Psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. Recent statistics in suicide prevention show that young people are increasingly posting their last words online. In this paper, we investigate whether it is possible to automatically identify suicide notes and discern them from other types of online discourse based on analysis of sentiments and linguistic features. Using supervised learning, we show that our model achieves an accuracy of 86.6%, outperforming previous work on a similar task by over 4%.
The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These efforts have lately been dominated by deep learning techniques which, as in other fields, tend to outperform traditional machine learning algorithms given sufficient amounts of training data. An important shortcoming of deep learning models is their lack of transparency—they operate as black boxes and typically do not provide rationales for their predictions, which can lead to a lack of trust in predicted outputs. In this article, a novel hybrid model for anomaly prediction in wind farms is proposed, which combines a recurrent neural network approach for accurate classification with an XGBoost decision tree classifier for transparent outputs. Experiments with an offshore wind turbine show that our model achieves a classification accuracy of up to 97%. The model is further able to generate detailed feature importance analyses for any detected anomalies, identifying exactly those components in a wind turbine that contribute to an anomaly. Finally, the feasibility of transfer learning is demonstrated for the wind domain by porting our “offshore” model to an unseen dataset from an onshore wind farm. The latter model achieves an accuracy of 65% and is able to detect 85% of anomalies in the unseen domain. These results are encouraging for application to wind farms for which no training data are available, for example, because they have not been in operation for long.
Natural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. In this paper we present a novel approach forsituated Natural Language Generationin dialogue that is based onhierarchical reinforcement learningand learns the best utterance for a context by optimisation through trial and error. The model is trained from human–human corpus data and learns particularly to balance the trade-off betweenefficiencyanddetailin giving instructions: the user needs to be given sufficient information to execute their task, but without exceeding their cognitive load. We present results from simulation and a task-based human evaluation study comparing two different versions of hierarchical reinforcement learning: One operates using a hierarchy of policies with a large state space and local knowledge, and the other additionally shares knowledge across generation subtasks to enhance performance. Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions that are better perceived by human users.
Currently, an estimated 36 million people worldwide are affected by Alzheimer's disease or related dementias. In the absence of a cure, non-pharmacological interventions, such as cognitive stimulation, which slow down the rate of deterioration can benefit people with dementia and their caregivers. Such interventions have shown to improve well-being and slow down the rate of cognitive decline. It has further been shown that cognitive stimulation in interaction with a computer is as effective as with a human. However, the need to operate a computer often represents a difficulty for the elderly and stands in the way of widespread adoption. A possible solution to this obstacle is to provide a spoken natural language interface that allows people with dementia to interact with the cognitive stimulation software in the same way as they would interact with a human caregiver. This makes the assistive technology accessible to users regardless of their technical skills and provides a fully intuitive user experience. This article describes a pilot study that evaluated the feasibility of computer-based cognitive stimulation through a spoken natural language interface. Prototype software was evaluated with 23 users, including healthy elderly people and people with dementia. Feedback was overwhelmingly positive.
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