Integrating deep-earth and surface processes for frontier-basin exploration. It is well-known that mantle convection has a profound influence on basin evolution, and the next step will be to quantify this relationship and provide the science that will make these concepts applicable to exploration. To do this, we will develop a workflow to link plate-reconstruction software with the mantle convection modelling to link plate motions mantle convection and the history of sedimentation systematically ....Integrating deep-earth and surface processes for frontier-basin exploration. It is well-known that mantle convection has a profound influence on basin evolution, and the next step will be to quantify this relationship and provide the science that will make these concepts applicable to exploration. To do this, we will develop a workflow to link plate-reconstruction software with the mantle convection modelling to link plate motions mantle convection and the history of sedimentation systematically for the first time for frontier basin-scale applications. We will apply these emerging technologies to the evolution of basins in the Arctic borderlands frontier for resource exploration and on the Australian continent.Read moreRead less
Privacy-preserving record linkage on multiple large databases. Record linkage has been recognised as a crucial infrastructure component in many information systems, however privacy concerns commonly prevent the linking of databases that contain personal information. This project will develop techniques that will enable the linking of multiple large databases without revealing any private information.
Special Research Initiatives - Grant ID: SR0566892
Funder
Australian Research Council
Funding Amount
$220,000.00
Summary
The EarthByte software and database system. Earth processes over geological timescales cannot be understood outside of a plate tectonic context. However, no standard tool exists to explore the causes and effects of lithosphere-mantle interaction in accordance with past plate configurations. Our aim is to develop a Palaeo-Geographic Information System called EarthByte that will connect the open source and architecture-independent GPlates and GMT software, and implement XML-based service interfac ....The EarthByte software and database system. Earth processes over geological timescales cannot be understood outside of a plate tectonic context. However, no standard tool exists to explore the causes and effects of lithosphere-mantle interaction in accordance with past plate configurations. Our aim is to develop a Palaeo-Geographic Information System called EarthByte that will connect the open source and architecture-independent GPlates and GMT software, and implement XML-based service interfaces and databases. EarthByte will create the foundation for an e-geoscience framework for grid-based data access and Earth process modelling by linking geological and geophysical observations to palaeogeographic models for constraining mantle convection and lithospheric deformation.Read moreRead less
Planet-scale reorganizations of the plate-mantle system. Vast sedimentary basins, fold belts and associated resources represent the main source of Australia's wealth, formed largely as consequences of major global tectonic events. We propose to connect two key national Simulation and Modelling infrastructures to a novel geodynamic modelling tool, developed specifically for modelling plate tectonics at the global and regional scale and suitable to unravel the causes and consequences of sudden gl ....Planet-scale reorganizations of the plate-mantle system. Vast sedimentary basins, fold belts and associated resources represent the main source of Australia's wealth, formed largely as consequences of major global tectonic events. We propose to connect two key national Simulation and Modelling infrastructures to a novel geodynamic modelling tool, developed specifically for modelling plate tectonics at the global and regional scale and suitable to unravel the causes and consequences of sudden global plate tectonic reorganizations. The knowledge-base derived from this work will considerably improve our understanding of catastrophic tectonic events affecting plate boundaries and plate interiors.Read moreRead less
The Subduction Reference Framework: unravelling the causes of long-term sea-level change. Long-term global sea level fluctuations have been a driving force of biogeography, climate change and organic evolution. We will assimilate images of subducted tectonic plates in the Earth's mantle into geodynamic models to establish a novel Subduction Reference Frame for the past 200 million years. This will form the basis for unravelling the effects of subduction on surface topography and sea-level chang ....The Subduction Reference Framework: unravelling the causes of long-term sea-level change. Long-term global sea level fluctuations have been a driving force of biogeography, climate change and organic evolution. We will assimilate images of subducted tectonic plates in the Earth's mantle into geodynamic models to establish a novel Subduction Reference Frame for the past 200 million years. This will form the basis for unravelling the effects of subduction on surface topography and sea-level change. The project outcomes will include predictive models of sedimentation and erosion in continental interiors, and will transform knowledge about the nature and magnitude of natural planetary change. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101808
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims ....Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims to enhance genetic programming for many practical problems. I contend that not only finding better tools for big data analytics is in the best interest of machine learning and big data communities, it also provides significant benefits for other communities and industries in Australia.Read moreRead less
Learning the Focus of Attention to Detect Distributed Coordinated Attacks. Cyber security analysts need to detect and respond to attacks as soon as possible, to minimise the damage attackers can inflict. However, the growth in highly distributed attacks that span multiple networks has meant that massive volumes of data need to be analysed. While machine learning techniques can help filter the data, we need techniques that can automatically provide a focus of attention for analysts on the most re ....Learning the Focus of Attention to Detect Distributed Coordinated Attacks. Cyber security analysts need to detect and respond to attacks as soon as possible, to minimise the damage attackers can inflict. However, the growth in highly distributed attacks that span multiple networks has meant that massive volumes of data need to be analysed. While machine learning techniques can help filter the data, we need techniques that can automatically provide a focus of attention for analysts on the most relevant observations. Our aim is to devise a novel suite of attention mechanisms that can focus the search of machine learning techniques for cyber security. The results of this project will improve the accuracy and efficiency of detecting distributed attacks across multiple networks.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compress ....Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compressing massive raw data, to recognition of abnormal waveforms preceding anomalies, and to retrieving and summarising similar past anomalies for creating descriptions of future anomalies. The project will demonstrate our method in health/environment monitoring applications, and its adoption will save resources, money and lives.Read moreRead less