Deep mining neurological abnormalities from brain signal data. This project aims to develop a reliable, robust and real-time analysis system for automatic and accurate detection of neurological abnormalities, and the prediction of impending neurological problems from brain signal data. The project expects to design novel algorithms for brain signal processing, data compression, and detection and prediction of neurological abnormalities from massive brain signal data. The project will lead to sig ....Deep mining neurological abnormalities from brain signal data. This project aims to develop a reliable, robust and real-time analysis system for automatic and accurate detection of neurological abnormalities, and the prediction of impending neurological problems from brain signal data. The project expects to design novel algorithms for brain signal processing, data compression, and detection and prediction of neurological abnormalities from massive brain signal data. The project will lead to significant improvement of existing methods in health monitoring applications in Australia and worldwide and hence will save lives, money and resources.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101465
Funder
Australian Research Council
Funding Amount
$419,498.00
Summary
Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods emp ....Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods empowering a Web-based observatory dashboard of fake news propagation. This achieves significant benefits for media organisations, governments, the public, and academia via timely alerts, data-journalism reports, and novel data visualisations of social media landscape to distinguish between legitimate and deceptive contents.Read moreRead less
Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-ti ....Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.Read moreRead less
Location-aware Frequent Pattern Mining from Uncertain Spatial Transaction Data. Traditional transaction data mining exploits transactions without location information. Technological advances like GPS-enabled devices or radar sensors now allow the collection of transactions with spatial information. This project will address inherent data uncertainty by developing technical solutions to mine uncertain spatial transactions, and initiate a new research direction, location-aware frequent pattern min ....Location-aware Frequent Pattern Mining from Uncertain Spatial Transaction Data. Traditional transaction data mining exploits transactions without location information. Technological advances like GPS-enabled devices or radar sensors now allow the collection of transactions with spatial information. This project will address inherent data uncertainty by developing technical solutions to mine uncertain spatial transactions, and initiate a new research direction, location-aware frequent pattern mining. It will deliver theoretical foundations and technology for knowledge discovery from uncertain spatial transaction data and expand research horizons. Outcomes will provide social and economic benefits, for example, significantly improved analysis and detection of crime, weather diagnosis, and market basket analysis for business. Read moreRead less
Large-scale spatio-temporal data hashing for efficient data analytics. This project aims to systematically investigate the challenging problem of hash function learning for large-scale spatio-temporal data. The project will generate high quality hash codes for spatio-temporal data objects, enabling efficient similarity computation and thus supporting various data mining tasks and applications. The project expects to devise a series of learning frameworks by addressing the specific challenges of ....Large-scale spatio-temporal data hashing for efficient data analytics. This project aims to systematically investigate the challenging problem of hash function learning for large-scale spatio-temporal data. The project will generate high quality hash codes for spatio-temporal data objects, enabling efficient similarity computation and thus supporting various data mining tasks and applications. The project expects to devise a series of learning frameworks by addressing the specific challenges of different types of spatio-temporal data, such as univariate and multivariate. The project will provide significant benefits to both academia and industry in massive spatio-temporal data analysing, for example traffic flow mining and optimisation, weather forecasting and financial fraud detection.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100950
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Building intelligence into online video services by learning user interests. This project aims to build an intelligent video streaming service by characterising users’ view interest patterns and predict user interest changes through learning data from Internet to address the challenge caused by astronomic video population. The outcomes of the project will be of great values for users and our society by intelligently filtering out valueless, harmful, illegal and unwanted videos in advance.
Big data fast response: real-time classification of big data stream. This project will provide a big data stream based data mining framework to build a real-time monitoring and decision making platform for business and government big data. By clever use of smart information, this breakthrough science will provide frontier technologies for managing and using huge data for social and economic benefits globally.
Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world appli ....Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world application platform to support information network analysis; theories for big network control, algorithms, and systematic solutions to enable user-centric knowledge search, including a new search engine for big information networks. By significantly improving IT, it will benefit Australian business, industry and the wider community.Read moreRead less
Novel data mining techniques for complex network analysis and control. This project will develop novel data mining theories and algorithms to analyse complex networks for safe information publishing and sharing across networks. It will enable smart information use in bioinformatics, social science and business intelligence, help protect against cybercrime and promote Australia's international research profile.
Assistive technologies for autism support harnessing social media. This project aims to tap social media to revolutionize early intervention therapy for children with autism. By creating open, extensible software for therapy delivery, and tools for parents to access high quality information and support, we will provide children a greater chance to achieve their potential and much-needed relief for parents and carers.