Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Target-agnostic analytics: building agile predictive models for big data. This project aims to develop target-agnostic analytics, creating models of data that can be queried about any variable or feature without having to be relearned. Government and business collect vast quantities of data, but these are wasted if we cannot use them to predict the future from the past. Presently, big-data analytics is effective at predicting a single pre-defined target variable, yet in many applications, what w ....Target-agnostic analytics: building agile predictive models for big data. This project aims to develop target-agnostic analytics, creating models of data that can be queried about any variable or feature without having to be relearned. Government and business collect vast quantities of data, but these are wasted if we cannot use them to predict the future from the past. Presently, big-data analytics is effective at predicting a single pre-defined target variable, yet in many applications, what we know about a system and what we want to find out are far more complex. This project expects to yield novel target-agnostic technologies with associated publications and open-source software. The project will expand the capabilities of machine learning, providing better use of the massive data assets collected across most public, commercial and industry sectors.Read moreRead less
Stay well: Analysing lifestyle data from smart monitoring devices. Pervasive health monitoring devices provide a rich data source with opportunity to continuously extract patterns and guide individuals towards their goals of wellbeing. To exploit this nexus between machine learning and pervasive computing, this project aims to solve the computational problems to analyse data from such wearable devices, applying rigorous statistical models to discover latent patterns and groupings. The significan ....Stay well: Analysing lifestyle data from smart monitoring devices. Pervasive health monitoring devices provide a rich data source with opportunity to continuously extract patterns and guide individuals towards their goals of wellbeing. To exploit this nexus between machine learning and pervasive computing, this project aims to solve the computational problems to analyse data from such wearable devices, applying rigorous statistical models to discover latent patterns and groupings. The significance lies in solving fundamental problems related to heterogeneous, multi-level, mixed-type time series data. The proposed outcomes are expected to enable monitoring of people 'in the wild', away from doctors and hospitals, thus significantly reducing the burgeoning cost of hospital visits and stays.Read moreRead less
Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make th ....Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.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.
A Computer-aided Diagnosis System For Gait Analysis
Funder
National Health and Medical Research Council
Funding Amount
$333,379.00
Summary
Gait analysis provides detailed information on how a patient walks to help orthopaedic surgeons decide on surgical treatments. However, gait analysis requires long and specific training and expertise in gait analysis is not widely available which restricts its benefits to the community. We will build the first computer-aided diagnosis system from renowned experts from The Royal Children’s Hospital Melbourne to assist clinicians interpreting gait analysis data nationally and internationally.
Identifying and Tracking Influential Events in Large Social Networks. This project aims to invent a novel model and techniques for identifying and tracking influential events in large and dynamic social networks in real time. The proposed model would take into account the structure and content of social networks, and the influence of events. The project also plans to develop efficient strategies for identifying and tracking events in large and dynamic social network environments based on the mod ....Identifying and Tracking Influential Events in Large Social Networks. This project aims to invent a novel model and techniques for identifying and tracking influential events in large and dynamic social networks in real time. The proposed model would take into account the structure and content of social networks, and the influence of events. The project also plans to develop efficient strategies for identifying and tracking events in large and dynamic social network environments based on the model, In particular, the project plans to investigate flexible social network query methods to make users’ event search easy. Finally the project plans to build an evaluation system to demonstrate the efficiency of the algorithms and effectiveness of the model.Read moreRead less
Biclique discovery in Big Data. This project aims to design algorithms to capture Big Data. Biclique is a popular graph model that can capture important cohesive structures in many applications. However, traditional biclique discovery algorithms which only focus on simple, small-scale, static and deterministic data are inadequate in the era of Big Data where data has Variety (various formats), Volume (large quantity), Velocity (dynamic update) and Veracity (uncertainty). This project expects to ....Biclique discovery in Big Data. This project aims to design algorithms to capture Big Data. Biclique is a popular graph model that can capture important cohesive structures in many applications. However, traditional biclique discovery algorithms which only focus on simple, small-scale, static and deterministic data are inadequate in the era of Big Data where data has Variety (various formats), Volume (large quantity), Velocity (dynamic update) and Veracity (uncertainty). This project expects to benefit real applications in both public and private sectors and add value to Australian manufactured products.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