The ten-volume set LNCS 12949 - 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 - 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic. The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, human computer interaction, software design engineering, and others. Part II of the set follows two general tracks: geometric modeling, graphics and visualization; advanced and emerging applications. Further sections include the proceedings of the workshops: International Workshop on Advanced Transport Tools and Methods (A2TM 2021); International Workshop on Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2021); International Workshop on Advancements in Applied Machine-learning and Data Analytics (AAMDA 2021). At the end of the book there is a block of short papers. The chapter "Spatial justice models: an exploratory analysis on fair distribution of opportunities" is published open access under a CC BY license (Creative Commons Attribution 4.0 International License).

Person re-identification is the problem of associating observations of targets in different non-overlapping cameras. Most of the existing learning-based methods have resulted in improved performance on standard re-identification benchmarks, but at the cost of time-consuming and tediously labeled data. Motivated by this, learning person re-identification models with limited to no supervision has drawn a great deal of attention in recent years. In this book, we provide an overview of some of the literature in person re-identification, and then move on to focus on some specific problems in the context of person re-identification with limited supervision in multi-camera environments. We expect this to lead to interesting problems for researchers to consider in the future, beyond the conventional fully supervised setup that has been the framework for a lot of work in person re-identification. Chapter 1 starts with an overview of the problems in person re-identification and the major research directions. We provide an overview of the prior works that align most closely with the limited supervision theme of this book. Chapter 2 demonstrates how global camera network constraints in the form of consistency can be utilized for improving the accuracy of camera pair-wise person re-identification models and also selecting a minimal subset of image pairs for labeling without compromising accuracy. Chapter 3 presents two methods that hold the potential for developing highly scalable systems for video person re-identification with limited supervision. In the one-shot setting where only one tracklet per identity is labeled, the objective is to utilize this small labeled set along with a larger unlabeled set of tracklets to obtain a re-identification model. Another setting is completely unsupervised without requiring any identity labels. The temporal consistency in the videos allows us to infer about matching objects across the cameras with higher confidence, even with limited to no supervision. Chapter 4 investigates person re-identification in dynamic camera networks. Specifically, we consider a novel problem that has received very little attention in the community but is critically important for many applications where a new camera is added to an existing group observing a set of targets. We propose two possible solutions for on-boarding new camera(s) dynamically to an existing network using transfer learning with limited additional supervision. Finally, Chapter 5 concludes the book by highlighting the major directions for future research.

This book contains high-quality research articles and reviews that promote research and reflect the most recent advances in intelligent wavelet based techniques for advanced multimedia applications as well as other emerging areas. In recent time, wavelet transforms have become useful in many signal, image and video processing applications, especially for multimedia security and surveillance. A few applications of wavelets in security and surveillance are watermarking, fusion, steganography, object detection, tracking, motion recognition and intention recognition, etc. Wavelets are well capable of analyzing signal, image and video at different resolution levels, popularly known as multiresolution analysis. The multiresolution analysis is advantageous in multimedia security and surveillance applications. It provides flexibility in selection of different resolution levels that leads to better accuracy. Furthermore, recently sparse representation has become an advancement to analyze wavelet coefficients. It is observed that wavelet transforms possess the invariance property which makes them suitable for many vision applications. This book provides a concise overview of the current state of the art and disseminates some of the novel and exciting ideas and techniques. In addition, it is also helpful for the senior undergraduate and graduate students, researcher, academicians, IT professional and providers, citizens, customers as well as policy makers working in this area as well as other emerging applications demanding state-of-the-art wavelet based multimedia applications.

While voice is widely used in speech recognition and speaker identification, its application in biomedical fields is much less common. This book systematically introduces the authors' research on voice analysis for biomedical applications, particularly pathological voice analysis. Firstly, it reviews the field to highlight the biomedical value of voice. It then offers a comprehensive overview of the workflow and aspects of pathological voice analysis, including voice acquisition systems, voice pitch estimation methods, glottal closure instant detection, feature extraction and learning, and the multi-audio fusion approaches. Lastly, it discusses the experimental results that have shown the superiority of these techniques. This book is useful to researchers, professionals and postgraduate students working in fields such as speech signal processing, pattern recognition, and biomedical engineering. It is also a valuable resource for those involved in interdisciplinary research.

This book presents a collection of high-quality research papers accepted to multi-conference consisting of International Conference on Image Processing and Communications (IP&C 2021), International Conference on Computer Recognition Systems (CORES 2021), International Conference on Advanced Computer Systems (ACS 2021) held jointly in Bydgoszcz, Poland (virtually), in June 2021. The accepted papers address current computer science and computer systems-related technological challenges and solutions, as well as many practical applications and results. The first part of the book deals with advances in pattern recognition and classifiers, the second part is devoted to image processing and computer vision, while the third part addresses practical applications of computer recognition systems. Machine learning solutions for security and networks are tackled in part four of the book, while the last part collects papers on progress in advanced computer systems. We believe this book will be interesting for researchers and practitioners in many fields of computer science and IT applications.

The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning.

The LNCS volume 12878 constitutes the proceedings of the 15th Chinese Conference on Biometric Recognition, held in Shanghai, China, in September 2021.The 53 papers presented in this book were carefully reviewed and selected from 72 submissions. The papers cover a wide range of topics such as multi-modal biometrics and emerging biometrics; hand biometrics; facial biometrics; and speech biometrics.

