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Detecting Drowsy Learners at the Wheel of e-Learning Platforms With Multimodal Learning Analytics., , , , , , and . IEEE Access, (2021)Multi-term and Multi-task Affect Analysis in the Wild., , , , , , and . CoRR, (2020)Detecting Learner Drowsiness Based on Facial Expressions and Head Movements in Online Courses., , , , , , , and . IUI Companion, page 124-125. ACM, (2020)Engagement estimation based on synchrony of head movements: application to actual e-learning scenarios., , and . IUI Companion, page 25-26. ACM, (2019)Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action., and . IUI Companion, page 103-104. ACM, (2020)Estimation of wakefulness in video-based lectures based on multimodal data fusion., , , , and . UbiComp/ISWC Adjunct, page 50-53. ACM, (2020)Uncertainty Prediction for Facial Action Units Recognition under Degraded Conditions., , , , , and . ICMLA, page 431-436. IEEE, (2022)MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition., , , and . WACV, page 6538-6548. IEEE, (2024)Mental fatigue estimation based on luminance changes in facial images., , and . SII, page 526-531. IEEE, (2016)Facial Action Unit Detection Based on Teacher-Student Learning Framework for Partially Occluded Facial Images., and . FG, page 1-5. IEEE, (2021)