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
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.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Interaction Mining for Cyberbullying Detection on Social Networks. This project plans to build an interactive mining system to detect cyberbullying on social networks that have a large number of participants and a variety of inputs, including conversation texts, time-variant changes and user profiles. The project is designed to change the existing cyberbullying prevention services from reactive keyword filtering to proactive social interaction pattern mining. The intended outcome will enable the ....Interaction Mining for Cyberbullying Detection on Social Networks. This project plans to build an interactive mining system to detect cyberbullying on social networks that have a large number of participants and a variety of inputs, including conversation texts, time-variant changes and user profiles. The project is designed to change the existing cyberbullying prevention services from reactive keyword filtering to proactive social interaction pattern mining. The intended outcome will enable the early detection and warning of cyberbullying and approach open a new way to discover interaction patterns with a large number of participants over evolving and complex social networks.Read moreRead less
Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. ....Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. Evaluation will be via development of several exemplar applications. The techniques and framework will be applicable to a broad range of economically important problems in areas as diverse as health, travel, scientific publication search, product marketing and software engineering.Read moreRead less
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less
Automation of species recognition and size measurement of fish from underwater stereo-video imagery. The project aims to develop algorithms to automate the processing of stereo-video images recorded to count and measure the size of fish. This will improve husbandry and monitoring for finfish aquaculture at reduced costs, create technology export for industry partners, and develop cost effective, non-destructive finfish sampling tools for marine agencies.
Special Research Initiatives - Grant ID: SR0354604
Funder
Australian Research Council
Funding Amount
$10,000.00
Summary
ARC Network in Imaging Science and Technology. The ARC Network in Imaging Science and Technology is a field of research network covering the fundamental science and technological development of applied imaging systems. The network will encompass all aspects of the imaging sciences from image formation, through image processing and analysis, and on to image visualisation. In particular, the network will focus on a number of application areas that utilise these core technologies: medical imaging; ....ARC Network in Imaging Science and Technology. The ARC Network in Imaging Science and Technology is a field of research network covering the fundamental science and technological development of applied imaging systems. The network will encompass all aspects of the imaging sciences from image formation, through image processing and analysis, and on to image visualisation. In particular, the network will focus on a number of application areas that utilise these core technologies: medical imaging; surveillance and security; materials science and metallurgy; environmental monitoring; and consumer imaging. In this way, the network will provide an environment for creative inter-disciplinary research to the socio-economic benefit of Australia.Read moreRead less