Extending fuzzy logic. Fuzzy logic is good for dealing with uncertain data somewhat like people do, and this technique has been used in train braking systems, computer animation etc, but can be slow for problems with large or complex data especially if the data are changing with time. The project will design efficient fuzzy logic algorithms capable of dealing with complex real world problems.
Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori ....Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.Read moreRead less
Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected ....Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected outcomes include the
development of software that allows the solution of hitherto unsolved machine
learning problems, and the ability to solve problems larger than those solvable
by the current generation of machine learning tools.Read moreRead less
User-task co-adaptation for effective interactive simulation environments. This project aims to deliver smart interactive simulation environments in which users and simulation tasks work together. This project aims to create novel adaptive algorithms to automatically discover those user and task features that vary together to smartly adapt users and simulation tasks to work together harmoniously, seamlessly and effectively. Interactive simulation environments are the backbone for evaluating conc ....User-task co-adaptation for effective interactive simulation environments. This project aims to deliver smart interactive simulation environments in which users and simulation tasks work together. This project aims to create novel adaptive algorithms to automatically discover those user and task features that vary together to smartly adapt users and simulation tasks to work together harmoniously, seamlessly and effectively. Interactive simulation environments are the backbone for evaluating concepts, designs, products and advanced training systems in industry and government organisations. By bringing the user naturally inside the simulation as a task's component, users can improve their experience while task performance is simultaneously optimised. Intended outcomes include novel dynamic user-task profiling algorithms and new adaptive algorithms for user-task co-adaptation. Practical outcomes may include robust and highly effective simulation environments.Read moreRead less
Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country.
E.g., Australian Bureau of Statistics ....Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country.
E.g., Australian Bureau of Statistics figures show that for 2004, investigations of some 35% of murders, 63% of kidnappings, and 80% of robberies are incomplete at 30 days. Terrorism investigations are harder in that usually there is no initial crime trigger for an investigation. Any assistance our tools can provide in will be of significant benefit to Australia.Read moreRead less
Computational Intelligence for Complex Structured Data. This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learnin ....Computational Intelligence for Complex Structured Data. This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learning of the syntax and semantics of individual gestures and actions, nor the multi-gesture information fusion required for understanding, which could significantly enhance efficiency, for example, in sorting through named entities in an investigation. All of this is done naturally by most human beings, using biological neural networks.Read moreRead less
Development of methods and algorithms to support multidisciplinary optimisation. This project will aim to develop a number of novel and computationally efficient schemes to deal with the key challenges facing multidisciplinary optimisation. These advancements will allow us to solve a number of challenging and intractable problems in science and engineering.
Discovery Early Career Researcher Award - Grant ID: DE170100128
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts ....Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts research. This project is expected to advance our understanding of information processing in the brain by providing a mechanistic approach to functional integration.Read moreRead less
Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses. Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats to Australian livestock health cannot be efficiently diagnosed or treated. New approaches based on high-throughput sequencing have recently been developed to study such viruses, but making sense of the sequence data is still a ....Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses. Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats to Australian livestock health cannot be efficiently diagnosed or treated. New approaches based on high-throughput sequencing have recently been developed to study such viruses, but making sense of the sequence data is still a complex problem. Together with the project's Partner Organisations, including YourGene Biosciences Australia and the CSIRO Australian Animal Health Laboratory, this project aims to develop new computational methods to broaden the scope of detection and analysis of unknown viruses, enhancing the capability for research into the causative viral agents of animal diseases.Read moreRead less
Mathematics and computing for integrated stockyard-centric management of mining supply chains. Blended mineral products, such as coal and iron ore, make a strong contribution to Australia's economy. Blending occurs in stockpiles, so to realise product value, stockyard and supply chain operational plans must align with blend targets. This project will provide new mathematical and computational planning tools to maximise this value.