994 resultados para data hiding


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The purpose of my research was to contribute to the improvement and sustainability of the Special Needs Activity Program, and develop program implementation strategies that had practical outcomes. I conducted an evaluative case study of S.N.A.P in order to determine what a quality adapted physical activity (APA) program is, why S.N.A.P is considered a quality APA program, and what institutional policies and practices exist to support it. Data was collected via interviews, questionnaires, and observations. Data analysis involved inductive and deductive methods, and a SWOTAR evaluation. Results indicate that quality APA programs include: ‘people’, ‘environment’, and ‘expectations’; there are benefits of experiential learning; activity stations that promote creativity are valuable; several stakeholders do not know the details about S.N.A.P but recognize its value; the institution values what S.N.A.P provides, yet, there is nothing being done to sustain it. Future research should investigate the feasibility of implementing S.N.A.P in various contexts.

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The furious pace of Moore's Law is driving computer architecture into a realm where the the speed of light is the dominant factor in system latencies. The number of clock cycles to span a chip are increasing, while the number of bits that can be accessed within a clock cycle is decreasing. Hence, it is becoming more difficult to hide latency. One alternative solution is to reduce latency by migrating threads and data, but the overhead of existing implementations has previously made migration an unserviceable solution so far. I present an architecture, implementation, and mechanisms that reduces the overhead of migration to the point where migration is a viable supplement to other latency hiding mechanisms, such as multithreading. The architecture is abstract, and presents programmers with a simple, uniform fine-grained multithreaded parallel programming model with implicit memory management. In other words, the spatial nature and implementation details (such as the number of processors) of a parallel machine are entirely hidden from the programmer. Compiler writers are encouraged to devise programming languages for the machine that guide a programmer to express their ideas in terms of objects, since objects exhibit an inherent physical locality of data and code. The machine implementation can then leverage this locality to automatically distribute data and threads across the physical machine by using a set of high performance migration mechanisms. An implementation of this architecture could migrate a null thread in 66 cycles -- over a factor of 1000 improvement over previous work. Performance also scales well; the time required to move a typical thread is only 4 to 5 times that of a null thread. Data migration performance is similar, and scales linearly with data block size. Since the performance of the migration mechanism is on par with that of an L2 cache, the implementation simulated in my work has no data caches and relies instead on multithreading and the migration mechanism to hide and reduce access latencies.

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Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE

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In this paper, we address the problem of robust information embedding in digital data. Such a process is carried out by introducing modifications to the original data that one would like to keep minimal. It assumes that the data, which includes the embedded information, is corrupted before the extraction is carried out. We propose a principled way to tailor an efficient embedding process for given data and noise statistics. © Springer-Verlag Berlin Heidelberg 2005.

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Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

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In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.