Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expecte ....Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expected outcomes are algorithms for public and private sector data curators to dial up or down their data access arrangements based on privacy risks and fidelity demands linked with different data types and uses. This project intends to enable Australians to securely benefit from valuable data in decision making.Read moreRead less
Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical application ....Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical applications such as better wellness tracking and lifestyle-related illness prevention, which will be particularly critical to Australia's aging population. This project will also serve as a vehicle to educate and train Australia’s young scholars and engineers.Read moreRead less
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, commun ....Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, community the ability to contribute to growing our knowledge-based society in a safe, transparent and trustworthy manner. Outcomes will include innovative instruments to teach machines, novel knowledge creation, trusted and transparent AI systems, and a new generation of human teachers specialised in educating AI systems.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less
Integrated Planning for Uncertainty-Centric Pilot Assistance Systems. This project aims to deliver a novel pilot assistance system to improve the viability, speed and safety of Helicopter Emergency Medical Services (HEMS) and Search and Rescue (SAR) missions. It will advance fundamental algorithms for probabilistic planning in partially observable scenarios to form the core technology of a pilot assistance system that accounts the various types of uncertainty faced by pilots in a typical HEMS/S ....Integrated Planning for Uncertainty-Centric Pilot Assistance Systems. This project aims to deliver a novel pilot assistance system to improve the viability, speed and safety of Helicopter Emergency Medical Services (HEMS) and Search and Rescue (SAR) missions. It will advance fundamental algorithms for probabilistic planning in partially observable scenarios to form the core technology of a pilot assistance system that accounts the various types of uncertainty faced by pilots in a typical HEMS/SAR missions. It will exploit recent advances in Partially Observable Markov Decision Processes (POMDPs) to recommend robust, safe, and pilot-aware mission and manoeuvring strategies to make HEMS/SAR operations safer for helicopter crews, and more effective for those in need of the service.Read moreRead less
Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental ....Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental policy formulation, network design, engineering, defence and cybersecurity; offering significant benefits to the researchers and practitioners in these fields. In addition to research outputs, it will strengthen international collaboration and build research capacity to put Australia at the forefront of this research.
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Preventing railway suicide: An open-systems perspective. Preventing railway suicide: An open-systems perspective. This project aims to develop an automated suicide risk detection system to reduce the incidence and impact of railway suicide, which has a devastating effect on victims’ families, station staff, train drivers, emergency workers, and bystanders. This project will use open-systems theory to develop two complementary information systems for more effective detection and reporting of suic ....Preventing railway suicide: An open-systems perspective. Preventing railway suicide: An open-systems perspective. This project aims to develop an automated suicide risk detection system to reduce the incidence and impact of railway suicide, which has a devastating effect on victims’ families, station staff, train drivers, emergency workers, and bystanders. This project will use open-systems theory to develop two complementary information systems for more effective detection and reporting of suicide risk; use these systems to investigate how different situational factors interact with different combinations of service interventions to influence suicide risk; and share the findings to reduce railway suicide in Australia and overseas.Read moreRead less