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| pub:research [2020/11/05 12:45] – Abstracts are available again kkutt | pub:research [2022/04/29 12:41] – 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 === | ||
| + | * K. Kutt, D. Drążyk, S. Bobek, and G. J. Nalepa, " | ||
| + | * DOI: [[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: | ||
| + | |||
| + | === 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 === | ||
| Line 9: | Line 31: | ||
| === MRC2020 === | === MRC2020 === | ||
| - | * L. Żuchowska, K. Kutt, K. Geleta, S. Bobek, and G. J. Nalepa, " | + | * L. Żuchowska, K. Kutt, K. Geleta, S. Bobek, and G. J. Nalepa, " |
| - | * {{http://mrc.kriwi.de/2020/download/ | + | * {{http://ceur-ws.org/Vol-2787/paper7.pdf|Full text available online}} |
| * ++Abstract | We propose an experimental framework for Affective Computing based of video games. We developed a set of specially designed mini-games, based of carefully selected game mechanics, to evoke emotions of participants of a larger experiment. We believe, that games provide a controllable yet overall ecological environment for studying emotions. We discuss how we used our mini-games as an important counterpart of classical visual and auditory stimuli. Furthermore, | * ++Abstract | We propose an experimental framework for Affective Computing based of video games. We developed a set of specially designed mini-games, based of carefully selected game mechanics, to evoke emotions of participants of a larger experiment. We believe, that games provide a controllable yet overall ecological environment for studying emotions. We discuss how we used our mini-games as an important counterpart of classical visual and auditory stimuli. Furthermore, | ||
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| * Presented at [[pub: | * Presented at [[pub: | ||
| * {{http:// | * {{http:// | ||
| + | * ++Abstract | In this paper we discuss selected important challenges in designing experiments that lead to data and information collection on affective states of participants. We aim at acquiring data that would be basis to formulate and evaluate computer methods for detection, identification and interpretation of such affective states, and ultimately human emotions.++ | ||
| === AfCAI2016a === | === AfCAI2016a === | ||
| Line 114: | Line 137: | ||
| * Presented at [[pub: | * Presented at [[pub: | ||
| * {{http:// | * {{http:// | ||
| + | * ++Abstract | We are aiming at developing a technology to detect, identify and interpret human emotional states. We believe, that it can be provided based on the integration of context-aware systems and affective computing paradigms. We are planning to identify and characterize affective context data, and provide knowledge-based models to identify and interpret affects based on this data. A working name for this technology is simply AfCAI: Affective Computing with Context Awareness for Ambient Intelligence.++ | ||
| + | |||
| + | ===== Projects ===== | ||
| + | |||
| + | * **Personality, | ||
| ===== Tools and Datasets ===== | ===== Tools and Datasets ===== | ||
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| ==== Prototypes of Affective Games ==== | ==== Prototypes of Affective Games ==== | ||
| + | * [[pub: | ||
| * [[pub: | * [[pub: | ||
| * [[pub: | * [[pub: | ||
| - | * [[pub:londonbridge|London Bridge]] (scrollrunner game) | + | * [[pub:prototypes# |
| ==== Datasets ==== | ==== Datasets ==== | ||
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