Depression, as a prevalent and severe psychological disorder, has become a burden to individuals, families and societies all over the world. Recently, some deep learning methods have been introduced for depression detection and achieved promising performance on a number of public datasets. Most of them rely on Long Short-Term Memory (LSTM) or Transformer to model multimodal time series data used for depression detection which fail in filtering noisy information within multiple modalities. Motivated by block-recurrent transformers, which has a strong ability to filter noisy information among single-modal time series, we propose novel block-recurrent transformers, called Bidirectional Multimodal Block-Recurrent Transformers (BMBRT), for multimodal data analysis and apply it to depression detection. BMBRT is extended from the block-recurrent transformers by introducing multimodal data enhancement module to obtain complementary information across modalities and designing a new multi-block transformer module for noise filtering. Experiments on three publicly available depression detection datasets show that our proposed method significantly outperforms current state-of-the-art methods.

As enrollment increases, teaching assistants (TAs) need help prioritizing responding to students' posts in on-line forums. In a graduate-level Artificial Intelligence forum, three instructors rank the urgency of posts which is compared to the ratings of 13 course TAs; correlation with the instructors' scores have an r=0.55, with a TA inter-rater reliability of 35%. However, when TAs used a codebook containing seven dimensions created by the instructors to define urgency levels, correlation increased to r=0.73 and reliability to 53%. The instructor rankings are also compared to cognitive presence ratings from the Community of Inquiry framework. Cognitive presence correlates with urgency with r=-0.68. These results suggest that recommendation agents that prioritize posts based on cognitive presence or the urgency codebook may be beneficial for TAs.