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الصفحة 37
الصفحة 37
Mathematical Methods in Computer Science : Essays in Memory of Thomas Beth
This Festschrift volume contains the proceedings of the conference Mathematical Methods in Computer Science, MMICS 2008, which was held during December 17-19, 2008, in Karlsruhe, Germany, in memory of Thomas Beth.The themes of the conference reflected the many interests of Thomas Beth. Although, these interests might seem diverse, mathematical methods and especially algebra as a language constituted the common denominator of all of his scientific achievements.
Mathematical Linguistics
Mathematical Linguistics introduces the mathematical foundations of linguistics to computer scientists, engineers, and mathematicians interested in natural language processing. The book presents linguistics as a cumulative body of knowledge from the ground up, with no prior knowledge of linguistics being assumed, covering more than the average two-semester introductory course in linguistics.This comprehensive, reader-friendly volume offers readers a high-level orientation, discussing the foundations of the field and presenting both the classical work and the most recent results. It covers an extremely rich array of topics including not only syntax and semantics but also phonology and morphology, probabilistic approaches, complexity, learnability, and the analysis of speech and handwriting.
Matching Properties of Deep Sub-Micron MOS Transistors
Matching Properties of Deep Sub-Micron MOS Transistors examines this interesting phenomenon. Microscopic fluctuations cause stochastic parameter fluctuations that affect the accuracy of the MOSFET. For analog circuits this determines the trade-off between speed, power, accuracy and yield.
Markov Models for Pattern Recognition : From Theory to Applications
Describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems.
Many-Core Computing : Hardware and software
Provides a timely and coherent account of the recent advances in many-core computing research. Starting with programming models, operating systems and their applications; it presents runtime management techniques, followed by system modelling, verification and testing methods, and architectures and systems. Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era. There is considerable interest in developing methods, tools, architectures and applications to support many-core computing.
Managing Risk and Information Security : Protect to Enable
Examine the evolving enterprise security landscape and discover how to manage and survive risk. While based primarily on the author’s experience and insights at major companies where he has served as CISO and CSPO, the book also includes many examples from other well-known companies and provides guidance for a management-level audience. Managing Risk and Information Security provides thought leadership in the increasingly important area of enterprise information risk and security. It describes the changing risk environment and why a fresh approach to information security is needed. Because almost every aspect of an enterprise is now dependent on technology not only for internal operations but increasing as a part of product or service creation, the focus of IT security must shift from locking down assets to enabling the business while managing and surviving risk. This edition discusses business risk from a broader perspective, including privacy and regulatory considerations. It describes the increasing number of threats and vulnerabilities and offers strategies for developing solutions. These include discussions of how enterprises can take advantage of new and emerging technologies—such as social media and the huge proliferation of Internet-enabled devices—while minimizing risk.
Managing Cyber Threats : Issues, Approaches, and Challenges
Brings together the latest techniques for managing cyber threats, developed by some of the world’s leading experts in the area. The book includes broad surveys on a number of topics, as well as specific techniques. It provides an excellent reference point for researchers and practitioners in the government, academic, and industrial communities who want to understand the issues and challenges in this area of growing worldwide importance.
Management of Data Center Networks
Discover state-of-the-art developments in DCNs from leading international voices in the field. Delivers a rigorous and insightful exploration of the network management challenges that present within intra- and inter-data center networks, including reliability, routing, and security. The book also discusses new architectures found in data center networks that aim to minimize the complexity of network management while maximizing Quality of Service, like Wireless/Wired DCNs, server-only DCNs, and more. offers: A thorough overview of the architectures of data center networks, including the classification of switch-centric, server-centric, enhanced, optical, and wireless DCN architectures An exploration of resource management in wired and wireless data center networks, including routing and wireless channel allocation and assignment challenges and criteria Practical discussions of inter-data center networks, including an overview of basic virtual network embedding Examinations of energy and security management in data center networks
Managed Software Evolution
This open access book presents the outcomes of the “Design for Future – Managed Software Evolution” .The different lifecycles of software and hardware platforms lead to interoperability problems in such systems. Instead of separating the development, adaptation and evolution of software and its platforms, as well as aspects like operation, monitoring and maintenance, they should all be integrated into one overarching process. Accordingly, the book is split into three major parts, the first of which includes an introduction to the nature of software evolution, followed by an overview of the specific challenges and a general introduction to the case studies used in the project. The second part of the book consists of the main chapters on knowledge carrying software, and cover tacit knowledge in software evolution, continuous design decision support, model-based round-trip engineering for software product lines, performance analysis strategies, maintaining security in software evolution, learning from evolution for evolution, and formal verification of evolutionary changes. In turn, the last part of the book presents key findings and spin-offs. The individual chapters there describe various case studies, along with their benefits, deliverables and the respective lessons learned. An overview of future research topics rounds out the coverage.
Malware Detection
Malware Detection, based on the Special ARO/DHS Workshop on Malware Detection at Rosslyn, VA, in 2005, captures the state of the art research in the area of malicious code detection, prevention and mitigation.
Machine-learning-assisted intelligent processing and optimization of complex systems
Focuses on the most recent developments in intelligent optimization methods and their applications in various test cases. The reprint covers various topics, including distributed multiagent modeling, metaheuristic algorithms, multisource data fusion, mobile computing and mobile sensing, machine learning-based intelligent processing for modeling complex manufacturing systems, and data-driven intelligent modeling
Machine Learning Refined : Foundations, Algorithms, and Applications
Provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology.
Machine learning methods for reverse engineering of defective structured surfaces
Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine Learning in Computer Vision
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Machine Learning for Multimedia Content Analysis
Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.
Machine learning for data streams : With practical examples in MOA
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA.
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020
Presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.
Machine learning for brain disorders
Organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
