Digital nomadism: How IT enables new forms of working and organising. This project aims to gain a better understanding of digital nomadism. Digital nomads use IT platforms to work remotely over the Internet while perpetually travelling. The project will develop new knowledge by better understanding of how IT transforms work and enable digital nomadism, the motivations and values of workers and their clients/organisations engaged in digital nomadism and the implications and consequences of digita ....Digital nomadism: How IT enables new forms of working and organising. This project aims to gain a better understanding of digital nomadism. Digital nomads use IT platforms to work remotely over the Internet while perpetually travelling. The project will develop new knowledge by better understanding of how IT transforms work and enable digital nomadism, the motivations and values of workers and their clients/organisations engaged in digital nomadism and the implications and consequences of digital nomadism for workers and clients/organisations. The project is expected to have a significant impact on policy and public discourse by providing an in-depth explanation and understanding of digital nomadism based on rigorous research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100479
Funder
Australian Research Council
Funding Amount
$427,116.00
Summary
A Unified Framework to Rapidly Fabricate Individualised Activity Sensors. This proposal aims to develop a unified computational framework which enables non-expert users to co-design and fabricate specialised physical activity sensors to address individualised sensing problems in applications such as rehabilitation, age-care and sports. Specifically, we will develop an analytical framework to classify complex sensing problems into fabricable primitive classes, namely i) conditional – limits of ac ....A Unified Framework to Rapidly Fabricate Individualised Activity Sensors. This proposal aims to develop a unified computational framework which enables non-expert users to co-design and fabricate specialised physical activity sensors to address individualised sensing problems in applications such as rehabilitation, age-care and sports. Specifically, we will develop an analytical framework to classify complex sensing problems into fabricable primitive classes, namely i) conditional – limits of activity, ii) differential – frequency of activity and iii) integrational – cumulative activity. And a co-design interface to synthesize them into complex activity sensors to fit individualised needs. Finally, we will evaluate the framework by deploying the created sensors in real-world settings and gathering data.Read moreRead less
Efficient and Scalable Similarity Query Processing on Big Streaming Graphs . This project aims to develop novel approaches for efficient and scalable similarity queries on big streaming graphs which are large-scale graphs where graph nodes and edges may arrive or expire at high speed. Three key challenges are expected to be addressed including high speed, large variety, and big volume of streaming graphs. Expected outcomes include new theories, novel indexing and query processing techniques, an ....Efficient and Scalable Similarity Query Processing on Big Streaming Graphs . This project aims to develop novel approaches for efficient and scalable similarity queries on big streaming graphs which are large-scale graphs where graph nodes and edges may arrive or expire at high speed. Three key challenges are expected to be addressed including high speed, large variety, and big volume of streaming graphs. Expected outcomes include new theories, novel indexing and query processing techniques, and advanced distributed algorithms as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications, such as e-commerce, cybersecurity, health, social networks, and bio-informatics.
