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pub:research [2021/01/07 11:14]
kkutt Sensors2021 added
pub:research [2021/02/02 18:50] (current)
kkutt ICAISC2020 added
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   * [[https://www.mdpi.com/1424-8220/21/1/163|Full text available online]]    * [[https://www.mdpi.com/1424-8220/21/1/163|Full text available online]] 
   * ++Abstract | In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 206 people, which consisted of two proof-of-concept demonstrations: a “classical” stimuli presentation part, and affective games that provide a rich and controllable environment for complex emotional stimuli. Several significant links between personality traits and the psychophysiological signals (electrocardiogram (ECG), galvanic skin response (GSR)), which were gathered while using the BITalino (r)evolution kit platform, as well as between personality traits and reactions to complex stimulus environment, are promising results that indicate the potential of the proposed adaptation mechanism.++   * ++Abstract | In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 206 people, which consisted of two proof-of-concept demonstrations: a “classical” stimuli presentation part, and affective games that provide a rich and controllable environment for complex emotional stimuli. Several significant links between personality traits and the psychophysiological signals (electrocardiogram (ECG), galvanic skin response (GSR)), which were gathered while using the BITalino (r)evolution kit platform, as well as between personality traits and reactions to complex stimulus environment, are promising results that indicate the potential of the proposed adaptation mechanism.++
 +
 +=== ICAISC2020 ===
 +  * S. Bobek, M. M. Tragarz, M. Szelążek, and G. J. Nalepa, "**Explaining Machine Learning Models of Emotion Using the BIRAFFE Dataset**," in ICAISC 2020, vol. 12416 LNAI, Springer, 2020, pp. 290–300.
 +  * DOI: [[https://doi.org/10.1007/978-3-030-61534-5_26|10.1007/978-3-030-61534-5_26]]
 +  * [[https://link.springer.com/chapter/10.1007/978-3-030-61534-5_26|Full text available online]] 
 +  * ++Abstract | Development of models for emotion detection is often based on the use of machine learning. However, it poses practical challenges, due to the limited understanding of modeling of emotions, as well as the problems regarding measurements of bodily signals. In this paper we report on our recent work on improving such models, by the use of explainable AI methods. We are using the BIRAFFE data set we created previously during our own experiment in affective computing.++
  
 === HAIIW2020 === === HAIIW2020 ===
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  • Last modified: 2021/02/02 18:50
  • by kkutt