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| pub:research [2021/01/07 10:19] – MRC2020 published kkutt | pub:research [2022/05/25 10:14] – AfCAI2022 paper published kkutt | ||
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| ===== Papers ===== | ===== Papers ===== | ||
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| + | === AfCAI2022 === | ||
| + | * K. Kutt, P. Sobczyk, and G. J. Nalepa, " | ||
| + | * {{ : | ||
| + | * ++Abstract | Facial expressions convey the vast majority of the emotional information contained in social utterances. From the point of view of affective intelligent systems, it is therefore important to develop appropriate emotion recognition models based on facial images. As a result of the high interest of the research and industrial community in this problem, many ready-to-use tools are being developed, which can be used via suitable web APIs. In this paper, two of the most popular APIs were tested: Microsoft Face API and Kairos Emotion Analysis API. The evaluation was performed on images representing 8 emotions—anger, | ||
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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.++ | ||
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| + | === 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 === | ||
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| * ++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.++ | * ++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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