Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Dario Pasquini"'
Publikováno v:
Diacronie. Studi di Storia Contemporanea, Vol 4, Iss 3, Pp 1-16 (2012)
The present essay discusses interpretations of satire offered by different disciplines. Furthermore it hypothesizes that both verbal and visual satirical texts provide particularly useful evidences of the emotions related to a certain historical peri
Externí odkaz:
https://doaj.org/article/27397697e4b44e16a438cd16e08f1ebb
Autor:
Dario Pasquini
Publikováno v:
European History Quarterly. 50:464-494
This article compares Italian and German memory cultures of Fascism and Nazism using an analysis of Italian and West- and East-German satirical magazines published from 1943 to 1963. In the early post-war period, as a consequence of the anti-Fascist
Autor:
Dario Pasquini
Publikováno v:
Journal of the History of Sexuality. 29:51-78
Publikováno v:
CCS
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the present paper, we expose vulnerabilities of the protocol and demonstrat
Autor:
Dario Pasquini
Publikováno v:
Modern Italy. 26:112-114
Publikováno v:
Pasquini, D, Francati, D & Ateniese, G 2022, Eluding Secure Aggregation in Federated Learning via Model Inconsistency . in CCS 2022-Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . Association for Computing Machinery, Proceedings of the ACM Conference on Computer and Communications Security, pp. 2429-2443, 28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022, Los Angeles, United States, 07/11/2022 . https://doi.org/10.1145/3548606.3560557
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the val
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f73ac397714eb77c29a006283d042b8f
Publikováno v:
Software Impacts 6 (2020). doi:10.1016/j.simpa.2020.100041
info:cnr-pdr/source/autori:D'Ambra P., Bernaschi M., Pasquini D./titolo:BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs/doi:10.1016%2Fj.simpa.2020.100041/rivista:Software Impacts/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:6
info:cnr-pdr/source/autori:D'Ambra P., Bernaschi M., Pasquini D./titolo:BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs/doi:10.1016%2Fj.simpa.2020.100041/rivista:Software Impacts/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:6
This is an original software publication. The software is certified by Ocean Code and is freely available. Sparse solvers are one of the building blocks of any technology for reliable and high-performance scientific and engineering computing. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b3c427736483389e0d390990842ada9
Publikováno v:
Computer Security – ESORICS 2020 ISBN: 9783030589509
ESORICS (1)
ESORICS (1)
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97dee626d4857253eff0b46a3647259e
http://hdl.handle.net/11573/1484881
http://hdl.handle.net/11573/1484881
Publikováno v:
IEEE Symposium on Security and Privacy
Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8a4e6dcac0a60711ad02829ba3104df
http://arxiv.org/abs/1910.04232
http://arxiv.org/abs/1910.04232