WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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  • Conference Object
    Citation - WoS: 5
    Citation - Scopus: 6
    Err@hri 2024 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Interactions
    (Assoc Computing Machinery, 2024) Spitale, Micol; Parreira, Maria Teresa; Stiber, Maia; Axelsson, Minja; Kara, Neval; Kankariyat, Garima; Gunes, Hatice; Kankariya, Garima
    Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions, encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors, to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Empathy Development in Digital Accessibility Through Real-Life Practices in a Programming Course: a Case Study
    (Assoc Computing Machinery, 2024) Inal, Yavuz; Cagiltay, Nergiz
    This case study adopted a project-based learning approach to a programming course based on real-life practices to help software engineering students develop empathy skills regarding digital accessibility. A project was assigned to first-year students to develop software for people with disabilities. The data were collected from each individual project of thirty-three students over four months. Students' efforts regarding analysis, design and development steps, and project outcomes were analyzed. The study results showed that students' experience level and knowledge about the accessibility domain were quite low initially. Regarding the target disability type in their projects, half of the students selected mental illness, followed by blindness, deafness, and physical illness. The students who gathered requirements from domain experts or target users made their products more accessible, indicating the importance of user involvement in empathy building in the development process. We also measured increased awareness of and knowledge about accessibility at the end of the course, leading us to discuss the effectiveness of real-life practices in teaching digital accessibility in programming courses.