الصفحة 67
الصفحة 67
Democracy in an Age of Globalisation
In Democracy in an Age of Globalisation, Otfried Höffe develops a comprehensive analysis of the demands, which the process of globalization exerts on the political organisations of humanity. The author starts from a diagnosis of the process of globalisation and frees its concept from its economistic narrowing: Globalisation is a comprehensive process which puts new strains on the economies and political systems of the world, the cultural and social structures of peoples. The scope of its challenges demands solutions, which transcend the powers of the classical nation-state. The question central to the book can be formulated as follows: "How can the social, moral and legal achievements of the nation-state be retained while its structure is reshaped to satisfy the requirements of a globalised world?"
Delay Differential Equations and Applications ; Proceedings of the NATO Advanced Study Institute held in Marrakech, Morocco, 9-21 September 2002
This Edition includes detailed discussion and analysis on: General Results and Linear Theory of Delay Equations in Finite Dimensional Spaces; Hopf Bifurcation, Centre Manifolds and Normal Forms for Delay Differential Equations; Functional Differential Equations in Infinite Dimensional Spaces; and Delay Differential Equations and Applications.
Deformable Models : Theory and Biomaterial Applications
Deformable Models: Theory and Biomaterial Applications is the second installation in the two-volume set Deformable Models which provides a wide cross-section of the methods and algorithms of variational and PDE methods in biomedical image analysis. The chapters are written by well-known researchers in this field, and the presentation style goes beyond an intricate abstraction of the theory into real application of the methods and description of the algorithms that were implemented. As such these chapters will serve the main goal of the editors of the volumes in bringing down to earth the latest in variational and PDE methods in modeling of soft tissues, covering the theory, algorithms, and applications of level sets and deformable models in medical image analysis.
Deformable Models : Biomedical and Clinical Applications
Deformable Models: Biomedical and Clinical Applications is the first entry in the two-volume set which provides a wide cross-section of the methods and algorithms of variational and Partial-Differential Equations (PDE) methods in biomedical image analysis. The chapters of Deformable Models: Biomedical and Clinical Applications are written by the well-known researchers in this field, and the presentation style goes beyond an intricate abstraction of the theory into real application of the methods and description of the algorithms that were implemented. As such these chapters will serve the main goal of the editors of these two volumes in bringing down to earth the latest in variational and PDE methods in modeling of soft tissues.
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 Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
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 architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
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.
Deep Integration, Global Firms, and Technology Spillovers
This book explores the impact of deep regional economic integration on spillovers of knowledge and technology across countries. Deep integration through signing deep regional trade agreements (DRTAs), which cover various policy areas in addition to tariff reductions, may or may not facilitate technology spillovers among their signatories. To understand the mechanism of the impact of deep integration on technology spillovers, this book starts by analyzing the behavior of global firms. Factors that affect global firms’ activities, such as export, foreign direct investment (FDI), offshore outsourcing, are examined. Micro data on Japanese firms are employed for the analysis. Then, the relationships between bilateral trade patterns and technology spillovers and between types of FDI and technology spillovers are investigated in detail.
Decrypted Secrets : Methods and Maxims of Cryptology
Cryptology, for millennia a "secret science", is rapidly gaining in practical importance for the protection of communication channels, databases, and software. Beside its role in computerized information systems (public key systems), more and more applications within computer systems and networks are appearing, which also extend to access rights and source file protection. The first part of this book treats secret codes and their uses - cryptography. The second part deals with the process of covertly decrypting a secret code - cryptanaly-sis - where in particular advice on assessing methods is given. The book presupposes only elementary mathematical knowledge.
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 under Deep Uncertainty : From Theory to Practice
Focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them.
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.
Decentralised Government in an Integrating World: Quantitative Studies for OECD Countries
The book offers a comprehensive empirical analysis of the determinants of changes in the distribution of expenditure and revenue-raising powers among fiscal tiers in OECD countries. Using a new indicator of fiscal decentralisation which accounts for subnational decision-making autonomy, common decentralisation trends are investigated.
Dealing with Uncertainties : A Guide to Error Analysis
Dealing with Uncertainties proposes and explains a new approach for the analysis of uncertainties. Firstly, it is shown that uncertainties are the consequence of modern science rather than of measurements. Secondly, it stresses the importance of the deductive approach to uncertainties.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
Data Warehousing and Knowledge Discovery ; Vol.4081 ; 8th International Conference, DaWaK 2006, Krakow, Poland, September 4-8, 2006, Proceedings
DaWaK aimed at providing the right and logical balance between data warehousing and knowledge discovery. In data warehousing the papers cover different research problems, such as advanced techniques in OLAP visuali- tion and multidimensional modelling, innovation of ETL processes and integration problems, materialized view optimization, very large data warehouse processing, data warehouses and data mining applications integration, data warehousing for real-life applications, e. g. , medical applications and spatial applications. In data mining and knowledge discovery, papers are focused on a variety of topics from data streams analysis and mining, ontology-based mining techniques, mining frequent item sets, clustering, association and classification, patterns and so on.
