a tremendous shift from the central management of individual records in a transactional way to a platform for data integration, fede- tion, search services, and data analysis. This book addresses these new issues in the area of data management from multiple perspectives, in the form of individual contributions, and it outlines future challenges in the context of data management.
Data feminism
We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science.
