This edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will also be able to learn about the use of visualisation techniques and technologies for the historical and forensic settings. The reader will be able to explore the utilisation of technologies from a number of fields to enable an engaging and meaningful visual representation of the biomedical sciences. The chapters presented in this volume cover such a diverse range of topics, with something for everyone. We present here chapters on technology enhanced learning in neuroanatomy; 3D printing and surgical planning; changes in higher education utilising technology, decolonising the curriculum and visual representations of the human body in education. We also showcase how not to use protective personal equipment inspired by the pandemic; anatomical and historical visualisation of obstetrics and gynaecology; 3D modelling of carpal bones and augmented reality for arachnid phobias for public engagement. In addition, we also present face modelling for surgical education in a multidisciplinary setting, military medical museum 3D digitising of historical pathology specimens and finally computational fluid dynamics.

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.

This book is focused on the challenges of digital transition in building construction, and potential solutions through the use of multi-criteria analysis. It provides clear explanations of proposed approaches from both a theoretical and practical point of view, including augmented reality and user-reporting. New Approaches for Multi-Criteria Analysis in Constructions begins by explaining classic multi-criteria analysis methods, such as Analytic Hierarchy Processes and the Simos-Roy-Figueira method, before moving on to discuss Augmented Reality - Decision Making. This new approach provides a tool to investigate user's perceptions, and utilises an interactive experience of the real-world environment combined with classic methods to provide a large amount of visual information. Finally, a detailed guide to user-reporting is presented. Offering new possibilities for applying multi-criteria analysis in a simpler, faster and more accessible way, this book supports analysis which considers users experience and perception. Architects, engineers, researchers and practitioners will be able to utilise augmented reality environments, multi-criteria analysis and user reporting for the building design choices, supply selection, maintenance strategies, risk and complex performance assessment.

This book constitutes revised selected papers of the 10th International Conference on Analysis of Images, Social Networks and Texts, AIST 2021, held in Tbilisi, Georgia, in December 2021. Due to the COVID-19 pandemic the conference was held in hybrid mode. The 17 full papers were carefully reviewed and selected from 118 submissions, out of which 92 were sent to peer review. The papers are organized in topical sections on natural language processing; computer vision; data analysis and machine learning; social network analysis; theoretical machine learning and optimisation.

This book constitutes the refereed proceedings of the 11th International Conference on Design, User Experience, and Usability, DUXU 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022. The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions. The DUXU 2022 proceedings comprise three volumes; they were organized in the following topical sections: Part I: Processes, Methods, and Tools for UX Design and Evaluation; User Requirements, Preferences, and UX Influential Factors; Usability, Acceptance, and User Experience Assessment. Part II: Emotion, Motivation, and Persuasion Design; Design for Well-being and Health.- Learning Experience Design; Globalization, Localization, and Culture Issues. Part III: Design Thinking and Philosophy; DUXU Case Studies; Design and User Experience in Emerging Technologies.

This book presents high-quality, peer-reviewed papers from the International Conference on "Innovations in Computational Intelligence and Computer Vision (ICICV 2021)," hosted by Manipal University Jaipur, Rajasthan, India, on August 5-6, 2021. Offering a collection of innovative ideas from researchers, scientists, academics, industry professionals and students, the book covers a variety of topics, such as artificial intelligence and computer vision, image processing and video analysis, applications and services of artificial intelligence and computer vision, interdisciplinary areas combining artificial intelligence and computer vision, and other innovative practices.

This book constitutes the proceedings of the 26th Annual Conference on Research in Computational Molecular Biology, RECOMB 2022, held in San Diego, CA, USA in May 2022. The 17 regular and 23 short papers presented were carefully reviewed and selected from 188 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.

The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

This volume constitutes selected papers presented at the First International Conference on Emerging Technology Trends in IoT and Computing, TIOTC 2021, held in Erbil, Iraq, in June 2021. The 26 full papers were thoroughly reviewed and selected from 182 submissions. The papers are organized in the following topical sections: Internet of Things (IOT): services and applications; Internet of Things (IOT) in healthcare industry; IOT in networks, communications and distributed computing; real world application fields in information science and technology.

