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.
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
Effective Fuzzy Systems for Complex Structured Data Using Fuzzy Signatures. We are developing systematic, heuristic and mathematical techniques to produce effective fuzzy systems for complex structured data. Many or most real world problems have data which has interdependent sub-components depending on the context (eg only female patients need be tested for pregnancy), and often has missing components. Our techniques use fuzzy signatures to extend simple fuzzy systems to deal with data with such ....Effective Fuzzy Systems for Complex Structured Data Using Fuzzy Signatures. We are developing systematic, heuristic and mathematical techniques to produce effective fuzzy systems for complex structured data. Many or most real world problems have data which has interdependent sub-components depending on the context (eg only female patients need be tested for pregnancy), and often has missing components. Our techniques use fuzzy signatures to extend simple fuzzy systems to deal with data with such complex (sub-)structure. This produces effective fuzzy systems with wide applicability to real problems, in telecommunications, and petroleum reservoir data.Read moreRead less
Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour. Predicting and modelling customer behaviour enables considerable savings in the telecommunications industry and elsewhere. The resulting predictive models facilitate identifying novice users, identifying fraud, responding to users' needs, guiding and advising users, and forwarding useful information.
We consider two cutting-edge data mining approaches, Minimum Message Length (developed and led by ....Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour. Predicting and modelling customer behaviour enables considerable savings in the telecommunications industry and elsewhere. The resulting predictive models facilitate identifying novice users, identifying fraud, responding to users' needs, guiding and advising users, and forwarding useful information.
We consider two cutting-edge data mining approaches, Minimum Message Length (developed and led by Monash) and Support Vector Machines, in order to create efficient tailor-made software.
Our software will respond to specific groups of users, and their changes over time, rather than just the average user. Moreover, it will integrate the functionalities of existing individual data mining software.Read moreRead less
Resource-bounded adaptive inference of accurate conditional probability estimates from data. This project will develop machine learning techniques with a valuable new capability: the ability to produce estimates of complex conditional probabilities to varying levels of expected accuracy depending upon the constraints of available computational resources. This will provide significant competitive advantage to developers of many types of online application by allowing them to maximise utilisation ....Resource-bounded adaptive inference of accurate conditional probability estimates from data. This project will develop machine learning techniques with a valuable new capability: the ability to produce estimates of complex conditional probabilities to varying levels of expected accuracy depending upon the constraints of available computational resources. This will provide significant competitive advantage to developers of many types of online application by allowing them to maximise utilisation of available computational resources when making inferences from data, together with the flexibility to trade-off accuracy and computing resources during system design. Australia will also benefit by strengthening its machine learning expertise, which is central to many complex and intelligent systems and the booming data mining industry.Read moreRead less
Supporting adaptive, interactive documents. The project will improve comprehensibility of technical material, reduce paper usage, encourage collaborative science, improve the reliability of published science (by allowing post-publication annotation and correction), and improve the accessibility of technical material for readers who are blind or have poor vision. The project also holds considerable potential for supporting Australian companies in the publishing and document processing industries.
Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent sys ....Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent systems that facilitate adaptation and change.
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