Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Gender inference"'
Publikováno v:
PeerJ Computer Science, Vol 10, p e2378 (2024)
The gender classification from names is crucial for uncovering a myriad of gender-related research questions. Traditionally, this has been automatically computed by gender detection tools (GDTs), which now face new industry players in the form of con
Externí odkaz:
https://doaj.org/article/e7f667ad6e0f442b8323b2d99c41b87a
Akademický článek
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Autor:
Lucía Santamaría, Helena Mihaljević
Publikováno v:
PeerJ Computer Science, Vol 4, p e156 (2018)
The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to
Externí odkaz:
https://doaj.org/article/f9e74c450776437b886d4d3a4978dbba
Autor:
Zhe Xu, Patrick Sturt
Publikováno v:
Xu, Z & Sturt, P 2023, ' The influence of stereotypical information on gender inference in Chinese ', SN Social Sciences, vol. 3, 30 . https://doi.org/10.1007/s43545-023-00618-6
This study investigates the influence of stereotypical information on the representation of gender in Chinese, applying a sentence evaluation paradigm. Participants were required to decide whether the second sentence about the gender of the character
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::878f3aa1fa15b2d29e890f22ddfb44e0
https://doi.org/10.1007/s43545-023-00618-6
https://doi.org/10.1007/s43545-023-00618-6
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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Publikováno v:
ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing
ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing, Association for Computing Machinery (ACM), 2020, 20 (2), pp.36-45. ⟨10.1145/3412816.3412819⟩
ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing, 2020, 20 (2), pp.36-45. ⟨10.1145/3412816.3412819⟩
ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing, Association for Computing Machinery (ACM), 2020, 20 (2), pp.36-45. ⟨10.1145/3412816.3412819⟩
ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing, 2020, 20 (2), pp.36-45. ⟨10.1145/3412816.3412819⟩
International audience; Users in online social networks are vulnerable to attribute inference attacks due to some published data. Thus, the picture owner's gender has a strong influence on individuals' emotional reactions to the photo. In this work,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa71994af59b3d4596d922b8e67f1c53
https://doi.org/10.1145/3412816.3412819
https://doi.org/10.1145/3412816.3412819
Publikováno v:
Information Processing & Management, 58(6)
Information Processing & Management, 58, 6, pp. 1-24
Information Processing & Management, 58, 1-24
Information Processing & Management, 58, 6, pp. 1-24
Information Processing & Management, 58, 1-24
In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential priva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13ff06377e421d146fff6234d4ed5c00
https://doi.org/10.1016/j.ipm.2021.102722
https://doi.org/10.1016/j.ipm.2021.102722
Publikováno v:
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing, Mar 2020, Brno, Czech Republic. pp.1826-1834, ⟨10.1145/3341105.3373943⟩
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing, Mar 2020, Brno, Czech Republic. pp.1826-1834, ⟨10.1145/3341105.3373943⟩
International audience; The picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4510c0603b627c247698e4bed5fe7907
https://inria.hal.science/hal-02974078/file/sac.pdf
https://inria.hal.science/hal-02974078/file/sac.pdf
Publikováno v:
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing, Mar 2020, Brno, Czech Republic. pp.1826-1834, ⟨10.1145/3341105.3373943⟩
SAC
SAC '20-35th ACM/SIGAPP Symposium on Applied Computing, Mar 2020, Brno, Czech Republic. pp.1826-1834, ⟨10.1145/3341105.3373943⟩
SAC
International audience; The picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cb3fb80594703a638355498f99ea963
https://hal.inria.fr/hal-02974078/document
https://hal.inria.fr/hal-02974078/document
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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