Hung-Hsuan Huang

With technological advancements in transmitting and storing large video files, more and more organizations are incorporating asynchronous video interviews as part of their personnel selection process. Automatic evaluation of these videos is a challenging machine learning setting because the samples are composed of time series input data but only one overall label is available. It is unclear which segments of the time series input (i.e., videos) are the most important ones for prediction. Not all nonverbal features, spoken words, and utterances contribute equally to the prediction; some segments of the videos might even introduce noise to the model. Processing all multimodal information is therefore inefficient. To address this challenge, we propose a framework to model the content of the answer via the full transcription and the speaking patterns of the interviewee via short clips. Our model learns to automatically select the most informative segment by previewing the acoustic modality using a technique called differentiable masking. The results show that our method outperforms existing approaches while being more efficient since only partial multimodal data are processed, and the interpretability of the model is enhanced.

This paper presents a virtual classroom game that the trainee teacher can practice how to manage in the aspect of inter-student relationship. The virtual students are driven as a multi-agent simulation, and the player of the game can intervene with the students and try to influence their relationships. The scores are measured by the number of isolated and fringe students.