Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to ....Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to increase prediction accuracy and reliability in dynamic sequences, and to support decision-making in complex situations to achieve robust and adaptive results. Anticipated outcomes can help data scientists with state-of-the-art skills to manage sequential data and benefit data-enabled innovation in Australia.Read moreRead less
Robust meta learning for risk-aware recommender systems. Recommender systems are the core of many online services but they are highly vulnerable to risks like shilling attacks, privacy leaks, and unexpected change. This project aims to develop new adversarial Bayesian-based, privacy-preserved and self-adaptive fuzzy meta learning methods and meta recommender systems that are robust to these risky, uncertain and dynamic environments. The anticipated outcomes should significantly improve the relia ....Robust meta learning for risk-aware recommender systems. Recommender systems are the core of many online services but they are highly vulnerable to risks like shilling attacks, privacy leaks, and unexpected change. This project aims to develop new adversarial Bayesian-based, privacy-preserved and self-adaptive fuzzy meta learning methods and meta recommender systems that are robust to these risky, uncertain and dynamic environments. The anticipated outcomes should significantly improve the reliability of recommender systems with particular benefits for online personalised service systems, e.g., e-government, e-business and e-Learning. The outcomes will also advance machine learning knowledge with a new robust meta learning schema for general data analytics and applications.Read moreRead less
How parents manage climate anxiety: coping and hoping for the whole family. This project studies how Australian parents manage climate anxiety for themselves and their families. Using mixed-methods/mixed-media approaches, it examines whether an increase in climate disasters is accelerating the spread of collective anxiety amongst families, how parents manage this anxiety for their children and partners, and if there are associated mental health burdens and gendered inequities in this management. ....How parents manage climate anxiety: coping and hoping for the whole family. This project studies how Australian parents manage climate anxiety for themselves and their families. Using mixed-methods/mixed-media approaches, it examines whether an increase in climate disasters is accelerating the spread of collective anxiety amongst families, how parents manage this anxiety for their children and partners, and if there are associated mental health burdens and gendered inequities in this management. It also looks at climate anxiety management across generations and climate histories, drawing out pessimistic/optimistic narratives about the future to enable action, resilience, and hope. It will produce an evidence base and photo-voice/documentary resources to help parents and support organisations combat climate anxiety.Read moreRead less