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 lawsuits of the Fourth overseas convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The a hundred thirty five papers during this quantity set offered including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are geared up in topical sections on attractiveness and synthesis of human impact, affect-sensitive purposes, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, correct insights from psychology, affective databases, evaluate and annotation tools.

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Extra resources 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.

G. self-report of overall prevalence of affect and behavior rather than transitions over seconds or minutes). For instance, Pekrun et al. [17] found that boredom was positively associated with attention problems in undergraduate students, and negatively associated with the use of elaboration and self-regulation strategies. Nottelmann and Hill [16] analyzed the relationship between anxiety and off-task behavior during high-stakes testing, finding a positive correlation. Larson and Richards [12] found that a student’s overall frequency of boredom in school was not statistically significantly correlated to their overall incidence of disruptive behavior (considered an extreme form of off-task behavior) as reported by their teacher.

A Physiological Approach to Affective Computing. In: Affective Computing: Focus on Emotion Expression, Synthesis, and Recognition, pp. 310–318. ITech Education and Publishing, Vienna (2008) 4. : Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System. , Heisterkamp, P. ) ADS 2004. LNCS (LNAI), vol. 3068, pp. 36–48. Springer, Heidelberg (2004) Affective Modeling from Multichannel Physiology: Analysis of Day Differences 13 5. : Emotion Recognition Based on Physiological Changes in Music Listening.

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