Zobrazeno 1 - 10
of 1 097
pro vyhledávání: '"Nalepa P"'
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
Autor:
Miroszewski, Artur, Asiani, Marco Fellous, Mielczarek, Jakub, Saux, Bertrand Le, Nalepa, Jakub
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum nature presents inherent challenges. One major challenge is the limited re
Externí odkaz:
http://arxiv.org/abs/2407.15776
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
Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at
Externí odkaz:
http://arxiv.org/abs/2407.04730
Autor:
Kotowski, Krzysztof, Haskamp, Christoph, Andrzejewski, Jacek, Ruszczak, Bogdan, Nalepa, Jakub, Lakey, Daniel, Collins, Peter, Kolmas, Aybike, Bartesaghi, Mauro, Martinez-Heras, Jose, De Canio, Gabriele
Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomal
Externí odkaz:
http://arxiv.org/abs/2406.17826
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:
Zaigrajew, Vladimir, Baniecki, Hubert, Tulczyjew, Lukasz, Wijata, Agata M., Nalepa, Jakub, Longépé, Nicolas, Biecek, Przemyslaw
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases. Since both fields ad
Externí odkaz:
http://arxiv.org/abs/2403.08017