Domain

This book describes how domain knowledge can be used in the design of interactive systems. It includes discussion of the theories and models of domain, generic domain architectures and construction of system components for specific domains. It draws on research experience from the Information Systems, Software Engineering and Human Computer Interaction communities.

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This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

Digital technology has made culture more accessible than ever before. Texts, audio, pictures and video can easily be produced, disseminated, used and remixed using devices that are increasingly user-friendly and affordable. However, along with this technological democratization comes a paradoxical flipside: the norms regulating culture's use - copyright and related rights - have become increasingly restrictive. This book brings together essays by academics, librarians, entrepreneurs, activists and policy makers, who were all part of the EU-funded Communia project. Together the authors argue that the Public Domain - that is, the informational works owned by all of us, be that literature, music, the output of scientific research, educational material or public sector information - is fundamental to a healthy society. The essays range from more theoretical papers on the history of copyright and the Public Domain, to practical examples and case studies of recent projects that have engaged with the principles of Open Access and Creative Commons licensing. The book is essential reading for anyone interested in the current debate about copyright and the Internet. It opens up discussion and offers practical solutions to the difficult question of the regulation of culture at the digital age.

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 offers a comparative analysis of the domain name registration systems utililsed in Australia and the United Kingdom. Taking an international perspective, the author analyses the global trends and dynamics of the domain name registration systems and explores the advantages and disadvantages of restrictive and less restrictive systems by addressing issues of consumer protection. The book examines the regulatory frameworks in the restrictive and unrestrictive registration systems and considers recent developments in this area. Jenny Ng also examines the legal and economic implications of these regulatory frameworks, drawing upon economic theory, regulatory and systems theory as well as applying rigorous legal analysis. In doing so, this work proposes ways in which such systems could be better designed to reflect the needs of the specific circumstances in individual jurisdictions. The Domain Name Registration System will be of particular interest to academics and students of IT law and e-commerce.

Despite being the leading paradigm in computer vision, deep neural networks are inherently limited by the visual and semantic information contained in their training set. In this thesis, we aim to design deep models operating with previously unseen visual domains and semantic concepts. We first describe different solutions for generalizing to new visual domains, applying variants of normalization layers to multiple challenging settings e.g. where new domain data is not available but arrives online or is described by metadata. In the second part, we incorporate new semantic concepts into pretrained deep models. We propose specific solutions for different problems such as multi-task/incremental learning and open-world recognition. Finally, we merge the two challenges: given images of multiple domains and categories, can we recognize unseen concepts in unseen domains? We propose an approach that is the first, promising step, towards solving this problem. Winner of the Competition “Prize for PhD Thesis 2020” arranged by Sapienza University Press.