This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.

This book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 - 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.

Der vorliegende Band des Kompendiums Medieninformatik beschaftigt sich mit der Medienpraxis. Er behandelt Aspekte der praktischen Informatik und ihre Anwendung in der Medientechnik wie die Entwicklung von Multimedia-Anwendungen, Grundlagen der Computergrafik sowie Theorie und Praxis von Mediendatenbanken. Hinzu kommen ausgewahlte Anwendungen der Medieninformatik: Mit Content-Related-Technologien konnen mediale Informationen in besonders effizienter Weise organisiert, strukturiert und an die richtigen Empfanger verteilt werden.

Zusammen mit dem Band "Mediennetze" beschreibt das Kompendium Medieninformatik die komplette Wertschopfungskette von digitalen Mediendaten: Erzeugung, Kodierung, Transport durch drahtgebundene oder drahtlose Netze bis hin zum Endnutzer."

Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.

IT-Produkte sind haufig sehr komplex. Daher spielen Berater bei Kaufentscheidungen eine wichtige Rolle. In dem Band setzen sich die Autoren mit dieser Situation auseinander und befassen sich mit ihren Auswirkungen auf Kommunikationsabteilungen von Unternehmen. Sie gehen auch auf die wachsende Bedeutung von Social Media und den damit einhergehenden Veranderungen der Beraterlandschaft ein. Zudem werden regionale Besonderheiten bzw. die Situation in Deutschland im internationalen Kontext betrachtet."

Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook - through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.

This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

The book collects the contributions to the NATO Advanced Study Institute on "Speech Recognition and Understanding: Recent Advances, Trends and Applications", held in Cetraro, Italy, during the first two weeks of July 1990. This Institute focused on three topics that are considered of particular interest and rich of i'p.novation by researchers in the fields of speech recognition and understanding: Advances in Hidden Markov modeling, connectionist approaches to speech and language modeling, and linguistic processing including language and dialogue modeling. The purpose of any ASI is that of encouraging scientific communications between researchers of NATO countries through advanced tutorials and presentations: excellent tutorials were offered by invited speakers that present in this book 15 papers which sum marize or detail the topics covered in their lectures. The lectures were complemented by discussions, panel sections and by the presentation of related works carried on by some of the attending researchers: these presentations have been collected in 42 short contributions to the Proceedings. This volume, that the reader can find useful for an overview, although incomplete, of the state of the art in speech understanding, is divided into 6 Parts.

This book constitutes the refereed proceedings of the 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks.

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.

This book constitutes the proceedings of the 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juarez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.

The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.

This book is open access. This book undertakes a multifaceted and integrated examination of biometric identification, including the current state of the technology, how it is being used, the key ethical issues, and the implications for law and regulation. The five chapters examine the main forms of contemporary biometrics-fingerprint recognition, facial recognition and DNA identification- as well the integration of biometric data with other forms of personal data, analyses key ethical concepts in play, including privacy, individual autonomy, collective responsibility, and joint ownership rights, and proposes a raft of principles to guide the regulation of biometrics in liberal democracies. Biometric identification technology is developing rapidly and being implemented more widely, along with other forms of information technology. As products, services and communication moves online, digital identity and security is becoming more important. Biometric identification facilitates this transition. Citizens now use biometrics to access a smartphone or obtain a passport; law enforcement agencies use biometrics in association with CCTV to identify a terrorist in a crowd, or identify a suspect via their fingerprints or DNA; and companies use biometrics to identify their customers and employees. In some cases the use of biometrics is governed by law, in others the technology has developed and been implemented so quickly that, perhaps because it has been viewed as a valuable security enhancement, laws regulating its use have often not been updated to reflect new applications. However, the technology associated with biometrics raises significant ethical problems, including in relation to individual privacy, ownership of biometric data, dual use and, more generally, as is illustrated by the increasing use of biometrics in authoritarian states such as China, the potential for unregulated biometrics to undermine fundamental principles of liberal democracy. Resolving these ethical problems is a vital step towards more effective regulation.

