Zobrazeno 1 - 10
of 592
pro vyhledávání: '"JA-LING WU"'
Publikováno v:
APSIPA Transactions on Signal and Information Processing, Vol 13, Iss 5 (2024)
Externí odkaz:
https://doaj.org/article/af78963bed05489da3b6317ec3c1c97c
Autor:
Chen-Hsiu Huang, Ja-Ling Wu
Publikováno v:
Entropy, Vol 26, Iss 5, p 357 (2024)
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting ne
Externí odkaz:
https://doaj.org/article/ad76ebc1a071478895fd48fa85bad697
Publikováno v:
Educational Technology & Society, Vol 25, Iss 3, Pp 105-121 (2022)
With the rapid increase of online learning and online degree programs, the need for a secure and fair scoring mechanisms in online learning becomes urgent. In this research, a secure scoring mechanism was designed and developed based on blockchain te
Externí odkaz:
https://doaj.org/article/54de366e3b5e420891fb162bd3e93fda
Autor:
Pin-Hung Juan, Ja-Ling Wu
Publikováno v:
Algorithms, Vol 17, Iss 2, p 52 (2024)
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of
Externí odkaz:
https://doaj.org/article/2c6a287a42cb4989baa0a20d7d140199
Publikováno v:
Cryptography, Vol 7, Iss 2, p 28 (2023)
Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate secu
Externí odkaz:
https://doaj.org/article/54dced183fc644c8ae4b6cbf11f97d37
Autor:
Yi-Wei Wang, Ja-Ling Wu
Publikováno v:
Algorithms, Vol 16, Iss 5, p 244 (2023)
This work presents an efficient and effective system allowing hospitals to share patients’ private information while ensuring that each hospital database’s medical records will not be leaked; moreover, the privacy of patients who access the data
Externí odkaz:
https://doaj.org/article/08b835e9d159414baf738f195558dd26
Autor:
Shao-Ming Lee, Ja-Ling Wu
Publikováno v:
Information, Vol 14, Iss 4, p 234 (2023)
Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental chall
Externí odkaz:
https://doaj.org/article/fcaae3e44c9844bbb4cadab3b8f95b77
Autor:
Tzu-Hsiang Kuo, Ja-Ling Wu
Publikováno v:
Mathematics, Vol 11, Iss 5, p 1227 (2023)
Secure comparison is a fundamental problem in multiparty computation. There are two different parties, each holding an l-bit integer, denoted by a and b, respectively. The goal of secure comparison is to compute the order relationship between a and b
Externí odkaz:
https://doaj.org/article/1c1ffa8f6ef34eb496ab5801d1cfb3bc
Publikováno v:
Entropy, Vol 25, Iss 2, p 272 (2023)
Privacy protection data processing has been critical in recent years when pervasively equipped mobile devices could easily capture high-resolution personal images and videos that may disclose personal information. We propose a new controllable and re
Externí odkaz:
https://doaj.org/article/2e0dd10fa0dc45d184b7f8e56a17dda6
Autor:
Yi-Lun Pan, Ja-Ling Wu
Publikováno v:
Entropy, Vol 24, Iss 7, p 982 (2022)
Steganography is one of the most crucial methods for information hiding, which embeds secret data on an ordinary file or a cover message for avoiding detection. We designed a novel rate-distortion-based large-capacity secure steganographic system, ca
Externí odkaz:
https://doaj.org/article/bbeacaa7b7304a37bbf0976a94f941cd