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| pub:research [2020/11/19 13:01] – [Prototypes of Affective Games] kkutt | pub:research [2025/02/01 12:14] (current) – kkutt | ||
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| ===== Papers ===== | ===== Papers ===== | ||
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| + | === KES2024 === | ||
| + | * J. Ignatowicz, K. Kutt, and G. J. Nalepa, “**Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods**, | ||
| + | * DOI: [[https:// | ||
| + | * [[https:// | ||
| + | * ++Abstract | Experiments in affective computing are based on stimulus datasets that, in the process of standardization, | ||
| + | |||
| + | === IWINAC2024a === | ||
| + | * K. Kutt and G. J. Nalepa, “**Emotion Prediction in Real-Life Scenarios: On the Way to the BIRAFFE3 Dataset**, | ||
| + | * DOI: [[https:// | ||
| + | * [[https:// | ||
| + | * ++Abstract | Despite over 20 years of research in affective computing, emotion prediction models that would be useful in real-life out-of-the-lab scenarios such as health care or intelligent assistants have still not been developed. The identification of the fundamental problems behind this concern led to the initiation of the BIRAFFE series of experiments, | ||
| + | |||
| + | === IWINAC2024b === | ||
| + | * K. Kutt, M. Kutt, B. Kawa, and G. J. Nalepa, “**Human-in-the-Loop for Personality Dynamics: Proposal of a New Research Approach**, | ||
| + | * DOI: [[https:// | ||
| + | * [[https:// | ||
| + | * ++Abstract | In recent years, one can observe an increasing interest in dynamic models in the personality psychology research. Opposed to the traditional paradigm—in which personality is recognized as a set of several permanent dispositions called traits—dynamic approaches treat it as a complex system based on feedback loops between individual and the environment. The growing attention to dynamic models entails the need for appropriate modelling tools. In this conceptual paper we address this demand by proposing a new approach called personality-in-the-loop, | ||
| + | |||
| + | === DSAA2023 === | ||
| + | * K. Kutt, Ł. Ściga, and G. J. Nalepa, " | ||
| + | * DOI: [[https:// | ||
| + | * ++Abstract | Current review papers in the area of Affective Computing and Affective Gaming point to a number of issues with using their methods in out-of-the-lab scenarios, making them virtually impossible to be deployed. On the contrary, we present a game that serves as a proof-of-concept designed to demonstrate that—being aware of all the limitations and addressing them accordingly—it is possible to create a product that works in-the-wild. A key contribution is the development of a dynamic game adaptation algorithm based on the real-time analysis of emotions from facial expressions. The obtained results are promising, indicating the success in delivering a good game experience.++ | ||
| + | |||
| + | === InfFusion2023 === | ||
| + | * J. M. Górriz //et al.//, " | ||
| + | * DOI: [[https:// | ||
| + | * [[https:// | ||
| + | * ++Abstract | Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, | ||
| + | |||
| + | === SciData2022 === | ||
| + | * K. Kutt, D. Drążyk, L. Żuchowska, M. Szelążek, S. Bobek, and G. J. Nalepa, " | ||
| + | * DOI: [[https:// | ||
| + | * [[https:// | ||
| + | * ++Abstract | Generic emotion prediction models based on physiological data developed in the field of affective computing apparently are not robust enough. To improve their effectiveness, | ||
| + | |||
| + | === 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, | ||
| + | |||
| + | === 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 71: | ||
| === 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 === | ||
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| * 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 ===== | ||
kkutt