Evaluating and developing the evidence-base and data mining approaches to strengthen the consumer product safety system in Australia. Consumer product-related injuries cause over 173,000 injuries per year though there is limited evidence about the causes and risks to enable early identification and warnings for consumers. This project will evaluate the evidence-base and develop new methods to support an early identification and surveillance system for product-related injuries.
Discovery Early Career Researcher Award - Grant ID: DE200101100
Funder
Australian Research Council
Funding Amount
$425,613.00
Summary
Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health c ....Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health care, empowering them with empathetic abilities is important for their success. The project will advance fundamental research in machine learning, affective computing and artificial intelligence to model human behavior, personality traits and emotions for an empathetic human-robot interaction.Read moreRead less
Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this i ....Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this issue through novel analyses of very subtle cues in facial and vocal expressions of affect embedded in a multimodal deep learning framework. Current approaches can successfully assist in binary classification tasks. This project will tackle the much more difficult problem of developing advanced affective sensing technology to simultaneously handle homogeneous and heterogeneous affect classes as well as continuous range estimates of affect intensity.Read moreRead less
Active and interactive analysis of prescription data for harm minimisation. Active and interactive analysis of prescription data for harm minimisation. This project aims to enhance prescription monitoring to reduce and prevent dangers to the public from inappropriate drug use. The project will develop a framework integrating active machine learning, interactive data mining, and data visualization into analysis of prescription data. The expected outcomes include online interactive analysis of lar ....Active and interactive analysis of prescription data for harm minimisation. Active and interactive analysis of prescription data for harm minimisation. This project aims to enhance prescription monitoring to reduce and prevent dangers to the public from inappropriate drug use. The project will develop a framework integrating active machine learning, interactive data mining, and data visualization into analysis of prescription data. The expected outcomes include online interactive analysis of large scale prescription data and a system that can interact with health professionals to provide high quality real time prescription monitoring, thereby improving patient outcomes and the efficiency of the healthcare system.Read moreRead less
The development of automated advanced data analysis techniques for the detection of aberrant patterns of prescribing controlled drugs. The state of the art in ICT for healthcare monitoring is rapidly advancing, however, the value of data depends on effective tools and techniques. This project will develop novel techniques for the detection of emerging patterns in the prescribing of controlled drugs, supporting Queensland Health’s role in patient harm minimisation.
Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental compu ....Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, and distributed solutions. The success of the project will directly contribute to the scientific foundation of Big Data computation. It will also contribute to the development of local industry involving cybersecurity, social media-based recommendation, network management, knowledge graphs, and E-business. Read moreRead less
Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber ....Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber-security, health, bio-informatics, social networks, and E-commerce. The success of the project will also facilitate the training of PhD graduates and postdoctoral research associates in the area of Big Data.
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Assistive technologies for autism support harnessing social media. This project aims to tap social media to revolutionize early intervention therapy for children with autism. By creating open, extensible software for therapy delivery, and tools for parents to access high quality information and support, we will provide children a greater chance to achieve their potential and much-needed relief for parents and carers.
Algorithms for collaborative micro-navigation based on spatio-temporal data management and data mining. Traffic congestion coupled with greenhouse gas emissions is a major challenge for modern society. This project will tackle this challenge by developing computer-assisted smart vehicles that can access and exchange real-time information about traffic conditions, leading to improved driving experience, safety and environmental sustainability.