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The ageing of the world population underscores the need for in-home monitoring systems that help elderly people remain safely in their homes. This paper introduces a statistical model that addresses one of the main concerns of elderly people and their carers, viz detection of safe movement in the house. Our model identifies unusually long periods of inactivity within different regions in a home using non-intrusive sensors. Our evaluation on two real-life datasets yields encouraging results.

We present a corpus-based approach for the automatic generation of email responses to help-desk requests. This is largely an extractive multi-document summarization task. However, in our application users have a very low tolerance for responses that contain incongruous sentences. To address this problem, we propose a method for extracting high-precision sentences for inclusion in a response, and a measure for predicting the completeness of a planned response. The idea is that complete, high-precision responses may be sent to users, while incomplete responses should be passed to operators. Our results show that a small but significant proportion (14%) of our automatically generated responses have a high degree of precision and completeness, and that our measure can reliably predict the completeness of a response.

The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged. Recent approaches explain an outcome by identifying the contributions of input features to this outcome. In environments involving large black-box models or complex inputs, this leads to computationally demanding algorithms. Further, these algorithms often suffer from low stability, with explanations varying significantly across similar examples. In this paper, we propose a Learning to Explain (L2E) approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. Once the explanation algorithm is distilled into an explainer network, it can be used to explain new instances. Our experiments on three classification tasks, which compare our approach to six explanation algorithms, show that L2E is between 5 and 7.5×10ˆ4 times faster than these algorithms, while generating more stable explanations, and having comparable faithfulness to the black-box model.