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Modern patient data tends to be large-scale and multi-dimensional, containing both spatial and temporal features. Learning good spatio-temporal features from large patient data is a challenging task, especially when there are missing observations. In this paper, we propose a spatio-temporal autoencoder (STAE), an unsupervised deep learning scheme, to learn features from large-scale and high-dimensional...
The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowd sensing (MCS) a promising approach for large-scale event detection and collective knowledge formation. In a typical MCS system, the crowdsourcer purchases sensing data from some mobile phone users (i.e., contributors) and sells it to consumers for revenue. This kind of sensing...
Graphical models have been widely applied in distributed network computation problems such as inference in large-scale sensor networks. While belief propagation (BP) based on message passing is a powerful approach to solving such distributed inference problems, one major challenge, in the context of wireless sensor networks, is how to systematically address the trade-off between energy efficiency...
The burgeoning cost of healthcare has forced industry experts and academics to rethink current healthcare systems. There is a compelling argument that the increasing costs are primarily due to the fundamentally reactionary approach used in healthcare today, with the focus being on treatment and cure rather than on prevention. Consequently, building a sustainable healthcare system requires a paradigm...
The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowdsensing a promising approach for large-scale data collection. In mobile crowdsensing, a specific situation is that the service provider (SP) needs to recruit contributors to fulfill sensing tasks requested by consumers. There are several challenges for contributor selection and...
We propose an ambient sensing-based incentive scheme that rewards or penalizes consumers depending on the amount of electricity they consume in the past time period and the ambient conditions under which the consumption took place. Our emphasis is to drive behaviour modification towards energy efficient behaviours. Direct interaction with users is achieved through the EnergySense smartphone application...
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