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 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 offered including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on popularity and synthesis of human have an effect on, 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

Example text

As such, data from the two environments will be considered together throughout the remainder of the paper. ’s [6] transition metric, L. L provides an indication of the probability of a transition or co-occurrence above and beyond the base rate of each affective category or behavior. For instance, on-task solitary behavior was the most common behavior in the Chemistry Virtual Lab; therefore, this behavior is likely to be the most common behavior that follows or co-occurs with any affective state in these environments.

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. : Psychophysiology: Human behavior and physiological response. Lawrence Erlbaum Associates Publishers, New Jersey (2007) 18. : Autonomic nervous system activity in emotion: A review. Biological Psychology 84, 394–421 (2010) 19. : Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn.

1 Day Differences and Non-stationarity of Affective Physiological Data Picard and colleagues [6] convincingly demonstrated that affective physiological data recorded from one subject across multiple days exhibited considerable day to day variations. They found that physiological data for a given emotion in a particular day (day data) yielded a higher clustering cohesion or tightness compared to data for the same emotion across multiple days. They attempted to address this problem by including day information as additional classification features; however, this did not yield a significant improvement in accuracy.

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