8,033 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Redefining Community in the Age of the Internet: Will the Internet of Things (IoT) generate sustainable and equitable community development?

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    There is a problem so immense in our built world that it is often not fully realized. This problem is the disconnection between humanity and the physical world. In an era of limitless data and information at our fingertips, buildings, public spaces, and landscapes are divided from us due to their physical nature. Compared with the intense flow of information from our online world driven by the beating engine of the internet, our physical world is silent. This lack of connection not only has consequences for sustainability but also for how we perceive and communicate with our built environment in the modern age. A possible solution to bridge the gap between our physical and online worlds is a technology known as the Internet of Things (IoT). What is IoT? How does it work? Will IoT change the concept of the built environment for a participant within it, and in doing so enhance the dynamic link between humans and place? And what are the implications of IoT for privacy, security, and data for the public good? Lastly, we will identify the most pressing issues existing in the built environment by conducting and analyzing case studies from Pomona College and California State University, Northridge. By analyzing IoT in the context of case studies we can assess its viability and value as a tool for sustainability and equality in communities across the world

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Socio-endocrinology revisited: New tools to tackle old questions

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    Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)

    Robustness against adversarial attacks on deep neural networks

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    While deep neural networks have been successfully applied in several different domains, they exhibit vulnerabilities to artificially-crafted perturbations in data. Moreover, these perturbations have been shown to be transferable across different networks where the same perturbations can be transferred between different models. In response to this problem, many robust learning approaches have emerged. Adversarial training is regarded as a mainstream approach to enhance the robustness of deep neural networks with respect to norm-constrained perturbations. However, adversarial training requires a large number of perturbed examples (e.g., over 100,000 examples are required for MNIST dataset) trained on the deep neural networks before robustness can be considerably enhanced. This is problematic due to the large computational cost of obtaining attacks. Developing computationally effective approaches while retaining robustness against norm-constrained perturbations remains a challenge in the literature. In this research we present two novel robust training algorithms based on Monte-Carlo Tree Search (MCTS) [1] to enhance robustness under norm-constrained perturbations [2, 3]. The first algorithm searches potential candidates with Scale Invariant Feature Transform method and makes decisions with Monte-Carlo Tree Search method [2]. The second algorithm adopts Decision Tree Search method (DTS) to accelerate the search process while maintaining efficiency [3]. Our overarching objective is to provide computationally effective approaches that can be deployed to train deep neural networks robust against perturbations in data. We illustrate the robustness with these algorithms by studying the resistances to adversarial examples obtained in the context of the MNIST and CIFAR10 datasets. For MNIST, the results showed an average training efforts saving of 21.1\% when compared to Projected Gradient Descent (PGD) and 28.3\% when compared to Fast Gradient Sign Methods (FGSM). For CIFAR10, we obtained an average improvement of efficiency of 9.8\% compared to PGD and 13.8\% compared to FGSM. The results suggest that these two methods here introduced are not only robust to norm-constrained perturbations but also efficient during training. In regards to transferability of defences, our experiments [4] reveal that across different network architectures, across a variety of attack methods from white-box to black-box and across various datasets including MNIST and CIFAR10, our algorithms outperform other state-of-the-art methods, e.g., PGD and FGSM. Furthermore, the derived attacks and robust models obtained on our framework are reusable in the sense that the same norm-constrained perturbations can facilitate robust training across different networks. Lastly, we investigate the robustness of intra-technique and cross-technique transferability and the relations with different impact factors from adversarial strength to network capacity. The results suggest that known attacks on the resulting models are less transferable than those models trained by other state-of-the-art attack algorithms. Our results suggest that exploiting these tree search frameworks can result in significant improvements in the robustness of deep neural networks while saving computational cost on robust training. This paves the way for several future directions, both algorithmic and theoretical, as well as numerous applications to establish the robustness of deep neural networks with increasing trust and safety.Open Acces

    Hunting Wildlife in the Tropics and Subtropics

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    The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core
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