Cited By
View all- Stasimioti MSosoni VChatzitheodorou K(2021)Investigating post-editing effortCognitive Linguistic Studies10.1075/cogls.00083.sta8:2(378-403)Online publication date: 22-Nov-2021
From capturing to generating human behavior: closing the interaction loop at the HUBIC Lab
Article No.: 72, Pages 1 - 6
Abstract
In this paper we are sharing the experience of setting up the Human Behavior, Interaction and Communication Lab (HUBIC Lab). The paper provides an overview of the process, from defining the needs, to designing and implementing the laboratory facilities necessary for capturing, analyzing and modelling human behavior in the context of research in human behaviour, interaction and communication and HCI.
References
[1]
Al Moubayed, S., Bohus, D., Esposito, A., Heylen, D., Koutsombogera, M., Papageorgiou, H., and Skantze, G. 2014. UM3I '14: Proceedings of the 2014 Workshop on Understanding and Modeling Multiparty, Multimodal Interactions. ACM, New York, NY, USA.
[2]
Cassell, J. 2000. Embodied conversational agents. MIT Press, Cambridge
[3]
Efthimiou, E., Fotinea, S. E., Vogler, C., Hanke, T., Glauert, J., Bowden, R., ... and Segouat, J. 2009. Sign language recognition, generation, and modelling: a research effort with applications in deaf communication. In International Conference on Universal Access in Human-Computer Interaction. Springer Berlin Heidelberg, 21--30.
[4]
Johnston, T. 2013. Towards a comparative semiotics of pointing actions in signed and spoken languages. Gesture, 13(2), 109--142
[5]
Jung, K., Kim, K. I., and Jain, A. K. 2004. Text information extraction in images and video: a survey. Pattern recognition, 37(5), 977--997.
[6]
Mattheyses, W., and Verhelst, W. 2015. Audiovisual speech synthesis: An overview of the state-of-the-art. Speech Communication, 66, 182--217.
[7]
Minker, W., Bühler, D., and Dybkjær, L. (Eds.). 2006. Spoken multimodal human-computer dialogue in mobile environments (Vol. 28). Springer Science & Business Media.
[8]
Özyurt, Ö., and Özyurt, H. 2015. Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52, 349--358.
[9]
Truong, H. M. 2016. Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185--1193.
[10]
Vinciarelli, A., Esposito, A., André, E., Bonin, F., Chetouani, M., Cohn, J. F., ... and Heylen, D. 2015. Open challenges in modelling, analysis and synthesis of human behaviour in human-human and human-machine interactions. Cognitive Computation, 7(4), 397--413.
[11]
Ye, Q., and Doermann, D. 2015. Text detection and recognition in imagery: A survey. IEEE transactions on pattern analysis and machine intelligence, 37(7), 1480--1500.
- From capturing to generating human behavior: closing the interaction loop at the HUBIC Lab
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Published In
November 2016
449 pages
ISBN:9781450347891
DOI:10.1145/3003733
Copyright © 2016 ACM.
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In-Cooperation
- Greek Com Soc: Greek Computer Society
- TEI: Technological Educational Institution of Athens
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Association for Computing Machinery
New York, NY, United States
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Published: 10 November 2016
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Cited By
View all- Stasimioti MSosoni VChatzitheodorou K(2021)Investigating post-editing effortCognitive Linguistic Studies10.1075/cogls.00083.sta8:2(378-403)Online publication date: 22-Nov-2021
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