Discovery Early Career Researcher Award - Grant ID: DE130100660
Funder
Australian Research Council
Funding Amount
$358,731.00
Summary
Simulating social networks to understand how neighbourhood factors influence health. Where you live and who you know has implications for your health. This study will use social network models to understand how social characteristics of neighbourhoods influence health. The new insights gained will help policy makers to develop better strategies for reducing health inequalities and improving health outcomes.
Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world envir ....Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world environments. Unlike robots, the proposed technology will be low cost, readily deployable and customisable, and will not have any physical limitations or maintenance requirements. It will thus have a wide range of applications from elderly care, healthcare care to educational training.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100630
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Relating function of complex networks to structure using information theory. This project aims to investigate networks in order to translate network function to a universal language of information flows. Network science has used common tools to reveal universal connection structures within various biological and man-made networks – our brains, social networks and power grids are all networks of interacting components. Yet there is no common method to study the function of these networks and how ....Relating function of complex networks to structure using information theory. This project aims to investigate networks in order to translate network function to a universal language of information flows. Network science has used common tools to reveal universal connection structures within various biological and man-made networks – our brains, social networks and power grids are all networks of interacting components. Yet there is no common method to study the function of these networks and how such function is coupled with structure. This project aims to relate network structure to function by using measures of information processing as a generally-applicable framework. This will deliver a theory of how structure gives rise to dynamics and how structure can be optimised for desired dynamics.Read moreRead less
Pollination in a new climate: evolutionary simulation of bee and flower interactions for predicting impacts of climate change on pollination. This project uses computer simulation to understand the potential impact of temperature variation associated with climate change on insect pollinator behaviour. The result will be a model of bee and flower interactions under future Australian conditions to be used for agricultural and environmental resource management and planning.
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less
A World Without Bees: simulating important agricultural insect pollinators. The project plans to develop a software model to assess the viability of crops under changes in pollinator populations, and recommend which floral traits should be breeding targets to ensure sustainable crops. Insects are essential to agriculture, but their populations are changing in poorly understood ways that are likely to affect human food supplies. This project plans to construct evolutionary agent-based models of c ....A World Without Bees: simulating important agricultural insect pollinators. The project plans to develop a software model to assess the viability of crops under changes in pollinator populations, and recommend which floral traits should be breeding targets to ensure sustainable crops. Insects are essential to agriculture, but their populations are changing in poorly understood ways that are likely to affect human food supplies. This project plans to construct evolutionary agent-based models of change in crop-pollinating insects: honeybees, bumblebees, stingless bees and flies. It then plans to model how these population changes affect production, predicting floral traits to breed into crop plants for ongoing pollination success. Another expected outcome is a flexible plant–pollinator simulation of insect-specific visual perception, foraging behaviour, physiological factors and inter-species interactions.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100761
Funder
Australian Research Council
Funding Amount
$430,504.00
Summary
Identifying biases in news using models of narrative framing. This project aims to develop tools to detect biased narratives and one-sided framing in news stories using novel natural language processing methods to understand the text more deeply. Unlike existing methods, which overly rely on surface word co-occurrences patterns, the novel methods will be able to capture narratives in a more holistic and intuitive manner. Expected outcomes include new modeling techniques grounded in theory and a ....Identifying biases in news using models of narrative framing. This project aims to develop tools to detect biased narratives and one-sided framing in news stories using novel natural language processing methods to understand the text more deeply. Unlike existing methods, which overly rely on surface word co-occurrences patterns, the novel methods will be able to capture narratives in a more holistic and intuitive manner. Expected outcomes include new modeling techniques grounded in theory and a tool to highlight biases with recommendations for diverse sets of news articles. By raising awareness to biased news reporting, the project will benefit Australians through more balanced public discourse on global challenges, such as climate change and health pandemics.Read moreRead less
Application Of A Machine Learning Approach For Effective Stock Management Of Farmed Abalone
Funder
Fisheries Research and Development Corporation
Funding Amount
$115,649.00
Summary
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals (as it is removed from the water) which can compromise growth and survival. ....Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals (as it is removed from the water) which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R & D improvements including accurate data collection during experimental trials (e.g. feeds, temperature). Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach, however specific application to the abalone industry is yet to be achieved. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems and, render the product data to be accessible and applicable for farmers. Objectives: 1. To develop and implement artificial intelligence as a method for accurately measuring and counting abalone at nursery, weaning and grow out. Read moreRead less