Affective Computing and Intelligent Interaction: 4th by Arvid Kappas (auth.), Sidney D’Mello, Arthur Graesser, Björn

By Arvid Kappas (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)

The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed complaints of the Fourth foreign convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set provided including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on reputation and synthesis of human impact, affect-sensitive purposes, methodological matters in affective computing, affective and social robotics, affective and behavioral interfaces, proper insights from psychology, affective databases, review and annotation tools.

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Extra info for Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I

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LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004) 13. : Estimation and decision fusion: A survey. Neurocomputing 71, 2650–2656 (2008) 14. : Classifier ensembles: Select real-world applications. Information Fusion 9, 4–20 (2008) 15. : An Ensemble Approach for Incremental Learning in Nonstationary Environments. , Roli, F. ) MCS 2007. LNCS, vol. 4472, pp. 490–500. Springer, Heidelberg (2007) 16. : International affective picture system (IAPS): Technical manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1995) 17.

D. Baker1, Gregory R. Moore1, Angela Z. Wagner2, Jessica Kalka2, Aatish Salvi1, Michael Karabinos3, Colin A. edu Abstract. We present an analysis of the affect that precedes, follows, and cooccurs with students’ choices to go off-task or engage in on-task conversation within two versions of a virtual laboratory for chemistry. This analysis is conducted using field observation data collected within undergraduate classes using the virtual laboratory software as part of their regular chemistry classes.

1 Introduction There is considerable motivation for measuring affect from physiological signals (for detailed review see [1]). Heart activity, respiration, facial muscle activity, galvanic skin response, body temperature, and blood pressure have all been considered as potential physiological channels for recognizing affective states [2, 3]. The literature is rife with a number of physiological-based affect detection systems that classify discrete emotions as well as primitive affective dimensions such as valence and arousal [4-6].

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