M. Sazzad Hussain

Automatic Cognitive Load Detection from Face, Physiology, Task Performance and Fusion During Affective Interference

Cognitive load is experienced during critical tasks and also while engaged emotional states are induced either by the task itself or by extraneous experiences. Emotions irrelevant to the working memory representation may interfere with the processing of relevant tasks and can influence task performance and behavior, making the accurate detection of cognitive load from nonverbal information challenging. This paper investigates automatic cognitive load detection from facial features, physiology and task performance under affective interference. Data was collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background and a combined classifier was used for detecting cognitive load levels. Results indicate that the face modality for cognitive load detection was more accurate under affective interference, whereas physiology and task performance were more accurate without the affective interference. Multimodal fusion improved detection accuracies, but it was less accurate under affective interferences. More specifically, the accuracy decreased with increasing intensity of emotional arousal.

Moderator Assistant: A Natural Language Generation-Based Intervention to Support Mental Health via Social Media

As online mental health support groups become increasingly popular, they require more support from volunteers and trained moderators who help their users through “interventions” (i.e., responding to questions and providing support). We present a system that supports such human interventions using Natural Language Generation (NLG) techniques. The system generates draft responses aimed at reducing moderators’ workload, and improving their efficacy. NLG and human interventions were compared through the ratings of 35 psychology interns. The NLG-based system was capable of generating messages that are grammatically correct with clear language. The system needs improvement, however, moderators can already use it as draft responses.

Driving curriculum and technological change to support writing in the engineering disciplines

Written communication skills are essential for engineers in the workplace, yet developing these skills in undergraduate engineering continues to be an issue. Curriculum innovation can be increased through the use of online writing tools that are designed to support integration of written tasks in engineering courses, along with the appropriate leadership communication and integration in the faculty teaching culture. Moreover, research has shown that there is a need to incorporate writing activities to enhance both learning and communication. The overall aim of this project is to create an integration model in collaboration with leadership, learning support and academics at several Australian universities. The integration model is based on a risk communication framework, which will support the use of engineering-specific online writing tools and drive curriculum innovation in undergraduate engineering. To create this model data is being collected from academics and students on use of the online writing tools.