This book constitutes the refereed proceedings of the 13th International Conference on Computer Vision Systems, ICVS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.

This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including machine learning, computer vision, signal processing, dynamical systems and scientific computing. This text introduces the differential geometry and Riemannian geometry concepts that will help applied mathematics, computer science and engineering students and researchers gain a firm mathematical grounding to use these tools confidently in their research. Its chart-last approach will prove more intuitive from an optimizer's viewpoint, and all definitions and theorems are motivated to build time-tested optimization algorithms. Starting from first principles, the text goes on to cover current research on topics including worst-case complexity and geodesic convexity. Readers will appreciate the tricks of the trade for conducting research and for numerical implementations sprinkled throughout the book.

Biometric recognition, or simply biometrics, is the science of establishing the identity of a person based on physical or behavioral attributes. It is a rapidly evolving field with applications ranging from securely accessing one's computer to gaining entry into a country. While the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate/graduate students. The three commonly used modalities in the biometrics field, namely, fingerprint, face, and iris are covered in detail in this book. Few other modalities like hand geometry, ear, and gait are also discussed briefly along with advanced topics such as multibiometric systems and security of biometric systems. Exercises for each chapter will be available on the book website to help students gain a better understanding of the topics and obtain practical experience in designing computer programs for biometric applications. These can be found at: http: //www.csee.wvu.edu/ ross/BiometricsTextBook/.

Designed for undergraduate and graduate students in computer science and electrical engineering, "Introduction to Biometrics" is also suitable for researchers and biometric and computer security professionals."

Medical imaging is an important topic which is generally recognised as key to better diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound.This book focuses primarily on state-of-the-art model-based segmentation techniques which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors based in both industry and academia and presents a host of new material including algorithms for:- brain segmentation applied to MR;- neuro-application using MR; - parametric and geometric deformable models for brain segmentation;- left ventricle segmentation and analysis using least squares and constrained least squares models for cardiac X-rays; - left ventricle analysis in echocardioangiograms;- breast lesion detection in digital mammograms;detection of cells in cell images.As an overview of the latest techniques, this book will be of particular interest to students and researchers in medical engineering, image processing, computer graphics, mathematical modelling and data analysis. It will also be of interest to researchers in the fields of mammography, cardiology, pathology and neurology.

This book contains revised and extended versions of selected papers from the 10th and 11th International Conference on Pattern Recognition, ICPRAM 2021 and 2022, held in February 2021 and 2022. Due to COVID-19 pandemic the conferences were held virtually. Both conferences received in total 204 submissions from which 8 full papers were carefully reviewed and selected for presentation in this volume. The papers span a wide range of investigation as well as development lines, which of course always reflect the last trends of research in the pattern recognition community.

Die SchAnheit der Natur mit dem Rechner nachzubilden, fasziniert die Computergraphik seit jeher. Im vorliegenden Buch werden Verfahren zur Erzeugung kA1/4nstlicher Pflanzenmodelle beschrieben und deren Anwendung in Bereichen wie Simulation, Virtual Reality, Botanik, Landschaftsplanung und Architektur. Die Modelle werden zu GArten, Parks und ganzen Landschaften kombiniert. Die Palette der Darstellungsformen reicht von tAuschend echt wirkenden Bildern bis zu abstrakten ReprAsentationen. Mit Ahnlichen Algorithmen kAnnen organische KArper hergestellt, verAndert und animiert werden. Die beigefA1/4gten Programme (Windows) erlauben dies auch dem Leser.

This three-volume set LNCS 13604-13606 constitutes revised selected papers presented at the Second CAAI International Conference on Artificial Intelligence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establish a global platform for international academic exchange, promote advanced research in AI and its affiliated disciplines such as machine learning, computer vision, natural language, processing, and data mining, amongst others.

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022.The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more.

This three-volume set LNCS 13604-13606 constitutes revised selected papers presented at the Second CAAI International Conference on Artificial Intelligence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI).The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establish a global platform for international academic exchange, promote advanced research in AI and its affiliated disciplines such as machine learning, computer vision, natural language, processing, and data mining, amongst others.