Discovery Early Career Researcher Award - Grant ID: DE140100007
Funder
Australian Research Council
Funding Amount
$391,947.00
Summary
An Adaptive and Intelligent Service Level Agreement Negotiation System for Web-Based Service-Oriented Grid Computing. This project will develop an intelligent negotiation system for Service Level Agreement (SLA) in web-based service-oriented grid computing. The specific aims include computational models for SLA negotiation representation, intelligent management of SLA negotiation procedures and adaptive learning for SLA negotiation system improvement. The significance of this project lies in its ....An Adaptive and Intelligent Service Level Agreement Negotiation System for Web-Based Service-Oriented Grid Computing. This project will develop an intelligent negotiation system for Service Level Agreement (SLA) in web-based service-oriented grid computing. The specific aims include computational models for SLA negotiation representation, intelligent management of SLA negotiation procedures and adaptive learning for SLA negotiation system improvement. The significance of this project lies in its promises to realise the automation of SLA negotiation through using intelligent and computational models, so as to greatly improve the efficiency of web-based service systems. The research results will enable software engineers to develop more robust and intelligent service-oriented systems through web-based computational grids.Read moreRead less
Natural language processing for automated validation of protein databases. The project aims to use natural language processing and information retrieval to reconcile and improve sources of biological information. Biological research has produced vast volumes of information about proteins, captured in structured resources (databases) and unstructured documents. However, the accuracy of much of this information is questionable. The project proposes to develop methods to validate data and reduce th ....Natural language processing for automated validation of protein databases. The project aims to use natural language processing and information retrieval to reconcile and improve sources of biological information. Biological research has produced vast volumes of information about proteins, captured in structured resources (databases) and unstructured documents. However, the accuracy of much of this information is questionable. The project proposes to develop methods to validate data and reduce the dramatic inconsistencies in protein information resources by leveraging observed correlations and complementarity between them, and specifically through targeted fact extraction from the biomedical literature. These methods will be applied at scale across millions of published articles, to infer and validate functional information.Read moreRead less
Exposing the anonymous attacker: detecting identity crimes using real-time entity resolution on large dynamic databases. Given the increasingly large costs of identity crimes in Australia, developing improved electronic identity verification techniques is highly significant in reducing losses from such crimes, making the Australian economy more competitive, and increasing consumer confidence in Australian financial institutions. Veda Advantage is widely used for identity verification by Australi ....Exposing the anonymous attacker: detecting identity crimes using real-time entity resolution on large dynamic databases. Given the increasingly large costs of identity crimes in Australia, developing improved electronic identity verification techniques is highly significant in reducing losses from such crimes, making the Australian economy more competitive, and increasing consumer confidence in Australian financial institutions. Veda Advantage is widely used for identity verification by Australian financial service providers, so the benefits of the techniques developed in this project will automatically flow through to the Australian community. These techniques will be sufficiently generic to be of use for real-time identity verification in a broad range of applications, including e-Government portals, electronic banking, online stores, or national security systems.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0561231
Funder
Australian Research Council
Funding Amount
$671,715.00
Summary
MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools acc ....MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools accessible through the Internet will enable researchers to archive, retrieve and exchange data and software; access distributed MR image databases and the latest MR image analysis tools; schedule analysis tasks on the grid compute engine, the outcomes of which will be visualized by the visualization workstations.Read moreRead less
eResearch in the Neurosciences: Building collaborations in Asia. The proposed Australasian collaboration on eResearch in Neuroscience will promote and maintain the good health of Australians by 'improving critical mass through collaboration and information sharing' through increased access to advanced imaging technology in Korea and analysis techniques in Japan. The collaboration will also promote frontier technologies for building and transforming Australian industries by developing a creative ....eResearch in the Neurosciences: Building collaborations in Asia. The proposed Australasian collaboration on eResearch in Neuroscience will promote and maintain the good health of Australians by 'improving critical mass through collaboration and information sharing' through increased access to advanced imaging technology in Korea and analysis techniques in Japan. The collaboration will also promote frontier technologies for building and transforming Australian industries by developing a creative and innovative research environment and enhancing Australian scientists' participation in breakthrough science. Great national benefit can be derived from international research collaboration, due to the contribution frontier technology can make to science and health. Read moreRead less
Modelling graph-of-graphs for solving document categorisation problems. Documents in the World Wide Web, such as scientific documents, exhibit a referencing structure as well as being structured objects themselves. This project addresses some inherent limitations of existing modelling techniques in order to improve on the quality of results, and to allow the addressing of some unsolved problems involving documents.
Discovery Early Career Researcher Award - Grant ID: DE200101283
Funder
Australian Research Council
Funding Amount
$400,998.00
Summary
Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoverie ....Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoveries and domain adaptation algorithms derived from synthetic data for real-world applications. The benefits are expected to be widespread across sectors such as transportation, security, and manufacturing, including safer robotic navigation, defect detection, and smart video surveillance to improve community safety.Read moreRead less
Multiview Complete Space Learning for Sparse Camera Network Research. Data analytics in video surveillance and social computing is a problem because data are represented by multiple heterogeneous features. This project will develop a multiview complete space learning framework to exploit heterogeneous properties to represent images obtained from sparse camera networks. It will integrate multiple features to identify people and understand behaviour, to build a database of activities occurring in ....Multiview Complete Space Learning for Sparse Camera Network Research. Data analytics in video surveillance and social computing is a problem because data are represented by multiple heterogeneous features. This project will develop a multiview complete space learning framework to exploit heterogeneous properties to represent images obtained from sparse camera networks. It will integrate multiple features to identify people and understand behaviour, to build a database of activities occurring in a wide area of surveillance. It will expand frontier technologies and safeguard Australia by providing warnings for hazardous (for example, overcrowding, trespassing), criminal, and terrorist situations. Results will be applicable internationally and enhance Australia’s role in machine learning and computer vision communities.Read moreRead less
Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false infor ....Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false information as well as recommendation of truthful news to counteract adversarial fake news. This project should generate technologies that enhance the integrity of the online echo system and benefit media providers and online population within Australia and across the world. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100045
Funder
Australian Research Council
Funding Amount
$377,829.00
Summary
Efficient and effective analytics for real-world time series forecasting. This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exp ....Efficient and effective analytics for real-world time series forecasting. This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exploit properties of big data accurately in a short time frame, which is transforming many industries. This should enable more accurate and reliable forecasts across industries as diverse as retail, food manufacturing, transport, mining, tourism, energy, and technology.Read moreRead less