9,998 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 Survey on Biomedical Text Summarization with Pre-trained Language Model

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    The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), provides a big challenge for clinicians and researchers to access clinical information efficiently. To address the problem, biomedical text summarization has been proposed to support clinical information retrieval and management, aiming at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, pre-trained language models (PLMs) have been the de facto standard of various natural language processing tasks in the general domain. Most recently, PLMs have been further investigated in the biomedical field and brought new insights into the biomedical text summarization task. In this paper, we systematically summarize recent advances that explore PLMs for biomedical text summarization, to help understand recent progress, challenges, and future directions. We categorize PLMs-based approaches according to how they utilize PLMs and what PLMs they use. We then review available datasets, recent approaches and evaluation metrics of the task. We finally discuss existing challenges and promising future directions. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master.Comment: 19 pages, 6 figures, TKDE under revie

    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

    Psychographic And Behavioral Segmentation Of Food Delivery Application Customers To Increase Intention To Use

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study presents a framework for segmenting Food Delivery Application (FDA) customers based on psychographic and behavioral variables as an alternative to existing segmentation. Customer segments are proposed by applying clustering methods to primary data from an electronic survey. Psychographic and behavioral constructs are formulated as hypotheses based on existing literature, and then evaluated as segmentation variables regarding their discriminatory power for customer segmentation. Detected relevant variables are used in the application of clustering techniques to find adequate boundaries within customer groupings for segmentation purposes. Characterization of customer segments is performed and enriched with implications of findings in FDA marketing strategies. This paper contributes to theory by providing new findings on segmentation that are relevant for an online context. In addition, it contributes to practice by detailing implications of customer segments in an online sales strategy, allowing marketing managers and FDA businesses to capitalize knowledge in their conversion funnel designs

    Detection of Hyperpartisan news articles using natural language processing techniques

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    Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders

    Varastest embrüotest pärit ekstratsellulaarsed vesiikulid: potentsiaal embrüokvaliteedi markeritena ja roll embrüo-emaka suhtluses

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    Väitekirja elektrooniline versioon ei sisalda publikatsiooneViljatus on globaalne rahvatervise probleem, mis mõjutab miljoneid inimesi. Abistav reproduktiivtehnoloogia, sealhulgas in vitro viljastamine, on aidanud mitmeid viljatuid inimesi. Küll on sellel metoodikal üheks kitsaskohaks implantatsiooni ebaõnnestumine isegi morfoloogiliselt parimate embrüotega. Seetõttu toimuvad jätkuvalt uuringud tuvastamaks paremaid meetodeid, mis hindavad embrüo kvaliteeti ja ennustavad siirdamise edukust, olles peamiselt embrüokasvusöötme baasil. Rakuvälised ehk ekstratsellulaarsed vesiikulid (EV) on membraaniga ümbritsetud nanoosakesed, mida toodavad peaaegu kõik rakutüübid erinevates füsioloogilistes ja patoloogilistes konditsioonides. Nende kaudu toimub rakuvaheline suhtlus. Mitmed uuringud, eriti vähi korral, on uurinud EVde potentsiaali biomarkerina ja ravimkandursüsteemina. Antud doktoritöö uuris implantatsiooni-eelse perioodi embrüost vabanenud EVde potentsiaali embrüokvaliteedi markerina ja embrüo-emaka suhtluse vahendajana. Katsed viidi läbi kasutades veise-embrüoid ja inimrakukultuuride põhiseid eksperimentaalmudeleid. Esimene uuring tõestas, et individuaalselt kasvatatud implantatsiooni-eelse perioodi veise-embrüod eritavad EVsid kasvusöötmesse ning nende kontsentratsiooni- ja suurusprofiil sõltub embrüo kvaliteedist ja arengustaadiumist. Järgnevalt katsetati munajuharakkudel implantatsiooni-eelse perioodi embrüost pärit EVde funktsionaalsust. Katse käigus selgus, et EVd kõrge kvaliteediga embrüotest muutsid munajuharakkude geeniekspressiooni, mida aga ei teinud halva kvaliteediga embrüote EVd. Suurenenud ekspressiooniga geenide hulgas olid mitmed interferoon-τ raja interferooni stimuleerivad geenid. Interferoon-τ peetakse mäletsejaliste tiinuse tuvastusmolekuliks. See leid viitab, et munajuha tunneb ära kvaliteetse embrüo. Viimaseks uuriti embrüo EVde funktsionaalsuse spetsiifilisust. Leiti, et endomeetrium reageerib vaid embrüo päritolu EVdele. Uuringute käigus tuvastati embrüost vabanenud EVde potentsiaal ja spetsiifilisus embrüokvaliteedi biomarkerina.Infertility is a global public health problem that affects millions of people in their reproductive life. Assisted reproductive technologies (ARTs) such as in-vitro fertilization have enabled many patients to overcome this issue. However, a bottleneck in ART success is the implantation failure even after the transfer of morphologically best embryos. Hence, investigations continue to identify better or complementary methods of assessing embryo quality and predicting transfer success, mainly based on the embryo culture media. Extracellular vesicles (EVs) are membrane-bound nanoparticles released by almost all types of cells under different physiological and pathological conditions. They mediate intercellular communication. Many studies, especially related to cancer, have investigated EVs' potential as biomarkers and therapeutic drug delivery systems. This project investigated preimplantation embryo-derived extracellular vesicles as a potential embryo quality marker and a mediator of embryo-maternal communication. Experiments were performed using bovine embryos and human cell-culture based experimental models. The first study showed that individually cultured preimplantation bovine embryos release EVs to their culture media, and their concentration and size profile are dependent on the quality and development stage of embryos. Subsequently, the functionality of preimplantation embryo-derived EVs were tested in the oviduct. It was observed that EVs from good quality embryos, but not the EVs from embryos of low developmental potential quality, could alter the gene expression of the oviduct. Among the up-regulated genes, many were interferon-stimulated genes of the interferon-τ pathway. Interferon-τ is considered the pregnancy recognition molecule in ruminant pregnancy. This finding suggests that the oviduct can serve as a biosensor of embryo quality. Finally, the functional specificity of embryonic EVs were investigated. It was observed that endometrium only react to embryonic EVs but not to the non-embryonic EVs. All these studies support the potential and specificity of embryo-derived EVs as a biomarker of embryo quality.https://www.ester.ee/record=b548409

