Reshaping superannuation practice in Australia using big data analytics. This project aims to reform superannuation investment practices in Australia. Using sophisticated data analytics and machine-learning techniques, combined with economic modelling and quantitative finance. The project will try to understand the broad characteristics of Australian superannuation investors and their practice from a ‘big data’ perspective. The expected outcomes of this project are the identification of key dete ....Reshaping superannuation practice in Australia using big data analytics. This project aims to reform superannuation investment practices in Australia. Using sophisticated data analytics and machine-learning techniques, combined with economic modelling and quantitative finance. The project will try to understand the broad characteristics of Australian superannuation investors and their practice from a ‘big data’ perspective. The expected outcomes of this project are the identification of key determinants for successful superannuation behaviour to inform decision-making for better superannuation practices and policies. It is expected that the insights arising from this project will contribute to safeguarding the future of Australia’s superannuation schemes, and to better financial security at retirement.Read moreRead less
Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p ....Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.Read moreRead less
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
Modelling and discovering complex interaction relations hidden in group behaviours in businesses, online and social communities. This project addresses the shortage in current behavior analysis by inventing innovative theories and algorithms for analysing complex relations and interactions in group behaviours. The outcomes of this project will enable effective detection of suspicious large groups, contributing to safer businesses and society and improved compliance in online and social communiti ....Modelling and discovering complex interaction relations hidden in group behaviours in businesses, online and social communities. This project addresses the shortage in current behavior analysis by inventing innovative theories and algorithms for analysing complex relations and interactions in group behaviours. The outcomes of this project will enable effective detection of suspicious large groups, contributing to safer businesses and society and improved compliance in online and social communities.Read moreRead less
Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be construct ....Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be constructed. All of these will construct a stable and efficient deep neural network, which is able to process large size videos captured from real scenarios in high efficiencies. Various fields, such as health care service and cybersecurity, will benefit hugely from this project.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100626
Funder
Australian Research Council
Funding Amount
$393,000.00
Summary
Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generat ....Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.Read moreRead less
Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expec ....Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expected outcomes will be a next-generation interpretable behaviour analysis system with versatile abilities to reason over various data structures and provide a high-level interpretability about its reasoning procedure. The benefits will span the research and industry sectors, e.g., retail, healthcare, service provider.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
Probabilistic graphical models for detecting outbreaks. This project will create a novel class of probabilistic graphical model algorithms for learning and inference in problems involving unfrequent events such as anomaly detection. The outcome will be a methodology for seamlessly integrating space-time correlated data that will enable the early prediction of outbreaks in a principled statistical manner.