Special Research Initiatives - Grant ID: SR0567386
Funder
Australian Research Council
Funding Amount
$98,000.00
Summary
Mechanisms for Ultra-secure Access to Large Repositories of Sensitive Data over the Grid. Large repositories of data that are typically geographically distributed and are subject to varying degrees of legal and ethical constraints are not available for open scientific research due to the sensitive and private nature of the information they contain, e.g. personal health records offer significant value for medical research, but are not readily available due to privacy legislation and the requireme ....Mechanisms for Ultra-secure Access to Large Repositories of Sensitive Data over the Grid. Large repositories of data that are typically geographically distributed and are subject to varying degrees of legal and ethical constraints are not available for open scientific research due to the sensitive and private nature of the information they contain, e.g. personal health records offer significant value for medical research, but are not readily available due to privacy legislation and the requirement to maintain end-user’s trust in healthcare information system. This project will build a demonstrator, based upon advanced cryptographic and information research and technologies to provide ultra-secure and sanitized access to this data via a data network grid.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101100
Funder
Australian Research Council
Funding Amount
$425,613.00
Summary
Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health c ....Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health care, empowering them with empathetic abilities is important for their success. The project will advance fundamental research in machine learning, affective computing and artificial intelligence to model human behavior, personality traits and emotions for an empathetic human-robot interaction.Read moreRead less
Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this i ....Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this issue through novel analyses of very subtle cues in facial and vocal expressions of affect embedded in a multimodal deep learning framework. Current approaches can successfully assist in binary classification tasks. This project will tackle the much more difficult problem of developing advanced affective sensing technology to simultaneously handle homogeneous and heterogeneous affect classes as well as continuous range estimates of affect intensity.Read moreRead less
Stream Data Classification in the Age of 5G Networks. This project aims to develop a novel stream data classification model to handle the challenges in the era of 5G networks, such as the scope of the stream data, the complexity of their relationship, the diversity of contained information and the incorrect readings of numerous sensors. The project addresses a significant knowledge gap by exploring and modelling the stronger correlation between data instances in the streams. The outcome is a sys ....Stream Data Classification in the Age of 5G Networks. This project aims to develop a novel stream data classification model to handle the challenges in the era of 5G networks, such as the scope of the stream data, the complexity of their relationship, the diversity of contained information and the incorrect readings of numerous sensors. The project addresses a significant knowledge gap by exploring and modelling the stronger correlation between data instances in the streams. The outcome is a system that is highly efficient, accurate and corrupted-data-tolerant classification solutions for individual stream data as well as multiple stream data. The expected benefits will be far-ranging and adaptable to many domains, such as smart home, medical and healthcare, transportation and manufacturing. Read moreRead less
Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental compu ....Structure Search Over Large Scale Heterogeneous Information Networks . Structure search on heterogeneous information networks (HINs) has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to develop novel techniques for efficiently conducting structure search on large scale HINs and lay the scientific foundations. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, and distributed solutions. The success of the project will directly contribute to the scientific foundation of Big Data computation. It will also contribute to the development of local industry involving cybersecurity, social media-based recommendation, network management, knowledge graphs, and E-business. Read moreRead less
Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber ....Cohesive Subgraph Discovery on Big Bipartite Graphs. This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber-security, health, bio-informatics, social networks, and E-commerce. The success of the project will also facilitate the training of PhD graduates and postdoctoral research associates in the area of Big Data.
Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100679
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and effic ....Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and efficiently support a set of primitive queries including rank-based queries, dominance-based queries and proximity-based queries. The results of this project will be an important complement to the development of data stream systems and will bring considerable social, economic and technological benefits to Australia.Read moreRead less
Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database i ....Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database indexing, event-influenced behaviour modelling and prediction. The success of this project is expected to enhance users’ online experience and improve e-commerce’s market value.Read moreRead less
Taming the uncertainty in trajectory data. This project aims to develop effective and efficient methods to manage large scale uncertain trajectory data. It provides individuals, business, government and social groups the ability to explore significant uncertain trajectories and their patterns, for important usages in location based services, logistic, transportation and tourism.
Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and re ....Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and retrieval across different media types and domains. The framework developed by the project uses deep learning methods to develop meaningful image attributes to positively bridge the modality gap and the domain gap when hash functions are affixed to data. This project will significantly advance the research of multimedia retrieval, and benefit a series of related research problems whenever heterogeneous multimedia data are involved in their applications.Read moreRead less