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pub:research [2021/01/07 11:14] – Sensors2021 added kkutt | pub:research [2021/11/16 17:32] – MRC 2021 papers kkutt | ||
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===== Papers ===== | ===== Papers ===== | ||
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+ | === MRC2021b === | ||
+ | * L. Żuchowska, K. Kutt, and G. J. Nalepa, " | ||
+ | * {{http:// | ||
+ | * ++Abstract | The paper presents the design of a game that will serve as a research environment in the BIRAFFE series experiment planned for autumn 2021, which uses affective and personality computing methods to develop methods for interacting with intelligent assistants. A key aspect is grounding the game design on the taxonomy of player types designed by Bartle. This will allow for an investigation of hypotheses concerning the characteristics of particular types of players or their stability in response to emotionally-charged stimuli occurring during the game.++ | ||
+ | |||
+ | === MRC2021a === | ||
+ | * K. Kutt, L. Żuchowska, S. Bobek, and G. J. Nalepa, " | ||
+ | * {{http:// | ||
+ | * ++Abstract | The paper provides insights into two main threads of analysis of the BIRAFFE2 dataset concerning the associations between personality and physiological signals and concerning the game logs' generation and processing. Alongside the presentation of results, we propose the generation of event-marked maps as an important step in the exploratory analysis of game data. The paper concludes with a set of guidelines for using games as a context-rich experimental environment.++ | ||
=== Sensors2021 === | === Sensors2021 === | ||
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* [[https:// | * [[https:// | ||
* ++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: | * ++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: | ||
+ | |||
+ | === ICAISC2020 === | ||
+ | * S. Bobek, M. M. Tragarz, M. Szelążek, and G. J. Nalepa, " | ||
+ | * DOI: [[https:// | ||
+ | * [[https:// | ||
+ | * ++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 === |