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The ability to accurately estimate the execution time of computationally expensive e-science algorithms enables better scheduling of workflows that incorporate those algorithms as their building blocks, and may give users an insight into the expected cost of workflow execution on cloud resources. When a large history of past runs can be observed, crude(More)
Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twit-ter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed(More)
In Human Activity Recognition (HAR) supervised and semi-supervised training are important tools for devising parametric activity models. For the best modelling performance, typically large amounts of annotated sample data are required. Annotating often represents the bottleneck in the overall modelling process as it usually involves retrospective analysis(More)
Bootstrapping activity recognition systems in ubiquitous and mobile computing scenarios often comes with the challenge of obtaining reliable ground truth annotations. A promising approach to overcome these difficulties involves obtaining online activity annotations directly from users. However, such direct engagement has its limitations as users typically(More)
Nowadays there is a high demand of computer controller multimedia home systems. A great variety of computer software media center systems is available on the market, software which transforms an ordinary computer into a home media system. This means it adds some functionality to the normal computer. Our goal is to develop such a user-friendly intuitive(More)
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