6,439 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

    One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

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    OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

    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

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    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

    Discovering the hidden structure of financial markets through bayesian modelling

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    Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole. We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors. Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces

    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)

    Estimating Directional Changes Trend Reversal in Forex Using Machine Learning

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    Most forecasting algorithms use a physical time scale data to study price movement in financial markets by taking snapshots in fixed schedule, making the flow of time discontinuous. The use of a physical time scale can make traders oblivious to significant activities in the market, which poses risks. For example, currency risk, the risk that exchange rate will change. Directional changes is a different and newer approach of taking snapshot of the market, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change according to a change in price a trader considers to be significant, which is expressed as a threshold. The trends in the summary are split into directional change (DC) and overshoot (OS) events. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to forecast when the next, alternate trend is expected to begin. First, we present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. Awareness of DC event and OS event lengths provide traders with an idea of when DC trends are expected to reverse and thus take appropriate action to increase profit or mitigate risk. Second, DC trends can be categorised into two distinct types: (1) trends with OS events; and (2) trends without OS events(i.e. OS event length is 0). Trends with OS events are those that continue beyond a period when they were first observed and trends without OS event are others that ends as soon as they were observed. To further improve trend reversal estimation accuracy, we identified these two categorises using classification techniques and estimated OS event length for trends that belong in the first category. We appraised whether this new knowledge could lead to an even greater excess return. Third, our novel trend reversal estimation approach was then used as part of a novel genetic algorithm (GA) based trading strategy. The strategy embedded an optimised trend reversal forecasting algorithm that was based on trend reversal point forecasted by multiple thresholds. We assessed the efficiency of our framework (i.e., a novel trend reversal approach and an optimised trading strategy) by performing an in-depth investigation. To assess our approach and evaluate the extent to which it could be generalised in Forex markets, we used five tailored thresholds to create 1000 DC datasets from 10, monthly 10- minute physical time data of 20 major Forex markets (i.e 5 thresholds * 10 months * 20 currency pairs). We compared our results to six benchmarks techniques, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings showed that our proposed approach can return a significantly higher profit at reduced risk, and statistically outperformed the other trading strategies compareds in a number of different performance metrics
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