What is successful public art today?: exploring how contemporary public art and memorial design shapes public engagement, perceptions and behaviour. Much public money is invested in public art and memorials. The research explores critical questions of value: what the public enjoys about such artworks, if and how artworks contribute amenity to public spaces, and whether recent artworks engage effectively with social memory, identity and politics. The research situates local practice within intern ....What is successful public art today?: exploring how contemporary public art and memorial design shapes public engagement, perceptions and behaviour. Much public money is invested in public art and memorials. The research explores critical questions of value: what the public enjoys about such artworks, if and how artworks contribute amenity to public spaces, and whether recent artworks engage effectively with social memory, identity and politics. The research situates local practice within international trends, to inform Australian designers, policymakers, art patrons and public space managers about recent innovations in technology, craft, creativity and critique, so they can create and choose public artworks and memorials which engage with the potentials of contemporary arts practice, the complexities of contemporary culture, and the diversity of social behaviour in public spaces.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
In Public / In Focus: Photography, Testimony and the Public Sphere. Photography plays an important but little understood role in the public sphere. Photographs invite viewers to identify with stories, events and others, and the ease with which photographs circulate in print and online makes them ideal for fostering discourse and debate. However, the increasing focus on testimony and witness in contemporary culture has recently altered the way that photography operates in public and raised some s ....In Public / In Focus: Photography, Testimony and the Public Sphere. Photography plays an important but little understood role in the public sphere. Photographs invite viewers to identify with stories, events and others, and the ease with which photographs circulate in print and online makes them ideal for fostering discourse and debate. However, the increasing focus on testimony and witness in contemporary culture has recently altered the way that photography operates in public and raised some significant problems for photography historians regarding the representation of events, others and the past. This project will respond to these problems, and produce a new understanding of the historical, social, cultural and political links between photography and the public sphere today.Read moreRead less
Sensory orchestration for multimodal literacy learning in primary education. This project aims to advance new learning and pedagogical models of sensory orchestration for the enhanced multimodal and digital literacy learning of primary students. Multimodal literacy is increasingly important in the Australian curriculum and international research, yet research and education largely prioritise visual texts. This project will generate pedagogical and learning models to optimise students’ broadened ....Sensory orchestration for multimodal literacy learning in primary education. This project aims to advance new learning and pedagogical models of sensory orchestration for the enhanced multimodal and digital literacy learning of primary students. Multimodal literacy is increasingly important in the Australian curriculum and international research, yet research and education largely prioritise visual texts. This project will generate pedagogical and learning models to optimise students’ broadened use of the senses in multimodal and digital literacy learning. It will develop new sensory literacy programs with primary schools, community organisations, and art museums.
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Information access through web-scale question-answer pair finding, ranking and matching. This project will aim to take web search to a new level of sophistication in accepting queries in the form of complex natural language questions, and returning a ranked list of natural language answers automatically extracted from a broad range of web user forums.
Graphic Encounters: Colonial Prints and the Inscription of Aboriginality. This project plans to collate the archive of prints depicting Indigenous Australians, from national and international collections, to ask how people's place in this newly encroached territory was inscribed by colonial prints. Before the 1890s, prints (engravings, etchings and lithographs) were the principal means of reproducing images. Prints disseminated imagery of Indigenous people and determined how they were 'put in th ....Graphic Encounters: Colonial Prints and the Inscription of Aboriginality. This project plans to collate the archive of prints depicting Indigenous Australians, from national and international collections, to ask how people's place in this newly encroached territory was inscribed by colonial prints. Before the 1890s, prints (engravings, etchings and lithographs) were the principal means of reproducing images. Prints disseminated imagery of Indigenous people and determined how they were 'put in the picture' of settlement. Our colonial-era cultural heritage includes many prints (engravings, etchings, lithographs, etcetera) of Aborigines, yet they have been overlooked and the story of their production, dissemination and consumption is untold. This project aims to collate and trace this visual archive of Indigenous Australians and present its imagery to all Australians, including descendants, in an exhibition and conference, catalogue, monograph and online database.Read moreRead less
Reconfiguring the World: China. Art. Agency, 1900s to Now. This research project will use modern and contemporary Chinese art to understand intercultural communication in the 21st century. It will do this by recovering forgotten histories and perspectives on modern and contemporary Chinese art in a global context. It will map the complexities of understanding Chinese art and charts the misunderstandings that have arisen in engaging with China.
Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to g ....Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to generate coherent and faithful text. Expected outcome include next-generation computational technologies for language understanding and generation. This should significantly benefit document-based language technologies and increase their applications in a range of cultural, industrial, and health settings.Read moreRead less
Adaptive Context-Dependent Machine Translation for Heterogeneous Text. While automatic machine translation technologies are undoubtedly useful to a wide range of users, they often produce incoherent outputs for many types of input, for example, medical, literature, or even conversational text. This project will develop new adaptive machine translation systems to handle many domains and text styles, including heterogeneous mixed-domain inputs. It will develop multi-task machine learning methods f ....Adaptive Context-Dependent Machine Translation for Heterogeneous Text. While automatic machine translation technologies are undoubtedly useful to a wide range of users, they often produce incoherent outputs for many types of input, for example, medical, literature, or even conversational text. This project will develop new adaptive machine translation systems to handle many domains and text styles, including heterogeneous mixed-domain inputs. It will develop multi-task machine learning methods for training collections of domain-specific translation systems while leveraging correlations between domains. This approach will reduce the big data requirements of current translation systems, and improve translation quality across a wide range of different language pairs and application domains.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less