Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit th ....Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit the future development of intelligent systems for dealing with missing data. This project directly supports a PhD student and two research assistants who will most likely continue their higher degree study. These contribute to regional tertiary education.Read moreRead less
Attentional asymmetries for navigation in healthy and clinical groups. This project plans to investigate how differences in attentional capacity between the left and right sides of the brain affect the ability to walk or manoeuvre vehicles between obstacles. To navigate our environment and avoid obstacles, we need to attend to stimuli that are important and ignore those that are not. Unfortunately, the brain’s attentional capacity is limited, which can result in errors and collisions. Using the ....Attentional asymmetries for navigation in healthy and clinical groups. This project plans to investigate how differences in attentional capacity between the left and right sides of the brain affect the ability to walk or manoeuvre vehicles between obstacles. To navigate our environment and avoid obstacles, we need to attend to stimuli that are important and ignore those that are not. Unfortunately, the brain’s attentional capacity is limited, which can result in errors and collisions. Using the techniques of cognitive neuroscience, the project aims to provide a better understanding of the cognitive and neural mechanisms that govern attention in an applied setting. It expects to identify the factors that exacerbate lapses in attention and collisions. The effect of everyday impediments such as mobile phones, alcohol and fatigue will be investigated together with means of minimising these attentional lapses and improving safety.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Closing the loop between salience and brain activity. This project aims to understand how animals exposed to an abundance of highly complex information decide what to attend to, that is, how they determine visual saliency. The project will approach this question by systematically tracking visual decision-making in the smallest animal brains, in closed-loop virtual reality environment. This approach will uncover basic working principles applicable to any system that needs to pay attention in a vi ....Closing the loop between salience and brain activity. This project aims to understand how animals exposed to an abundance of highly complex information decide what to attend to, that is, how they determine visual saliency. The project will approach this question by systematically tracking visual decision-making in the smallest animal brains, in closed-loop virtual reality environment. This approach will uncover basic working principles applicable to any system that needs to pay attention in a visually cluttered world, from insects to humans or autonomous vehicles.Read moreRead less
The relative impacts of sleep, wake and the internal body clock on human performance. The 24h society presents a number of challenges to the shiftworker. First, shiftworkers have to maintain a balance between the competing needs of work, family, leisure and social life. Second, shiftwork has been identified as a risk factor for obesity, diabetes and heart disease. Third, shiftworkers have an increased risk of injury and death at work. This project will use an innovative research protocol to prov ....The relative impacts of sleep, wake and the internal body clock on human performance. The 24h society presents a number of challenges to the shiftworker. First, shiftworkers have to maintain a balance between the competing needs of work, family, leisure and social life. Second, shiftwork has been identified as a risk factor for obesity, diabetes and heart disease. Third, shiftworkers have an increased risk of injury and death at work. This project will use an innovative research protocol to provide critical information about the independent and combined effects of sleep loss and body clock disruption on human performance. Work schedules designed on the basis of a better understanding of sleep loss and circadian disruption will result in healthier employees, safer workplaces, and reduced costs to the community.Read moreRead less
Privacy preserving data sharing in data mining environments. Preserving privacy in data mining among various enterprises and organisations is essential for many real world applications in areas like health surveillance, business analysis, fraud detection and terror protection. Efficient and effective techniques are badly needed to protect privacy in data sharing and data mining. The developed cutting-edge techniques in this project will be implemented in freely available open source software too ....Privacy preserving data sharing in data mining environments. Preserving privacy in data mining among various enterprises and organisations is essential for many real world applications in areas like health surveillance, business analysis, fraud detection and terror protection. Efficient and effective techniques are badly needed to protect privacy in data sharing and data mining. The developed cutting-edge techniques in this project will be implemented in freely available open source software tools, empowering Australian organisations to utilise the techniques to develop intelligent systems in data sharing environments. These techniques will ultimately lead to better utilisation of the information available in many enterprises and organisations.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to ....Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.Read moreRead less