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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 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.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Law and the Semantic Web : Legal Ontologies, Methodologies, Legal Information Retrieval, and Applications
As part of this objective, ICT (information and communication technologies) services should become available for every citizen, and for all schools, homes and businesses. The book you have in front of you is about Semantic Web technology and law. Law is something omnipresent; all citizens — at some points in their lives — have to deal with it. In addition, law involves a large group of professionals, and is a mul- billion business world wide. Information technology is important because it that can improve citizens’ interaction with law, as well as improve legal professionals’ work environment. Legal professionals dedicate a significant amount of their time to finding, reading, analyzing and synthesizing information in order to take decisions, and prepare advice and trials, among other tasks. As part of the “Semantic-Based Knowledge and Content Systems” Strategic Objective, the European Commission is funding projects to construct technology to make the Semantic Web vision come true. 1 The articles in this book are related to two current foci of the Strategic Objective : • Knowledge acquisition and modelling, capturing knowledge from raw information and multimedia content in webs and other distributed repositories to turn poorly structured information into machi- processable knowledge.
Knowledge graphs and big data processing
This book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights.
Knowledge and Information Visualization : Searching for Synergies
The basic ideas underlying knowledge visualization and information vi- alization are outlined. In a short preview of the contributions of this volume, the idea behind each approach and its contribution to the goals of the book are outlined. 2 The Basic Concepts of the Book Three basic concepts are the focus of this book: "data", "information", and "kno- edge". There have been numerous attempts to define the terms "data", "information", and "knowledge", among them, the OTEC Homepage "Data, Information, Kno- edge, and Wisdom" (Bellinger, Castro, & Mills, see http://www.syste- thinking.org/dikw/dikw.htm): Data are raw. They are symbols or isolated and non-interpreted facts. Data rep- sent a fact or statement of event without any relation to other data. Data simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself.
Knowledge and Data Management in GRIDs
Knowledge and Data Management in GRIDs is the third volume of the CoreGRID series and brings together scientific contributions by researchers and scientists working on storage, data, and knowledge management in GRID and Peer-to-Peer systems. This volume presents the latest GRID solutions and research results in key areas of knowledge and data management such as distributed storage management, GRID databases, Semantic GRID and GRID-aware data mining.
Journal on Data Semantics XI
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
Journal on Data Semantics X
Web semantics and semi-structured data , Semantic caching , Data warehousing and semantic data mining , Spatial, temporal, multimedia and multimodal semantics , Semantics in data visualization , Semantic services for mobile users , Supporting tools , Applications of semantic-driven approaches These topics are to be understood as specifically related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not from the topics in the list. While the physical appearance of the journal issues is like the books from the we- known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board.
Journal on Data Semantics VIII
Springer's LNCS Journal on Data Semantics aims at providing a highly visible dissemination channel for most remarkable work that in one way or another addresses research and development on issues related to the semantics of data. The target domain ranges from theories supporting the formal definition of semantic content to innovative domain-specific application of semantic knowledge. This publication channel should be of the highest interest to researchers and advanced practitioners working on the Semantic Web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
Journal on Data Semantics VII
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. Based on the highly visible publication platform Lecture Notes in Computer Science, this new journal is widely disseminated and available worldwide. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
Journal on Data Semantics VI
Data warehousing and semantic data mining • Spatial, temporal, multimedia and multimodal semantics • Semantics in data visualization • Semantic services for mobile users • Supporting tools • Applications of semantic-driven approaches These topics are to be understood as speci?cally related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearanceof the journal issues looks like the books from the well-known Springer LNCS series, the mode of operation is that of a jo- nal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of best papers from major conferences addressing data semantics issues. Special issues, foc- ing on a speci?c topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board.
Journal on Data Semantics V
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. Based on the highly visible publication platform Lecture Notes in Computer Science, this new journal is widely disseminated and available worldwide. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
Journal on Data Semantics IX
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge.
Journal on Data Semantics III
– semantic caching – data warehousing and semantic data mining – spatial, temporal, multimedia and multimodal semantics – semantics in data visualization – semantic services for mobile users – supporting tools – applications of semantic-driven approaches These topics are to be understood as speci?cally related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearance of the journal issues looks like the books from the well-known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of best papers from major conferences addressing data semantics issues. Special issues, focusing on a speci?c topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board. Finally, it is also possible that a journal issue be devoted to a single text.
Journal on Data Semantics II
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. Based on the highly visible publication platform Lecture Notes in Computer Science, this new journal is widely disseminated and available worldwide. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
Java 17 Recipes : A Problem-Solution Approach
Quickly find solutions to dozens of common programming problems encountered while building Java applications, with recipes presented in the popular problem-solution format. Look up the programming problem that you want to resolve. Read the solution. Apply the solution directly in your own code. Problem solved! covers of some of the newest features, APIs, and more such as pattern matching for switch, Restore Always-Strict-Floating-Point-Semantics, enhanced pseudo-random number generators, the vector API, sealed classes, and enhancements in the use of String. Source code for all recipes is available in a dedicated GitHub repository. This must-have reference belongs in your library. You will learn : Look up solutions to everyday problems involving Java SE 17 LTS and other recent releases / Develop Java SE applications using the latest in Java SE technology / Incorporate Java major features introduced in versions 17, 16, and 15 into your code
Issues in Multi-Agent Systems : The AgentCities.ES Experience
The purpose of this book is to present current status of this technology by looking at its application in different domains, such as electronic markets, e-tourism, ambience intelligence, and complex system analysis.It starts by discussing software engineering issues for the development of multi-agent systems, how much it costs to build a multi-agent system, and which methods and tools are currently available. Next chapters present some of the most relevant aspects that are considered for the development of multi-agent systems.
Computable Models of the Law : Languages, Dialogues, Games, Ontologies
This book originate from a workshop held at the European University Institute of Florence, Italy, in December 2006. The workshop was devoted to the discussion of the different ways of understanding and explaining contemporary law, for the purpose of building computable models of it -- especially models enabling the development of computer applications for the legal domain.
