Affective Computing and Intelligent Interaction: Fourth by Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou,
By Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, Shrikanth S. Narayanan (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 a hundred thirty five papers during this quantity set awarded including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are geared up in topical sections on popularity and synthesis of human impact, affect-sensitive functions, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, suitable insights from psychology, affective databases, evaluate and annotation tools.
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Additional info for Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II
59, 157–183 (2003) 11. : Evidence for a three-factor theory of emotions. Journal of Research in Personality 11, 273–294 (1977) 12. : An interval type-2 fuzzy logic system to translate between emotion-related vocabularies. In: Proceedings of Interspeech, Brisbane, Australia (September 2008) 13. : Using interval type-2 fuzzy logic to translate emotion words from spanish to english. In: IEEE World Conference on Computational Intelligence (WCCI) FUZZ-IEEE Workshop (2010) 14. : A Mathematical Introduction to Logic, 2nd edn.
MIT Press, Cambridge (2003) 7. : Co-training and self-training for word sense disambiguation. In: Natural Language Learning (CoNLL 2004), Boston (May 2004) 8. Penman: The penman user guide. Tech. , Technical Report, Information Science Institute, Marina Del ray, CA 90292 (1989) 9. : An architecture for data-to-text systems. In: Proceedings of ENLG 2007, pp. 97–104 (2007) 10. : Building Natural Language Generation Systems. Studies in Natural Language Processing. com Abstract. We have already confirmed that the artificial subtle expressions (ASEs) from a robot can accurately and intuitively convey its internal states to participants .
However, the stimuli are predetermined and the responses are constrained, often as a Likert scale or prescribed set of emotion categories. In EMO20Q, potentially any word can be picked as an emotion word and any question can be asked of it; it is only limited by the game’s rules and the subjects’ good-faith judgment. Thus, in contrast with purely elicited experiments, EMO20Q can be presumed to have higher experimental validity and less experimental bias. The player/subjects of EMO20Q are less constrained by the elicitation methodology and we can assume that their honesty and cooperation is comparable to elicitation experiments, or improved due to the supervision of the other player and their shared communicative goals.