Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Fausto, Giunchiglia"'
Autor:
Nicolò Alessandro Girardini, Simone Centellegher, Andrea Passerini, Ivano Bison, Fausto Giunchiglia, Bruno Lepri
Publikováno v:
EPJ Data Science, Vol 12, Iss 1, Pp 1-22 (2023)
Abstract One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the impact of the pandemic on their psychological well-being and a
Externí odkaz:
https://doaj.org/article/db674468c3a643e88f0bcbba5ad854fc
Autor:
Hlaudi Daniel Masethe, Mosima Anna Masethe, Sunday Olusegun Ojo, Fausto Giunchiglia, Pius Adewale Owolawi
Publikováno v:
Information, Vol 15, Iss 9, p 540 (2024)
In natural language processing, word sense disambiguation (WSD) continues to be a major difficulty, especially for low-resource languages where linguistic variation and a lack of data make model training and evaluation more difficult. The goal of thi
Externí odkaz:
https://doaj.org/article/59ff08e378a34bed97fc2430a5705f86
Publikováno v:
Frontiers in Psychology, Vol 14 (2023)
Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, d
Externí odkaz:
https://doaj.org/article/b4b6c546008a4041a5c4c0f370fceee7
Autor:
Wanyi Zhang, Qiang Shen, Stefano Teso, Bruno Lepri, Andrea Passerini, Ivano Bison, Fausto Giunchiglia
Publikováno v:
EPJ Data Science, Vol 10, Iss 1, Pp 1-22 (2021)
Abstract Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shopping and online behaviors. However, the existing researches have been often limited to a sing
Externí odkaz:
https://doaj.org/article/4f95591bb5b24cfebb8b5ac2808029af
Publikováno v:
Entropy, Vol 25, Iss 4, p 638 (2023)
Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algor
Externí odkaz:
https://doaj.org/article/f1d761c08cf14aceb9f5c45844a5a238
Publikováno v:
Algorithms, Vol 15, Iss 4, p 109 (2022)
Mobile Crowd Sensing (MCS) is a novel IoT paradigm where sensor data, as collected by the user’s mobile devices, are integrated with user-generated content, e.g., annotations, self-reports, or images. While providing many advantages, the human invo
Externí odkaz:
https://doaj.org/article/fa1986511c0a47bea23517bd38d13e93
Publikováno v:
Algorithms, Vol 15, Iss 4, p 116 (2022)
The rapid development of online social media makes abuse detection a hot topic in the field of emotional computing. However, most natural language processing (NLP) methods only focus on linguistic features of posts and ignore the influence of users
Externí odkaz:
https://doaj.org/article/2f90fa7500624312ab2a578ba925a3de
Autor:
Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, Alan Hartman, Tsvi Kuflik
Publikováno v:
ACM Computing Surveys. 55:1-37
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders—including developers, end
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77bf871241a2b6c8a569ebda80a4104f
https://doi.org/10.1145/3527152
https://doi.org/10.1145/3527152
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d298d9f7719869a515f9b701701cdc4
https://doi.org/10.1109/icassp49357.2023.10094574
https://doi.org/10.1109/icassp49357.2023.10094574
Autor:
Lan Huang, Yuanwei Zhao, Bo Wang, Dongxu Zhang, Rui Zhang, Subhashis Das, Simone Bocca, Fausto Giunchiglia
Publikováno v:
Algorithms, Vol 14, Iss 8, p 241 (2021)
Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of
Externí odkaz:
https://doaj.org/article/67dd3ad42c66482a8cf2a76bd3d92d9f