926 resultados para Legislation as topic


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Australia has followed the course taken by other English-speaking countries in recent years of enacting legislation that requires convicted sexual offenders to register personal details with law enforcement agencies. These laws have been enacted to protect the public from the perceived threat posed by sex offenders, but have been written with little apparent reference to the available research literature about the nature and extent of this threat. In addition, there is no empirical evidence supporting the effectiveness of legislatively based sex offender registries to either reduce sexual offending or to enable the police to investigate sex crimes and apprehend offenders. This article compares and contrasts the current laws governing sex offender registration enacted by the various states and territories in Australia, and offers a critical analysis of their provisions in light of the research literature on sexual offending.

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In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling.

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Between 1945 and 1948, Michael Polanyi, Michael Oakeshott, and Karl Popper respectively discussed the nature of tradition, and the part that traditions play in free societies. This article analyzes these thinkers’ ideas of tradition. Polanyi depicted tradition as knowledge that is embodied in skilled practice, and tradition for Oakeshott consists in activities that are suffused with practical knowledge and technique. Popper emphasized rational criticizability, whereas Polanyi and Oakeshott emphasized the tacit dimension of traditions.

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Aim.  The aim of this paper was to examine the nurse practitioner legislative framework in Australia from a critical social theory perspective. Background.  National regulation for nurses and midwives has superseded all previous state legislation with effect from July 2010. The aim of this change was to streamline regulation processes across all health professionals requiring regulation, in order to eliminate diverse state-based regulatory policies that were identified as hindering transferability of the workforce across Australia. This paper explores the changes with reference to nurse practitioners. Since their introduction to Australia different legislative practices between states have presented difficult endorsement procedures which have affected employment. Data sources.  Information for the paper is drawn from a doctoral study which examined the politics of advancing nursing in Australia, with particular reference to the discourses of nurse practitioners. This is augmented by more recent legislative documents and policies, as well as media reports, to examine the process of change in legislation and the unfolding discourses on employment and practice. Implications to nursing.  Nurse practitioner endorsement may be more complicated, defeating the original premise of transferability of a skilled workforce across state jurisdictions. Conclusion.  This paper exposes the influence that powerful discourses can have on a major change to professional practice.

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This dataset is a list of all Papua New Guinean legislation (Acts and Regulations) during Australia's colonial administration of the country. It includes data on legislation from 1925 through to 1974 and identifies the act/regulation title, the act number, the act commencement date and chapter number.

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Legislation and standards are alleged to be one of the key solutions for improving accessibility and Universal Design implementation in Malaysia including its implementation in housing design. In response to this concern, the government of Malaysia has taken considerable steps in articulating professional practice obligations as demonstrated in continual improvements in relevant new laws and standards (Malaysian Standard (MS)). The findings from a preliminary study have however evidenced a clear gap between having laws and standards and ensuring their implementation in the construction industry. This paper reviews the issues faced by the existing Malaysian enforcement and practices to Universal Design. The findings emphasise awareness, understanding and practice implications for the legislation and its standards in Malaysia, and problems and assumptions perceived. Findings indicate that there is lack of understanding and awareness of the current legislation and standards in the construction industry, in addition to the insufficiency of comprehensive guidelines to regulate Universal Design in Malaysia.

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Probabilistic topic models have become a standard in modern machine learning with wide applications in organizing and summarizing ‘documents’ in high-dimensional data such as images, videos, texts, gene expression data, and so on. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics than bag-of-word interpretation, but also more informative for classification tasks. This paper describes the Topic Model Kernel (TMK), a high dimensional mapping for Support Vector Machine classification of data generated from probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks from real world datasets. We outperform existing kernels on the distributional features and give the comparative results on non-probabilistic data types.