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--- pub:research [2021/02/02 18:50] – ICAISC2020 added kkutt
+++ pub:research [2022/06/07 11:38] – kkutt
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 ===== Papers =====
+=== 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 ===
+  * K. Kutt, P. Sobczyk, and G. J. Nalepa, "**Evaluation of Selected APIs for Emotion Recognition from Facial Expressions**," in Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022 Proceedings, Part II, 2022, pp. 65–74.
+  * {{ :pub:kkt2022afcai.pdf |Full text draft available}}
+  * ++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, contempt, disgust, fear, joy, sadness, surprise and neutral—distributed in 4 benchmark datasets: Cohn-Kanade (CK), Extended Cohn-Kanade (CK+), Amsterdam Dynamic Facial Expression Set (ADFES) and Radboud Faces Database (RaFD). The results indicated a significant advantage of the Microsoft API in the accuracy of emotion recognition both in photos taken en face and at a 45∘ angle. Microsoft’s API also has an advantage in the larger number of recognised emotions: contempt and neutral are also included.++
+=== MRC2021b ===
+  * L. Żuchowska, K. Kutt, and G. J. Nalepa, "**Bartle Taxonomy-based Game for Affective and Personality Computing Research**," in MRC@IJCAI 2021, 2021, pp. 51–55.
+  * {{http://ceur-ws.org/Vol-2995/paper7.pdf|Full text available online}}
+  * ++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, "**People in the Context – an Analysis of Game-based Experimental Protocol**," in MRC@IJCAI 2021, 2021, pp. 46–50.
+  * {{http://ceur-ws.org/Vol-2995/paper6.pdf|Full text available online}}
+  * ++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 ===
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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.++
+===== Projects =====
+  * **Personality, Affective Context and the Brain (PANBA)** (01.2021-05.2022; research minigrant in the [[https://id.uj.edu.pl/en_GB/digiworld|DigiWorld Priority Research Area UJ]], project no. U1U/P06/NO/02.02; leader: [[pub:kkt|Krzysztof Kutt]]) aims to continue the efforts made in [[pub:biraffe|BIRAFFE1 and BIRAFFE2 oriented towards developing methods for affective personalization of intelligent systems]]. The project is aimed at analyzing data from the BIRAFFE2 experiment and preparing a new research procedure (BIRAFFE3) that includes the use of EEG. For more details, see [[https://geist.re/pub:projects:panba:start|the dedicated page in GEIST.re wiki]].
 ===== Tools and Datasets =====