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.
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
Quantifying intake of food prepared outside home during emerging adulthood. This project aims to explore the role that food prepared outside the home versus within the home plays in the diets of young adults. The project plans to use data mining to understand where, when and with whom young adults buy food, and the nutritional composition and overall contribution to diet of these foods. The project will use a newly designed and tested Smartphone application to transform data collection and proce ....Quantifying intake of food prepared outside home during emerging adulthood. This project aims to explore the role that food prepared outside the home versus within the home plays in the diets of young adults. The project plans to use data mining to understand where, when and with whom young adults buy food, and the nutritional composition and overall contribution to diet of these foods. The project will use a newly designed and tested Smartphone application to transform data collection and processing, and enable continuous digital photography. The knowledge gained is intended to form the evidence base for formulation of policy and electronic and mobile-health promotion to reverse the problem of young adults gaining more weight than any other group in Australia.Read moreRead less
Bio-Acoustic Observatory: Engaging Birdwatchers to Monitor Biodiversity by Collaboratively Collecting and Analysing Big Audio Data. This project will research how to crowd-source the collection and analysis of environmental animal sounds (for example, birds, frogs). This will enable a bio-acoustic observatory which provides a scalable, objective and permanent record of the environment, something hitherto impossible. The project will investigate how to engage the community of birdwatchers to exte ....Bio-Acoustic Observatory: Engaging Birdwatchers to Monitor Biodiversity by Collaboratively Collecting and Analysing Big Audio Data. This project will research how to crowd-source the collection and analysis of environmental animal sounds (for example, birds, frogs). This will enable a bio-acoustic observatory which provides a scalable, objective and permanent record of the environment, something hitherto impossible. The project will investigate how to engage the community of birdwatchers to extend their pastime online with new kinds of interactive tools to enable collaborative analysis of big audio data, and new kinds of birding experiences. Outcomes will be: new approaches to physical/virtual engagement in human-computer interaction; new approaches to analysing big data; a new validated ecological monitoring technique and concepts for sustainable knowledge generation communities.Read moreRead less
Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical application ....Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical applications such as better wellness tracking and lifestyle-related illness prevention, which will be particularly critical to Australia's aging population. This project will also serve as a vehicle to educate and train Australia’s young scholars and engineers.Read moreRead less
Visual Analytics for Next Generation Sequencing. Next-generation sequencing technologies have brought a revolution in biology and healthcare, while taxing the ability of scientists and clinicians to identify and process relevant data, to make sense of it all and communicate it to others in a concise and meaningful way. This project aims to tackle this problem through fundamentally new approaches to data selection and visualisation at very large scale, actively encoding for insight into underlyin ....Visual Analytics for Next Generation Sequencing. Next-generation sequencing technologies have brought a revolution in biology and healthcare, while taxing the ability of scientists and clinicians to identify and process relevant data, to make sense of it all and communicate it to others in a concise and meaningful way. This project aims to tackle this problem through fundamentally new approaches to data selection and visualisation at very large scale, actively encoding for insight into underlying biological and biomedical processes, bringing sustainable discovery of new relationships and variations within the data. The project aims to support new approaches to medical diagnosis and treatment, and offer crucial lessons to address the broader challenge of understanding large, complex data sets.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101002
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Efficiently querying uncertain spatial space. Location-based services are becoming increasingly popular due to exponentially increased usage of smartphones and cheap wireless network. This project aims to provide efficient solutions for various location-based queries applicable to different travelling domains such as road networks, Euclidean space with obstacles and indoor space.
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Probabilistic search over large-scale uncertain graphs. Efficiently conducting structure-based search is fundamental in many real applications. The project aims to develop effective searching techniques for large-scale imprecise and/or uncertain graphs. This project will develop, analyse, implement, and evaluate novel indexing and query processing techniques to efficiently conduct structure-based probabilistic queries over large uncertain graphs, including structure search, structure similarity ....Probabilistic search over large-scale uncertain graphs. Efficiently conducting structure-based search is fundamental in many real applications. The project aims to develop effective searching techniques for large-scale imprecise and/or uncertain graphs. This project will develop, analyse, implement, and evaluate novel indexing and query processing techniques to efficiently conduct structure-based probabilistic queries over large uncertain graphs, including structure search, structure similarity search, all-matches, vertex-pair similarity search and top-k search. The success of this project will be an important complement to the current development of graph database management technology and will bring considerable social, economic and technological benefits to Australia.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less