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| pub:research [2023/11/09 19:18] – [Papers] 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**,” //Procedia Computer Science//, vol. 246, pp. 3073–3082, 2024 |
| | * DOI: [[https://doi.org/10.1016/j.procs.2024.09.365|10.1016/j.procs.2024.09.365]] |
| | * [[https://doi.org/10.1016/j.procs.2024.09.365|Full text available online]] |
| | * ++Abstract | Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain.++ |
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| | === IWINAC2024a === |
| | * K. Kutt and G. J. Nalepa, “**Emotion Prediction in Real-Life Scenarios: On the Way to the BIRAFFE3 Dataset**,” in //Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024//, J. M. Ferrández Vicente, M. Val Calvo, and H. Adeli, Eds., Cham: Springer, 2024, pp. 465–475 |
| | * DOI: [[https://doi.org/10.1007/978-3-031-61140-7_44|10.1007/978-3-031-61140-7_44]] |
| | * [[https://www.researchgate.net/publication/381032511_Emotion_Prediction_in_Real-Life_Scenarios_On_the_Way_to_the_BIRAFFE3_Dataset|Full text available @ResearchGate]] |
| | * ++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, whose main goal is to develop a set of techniques, tools and good practices to introduce personalized context-based emotion processing modules in intelligent systems/assistants. The aim of this work is to present the work-in-progress concept of the third experiment in the BIRAFFE series and discuss the results of the pilot study. After all conclusions have been drawn up, actual study will be carried out, and then the collected data will be processed and made available under the creative commons license as BIRAFFE3 dataset.++ |
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| | === IWINAC2024b === |
| | * K. Kutt, M. Kutt, B. Kawa, and G. J. Nalepa, “**Human-in-the-Loop for Personality Dynamics: Proposal of a New Research Approach**,” in //Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024//, J. M. Ferrández Vicente, M. Val Calvo, and H. Adeli, Eds., Cham: Springer, 2024, pp. 455–464. |
| | * DOI: [[https://doi.org/10.1007/978-3-031-61140-7_43|10.1007/978-3-031-61140-7_43]] |
| | * [[https://www.researchgate.net/publication/381035542_Human-in-the-Loop_for_Personality_Dynamics_Proposal_of_a_New_Research_Approach|Full text available @ResearchGate]] |
| | * ++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, which combines state-of-the-art psychological models with the human-in-the-loop approach used in the design of intelligent systems. This new approach has a potential to open new research directions including the development of new experimental frameworks for research in personality psychology, based on simulations and methods used in the design of intelligent systems. It will also enable the development of new dynamic models of personality in silico. Finally, the proposed approach extends the field of intelligent systems design with new possibilities for processing personality-related data in these systems.++ |
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| | === DSAA2023 === |
| | * K. Kutt, Ł. Ściga, and G. J. Nalepa, "**Emotion-based Dynamic Difficulty Adjustment in Video Games**," in DSAA 2023, pp. 1–5. |
| | * DOI: [[https://doi.org/10.1109/DSAA60987.2023.10302578|10.1109/DSAA60987.2023.10302578]] |
| | * ++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.++ |
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| === InfFusion2023 === | === InfFusion2023 === |
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