47 resultados para Context data


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Conceptualisations of disability that emphasise the contextual and cultural nature of disability and the embodiment of these within a national system of data collection present a number of challenges especially where this process is devolved to schools. The requirement for measures based on contextual and subjective experiences gives rise to particular difficulties in achieving parity in the way data is analysed and reported. This paper presents an account of the testing of a tool intended for use by schools as they collect data from parents to identify children who meet the criteria of disability established in Disability Discrimination Acts (DDAs). Data were validated through interviews with parents and teachers and observations of children and highlighted the pivotal role of the criterion of impact. The findings are set in the context of schools meeting their legal duties to identify disabled children and their support needs in a way that captures the complexity of disabled children’s school lives and provides useful and useable data.

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Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.