14,955 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

    Offline and Online Models for Learning Pairwise Relations in Data

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    Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting

    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

    Perceptions of surveillance: exploring feelings held by Black community leaders in Boston toward camera enforcement of roadway infractions

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    Roadway camera enforcement programs have been found to effectively reduce vehicle travel speeds, as well as decrease the number and severity of collisions. Despite a wealth of evaluative research confirming this enforcement approach's aptitude at promoting safer roadway behavior, fewer than 50 % of US states currently host camera-based programs. Public opposition is frequently cited as the cause for the slow proliferation of this enforcement strategy. However, with public demand for police reform having an increasing presence on the national political stage, how might feelings toward camera technology currently stand among groups most marginalized by existing enforcement systems, and how might those feelings vary by type of enforcement application? Through a series of focus groups, this work centers Black voices on matters of surveillance and roadway enforcement by discussing sentiment toward camera programs with Black community leaders. This discussion is contextually situated in Boston, Massachusetts, where legislation that would allow for camera enforcement of roadway infractions is actively being deliberated in the State Senate. Findings culminate in a list of right-sizing and procedural recommendations for policy makers hoping to gain support for camera enforcement, improve roadway safety, and advance racial equity in our systems of policing and governance

    Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks

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    The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity

    Annual SHOT Report 2018

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    SHOT is affiliated to the Royal College of PathologistsAll NHS organisations must move away from a blame culture towards a just and learning culture. All clinical and laboratory staff should be encouraged to become familiar with human factors and ergonomics concepts. All transfusion decisions must be made after carefully assessing the risks and benefits of transfusion therapy. Collaboration and co-ordination among staff is vital

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Victims' Access to Justice in Trinidad and Tobago: An exploratory study of experiences and challenges of accessing criminal justice in a post-colonial society

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    This thesis investigates victims' access to justice in Trinidad and Tobago, using their own narratives. It seeks to capture how their experiences affected their identities as victims and citizens, alongside their perceptions of legitimacy regarding the criminal justice system. While there have been some reforms in the administration of criminal justice in Trinidad and Tobago, such reforms have not focused on victims' accessibility to the justice system. Using grounded theory methodology, qualitative data was collected through 31 in-depth interviews with victims and victim advocates. The analysis found that victims experienced interpersonal, structural, and systemic barriers at varying levels throughout the criminal justice system, which manifested as institutionalized secondary victimization, silencing and inequality. This thesis argues that such experiences not only served to appropriate conflict but demonstrates that access is often given in a very narrow sense. Furthermore, it shows a failure to encompass access to justice as appropriated conflicts are left to stagnate in the system as there is often very little resolution. Adopting a postcolonial lens to analyse victims' experiences, the analysis identified othering practices that served to institutionalize the vulnerability and powerlessness associated with victim identities. Here, it is argued that these othering practices also affected the rights consciousness of victims, delegitimating their identities as citizens. Moreover, as a result of their experiences, victims had mixed perceptions of the justice system. It is argued that while the system is a legitimate authority victims' endorsement of the system is questionable, therefore victims' experiences suggest that there is a reinforcement of the system's legal hegemony. The findings suggest that within the legal system of Trinidad and Tobago, legacies of colonialism shape the postcolonial present as the psychology and inequalities of the past are present in the interactions and processes of justice. These findings are relevant for policymakers in Trinidad and Tobago and other regions. From this study it is recognized that, to improve access to justice for victims, there needs to be a move towards victim empowerment that promotes resilience and enhances social capital. Going forward it is noted that there is a need for further research

    Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs

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    Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante
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