Zobrazeno 1 - 10
of 186
pro vyhledávání: '"NALEPA, Grzegorz J."'
Autor:
Bobek, Szymon, Korycińska, Paloma, Krakowska, Monika, Mozolewski, Maciej, Rak, Dorota, Zych, Magdalena, Wójcik, Magdalena, Nalepa, Grzegorz J.
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts i
Externí odkaz:
http://arxiv.org/abs/2411.02419
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
Autor:
Bobek, Szymon, Korycińska, Paloma, Krakowska, Monika, Mozolewski, Maciej, Rak, Dorota, Zych, Magdalena, Wójcik, Magdalena, Nalepa, Grzegorz J.
This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understan
Externí odkaz:
http://arxiv.org/abs/2410.15952
As part of ongoing research projects, three Jagiellonian University units -- the Jagiellonian University Museum, the Jagiellonian University Archives, and the Jagiellonian Library -- are collaborating to digitize cultural heritage documents, describe
Externí odkaz:
http://arxiv.org/abs/2407.06976
In response to several cultural heritage initiatives at the Jagiellonian University, we have developed a new digitization workflow in collaboration with the Jagiellonian Library (JL). The solution is based on easy-to-access technological solutions --
Externí odkaz:
http://arxiv.org/abs/2407.06972
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 affect
Externí odkaz:
http://arxiv.org/abs/2406.16187
Autor:
Jakubowski, Jakub, Wojak-Strzelecka, Natalia, Ribeiro, Rita P., Pashami, Sepideh, Bobek, Szymon, Gama, Joao, Nalepa, Grzegorz J
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be p
Externí odkaz:
http://arxiv.org/abs/2405.12785
Autor:
Bobek, Szymon, Nalepa, Grzegorz J.
Explainable artificial intelligence (XAI) is one of the most intensively developed area of AI in recent years. It is also one of the most fragmented with multiple methods that focus on different aspects of explanations. This makes difficult to obtain
Externí odkaz:
http://arxiv.org/abs/2310.14894
Autor:
Pashami, Sepideh, Nowaczyk, Slawomir, Fan, Yuantao, Jakubowski, Jakub, Paiva, Nuno, Davari, Narjes, Bobek, Szymon, Jamshidi, Samaneh, Sarmadi, Hamid, Alabdallah, Abdallah, Ribeiro, Rita P., Veloso, Bruno, Sayed-Mouchaweh, Moamar, Rajaoarisoa, Lala, Nalepa, Grzegorz J., Gama, João
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deci
Externí odkaz:
http://arxiv.org/abs/2306.05120
Autor:
Rodriguez-Fernandez, Victor, Montalvo, David, Piccialli, Francesco, Nalepa, Grzegorz J., Camacho, David
Publikováno v:
Knowledge-Based Systems, 277, 2023, p.110793
The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation a
Externí odkaz:
http://arxiv.org/abs/2302.03858