261 resultados para Legislative Action
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In this paper we explore some of the ethical issues associated with conducting Ethnographic Action Research (Tacchi, 2004; Tacchi et al., 2003) for understanding and facilitating distributed collaboration. Ethnography and action research are increasingly popular qualitative approaches to researching computer-supported collaboration and we are applying them together in a project within a distributed research centre. We identify ethical principles applied to the conduct of research in Australia and we briefly describe a number of ethical problems that arise due to the nature of Ethnographic Action Research.
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Baby Boomers are a generation of life long association joiners, but following generations prefer spontaneous and episodic volunteering. This trend is apparent not only during natural disasters, but in most other spheres of volunteering. Legal liability for such volunteers is a growing concern, which unresolved, may dampen civic participation. We critically examine the current treatment of these liabilities through legislation, insurance and risk management.
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In this paper we present a novel application of scenario methods to engage a diverse constituency of senior stakeholders, with limited time availability, in debate to inform planning and policy development. Our case study project explores post-carbon futures for the Latrobe Valley region of the Australian state of Victoria. Our approach involved initial deductive development of two ‘extreme scenarios’ by a multi-disciplinary research team, based upon an extensive research programme. Over four workshops with the stakeholder constituency, these initial scenarios were discussed, challenged, refined and expanded through an inductive process, whereby participants took ‘ownership’ of a final set of three scenarios. These were both comfortable and challenging to them. The outcomes of this process subsequently informed public policy development for the region. Whilst this process did not follow a single extant structured, multi-stage scenario approach, neither was it devoid of form. Here, we seek to theorise and codify elements of our process – which we term ‘scenario improvisation’ – such that others may adopt it.
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The role of the creative industries – arts and artists – in helping to drive the changes in laws and behaviours that are necessary to tackle climate change, while not superficially obvious, is a deep one. Arts and artists of all kinds, as cultural practitioners, have been closely entwined with social change and social control since time immemorial, in large part because they help shape our understanding of the world, framing ideas, prefiguring change, and opening hearts and minds to new ways of thinking. They have played a major role in campaigns for law reform on many issues, and climate change should be no exception. Indeed, with climate change increasingly being seen as a deeply cultural issue, and its solutions as cultural ones to do with changing the way we understand our world and our place in it, the role of cultural practitioners in helping to address it should also increasingly be seen as central. It is curious, then, how comparatively little artistic engagement with climate change has taken place, how little engagement with the arts the climate movement has attempted, and how little theoretical and critical analysis has been undertaken on the role of the creative arts in climate change action. Through a literature review and a series of interviews with individuals working in relevant fields in Australia, this study examines and evaluates the role of the creative industries in climate change action and places it in a historical and theoretical context. It covers examples of the kind of artistic and activist collaborations that have been undertaken, the different roles in communication, campaigning for law reform, and deep culture change that arts and artists can play, and the risks and dangers inherent in the involvement of artists, both to climate change action and to the artist. It concludes that, despite the risks, a deeper and more thoughtful engagement of and by the creative industries in climate action would not only be useful but is perhaps vital to the success of the endeavours.
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Deep convolutional network models have dominated recent work in human action recognition as well as image classification. However, these methods are often unduly influenced by the image background, learning and exploiting the presence of cues in typical computer vision datasets. For unbiased robotics applications, the degree of variation and novelty in action backgrounds is far greater than in computer vision datasets. To address this challenge, we propose an “action region proposal” method that, informed by optical flow, extracts image regions likely to contain actions for input into the network both during training and testing. In a range of experiments, we demonstrate that manually segmenting the background is not enough; but through active action region proposals during training and testing, state-of-the-art or better performance can be achieved on individual spatial and temporal video components. Finally, we show by focusing attention through action region proposals, we can further improve upon the existing state-of-the-art in spatio-temporally fused action recognition performance.
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Contribution to ARI Remix. ARI remix is a three-year digital humanities, artist interviews and oral history project collecting and presenting memories of Australian Artist-run culture in Queensland, New South Wales and the Australian Capital Territory between 1980 and 2000. Its focus is fleshing out and illuminating the ephemeral and neglected histories of the many lively and socially engaged artistic scenes along the east coast of Australia during the last two decades of the 20th century.
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Strategies that confine antibacterial and/or antifouling property to the surface of the implant, by modifying the surface chemistry and morphology or by encapsulating the material in an antibiotic-loaded coating, are most promising as they do not alter bulk integrity of the material. Among them, plasma-assisted modification and catechol chemistry stand out for their ability to modify a wide range of substrates. By controlling processing parameters, plasma environment can be used for surface nano structuring, chemical activation, and deposition of biologically active and passive coatings. Catechol chemistry can be used for material-independent, highly-controlled surface immobilisation of active molecules and fabrication of biodegradable drug-loaded hydrogel coatings. In this article, we comprehensively review the role plasma-assisted processing and catechol chemistry can play in combating bacterial colonisation on medically relevant coatings, and how these strategies can be coupled with the use of natural antimicrobial agents to produce synthetic antibiotic-free antibacterial surfaces.
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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food composition for NSW Health
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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food labeling for NSW Health
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Reviews and synthesizes evidence to make recommendations on policy actions improve food environments in the area of food promotion for NSW Health
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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food pricing for NSW Health
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Reviews and synthesizes evidence to produce evidence-based recommendations on policy actions to improve food provision for NSW Health
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Reviews and synthesizes nutrition policy actions to improve food retail for NSW Health
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Summaries evidence across seven domains of potential food policy action to improve food environments and food supply to prevent obesity for NSW Health
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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.