An knowledge-based approach to multi-document text summarisation for automated meta-analysis of the scientific literature. The biomedical sciences produce literature at an exponential rate, and the size of this knowledge base far exceeds the capacity of humans to keep up with the growth in new knowledge. This project will develop computational text summarisation methods to abstract the content of scientific journal articles reporting clinical trials, and develop multi-document summarisation meth ....An knowledge-based approach to multi-document text summarisation for automated meta-analysis of the scientific literature. The biomedical sciences produce literature at an exponential rate, and the size of this knowledge base far exceeds the capacity of humans to keep up with the growth in new knowledge. This project will develop computational text summarisation methods to abstract the content of scientific journal articles reporting clinical trials, and develop multi-document summarisation methods to synthesise these abstracts using automated statistical meta-analysis methods. These methods have broad potential to improve text-summarisation technologies in general, to profoundly enhance our ability to integrate published knowledge, and to make a highly significant and specific contribution to improving the quality of evidence used in health decision-making. Read moreRead less
Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are a ....Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are at high risk of incurring debt and defaulting on paying taxes. In turn, the early identification of clients in financial distress will allow the ATO to give them assistance so that they can reduce their debts and meet their financial obligations.Read moreRead less
Agent-Oriented Concept Management. This project will develop innovative agent-oriented approaches to managing information. An understanding of the concepts used by a system will enhance its ability to share information. Knowledge and concept management are key components of an information economy, and it is widely accepted that the success of the next generation of information systems will be their agent-oriented capability and their ability to interact with each other. In response, internation ....Agent-Oriented Concept Management. This project will develop innovative agent-oriented approaches to managing information. An understanding of the concepts used by a system will enhance its ability to share information. Knowledge and concept management are key components of an information economy, and it is widely accepted that the success of the next generation of information systems will be their agent-oriented capability and their ability to interact with each other. In response, international funding agencies have targeted agent-oriented technologies as essential ingredients for prosperity in the 21st century. This project will help to ensure that Australia shares in that prosperity.Read moreRead less
Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will ....Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will formally specify and develop a model to validate loose association rules while minimising data leakage risks. The outcomes will benefit Australians through enabling sharing and linking increasingly large datasets securely and cheaply.Read moreRead less
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
Data mining complex transactional and criminal networks. Money laundering, if undetected, poses a major concern for governments and communities. The software system platform for detecting money laundering networks from this project will be the first that can assist intelligence data analysts to detect unknown money laundering networks faster and more accurately, helping fight crimes more efficiently.
Interest-based negotiation: theory and practice. The results of the project will provide theoretically sound technologies that can better support distributed resource and task allocation in complex problem solving settings. The combination of theoretical and practical outcomes from the research will enable these technologies to be applied in decision-making settings beyond those studied directly in the project. The project contributes to Australia's ability to compete in the emerging internation ....Interest-based negotiation: theory and practice. The results of the project will provide theoretically sound technologies that can better support distributed resource and task allocation in complex problem solving settings. The combination of theoretical and practical outcomes from the research will enable these technologies to be applied in decision-making settings beyond those studied directly in the project. The project contributes to Australia's ability to compete in the emerging international market for knowledge-based software applications. The project's key connections and integration with European research will also strengthen research collaboration and training opportunities for Australian based students. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less
Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will esta ....Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.Read moreRead less
Unifying Modern Approaches in Machine Learning. The proposed research will lead to better algorithms for some important machine learning problems that could lead to better tools for extracting useful knowledge from data such as in bioinformatics and sensor networks; it will strengthen an international collaboration with one of the world's top centres of machine learning research; it will contribute to an open source toolkit of machine learning algorithms which will put Australia on the map as a ....Unifying Modern Approaches in Machine Learning. The proposed research will lead to better algorithms for some important machine learning problems that could lead to better tools for extracting useful knowledge from data such as in bioinformatics and sensor networks; it will strengthen an international collaboration with one of the world's top centres of machine learning research; it will contribute to an open source toolkit of machine learning algorithms which will put Australia on the map as a provider of sophisticated machine learning software; it will provide training opportunities for several PhD students and a postdoc to work with some of the best machine learning researchers in the world.Read moreRead less