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Australian State/Territory : QLD
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Research Topic : intelligence
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  • Funded Activity

    Linkage Projects - Grant ID: LP0235856

    Funder
    Australian Research Council
    Funding Amount
    $135,270.00
    Summary
    QUA:Queensland digital Ultra-Atlas. This project aims to design and develop a digital ultra-atlas of Queensland which integrates the capabilities of GIS(geographical information system) with interactive 4D graphical modelling,knowledge extraction and context-based query and retrieval. This ultra-atlas would allow users to discover knowledge on natural resources,cultural characteristics and historical changes,as well as simulating different effects by providing advanced search capabilities and en .... QUA:Queensland digital Ultra-Atlas. This project aims to design and develop a digital ultra-atlas of Queensland which integrates the capabilities of GIS(geographical information system) with interactive 4D graphical modelling,knowledge extraction and context-based query and retrieval. This ultra-atlas would allow users to discover knowledge on natural resources,cultural characteristics and historical changes,as well as simulating different effects by providing advanced search capabilities and engaging display of spatial and thematically-linked data. Such an ultra-atlas would have enormous impact on facilitating strategic planning and performance in many applications(e.g fire control,environment and urban planning).
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    Funded Activity

    Discovery Projects - Grant ID: DP0451129

    Funder
    Australian Research Council
    Funding Amount
    $195,000.00
    Summary
    Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer me .... Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer methods can be used to infer confidential information about individuals. This project develops new techniques to ensure privacy and alleviate public concerns such as secondary use of personal data. We shall develop new methods for replacing original data with data that exhibits approximately the same patterns, but conceals sensitive data.
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    Funded Activity

    Discovery Projects - Grant ID: DP0452676

    Funder
    Australian Research Council
    Funding Amount
    $150,000.00
    Summary
    Techniques to use stereo vision for improving person identification systems based on face recognition. The broad aim of this project is to use three-dimensional information available by processing images from stereo cameras in order to bridge the gap between constrained face recognition systems and viable systems that work well under varying illumination, changes in pose and variations in spectacles, facial hair and attire. Such a system will be useful in passenger verification at airports and a .... Techniques to use stereo vision for improving person identification systems based on face recognition. The broad aim of this project is to use three-dimensional information available by processing images from stereo cameras in order to bridge the gap between constrained face recognition systems and viable systems that work well under varying illumination, changes in pose and variations in spectacles, facial hair and attire. Such a system will be useful in passenger verification at airports and as a component of personal identification systems to counter terrorism. The key to successful face location and recognition is an effective combination of all data - range, luminance and colour - and techniques for this will be the discovered outcomes.
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    Funded Activity

    Discovery Projects - Grant ID: DP0558879

    Funder
    Australian Research Council
    Funding Amount
    $463,000.00
    Summary
    Mining Distributed, High-Speed, Time-Variant Data Streams. With the high-speed and large volume of data generation, the data mining research community is facing an unprecedented challenge to provide instant data mining outcomes for prompt usage. Getting access to derived information from multiple, dynamically changing data is vital for many business, science and security services. Extended networks of sensors and other devices assist many environments with data collection that should be correlat .... Mining Distributed, High-Speed, Time-Variant Data Streams. With the high-speed and large volume of data generation, the data mining research community is facing an unprecedented challenge to provide instant data mining outcomes for prompt usage. Getting access to derived information from multiple, dynamically changing data is vital for many business, science and security services. Extended networks of sensors and other devices assist many environments with data collection that should be correlated and processed towards discovery of dependencies, regularities and patterns. Data mining tools, especially of this new generation, are capable of dealing with data streams, and they offer great benefits for users from many industry sectors; defence, health management, security, commerce and science.
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    Funded Activity

    Discovery Projects - Grant ID: DP0345901

    Funder
    Australian Research Council
    Funding Amount
    $165,000.00
    Summary
    Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discove .... Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.
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