This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

This book gives a broad overview of research on sound simulation driven by a variety of applications. Vibrating objects produce sound, which then propagates through a medium such as air or water before finally being heard by a listener. As a crucial sensory channel, sound plays a vital role in many applications. There is a well-established research community in acoustics that has studied the problems related to sound simulation for six decades. Some of the earliest work was motivated by the design of concert halls, theaters, or lecture rooms with good acoustic characteristics. These problems also have been investigated in other applications, including noise control and sound design for urban planning, building construction, and automotive applications. Moreover, plausible or realistic sound effects can improve the sense of presence in a virtual environment or a game. In these applications, sound can provide important clues such as source directionality and spatial size. The book first surveys various sound synthesis methods, including harmonic synthesis, texture synthesis, spectral analysis, and physics-based synthesis. Next, it provides an overview of sound propagation techniques, including wave-based methods, geometric-based methods, and hybrid methods. The book also summarizes various techniques for sound rendering. Finally, it surveys some recent trends, including the use of machine learning methods to accelerate sound simulation and the use of sound simulation techniques for other applications such as speech recognition, source localization, and computer-aided design.

This book explores the visualization of three-dimensional non-Euclidean spaces using ray-tracing techniques in Graphics Processing Unit (GPU). This is a trending topic in mathematical visualization that combines the mathematics areas of geometry and topology, with visualization concepts of computer graphics. Several conditions made this a special moment for such topic. On one hand, the development of mathematical research, computer graphics, and algorithms have provided the necessary theoretical framework. On the other hand, the evolution of the technologies and media allows us to be immersed in three-dimensional spaces using Virtual Reality. The content of this book serves both experts in the areas and students. Although this is a short book, it is self-contained since it considers all the ideas, motivations, references, and intuitive explanations of the required fundamental concepts.

This book gathers selected research papers presented at the First Asian Symposium on Cellular Automata Technology (ASCAT 2022), organized online by academicians from Kolkata, India, during March 3-5, 2022. The book presents one of the most emergent areas in natural computing, cellular automaton (CA). CA is a paradigm of uniform fine-grained parallel computation which has been explored to understand complex systems by developing its model at the microscopic level. The book discusses many real-life problems in the domain of very large-scale integration (VLSI) design and test, pattern recognition and classification, cryptography, pseudo-random pattern generation, image processing, sensor networks, material science, etc., by using CA.

This book deepens the understanding of people through smartphone data obtained via mobile sensing and applies psychological insights for social networking applications. The author first introduces TYDR, an application for researching smartphone data and user personality. A novel, structured privacy model for mobile sensing applications is developed and the obtained empirical results help researchers gauge what data they can expect users to share in daily-life studies. The new research findings, the concept of mobile sensing, and psychological insights about the formation and structure of real-life social networks are integrated into the field of social networking. Finally, for this novel integration, the author presents concepts, decentralized software architectures, and fully realized prototypes that recommend new contacts, media, and locations to individual users and groups of users.

This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic. The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021). The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.

This volume constitutes selected papers presented at the 28th International Conference on Systems, Signals and Image Processing, IWSSIP 2021, held in Bratislava, Slovakia, in June 2021. Due to the COVID-19 pandemic the conference was held online. The presented 14 full and 5 short papers were thorougly reviewed and selected from the 76 submissions. The papers focus on various aspects of advanced signal processing in different scientific areas, including filter design, Fourier and other transforms, feature extraction, machine learning and system adaptation to user-oriented products like 5G networks, IoT, virtual teleport or tele-surgery operations.

This volume constitutes the selected papers presented at the First International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2021, hed in Varanasi, India, in December 2021. Due to the COVID-19 pandemic the conference was held online. The 61 papers presented were thoroughly reviewed and selected from 593 submissions. They are organized in topical sections on advanced network technologies and intelligent computing. ;

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field.

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability. After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn Solve problems in computer vision with PyTorch. Implement transfer learning and perform image classification, object detection, image segmentation, and other computer vision applications Design and develop production-grade computer vision projects for real-world industry problems Interpret computer vision models and solve business problems Who This Book Is For Data scientists and machine learning engineers interested in building computer vision projects and solving business problems

This volume constitutes the refereed proceedings of the 13th International Conference on Intelligent Human Computer Interaction, IHCI 2021, which took place in Kent, OH, USA, in December 2021. The 59 full and 9 short papers included in these proceedings were carefully reviewed and selected from a total of 142 submissions. The papers were organized in topical sections named human centered AI; and intelligent interaction and cognitive computing

This volume constitutes reviewed and selected papers from the 11th International Advanced Computing Conference, IACC 2021, held in December 2021. The 47 full papers and 4 short papers presented in the volume were thorougly reviewed and selected from 246 submissions. The papers are organized in the following topical sections: application of artificial intelligence and machine learning in healthcare; application of AI for emotion and behaviour prediction; problem solving using reinforcement learning and analysis of data; advance uses of RNN and regression techniques; special intervention of AI.

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future.

This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic.The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training.