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it "Deep Biometrics". The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications. Highlights the impact of deep learning over the field of biometrics in a wide area; Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.; Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

This book gathers selected papers presented at the International Conference on Advancements in Computing and Management (ICACM 2019). Discussing current research in the field of artificial intelligence and machine learning, cloud computing, recent trends in security, natural language processing and machine translation, parallel and distributed algorithms, as well as pattern recognition and analysis, it is a valuable resource for academics, practitioners in industry and decision-makers.

This book constitutes refereed proceedings of the 27th International Workshop on Frontiers of Computer Vision, IW-FCV 2021, held in Daegu, South Korea, in February 2021. The 17 full papers and 8 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers in the volume are organized according to the following topics: real-world applications; segmentation / object tracking; recognition; human behaviour; algorithm / application.

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists' preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice

This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging.

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.

This book constitutes the refereed proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events.
This 20 revised full papers presented together with 9 poster papers were carefully reviewed and selected from 59 submissions. The wide range of topics in this volume reflect the current state of research in the field, including representations, theory, novel operators and techniques, self organization, and applications.

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.

The book gives an introduction into the theory and practice of the transdisciplinary field of Character Computing, introduced by Alia El Bolock. The latest scientific findings indicate that "One size DOES NOT fit all" in terms of how to design interactive systems and predict behavior to tailor the interaction experience. Emotions are one of the essential factors that influence people's daily experiences; they influence decision making and how different emotions are interpreted by different individuals. For example, some people may perform better under stress and others may break. Building upon Rosalind Picard's vision, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions and how different characters perceive and react to these emotions, hence having richer and truly tailored interaction experiences. Psychological processes or personality traits are embedded in the existing fields of Affective and Personality Computing. However, this book is the first that systematically addresses this including the whole human character; namely our stable personality traits, our variable affective, cognitive and motivational states as well as our morals, beliefs and socio-cultural embedding. The book gives an introduction into the theory and practice of the transdisciplinary field of Character Computing. The emerging field leverages Computer Science and Psychology to extend technology to include the whole character of humans and thus paves the way for researchers to truly place humans at the center of any technological development. Character Computing is presented from three main perspectives: Profiling and sensing the character Leveraging characters to build ubiquitous character-aware systems Investigating how to extend Artificial Intelligence to create artificial characters

This book constitutes the proceedings of the 13th Mexican Conference on Pattern Recognition, MCPR 2021, which was planned to be held in Mexico City, Mexico, in June 2021. The conference was instead held virtually. The 35 papers presented in this volume were carefully reviewed and selected from 75 submissions. They are organized in the following topical sections: artificial intelligence techniques and recognition; pattern recognition techniques; neural networks and deep learning; computer vision; image processing and analysis; and medical applications of pattern recognition.

This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Reproducible Research in Pattern Recognition, RRPR 2021, held as a virtual event, in January 2021. The 8 revised full papers, presented together with 6 short papers, were carefully reviewed and selected from 18 submissions. The papers were organized into three main categories. The first contributions focused on reproducible research frameworks. The second category focused on reproducible research results and the last category included ICPR companion papers describing implementation and details that are an absolute requirement for reproducibility.

This book constitutes the proceedings of the 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, which took place during May 16-20, 2021. The conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic. The 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. They were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging.

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The second volume includes 83 papers organized in topical sections on diffusion weighted imaging, fMRI, statistical analysis and shape modeling, and registration.

This book constitutes the refereed proceedings of the International Workshop on Prostate Cancer Imaging, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 15 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 19 submissions. The papers cover the clinical areas of radiology, radiation oncology, and image guided intervention, addressing topics such as prostate segmentation, multi-modal prostate registration, and computer-aided diagnosis and classification of prostate cancer.

This authoritative and comprehensive handbook is the definitive work on the current state of the art of Biometric Presentation Attack Detection (PAD) - also known as Biometric Anti-Spoofing. Building on the success of the previous, pioneering edition, this thoroughly updated second edition has been considerably expanded to provide even greater coverage of PAD methods, spanning biometrics systems based on face, fingerprint, iris, voice, vein, and signature recognition. New material is also included on major PAD competitions, important databases for research, and on the impact of recent international legislation. Valuable insights are supplied by a selection of leading experts in the field, complete with results from reproducible research, supported by source code and further information available at an associated website. Topics and features: reviews the latest developments in PAD for fingerprint biometrics, covering optical coherence tomography (OCT) technology, and issues of interoperability; examines methods for PAD in iris recognition systems, and the application of stimulated pupillary light reflex for this purpose; discusses advancements in PAD methods for face recognition-based biometrics, such as research on 3D facial masks and remote photoplethysmography (rPPG); presents a survey of PAD for automatic speaker recognition (ASV), including the use of convolutional neural networks (CNNs), and an overview of relevant databases; describes the results yielded by key competitions on fingerprint liveness detection, iris liveness detection, and software-based face anti-spoofing; provides analyses of PAD in fingervein recognition, online handwritten signature verification, and in biometric technologies on mobile devicesincludes coverage of international standards, the E.U. PSDII and GDPR directives, and on different perspectives on presentation attack evaluation. This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.