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pro vyhledávání: '"Candon, Kate"'
People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However,
Externí odkaz:
http://arxiv.org/abs/2409.07560
Autor:
Candon, Kate, Georgiou, Nicholas C., Zhou, Helen, Richardson, Sidney, Zhang, Qiping, Scassellati, Brian, Vázquez, Marynel
Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressi
Externí odkaz:
http://arxiv.org/abs/2402.00190
While neural network binary classifiers are often evaluated on metrics such as Accuracy and $F_1$-Score, they are commonly trained with a cross-entropy objective. How can this training-evaluation gap be addressed? While specific techniques have been
Externí odkaz:
http://arxiv.org/abs/2009.01367
Autor:
Brawer, Jake, Ghose, Debasmita, Candon, Kate, Meiying Qin, Roncone, Alessandro, Vázquez, Marynel, Scassellati, Brian
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2023, p525-533, 9p
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2023, p398-407, 10p
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2023, p290-300, 11p