Interacting with visualisations of extremely large graph structures on large displays. The latest technological progressions have delivered very large data sets that can be modelled as graphs or networks. Examples include: social networks, biological data, and software structures. This project will develop techniques to allow users to visualise the graphs in the entirety and directly interact with data.
Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less
A process model of visual working memory. This project aims to develop a process model of encoding of items into memory. Working memory is central to almost all cognitive functions, but little is known about short-term memory for visual information. Progress in this area is slow because of a focus on models that do not specify the processes underlying memory, and no model explains the processes that would limit the number of items the memory can hold to four. A process model is expected to addre ....A process model of visual working memory. This project aims to develop a process model of encoding of items into memory. Working memory is central to almost all cognitive functions, but little is known about short-term memory for visual information. Progress in this area is slow because of a focus on models that do not specify the processes underlying memory, and no model explains the processes that would limit the number of items the memory can hold to four. A process model is expected to address fundamental issues in visual working memory.Read moreRead less
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Square Eyes or All Lies? Understanding Children's Exposure to Screens. This project will examine Australian parents’ number one concern about their children’s health and behaviour – their interactions with electronic screens. Current screen time guidelines are based on low-quality evidence and lack the nuance required to address this complex issue. This project will use innovative technology to resolve these weaknesses. Wearable cameras will measure what children are doing on screens, and where, ....Square Eyes or All Lies? Understanding Children's Exposure to Screens. This project will examine Australian parents’ number one concern about their children’s health and behaviour – their interactions with electronic screens. Current screen time guidelines are based on low-quality evidence and lack the nuance required to address this complex issue. This project will use innovative technology to resolve these weaknesses. Wearable cameras will measure what children are doing on screens, and where, when, and how long they are doing it. The project will also investigate how screen time impacts children’s development and how it is influenced by their environment. This evidence will benefit children by improving screen time guidelines, and help parents understand the impact of screen time on children’s development.
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What Can You Trust in the Large and Noisy Web? This project will develop innovative techniques to efficiently and effectively distill truthful information from the inherently unreliable and large-scale Web environment, where misinformation has been widely regarded as a grand challenge for the next decade. The results of this project will not only maintain Australia’s leadership in this frontier research area, but also support many important applications that safeguard Australian people and econo ....What Can You Trust in the Large and Noisy Web? This project will develop innovative techniques to efficiently and effectively distill truthful information from the inherently unreliable and large-scale Web environment, where misinformation has been widely regarded as a grand challenge for the next decade. The results of this project will not only maintain Australia’s leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as emergency and disaster management and online healthcare. This project also serves as an excellent vehicle for the education and training of Australia’s next generation of scholars and engineers.Read moreRead less
Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time ....Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time, and confidence, suitable for application to computerized testing scenarios. The models and testing methods validated in the laboratory will be refined for application in eyewitness memory settings, facilitating better evaluation of identification evidence, and potentially reducing wrongful convictions.Read moreRead less
Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of ....Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of science and technology. A large number of datasets will be investigated to benchmark the new methods. The expected outcomes will help support our national priorities with new data analytic capabilities. With a strong and interdisciplinary team in three continents, the project will attract international collaboration. 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
Normal forms and Chern-Moser connection in the study of Cauchy-Riemann Manifolds. This research project is aimed at a systematic study of Cauchy-Riemann manifolds, their holomorphic mappings and automorphisms, by means of a unifying approach based on
Chern-Moser type normal forms. The importance of Cauchy-Riemann manifolds stems from the fact that they bridge complex structure and holomorphy with the Riemannian nature of real manifolds. Construction of an analogue of the Chern-Moser normal form ....Normal forms and Chern-Moser connection in the study of Cauchy-Riemann Manifolds. This research project is aimed at a systematic study of Cauchy-Riemann manifolds, their holomorphic mappings and automorphisms, by means of a unifying approach based on
Chern-Moser type normal forms. The importance of Cauchy-Riemann manifolds stems from the fact that they bridge complex structure and holomorphy with the Riemannian nature of real manifolds. Construction of an analogue of the Chern-Moser normal form for multicodimensional Levi-nondegenerate CR-manifolds and extension of CR-mappings between them are major goals in complex analysis. Identification of Chern-Moser chains and equivariant linearisation of isotropy automorphisms are major goals in geometry.Read moreRead less