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 court cases 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 rigorously reviewed and chosen from 196 submissions. The papers are prepared in topical sections on acceptance and synthesis of human impact, affect-sensitive functions, methodological matters 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 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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The ensemble approach can be viewed as a constantly emerging mechanism, so we expect its generalization capability will increase as the ensemble size grows. This requires developing strategies to manage the ensemble size and its structure. 12 O. Alzoubi et al. 4 Conclusions, Limitations and Future Work We have shown that physiological data exhibits day variations. The challenge that this phenomenon introduces is of paramount importance to physiological based affect detection systems. In order to be able to detect affect from future physiological data, there is a need for a classification system that can handle these day variations.

There is a critical need for basic research on how physiological signals vary over time before effective solutions can be proposed. This study contributes to this goal by systematically analyzing day variations in physiological data collected from four subjects over five recording sessions each. We also propose and evaluate an algorithm that has the potential to capitalize on, instead of being crippled by day variations in physiological signals. g. averaging or voting) rather than relying on a single classifier [13].

Toward Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1175–1191 (2001) 7. : Emotion recognition system using short-term monitoring of physiological signals. Medical and Biological Engineering and Computing 42, 419–427 (2004) 8. : Comparing Two Emotion Models for Deriving Affective States from Physiological Data. , Beale, R. ) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 35–50. Springer, Heidelberg (2008) 9.

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