Assessments for writing with generative artificial intelligence . This project aims to develop a novel assessment framework for writing with generative artificial intelligence—a new technology capable of producing text with humanlike fluency. This project endeavours to produce new knowledge at the intersection of learning analytics, the learning sciences, and educational technology using innovative methods for data capture and analysis. Expected outcomes of this project include the first valid, ....Assessments for writing with generative artificial intelligence . This project aims to develop a novel assessment framework for writing with generative artificial intelligence—a new technology capable of producing text with humanlike fluency. This project endeavours to produce new knowledge at the intersection of learning analytics, the learning sciences, and educational technology using innovative methods for data capture and analysis. Expected outcomes of this project include the first valid, feasible, and reliable framework for assessing writing composed with the help of artificial intelligence. This should provide significant benefits to (a) writing assessment in higher education, (b) student learning, and (c) our understanding of collaborations between humans and artificial intelligence.Read moreRead less
DAFF National Agriculture Traceability Regulatory Technology Research And Insights Grant: Australian AgriFood Data Exchange - Ag Sector Traceability Transformation Delivered Through An Interoperable Data Platform And Exchange
Funder
Fisheries Research and Development Corporation
Funding Amount
$500,000.00
Summary
Regulatory efficiency and compliance across agricultural supply chains is hindered by inefficient, incompatible or unavailable data and systems that prevent creation of robust, interoperable traceability solutions. The Australian AgriFood Data Exchange (AAFDX) will solve this challenge by creating a secure, cloud-based platform enabling government, industry and other participants to share, re-use and merge data from disparate systems in a secure, controlled manner. The AAFDX will be a modern, ef ....Regulatory efficiency and compliance across agricultural supply chains is hindered by inefficient, incompatible or unavailable data and systems that prevent creation of robust, interoperable traceability solutions. The Australian AgriFood Data Exchange (AAFDX) will solve this challenge by creating a secure, cloud-based platform enabling government, industry and other participants to share, re-use and merge data from disparate systems in a secure, controlled manner. The AAFDX will be a modern, efficient, internationally recognised data infrastructure enabling regulators and industry to better manage compliance, stimulate innovation and supply chain performance, assure consumers, coordinate biosecurity and export market access, through enhanced traceability. The funding will build the minimal viable product, with expansion to specific traceability and compliance applications. The AAFDX will endure beyond the funding period with partner co-investment and a user pays revenue stream Objectives: 1. Deliver a minimum viable product (MVP) of the Australian Agrifood Data Exchange services 2. Develop a platform that facilitates applications/solutions that increase traceability, productivity, compliance, profitability 3. Develop governance arrangements to ensure that data security, and in turn users trust in ag-tech is not compromised 4. Build digital maturity of the fisheries and aquaculture sectors to engage in the potential, permissioned shared data offers Read moreRead less
ARDC: Food Security Data Challenges: Increasing Food Security Through Liberation Of Fishing And Aquaculture Data
Funder
Fisheries Research and Development Corporation
Funding Amount
$1,001,708.12
Summary
The development of digital and data systems (DSS) across fisheries & aquaculture, as well as the agriculture sector more broadly is disparate. How data is collected, how it is stored, and how it can subsequently be used is greatly influenced by factors such as sector digital maturity, or available funding to develop (or upgrade) DSS.
This project seeks to develop a national fisheries and aquaculture data ingestion and storage system (Activity 1), ensuring that information derived from f ....The development of digital and data systems (DSS) across fisheries & aquaculture, as well as the agriculture sector more broadly is disparate. How data is collected, how it is stored, and how it can subsequently be used is greatly influenced by factors such as sector digital maturity, or available funding to develop (or upgrade) DSS.
This project seeks to develop a national fisheries and aquaculture data ingestion and storage system (Activity 1), ensuring that information derived from fisheries and aquaculture activities is findable, accessible, interoperable and reusable (FAIR). The ingestion and storage system will be bolstered by a complementary data catalogue (detailing the data sets available on the platform) (Activity 2) and analytical tools (able to gain insights without moving data outside the storage platform) (Activity 6). The platform will be underpinned by metadata (Activity 3) and a robust governance framework (Activity 4). Use of the system will be tested through 3 case studies, supporting capacity and capability improvement of the sector (Activity 5).
