الصفحة 3
الصفحة 3
The Geospatial Web : How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society
Summarizes the latest research on the Geospatial Web’s technical foundations, describes information services and collaborative tools built on top of geo-browsers, and investigates the environmental, social and economic impacts of geospatial applications. The role of contextual knowledge in shaping the emerging network society deserves particular attention. By integrating geospatial and semantic technology, such contextual knowledge can be extracted automatically – for example, when processing Web documents to identify relevant content for customized news services.
The digital health in the pandemic era
Digital health, virtual assistance, and telemedicine are terms often used interchangeably to refer to remote medical assistance, monitoring and care. Several studies and insights have developed these issues, analyzing the advantages and disadvantages and successes and failures and offering reflections on the implications and issues of these technologies in the health domain. The results of these investigations are affecting the redesign of hospital and outpatient management based on digital innovation using eHealth and mHealth. During the COVID-19 pandemic, this approach made it possible to offer assistance and continue care at home, protecting patients, preserving health workers, limiting the spread of the virus, and reducing the need for hospitalization. This reprint contains contributions dealing with the development of DH during the COVID-19 pandemic.
The Adaptive Web : Methods and Strategies of Web Personalization
Following the increase in of the information available on the Web, the diversity of its users and the complexity of Web applications, researchers started developing adaptive Web systems that tailored their appearance and behavior to each individual user or user group. Adaptive systems were designed for different usage contexts, exploring different kinds of personalization. Web personalization has evolved into a large research field attracting scientists from different communities such as hypertext, user modeling, machine learning, natural language generation, information retrieval, intelligent tutoring systems, cognitive science, and Web-based education.
The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy ; SPIoT-2020, Vol.1
Presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies.
Text Mining for Information Professionals : An Uncharted Territory
Focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. Contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. They contain the code, data, and notebooks for the case studies; a summary of all the stories shared by the librarians/faculty; and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment.
Text Data Mining
Offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview.
Text Analytics : An introduction to the science and applications of unstructured information analysis
A concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.
Tensor Network Contractions : Methods and Applications to Quantum Many-Body Systems
This book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.
Symbol Grounding and Beyond ; 3rd International Workshop on the Emergence and Evolution of Linguistic Communications, EELC 2006, Rome, Italy, September 30-October 1, 2006, Proceedings
Focuses on the evolution and emergence of language. This isa fast-growing interdisciplinary research area with researchers coming from dis-ciplines such as anthropology, linguistics, psychology, primatology, neuroscience,cognitive science and computer science. Although most papers focus on evolu-tion, a number of papers focus more on language acquisition.
Symbiotic interaction ; 5th International workshop, Symbiotic 2016, Padua, Italy, September 29–30, 2016, revised selected papers
The 12 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 23 submissions. The idea of symbiotic systems put forward in this workshop capitalizes on the computers’ ability to implicitly detect the users goals, preferences or/and psycho-physiological states and thereby enhancing human-computer interaction (HCI). The papers present an overview of the symbiotic relationships between humans and computers with emphasis on user-driven research on symbiotic systems, adaptive systems, implicit input data, physiological computing and BCI, but also on understanding the nature of the interdependence and agency between computers and humans more broadly.
Switching and learning in feedback systems : European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10, 2003, Revised Lectures and Selected Papers
A central theme in the study of dynamic systems is the modelling and control of uncertain systems. While ‘uncertainty’ has long been a strong motivating factor behind many techniques developed in the modelling, control, statistics and mathematics communities, the past decade, in particular, has witnessed remarkable progress in this area with the emergence of a number of powerful new methods for both modelling and controlling uncertain dynamic systems. The specific objective of this book is to describe and review some of these exciting new approaches within a single volume. Our approach was to invite some of the leading researchers in this area to contribute to this book by submitting both tutorial papers on their speci?c area of research, and to submit more focussed research papers to document some of the latest results in the area. We feel that collecting some of the main results together in this manner is particularly important as many of the important ideas that emerged in the past decade were derived in a variety of academic disciplines.
Sustainable Construction in the Era of the Fourth Industrial Revolution
Provides readers with an understanding of various concepts, benefits, and practices that the adoption of Fourth Industrial Revolution (4IR) technolo>gies can bring when working towards sustainable construction goals. As digitalization continues to advance rapidly, the pressures on stakeholders in the architecture, engineering, construction, and operation (AECO) industry to revamp and restructure their activities and outputs become increasingly prev>alent. This research book explains the importance of various digital tools and principles to achieve sustainable construction projects. It adopts various stand>ards and concepts to highlight how 4IR technologies could assist and accelerate construction sustainability. It is the first book to link construction management with various digital tools to enhance construction projects’ sustainability. It also provides an in-depth insight into the concept of sustainable construction 4.0 across both developing and developed countries for construction professionals, sustainability experts, researchers, educators, and other stakeholders.The book can be adopted as a research guide, framework, and reference on sustainable construction, the concept of sustainable projects, digitalization in the construction industry, and the 4IR.
Survey of Text Mining II : Clustering, Classification, and Retrieval
Continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining.
Support Vector Machines : Theory and Applications
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in the respective fields.
Support Vector Machines
Explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods.The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field.The book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques.
Super-Recursive Algorithms
New discoveries about algorithms are leading scientists beyond the Church-Turing Thesis, which governs the "algorithmic universe" and asserts the conventionality of recursive algorithms. A new paradigm for computation, the super-recursive algorithm, offers promising prospects for algorithms of much greater computing power and efficiency. Super-Recursive Algorithms provides an accessible, focused examination of the theory of super-recursive algorithms and its ramifications for the computer industry, networks, artificial intelligence, embedded systems, and the Internet. The book demonstrates how these algorithms are more appropriate as mathematical models for modern computers, and how these algorithms present a better framework for computing methods in such areas as numerical analysis, array searching, and controlling and monitoring systems. In addition, a new practically-oriented perspective on the theory of algorithms, computation, and automata, as a whole, is developed. Problems of efficiency, software development, parallel and distributed processing, pervasive and emerging computation, computer architecture, machine learning, brain modeling, knowledge discovery, and intelligent systems are addressed.
Subspace, Latent Structure and Feature Selection ; Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers
The inspiration for this volume was a workshop held under the auspices of thePASCAL Network of Excellence. The aimof this preface is to provide an overview of the contributions to this volume,placing this research in its wider context.The aim of the workshop was to bring together researchers working on sub-space and latent variable techniques in different research communities in orderto create bridges and enable cross-fertilization of ideas.
String Processing and Information Retrieval ; Vol. 4209 ; 13th International Conference, SPIRE 2006, Glasgow, UK, October 11-13, 2006, Proceedings
This volume contains the papers presented at the 13th International Symposium on String Processing and Information Retrieval (SPIRE), held October 11-13, 2006, in Glasgow, Scotland. The papers in this volume were selected from 102 papers submitted from over 20 di?erent countries in response to the Call for Papers.
Statistical quantitative methods in finance : From theory to quantitative portfolio management
Explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. additionally, the book delves into non-linear methods and bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. the book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. these enhancements are illustrated through real-world examples from finance and econometrics, accompanied by python code.
Statistical Network Analysis : Models, Issues, and New Directions; ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers
This volume was prepared to share with a larger audience the exciting ideas and work presented at an ICML 2006 workshop of the same title. Network models have a long history. Sociologists and statisticians made major advances in the 1970s and 1980s, culminating in part with a number of substantial databases and the class of exponential random graph models and related methods in the early 1990s. Physicists and computer scientists came to this domain cons- erably later, but they enriched the array of models and approaches and began to tackle much larger networks and more complex forms of data.
