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
Towards in-vehicle situation awareness using visual and audio sensors. This project aims to characterise driver awareness, activity and interactions with other vehicle occupants using visual and audio cues from internally mounted sensors. Road accidents cost Australia an estimated $30 billion per year and tragic loss of thousands of lives, yet the vast majority of severe vehicle crashes are linked to driver fatigue or distraction. The expected project outcomes include advanced artificial intelli ....Towards in-vehicle situation awareness using visual and audio sensors. This project aims to characterise driver awareness, activity and interactions with other vehicle occupants using visual and audio cues from internally mounted sensors. Road accidents cost Australia an estimated $30 billion per year and tragic loss of thousands of lives, yet the vast majority of severe vehicle crashes are linked to driver fatigue or distraction. The expected project outcomes include advanced artificial intelligence to infer and predict dangerous driver and passenger behaviour. This has the potential to significantly benefit society by advancing autonomous driving capabilities and reducing driver-induced accidents and fatalities, ensuring that every driver, passenger and pedestrian arrives home safely at the end of each day.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101129
Funder
Australian Research Council
Funding Amount
$442,000.00
Summary
Synergy between future 21-cm experiments and physical cosmology. The nature of dark matter and formation of the first galaxies are both unsolved mysteries. During the first 500 million years, our universe was filled with hydrogen atoms illuminated by the first galaxies. The 21-cm radiation from this gas encodes properties of unseen galaxies and dark matter during this so-called cosmic dawn. This project aims to build an innovative framework to leverage future 21-cm experiments using The Square K ....Synergy between future 21-cm experiments and physical cosmology. The nature of dark matter and formation of the first galaxies are both unsolved mysteries. During the first 500 million years, our universe was filled with hydrogen atoms illuminated by the first galaxies. The 21-cm radiation from this gas encodes properties of unseen galaxies and dark matter during this so-called cosmic dawn. This project aims to build an innovative framework to leverage future 21-cm experiments using The Square Kilometre Array to observe cosmic dawn, and to forecast the optimal constraints on dark matter physics. Additional outcomes include the largest cosmological simulation of the first galaxies powered by neural networks and improved knowledge of their properties using Bayes' theorem and The James Webb Space Telescope.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100863
Funder
Australian Research Council
Funding Amount
$405,398.00
Summary
Privacy Coupling: When Your Personal Devices Betray You. This project aims to propose novel privacy preserving schemes that can protect the privacy of individuals in the era of Internet of things and machine learning. In the recent years, most Australian organizations have been a target of privacy and cybersecurity attacks, affecting their data and network systems. The expected outcomes of this project are privacy preserving schemes that can prevent attackers from compromising the private inform ....Privacy Coupling: When Your Personal Devices Betray You. This project aims to propose novel privacy preserving schemes that can protect the privacy of individuals in the era of Internet of things and machine learning. In the recent years, most Australian organizations have been a target of privacy and cybersecurity attacks, affecting their data and network systems. The expected outcomes of this project are privacy preserving schemes that can prevent attackers from compromising the private information of individuals in IoT and machine learning services, and thus significantly improve the protection against cybersecurity attacks. Significant benefits in social wellbeing and security are expected for all industry, government, and service sectors that collect data about people.Read moreRead less
Physical Layer Security for Wireless Machine-Type Communications. This project aims to provide new understanding and design guidelines to secure wireless communications among low-cost resource-constrained devices. This is achieved by advancing the fundamental theory of an emerging security paradigm named physical layer security. Expected outcomes of this project include a communication-theoretic framework to characterise the secrecy performance of communications over wireless networks, followed ....Physical Layer Security for Wireless Machine-Type Communications. This project aims to provide new understanding and design guidelines to secure wireless communications among low-cost resource-constrained devices. This is achieved by advancing the fundamental theory of an emerging security paradigm named physical layer security. Expected outcomes of this project include a communication-theoretic framework to characterise the secrecy performance of communications over wireless networks, followed by novel signal processing and transmission designs. The research outcomes should provide innovative solutions to safeguard commercial and industry Internet of Things networks, benefiting Australia's digital transformation.Read moreRead less
Understanding gender inequality in the post-pandemic future of work. This project examines the impact of the COVID-19 pandemic and economic crisis on the working futures of young women and men in three advanced market economies where the pandemic hit with varying degrees of severity. Young people have experienced the greatest upheaval of all workers, and the impact has been gendered. Recovery strategies will have lasting consequences for women’s and men’s working futures. The project will produc ....Understanding gender inequality in the post-pandemic future of work. This project examines the impact of the COVID-19 pandemic and economic crisis on the working futures of young women and men in three advanced market economies where the pandemic hit with varying degrees of severity. Young people have experienced the greatest upheaval of all workers, and the impact has been gendered. Recovery strategies will have lasting consequences for women’s and men’s working futures. The project will produce macro-level mapping of post-pandemic national work/care regimes, and micro-level survey data on young people’s experience of and attitudes to the future of work in Australia, the UK and Japan, to deliver insights on the gendered economic and social impact of the pandemic and inform a more inclusive global recovery.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
COVID-19, health and labour market marginalisation . This project aims to investigate the impact of the COVID-19 pandemic on labour market marginalisation in Australia. It seeks to generate new insights about whether the global economic shock had a disproportionately negative effect on the employment circumstances of working-age Australians with mental health and musculoskeletal/pain conditions, which are the leading causes of disability in Australia. The expected outcomes of this project includ ....COVID-19, health and labour market marginalisation . This project aims to investigate the impact of the COVID-19 pandemic on labour market marginalisation in Australia. It seeks to generate new insights about whether the global economic shock had a disproportionately negative effect on the employment circumstances of working-age Australians with mental health and musculoskeletal/pain conditions, which are the leading causes of disability in Australia. The expected outcomes of this project include improved policy responses to direct effective support and assistance to those with the greatest need, and new resources for the research community. This should lead to significant benefits through reduced inequalities and improved social, economic and workforce outcomes for vulnerable Australians.Read moreRead less
Democratisation of Deep Learning: Neural Architecture Search at Low Cost. The need to manually design Deep Learning-based Neural Networks (DNNs) limits their usage to AI experts and hinders the exploitation of their true potential more broadly, e.g., in farming, humanities. We aim to replace this tedious process through novel AI methods capable of generating DNNs that can perform significantly better and at a lower computational cost than manually designed DNNs. We further expand this idea to so ....Democratisation of Deep Learning: Neural Architecture Search at Low Cost. The need to manually design Deep Learning-based Neural Networks (DNNs) limits their usage to AI experts and hinders the exploitation of their true potential more broadly, e.g., in farming, humanities. We aim to replace this tedious process through novel AI methods capable of generating DNNs that can perform significantly better and at a lower computational cost than manually designed DNNs. We further expand this idea to solve complex real-world problems with both labelled and unlabelled data found in various applications including energy and climate change. The expected outcomes include the novel AI methods, highly trained AI researchers and a number of critical applications that will bring significant benefits to Australia and the world.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