The production of and use of data cuts across industry and government, and covers activities throughout the fisheries supply chain (from pre-fishing quota management, to post-fishing processing and subsequent traceability). Consequently there is no one organisation across fishing and aquaculture that is best placed to co-ordinate and trial this technology. FRDC is capable to the leadership required to ensure a fit for purpose product for end users, additionally the leverage of Australia Research Data Commons investment will contribute to the development of a nationally coherent eResearch infrastructure
This project received investment from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). Objectives: 1. Develop a new cloud-based fisheries data storage platform to enable ingestion, management, and sharing of datasets 2. Develop a CKAN-based data catalogue, a searchable fisheries data source allowing users to browse, combine, share, and access exchangeable data assets 3. Create best practice metadata standards that will be identified, documented, and then operationalised through the data catalogue and storage platform 4. Develop and operationalise a fisheries-focused data governance framework 5. Enhance capacity and capacity to use the platform through demonstration of 3 unique case studies 6. Develop use-case relevant suite of reporting and analysis tools to allow researchers to gain insights without moving data outside the storage platform Read moreRead less
Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the n ....Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the next generation of intelligent systems to accommodate data in a noisy and hostile environment. This should benefit science, society, and the economy nationally and internationally through the applications to trustworthily analyse their corresponding complex data. Read moreRead less
Australian Fisheries And Aquaculture Statistics 2022
Funder
Fisheries Research and Development Corporation
Funding Amount
$60,000.00
Summary
Statistics on Australian fisheries production and trade seeks to meet the needs of the fishing and aquaculture industry, fisheries managers, policymakers and researchers. It can assist in policy decisions, industry marketing strategies and the allocation of research funding or priorities. The gross value of production for specific fisheries are used for determining the research and development levies collected by government.
The neutrality and integrity of GVP estimates is therefore im ....Statistics on Australian fisheries production and trade seeks to meet the needs of the fishing and aquaculture industry, fisheries managers, policymakers and researchers. It can assist in policy decisions, industry marketing strategies and the allocation of research funding or priorities. The gross value of production for specific fisheries are used for determining the research and development levies collected by government.
The neutrality and integrity of GVP estimates is therefore important due to their forming the basis for research levies for each fishery. At the international level, the Department of Agriculture through the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) contributes to a number of international databases. These include databases managed by the Food and Agriculture Organisation (FAO) and the Organisation for Economic Cooperation and Development (OECD). Information at the international level can assist in international negotiations on issues such as trans-boundary fisheries and analysis of trade opportunities. Objectives: 1. To maintain and improve the data base of production, gross value of production and trade statistics for the Australian fishing industry, including aquaculture. 2. To provide these data in an accessible form. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100495
Funder
Australian Research Council
Funding Amount
$422,154.00
Summary
Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-use ....Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-user experience. Expected outcomes of this research include new knowledge, toolkits and algorithms for use in developing machine-learning based secure, efficient and fault-tolerant technologies for software applications, mobile services, cloud computing, autonomous vehicles and advanced manufacturing processes.Read moreRead less
Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new ....Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new findings about doctoral learning profiles in a direct intervention (DOCLearnPro) that targets individual differences across students in doctoral and master’s degrees to improve learning outcomes significantly and contribute theoretically, methodologically and substantively in order to advance understanding of researcher development.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101089
Funder
Australian Research Council
Funding Amount
$436,847.00
Summary
Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a metho ....Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a methodology to disinherit risks of pre-trained models and a new fuzzy relation based distributional discrepancy in heterogeneous transfer learning scenarios. The outcomes should significantly improve the reliability of machine learning with benefits for safety learning in data analytics.Read moreRead less
Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of n ....Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of nearly pure additions to fulfil the requisites of accuracy, robustness, calibration and generalisation in real-world computer vision tasks. The success of this project will benefit deep learning-based products on smartphones or robots in health and cybersecurity.Read moreRead less