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Taming Large-Volume Dynamic Graphs in the Cloud. This project aims to develop efficient and scalable algorithms to process large-volume dynamic graphs in the cloud. The project expects to address key challenges and lay theoretical foundations in large-volume dynamic graph processing, which plays an important role in developing general-purpose, real-time structural search engines. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big graphs that ....Taming Large-Volume Dynamic Graphs in the Cloud. This project aims to develop efficient and scalable algorithms to process large-volume dynamic graphs in the cloud. The project expects to address key challenges and lay theoretical foundations in large-volume dynamic graph processing, which plays an important role in developing general-purpose, real-time structural search engines. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big graphs that evolve rapidly over time. These enable users to monitor and analyse structural information in large dynamic networks in real time. The project expects to open up a new research direction for graph processing to enrich frontier technologies and benefit many key applications in Australia.Read moreRead less
Efficient and Scalable Processing of Dynamic Heterogeneous Graphs . This project aims to develop efficient and scalable algorithms to process large-scale dynamic heterogeneous graphs where graph nodes and edges are of multiple types and the graph structure updates dynamically. Key challenges are expected to be addressed including complex structure, high speed, and large volume of dynamic heterogeneous graphs. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incre ....Efficient and Scalable Processing of Dynamic Heterogeneous Graphs . This project aims to develop efficient and scalable algorithms to process large-scale dynamic heterogeneous graphs where graph nodes and edges are of multiple types and the graph structure updates dynamically. Key challenges are expected to be addressed including complex structure, high speed, and large volume of dynamic heterogeneous graphs. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, distributed algorithms as well as a system prototype to demonstrate the practical value. Success of this project will open up a new research direction to enrich frontier technologies and benefit many key applications in Australia including cybersecurity, e-commerce, health and social networks.Read moreRead less
Directionality-Aware Cohesive Subgraph Search over Directed Graphs. Searching cohesive subgraphs around a set of user-specified seed vertices in big graphs has many applications including cybersecurity, crime detection, social marketing and public health. This project aims to investigate directionality-aware search of cohesive subgraphs over directed graphs by designing effective models and developing efficient and scalable algorithms. This project expects to address key challenges and lay scien ....Directionality-Aware Cohesive Subgraph Search over Directed Graphs. Searching cohesive subgraphs around a set of user-specified seed vertices in big graphs has many applications including cybersecurity, crime detection, social marketing and public health. This project aims to investigate directionality-aware search of cohesive subgraphs over directed graphs by designing effective models and developing efficient and scalable algorithms. This project expects to address key challenges and lay scientific foundations for searching big directed graphs. The expected outcomes include novel models, computing paradigms, algorithms, indexing techniques, and distributed solutions. The success of the project will not only provide technological breakthroughs but also benefit the development of key industries in AustraliaRead moreRead less
Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultu ....Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultural issues. This project is expected to benefit industries with widely distributed multi-cultural workforces such as mining, defence and medicine.Read moreRead less
Advanced search of cohesive subgraphs in big graphs. This project aims to study advanced cohesive subgraph searches, as well as design efficient and scalable techniques to conduct such searches. Cohesive subgraph search over big graphs is demanded by many applications, such as risk management, analysis of users’ behaviours, cybersecurity, crime detection, social marketing and community search. This project will develop, analyse, implement, and evaluate novel indexing and data processing techniqu ....Advanced search of cohesive subgraphs in big graphs. This project aims to study advanced cohesive subgraph searches, as well as design efficient and scalable techniques to conduct such searches. Cohesive subgraph search over big graphs is demanded by many applications, such as risk management, analysis of users’ behaviours, cybersecurity, crime detection, social marketing and community search. This project will develop, analyse, implement, and evaluate novel indexing and data processing techniques to support a set of advanced cohesive subgraph searches. This will provide significant benefits to many applications such as the next generation of fintech, cybersecurity, e-commerce, crime detection and social network analysis.Read moreRead less
Effective, efficient and scalable processing of the graph of graphs. This project aims to develop novel approaches to realise the value of the graph of graphs (GoG), which has been widely used to capture the relations among the structured entities. Several key challenges will be addressed: better models to capture the similarity and cohesiveness of the structured entities, increased efficiency, and greater scalability of the processing and analytics of the GoG. The novel models and algorithms de ....Effective, efficient and scalable processing of the graph of graphs. This project aims to develop novel approaches to realise the value of the graph of graphs (GoG), which has been widely used to capture the relations among the structured entities. Several key challenges will be addressed: better models to capture the similarity and cohesiveness of the structured entities, increased efficiency, and greater scalability of the processing and analytics of the GoG. The novel models and algorithms developed within this project will be incorporated into a prototype for both evaluation and to demonstrate real-world practical value for business, industry, and academia. Success in this project should see significant benefits for many important applications such as health, cyber-security and e-commerce.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100245
Funder
Australian Research Council
Funding Amount
$410,518.00
Summary
Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep lea ....Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep learning schema for data modelling and sequential decision-making knowledge with a novel deep reinforcement learning methodology. These developments have immediate applications in autonomous vehicles, advanced manufacturing, and dynamic pricing, with scientific, economic, and social benefits for Australia and the world.Read moreRead less