Special Research Initiatives - Grant ID: SR0567201
Funder
Australian Research Council
Funding Amount
$77,428.00
Summary
e-Archaeology: Towards an Australian Archaeological Data Grid. The central goal of this pilot project is the development and implementation of an Australian archaeological digital collection platform based on existing High Performance Computing techniques and infrastructure. This collection will facilitate the dissemination and interchange of archaeological data across disciplines and institutions and across the public and private sectors; enhance archaeological research; and contribute to disco ....e-Archaeology: Towards an Australian Archaeological Data Grid. The central goal of this pilot project is the development and implementation of an Australian archaeological digital collection platform based on existing High Performance Computing techniques and infrastructure. This collection will facilitate the dissemination and interchange of archaeological data across disciplines and institutions and across the public and private sectors; enhance archaeological research; and contribute to discourses about Australian cultural heritage and identity. Our case study will be the Mill Point Archaeological Project in southeast Queensland. This initiative will build on existing developments made overseas and through strategic collaborations between UQ, ANU and the San Diego Supercomputer Center at UCSD.Read moreRead less
Special Research Initiatives - Grant ID: SR0567263
Funder
Australian Research Council
Funding Amount
$82,831.00
Summary
Development of Tool Interfaces and Data Standards for Enabling Remote Secondary Analysis of Qualitative Data. This proposal will promote sharing and rapid secondary analysis of natural language and other qualitative data by providing interface standards between qualitative analysis systems, and will enhance the integration of analytic methods through improved archiving. This will be achieved by the construction and dissemination of open standards for the efficient, flexible transport and storage ....Development of Tool Interfaces and Data Standards for Enabling Remote Secondary Analysis of Qualitative Data. This proposal will promote sharing and rapid secondary analysis of natural language and other qualitative data by providing interface standards between qualitative analysis systems, and will enhance the integration of analytic methods through improved archiving. This will be achieved by the construction and dissemination of open standards for the efficient, flexible transport and storage of annotation metadata, along with demonstrated implementations of these standards in qualitative tools. At the current stage of development of e-Social Science, the provision of middleware for collaborative work is more important than large-scale computational capability. The initiative will leverage existing ARC investment in qualitative data archiving.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100105
Funder
Australian Research Council
Funding Amount
$458,823.00
Summary
Towards Evolvable and Sustainable Multimodal Machine Learning. Machine learning is commonly limited to a single operational modality. To enable image, sound and language comprehension simultaneously would require machines to reuse knowledge and understand concepts from multimodal data. The project aims to build a sparse model and present a set of innovative algorithms to enhance model generalisation for addressing distributional and semantic shifts and minimise the computational and labelling co ....Towards Evolvable and Sustainable Multimodal Machine Learning. Machine learning is commonly limited to a single operational modality. To enable image, sound and language comprehension simultaneously would require machines to reuse knowledge and understand concepts from multimodal data. The project aims to build a sparse model and present a set of innovative algorithms to enhance model generalisation for addressing distributional and semantic shifts and minimise the computational and labelling costs for training multimodal systems. Its outcomes will enable evolvable learning of models to suit varying testing scenarios after deployment and whilst reducing energy consumption and carbon emission. The application of these techniques could benefit sectors such as E-commerce, agriculture and transport.Read moreRead less
Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures fo ....Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures for resource-constrained environments. This will be achieved by developing theories and techniques for complex real-world multi-modal data retrieval throughout the data life-cycle. The expected outcomes will significantly contribute to building capability in emerging technologies in the context of responsible data science. Read moreRead less
Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. ....Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. Further, the evaluation methods to be developed will not rely on access to test data, allowing cost-effective, private, and safe AI for high-stakes decision support. The outcomes will benefit Australia by accelerating economic investment and fostering greater social acceptance of AI.Read moreRead less
Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and wo ....Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and worldwide to reliably identify potential privacy risks and issues on their applications. The intended outcomes should endow data controllers with the capability of evidencing their compliance of data protection legislations such as Australia Privacy Act 1988 and EU General Data Protection Regulation (GDPR).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
Synergising multimedia content understanding with social data analysis. This project aims to develop novel approaches to explore synergies within big social multimedia data from both social and multimedia perspective. It provides individuals, groups, and businesses the ability to tap into the wisdom of crowds to enlarge knowledge base, enhance user experience, understand the pulse of crowds and make informed decision.