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

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    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    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

    Investigating illicit drug use in adolescent students in England

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    Abstract The Smoking Drinking Drug Use Survey of adolescents aged 11 to 15 years living in England shows that lifetime drug use by adolescents aged 11 to 15 years has increased (15% to 24%) from 2014 to 2018 (NHS Digital, 2017, 2021b). This upward trend is despite the implementation of drug policies focused on reducing supply, possession, and manufacture of illicit drugs. Based on the premise that drug use is a socially learnt behaviour, the main objective of this research is to investigate whether social learning factors (imitation, parental reinforcement, peer association and attitudes to drug use) mediate drug use in adolescents aged 11 to 15 years living in England. The second objective is to identify which social learning factors mediate drug use by ages, region, and gender. Using the Social Structure Social Learning (SSSL) theory as a framework for the research, this study contributes to the literature by identifying a) the strongest social learning behaviour for each age, gender and region in England and b) the mechanism (mediation) by which social learning affects drug use. This research employs rich data on drug use drawn from the Smoking Drinking Drug Use Survey 2016, a cross-sectional survey of adolescents aged 11-15 years across England (as of October 2021 the data for the most recent survey 2018 was not available for analysis). Mediation analysis was used to evaluate which social learning factors mediate the association between age, gender, region and drug use. The results showed that there were differences in learning behaviours that were specific to age, gender and region. For example, the most significant social learning behaviour for drug use among boys was “imitation of friends”, whilst for females, it was “peer association” among females (i.e. having a perception that peers are using drugs). In addition, having “positive attitudes to glue” (i.e. “it is ok to try glue”) was the strongest learning behaviour for drug use among younger individuals (i.e. at ages 11 to 13). Furthermore, whilst in Northern England, the strongest learning behaviour was having “positive attitudes to cannabis”, in London peer association was found to be the strongest learning pathway to drug use. Family disapproval of drug use (“persuade me not to take drugs”) was found to be a protective factor against drug use for all ages except for age 11 and 12 years and those living in the East Midlands and London. In these cases, more authoritarian parenting –– strong parental disapproval (“stop me from taking drugs”) was found to be a protective factor. This research offers two main contributions to the literature. First, it shows empirical linkages between constructs built using SSSL theory that have not been previously explored within a population of young adolescents in England. Second, it identifies the effects and degree to which social learning affects the relationship between drug use and social structure. Overall, this research also contributes to an improved theoretical rationale for existing SSSL associations; that is, social learning can behave as a mediator or a moderator depending on the context. The evidence produced by this thesis could also have potentially relevant policy implications. More specifically, the differences in the social learning behaviours may suggest the need to implement more targeted prevention policies aimed by age, gender and regional groups of young adolescents

    Network Geometry

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    Networks are finite metric spaces, with distances defined by the shortest paths between nodes. However, this is not the only form of network geometry: two others are the geometry of latent spaces underlying many networks and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale invariance, self-similarity and other forms of fundamental symmetries in networks. Network geometry is also of great use in a variety of practical applications, from understanding how the brain works to routing in the Internet. We review the most important theoretical and practical developments dealing with these approaches to network geometry and offer perspectives on future research directions and challenges in this frontier in the study of complexity
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