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Titlebook: Affective Computing and Intelligent Interaction; Fourth International Sidney D’Mello,Arthur Graesser,Jean-Claude Martin Conference proceedi

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11#
發(fā)表于 2025-3-23 10:33:29 | 只看該作者
“That’s Aggravating, Very Aggravating”: Is It Possible to Classify Behaviors in Couple Interactions cal and theoretical challenges in observational practice. Technology holds the promise of mitigating some of these difficulties by assisting in the evaluation of higher level human behavior. In this work we attempt to address two questions: (1) Does the lexical channel contain the necessary informat
12#
發(fā)表于 2025-3-23 15:01:34 | 只看該作者
Predicting Facial Indicators of Confusion with Hidden Markov Modelsctive state is confusion, which has been positively associated with effective learning. Although identifying episodes of confusion presents significant challenges, recent investigations have identified correlations between confusion and specific facial movements. This paper builds on those findings
13#
發(fā)表于 2025-3-23 20:59:13 | 只看該作者
14#
發(fā)表于 2025-3-23 23:00:49 | 只看該作者
Using Individual Light Rigs to Control the Perception of a Virtual Character’s Personalityystem composed of three dynamic lights that can be configured using an interactive editor. To study the effect of character-centric lighting on observers, we created four lighting configurations derived from the photography and film literature. A user study with 32 subjects shows that the lighting s
15#
發(fā)表于 2025-3-24 03:12:02 | 只看該作者
Call Center Stress Recognition with Person-Specific Modelsad the same job profile, we found large differences in how individuals reported stress levels, with similarity from day to day within the same participant, but large differences across the participants. We examined two ways to address the individual differences to automatically recognize classes of
16#
發(fā)表于 2025-3-24 08:23:22 | 只看該作者
17#
發(fā)表于 2025-3-24 13:55:29 | 只看該作者
Multiple Instance Learning for Classification of Human Behavior Observationswho are asked to evaluate several aspects of the observations along various dimensions. This can be a tedious task. We propose that automatic classification of behavioral patterns in this context can be viewed as a multiple instance learning problem. In this paper, we analyze a corpus of married cou
18#
發(fā)表于 2025-3-24 16:51:42 | 只看該作者
19#
發(fā)表于 2025-3-24 23:00:24 | 只看該作者
20#
發(fā)表于 2025-3-25 00:20:25 | 只看該作者
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