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ORCIDThe Handbook of Multimodal-Multisensor Interfaces, Volume 2, 2018
- Sharon L. Oviatt, Björn W. Schuller, Philip R. Cohen, Daniel Sonntag, Gerasimos Potamianos, Antonio Krüger
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The Handbook of Multimodal-Multisensor Interfaces: Foundations, User Modeling, and Common Modality Combinations - Volume 2. Association for Computing Machinery 2018, ISBN 978-1-970001-71-6 - Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency:
Challenges and applications in multimodal machine learning. 17-48 - Ethem Alpaydin:
Classifying multimodal data. 49-69 - Yannis Panagakis, Ognjen Rudovic, Maja Pantic:
Learning for multimodal and affect-sensitive interfaces. 71-98 - Gil Keren, Amr El-Desoky Mousa, Olivier Pietquin, Stefanos Zafeiriou, Björn W. Schuller:
Deep learning for multisensorial and multimodal interaction. 99-128 - Björn W. Schuller:
Multimodal user state and trait recognition: an overview. 129-165 - Sidney K. D'Mello, Nigel Bosch, Huili Chen:
Multimodal-multisensor affect detection. 167-202 - Alessandro Vinciarelli:
Multimodal analysis of social signals. 203-226 - Johannes Wagner, Elisabeth André:
Real-time sensing of affect and social signals in a multimodal framework: a practical approach. 227-261 - Jean-Claude Martin, Céline Clavel, Matthieu Courgeon, Mehdi Ammi, Michel-Ange Amorim, Mohamed Yacine Tsalamlal, Yoren Gaffary:
How do users perceive multimodal expressions of affects? 263-285 - Jianlong Zhou, Kun Yu, Fang Chen, Yang Wang, Syed Z. Arshad:
Multimodal behavioral and physiological signals as indicators of cognitive load. 287-329 - Sharon L. Oviatt, Joseph F. Grafsgaard, Lei Chen, Xavier Ochoa:
Multimodal learning analytics: assessing learners' mental state during the process of learning. 331-374 - Jeffrey F. Cohn, Nicholas Cummins, Julien Epps, Roland Goecke, Jyoti Joshi, Stefan Scherer:
Multimodal assessment of depression from behavioral signals. 375-417 - Mihai Burzo, Mohamed Abouelenien:
Multimodal deception detection. 419-453 - Samy Bengio, Li Deng, Louis-Philippe Morency, Björn W. Schuller:
Perspectives on predictive power of multimodal deep learning: surprises and future directions. 455-472
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