Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project ....Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project expects to provide practical data analysis approaches and establish the theoretical foundations for data mining with multiple sources of brain data.Read moreRead less
Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer ....Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less
Monitoring intuitive expertise in the context of airport security screening. During airport security screening and processing, confusion and error are greatest when systems or contexts are unfamiliar. Poorly designed systems compromise the interactions of airport security personnel and decrease their ability to promptly and accurately respond to situations. This project aims to deliver a suite of automated methods to monitor security operator knowledge and engagement, to assess the real-time sec ....Monitoring intuitive expertise in the context of airport security screening. During airport security screening and processing, confusion and error are greatest when systems or contexts are unfamiliar. Poorly designed systems compromise the interactions of airport security personnel and decrease their ability to promptly and accurately respond to situations. This project aims to deliver a suite of automated methods to monitor security operator knowledge and engagement, to assess the real-time security screening context, and to detect unusual passenger behaviour at the screening check-point. This monitoring aims to provide new knowledge and techniques to enhance security operator performance, refine the screening process, improve passenger experience and, most critically, ensure safety at Australian airports.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100850
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
Dynamic adaptive software configuration. The aim of this project is to use computational intelligence in software engineering processes to achieve self-optimising products. Many mobile device users bemoan inadequate battery life, and device size is largely determined by the battery. In addition, information and communications technology uses six per cent of the global electricity production. The expected outputs of this project will be packages of optimisation components that software engineers ....Dynamic adaptive software configuration. The aim of this project is to use computational intelligence in software engineering processes to achieve self-optimising products. Many mobile device users bemoan inadequate battery life, and device size is largely determined by the battery. In addition, information and communications technology uses six per cent of the global electricity production. The expected outputs of this project will be packages of optimisation components that software engineers can incorporate into next generation products: the products analyse their collected usage data, perform what-if analyses, and optimise their configurations accordingly for the next usage period. Hence, the products may respond faster, be more reliable, and consume less energy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101174
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Building a mechanical quantum memory from superfluid helium. This project aims to implement a quantum computer memory module which can be integrated in a future hybrid quantum computer, where the advantages of different quantum platforms are combined. The memory module will be achieved by coupling a superconducting qubit to the surface vibrations of superfluid helium and exchanging quantum information between the qubit and helium. By simulating chemical reactions, the project expects to find cle ....Building a mechanical quantum memory from superfluid helium. This project aims to implement a quantum computer memory module which can be integrated in a future hybrid quantum computer, where the advantages of different quantum platforms are combined. The memory module will be achieved by coupling a superconducting qubit to the surface vibrations of superfluid helium and exchanging quantum information between the qubit and helium. By simulating chemical reactions, the project expects to find cleaner alternatives of current industrial processes, reducing environmental impact. The outcomes should provide significant benefits for testing the validity of quantum mechanics, and by contributing to the realisation of a quantum computer, contribute to broad socio-economic benefits.Read moreRead less
Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new appr ....Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new approach to the process of autonomously gathering information about objects in outdoor environments, will synergise with existing classification and motion planning methods, and will support Australia's continued role as a leader in field robotics research.Read moreRead less