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Field of Research : Computer-Human Interaction
Field of Research : Human Information Behaviour
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    Discovery Projects - Grant ID: DP210103923

    Funder
    Australian Research Council
    Funding Amount
    $410,000.00
    Summary
    Improving Interactions for Digital Browsing of Large Collections. Browsing for information is an established and fundamental part of how people find the knowledge that they need. However, our current understanding of how browsing succeeds or fails is poor. This is because we have limited empirical data, and until recently, the available technologies to create detailed data on what people actually look at and when were very limited. As a result, digital browsing methods have been almost universal .... Improving Interactions for Digital Browsing of Large Collections. Browsing for information is an established and fundamental part of how people find the knowledge that they need. However, our current understanding of how browsing succeeds or fails is poor. This is because we have limited empirical data, and until recently, the available technologies to create detailed data on what people actually look at and when were very limited. As a result, digital browsing methods have been almost universally inferior to real-world counterparts. Given the lack of fundamental theories to inform design, this is unsurprising. After creating a detailed and systematic account of user behaviour in browsing, we will create novel designs that will accelerate the discovery of information, particularly for innovative work.
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    Funded Activity

    Linkage Projects - Grant ID: LP140100995

    Funder
    Australian Research Council
    Funding Amount
    $275,000.00
    Summary
    Computational Intelligence for Complex Structured Data. This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learnin .... Computational Intelligence for Complex Structured Data. This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learning of the syntax and semantics of individual gestures and actions, nor the multi-gesture information fusion required for understanding, which could significantly enhance efficiency, for example, in sorting through named entities in an investigation. All of this is done naturally by most human beings, using biological neural networks.
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