Travis J. Wiltshire

Chris van der Lee, Thiago Castro Ferreira, Chris Emmery et al.

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.

Evy van Weelden, Jos M. Prinsen, Caterina Ceccato et al.

Real-time adjustments to task difficulty during flight training are crucial for optimizing performance and managing pilot workload. This study evaluated the functionality of a pre-trained brain-computer interface (BCI) that adapts training difficulty based on real-time estimations of workload from brain signals. Specifically, an EEG-based neuro-adaptive training system was developed and tested in Virtual Reality (VR) flight simulations with military student pilots. The neuro-adaptive system was compared to a fixed sequence that progressively increased in difficulty, in terms of self-reported user engagement, workload, and simulator sickness (subjective measures), as well as flight performance (objective metric). Additionally, we explored the relationships between subjective workload and flight performance in the VR simulator for each condition. The experiments concluded with semi-structured interviews to elicit the pilots' experience with the neuro-adaptive prototype. Results revealed no significant differences between the adaptive and fixed sequence conditions in subjective measures or flight performance. In both conditions, flight performance decreased as subjective workload increased. The semi-structured interviews indicated that, upon briefing, the pilots preferred the neuro-adaptive VR training system over the system with a fixed sequence, although individual differences were observed in the perception of difficulty and the order of changes in difficulty. Even though this study shows performance does not change, BCI-based flight training systems hold the potential to provide a more personalized and varied training experience.

Dan Hudson, Travis J. Wiltshire, Martin Atzmueller

In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to generate hypotheses about the relationships between the variables describing a specific process and its dynamics. We detail our approach in a concrete instantiation and exemplary implementation, specifically in the field of teamwork research. Using a real-world dataset of team interactions we include results from an example data analytics application of our proposed approach, showcase novel analysis options, and discuss possible implications of the results from the perspective of teamwork research.