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Research Topic : Computer Vision
Australian State/Territory : ACT
Field of Research : Software Engineering
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  • Researchers (14)
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  • Funded Activity

    Discovery Projects - Grant ID: DP140103878

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Micro Virtual Machines: Abstraction, contained. This project will address a systemic source of inefficiency in widely used software which leads to many programs running as much as ten times slower and using considerably more energy than necessary, shortening battery life on mobile phones and increasing costs for large server farms. This inefficiency is endemic because it is due to the underlying languages rather than the particular software. This project will address this problem by developing a .... Micro Virtual Machines: Abstraction, contained. This project will address a systemic source of inefficiency in widely used software which leads to many programs running as much as ten times slower and using considerably more energy than necessary, shortening battery life on mobile phones and increasing costs for large server farms. This inefficiency is endemic because it is due to the underlying languages rather than the particular software. This project will address this problem by developing a high efficiency substrate, called a micro virtual machine, on which languages may be built.
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    Funded Activity

    Discovery Projects - Grant ID: DP140102185

    Funder
    Australian Research Council
    Funding Amount
    $440,000.00
    Summary
    Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. .... Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. Evaluation will be via development of several exemplar applications. The techniques and framework will be applicable to a broad range of economically important problems in areas as diverse as health, travel, scientific publication search, product marketing and software engineering.
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    Funded Activity

    Linkage Projects - Grant ID: LP100100588

    Funder
    Australian Research Council
    Funding Amount
    $805,000.00
    Summary
    Advancing Medical Image Analysis through High Performance Heterogeneous Computing, Numerical Simulation, and Novel Human Computer Interfaces. This project will link Australian researchers with a major multi-national IT company. The engagement of world-class personnel from Microsoft will provide unprecedented opportunities for graduate students to experience research in both an academic and an industrial setting. The participation of Microsoft product division offers the potential to transform th .... Advancing Medical Image Analysis through High Performance Heterogeneous Computing, Numerical Simulation, and Novel Human Computer Interfaces. This project will link Australian researchers with a major multi-national IT company. The engagement of world-class personnel from Microsoft will provide unprecedented opportunities for graduate students to experience research in both an academic and an industrial setting. The participation of Microsoft product division offers the potential to transform the outcomes of this project into widely-used software solutions. The project will pave the way for more widespread and reliable evidenced-based computer-aided diagnosis and image-guided treatment. It will produce well-trained and sought-after graduates and research associates with extensive inter-disciplinary knowledge of medical image analysis and high-performance computing.
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    Funded Activity

    Discovery Projects - Grant ID: DP0452011

    Funder
    Australian Research Council
    Funding Amount
    $195,000.00
    Summary
    Improving Modern Programming Language Performance: A Memory-Conscious Approach. The performance of modern programming languages such as Java and C# lags that of imperative languages such as C and Fortran. A significant source of the performance gap is poor memory behavior, which future computer architectures will exacerbate. This project addresses the problem of poor memory behavior in modern programming languages such as Java and C# through an integrated attack that incorporates new garbage c .... Improving Modern Programming Language Performance: A Memory-Conscious Approach. The performance of modern programming languages such as Java and C# lags that of imperative languages such as C and Fortran. A significant source of the performance gap is poor memory behavior, which future computer architectures will exacerbate. This project addresses the problem of poor memory behavior in modern programming languages such as Java and C# through an integrated attack that incorporates new garbage collection algorithms, run-time techniques that optimize running programs, and new compiler analyses with both static and dynamic optimizations. The project will give Australia an international presence in a research area of great academic and commercial importance.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE170101081

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t .... Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210101348

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an .... Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.
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    Funded Activity

    Discovery Projects - Grant ID: DP140101540

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Machine-checked Foundations for Verified Vote Counting. The project will deliver a general methodology for developing formal logical specifications of the Acts of Parliament for many common systems for counting votes in preferential elections. The project will deliver corresponding computer programs to count votes according to these systems and will deliver formal independently checkable proofs that the programs meet their specification. Such formally verified computer programs provide a legally .... Machine-checked Foundations for Verified Vote Counting. The project will deliver a general methodology for developing formal logical specifications of the Acts of Parliament for many common systems for counting votes in preferential elections. The project will deliver corresponding computer programs to count votes according to these systems and will deliver formal independently checkable proofs that the programs meet their specification. Such formally verified computer programs provide a legally sound basis for counting votes by computer. The methodology will also allow electoral commissioners to improve the natural language descriptions of the relevant Acts of Parliament which are often woefully out of date with current practice.
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    Showing 1-7 of 7 Funded Activites

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