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Scheme : Discovery Projects
Research Topic : Cloud computing
Australian State/Territory : TAS
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  • Funded Activity

    Discovery Projects - Grant ID: DP160100601

    Funder
    Australian Research Council
    Funding Amount
    $299,031.00
    Summary
    Automated internet warnings to prevent viewing of minor-adult sex images. Since the advent of the internet and digital cameras, the market for child exploitation material (CEM) has boomed. This project aims to explore how the visual appearance of warning messages influences internet users. It plans to conduct a randomised controlled experiment with naïve participants on a real-life website to test the effectiveness of messages designed to discourage viewers of legal ‘barely legal’ pornography. I .... Automated internet warnings to prevent viewing of minor-adult sex images. Since the advent of the internet and digital cameras, the market for child exploitation material (CEM) has boomed. This project aims to explore how the visual appearance of warning messages influences internet users. It plans to conduct a randomised controlled experiment with naïve participants on a real-life website to test the effectiveness of messages designed to discourage viewers of legal ‘barely legal’ pornography. It is anticipated that results will assist policing efforts by indicating whether warnings can be used to dissuade first-time CEM viewers and whether differences exist between harm or deterrent-focused messages.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP170102885

    Funder
    Australian Research Council
    Funding Amount
    $469,000.00
    Summary
    Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultu .... Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultural issues. This project is expected to benefit industries with widely distributed multi-cultural workforces such as mining, defence and medicine.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220100795

    Funder
    Australian Research Council
    Funding Amount
    $485,000.00
    Summary
    Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire .... Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool.
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    Showing 1-3 of 3 Funded Activites

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