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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 overseas 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 rigorously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on attractiveness and synthesis of human have an effect on, affect-sensitive functions, methodological matters in affective computing, affective and social robotics, affective and behavioral interfaces, appropriate insights from psychology, affective databases, review 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

We would also like to thank Lisa Rossi, Carolyn Rosé, Ma. Mercedes Rodrigo, Sidney D’Mello, Vincent Aleven, and Timothy Nokes for helpful comments and suggestions. References 1. : Better to be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners’ Cognitive-Affective States During Interactions with Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies 68(4), 223–241 (2010) 2. : Human Classification of Low-Fidelity Replays of Student Actions.

940–943 (2005) 3. : 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.

Updating of the weights is done automatically based on incoming data, which makes this approach suitable for online applications. The pseudo code presented in Table 1 describes the major steps of the algorithm. Table 1. ,Dn}, Initialize all classifier weights; wi = 1. i =1:n ─ Classification: For a new example x, calculate the support for each class as the sum of the weights of all classifiers Di that suggest class label ck for x. Set x to the class with largest support. k=1:number of classes ─ Updating: if x is classified correctly by classifier Di then its weight is increased (promotion) by wi = alpha * wi,, where alpha > 1.

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