Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven p ....Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven patient group discovery, which could more precisely identify the patient cohorts most likely to benefit from a specific policy; and a model to predict the efficacy of policy options, which could increase the sustainability of the national health system by enabling smarter, more efficient policy decision-making.Read moreRead less
Automatic speech-based assessment of mental state via mobile device. This project aims to create the first mobile, device-based automatic assessment of mental state from acoustic speech. Focusing on novel approaches for eliciting speech, for regression-based scoring of mental state and for longitudinal modelling of speech, the project takes speech processing out of the laboratory and into realistic environments. The project is significant because elicitation approach and longitudinal modelling h ....Automatic speech-based assessment of mental state via mobile device. This project aims to create the first mobile, device-based automatic assessment of mental state from acoustic speech. Focusing on novel approaches for eliciting speech, for regression-based scoring of mental state and for longitudinal modelling of speech, the project takes speech processing out of the laboratory and into realistic environments. The project is significant because elicitation approach and longitudinal modelling have been acknowledged by the research community as challenges that are valuable to investigate, and because conventional regression methods are sub-optimal on ordinal mental state scales. This is significant commercially because mobile devices allow individually tailored, frequent and low-cost mental state assessment. Expected outcomes will include commercial-ready technology, trialled on Australians, accessible to everyone with a mobile device and concentration of Australian research and development capability in a rapidly growing application area.Read moreRead less
Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical b ....Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical books. Libraries and legal, social and data science researchers will investigate eBook lending practices and understand their social impacts. The project will identify ways of reforming policy, law, and practice to help libraries fulfil their public interest missions. This project is expected to enable libraries to extract more value from existing public investments.Read moreRead less
A cloud computing environment for managing foreign exchange risk. This project involves using parallel computing and machine learning to improve the real-time handling of a bank's foreign exchange risk. It will lead to improved techniques to manage exchange rate variations, enabling banks to better assess their risk and will ultimately lead to improved services for Australian companies.
Hardware-based accelerators for real-time machine learning. This project will tackle the challenge of applying real-time machine learning to massive high-frequency data. This project will leverage advancements in machine learning and hardware synthesis to implement computationally complex machine-learning algorithms on hardware-accelerated platforms, avoiding overhead delays incurred by software running on a processor.
Active Management of Complex Non-self-finalising Behaviours through Deep Analytics. This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no su ....Active Management of Complex Non-self-finalising Behaviours through Deep Analytics. This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no sufficient theories or effective systems in data mining and behavioural science to systematically learn the intent, impact and patterns of NSF behaviours, and to suggest cost-effective responses to these behaviours. This project aims to ensure Australia’s leading role in innovation for evidence-driven enterprise behaviour analytics and management.Read moreRead less
Detecting significant changes in organisation-customer interactions leading to non-compliance. The instant detection of risky customer and/or group dynamics and business policy and/or process changes dispersed in normal interactions can avoid immense losses and inconsistent policies for Government and industries, such as preventing Centrelink customer debt. This project will deliver novel analytical techniques and smart information use to effectively detect the above-mentioned changes leading to ....Detecting significant changes in organisation-customer interactions leading to non-compliance. The instant detection of risky customer and/or group dynamics and business policy and/or process changes dispersed in normal interactions can avoid immense losses and inconsistent policies for Government and industries, such as preventing Centrelink customer debt. This project will deliver novel analytical techniques and smart information use to effectively detect the above-mentioned changes leading to non-compliance. It will enhance service quality, compliance, payment accuracy and policy design for the Australian Government and industries such as Centrelink, the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), banking and insurance. The resulting systems, the researchers trained and resulting publications will significantly enhance Australia's leading role in tackling change-driven non-compliance.Read moreRead less
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Knowledge discovery and recommendation of multimedia data in healthcare. The project aims to develop tools to abstract/streamline the ever-growing information-rich multimedia contents into easily discoverable knowledges. Advanced multimedia knowledge graph will be first time developed to accurately exploit hidden knowledge for health industry, and served to generate right information recommendation for healthcare professionals (HCP) at the right time. The proposed technology will improve HCPs' c ....Knowledge discovery and recommendation of multimedia data in healthcare. The project aims to develop tools to abstract/streamline the ever-growing information-rich multimedia contents into easily discoverable knowledges. Advanced multimedia knowledge graph will be first time developed to accurately exploit hidden knowledge for health industry, and served to generate right information recommendation for healthcare professionals (HCP) at the right time. The proposed technology will improve HCPs' communication, keep them up to date, and enhance their speedy reaction to constantly changing situations/diseases, thus reducing poor patient outcomes and unnecessary hospital costs. It will make significant impact to a range of industries, e.g. healthcare, where personalised professional recommendation is demanded.Read moreRead less