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pub:research [2022/05/25 10:14] – AfCAI2022 paper published kkuttpub:research [2024/01/25 12:26] (current) kkutt
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 ===== Papers ===== ===== Papers =====
 +
 +=== 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.++
 +
 +=== InfFusion2023 ===
 +  * J. M. Górriz //et al.//, "**Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends**," Inf. Fusion, vol. 100, p. 101945, 2023.
 +  * DOI: [[https://doi.org/10.1016/j.inffus.2023.101945|10.1016/j.inffus.2023.101945]]
 +  * [[https://doi.org/10.1016/j.inffus.2023.101945|Full text available online]] 
 +  * ++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, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.++
 +
 +=== SciData2022 ===
 +  * K. Kutt, D. Drążyk, L. Żuchowska, M. Szelążek, S. Bobek, and G. J. Nalepa, "**BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments**," Sci. Data, vol. 9, no. 1, p. 274, 2022
 +  * DOI: [[https://doi.org/10.1038/s41597-022-01402-6|10.1038/s41597-022-01402-6]]
 +  * [[https://doi.org/10.1038/s41597-022-01402-6|Full text available online]] 
 +  * ++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, one needs to personalize them to specific individuals and incorporate broader contextual information. To address the lack of relevant datasets, we propose the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE2) dataset. In addition to the classical procedure in the stimulus-appraisal paradigm, it also contains data from an affective gaming session in which a range of contextual data was collected from the game environment. This is complemented by accelerometer, ECG and EDA signals, participants’ facial expression data, together with personality and game engagement questionnaires. The dataset was collected on 102 participants. Its potential usefulness is presented by validating the correctness of the contextual data and indicating the relationships between personality and participants’ emotions and between personality and physiological signals.++
  
 === AfCAI2022 === === AfCAI2022 ===
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