Zobrazeno 1 - 10
of 206
pro vyhledávání: '"XUANHUA SHI"'
Publikováno v:
大数据, Vol 10, Pp 172-188 (2024)
In the era of big data, deep neurals network models represented by LSTM have the ability to process massive data, and have excellent performance in the fields of language processing, speech recognition and time series data prediction.However, with th
Externí odkaz:
https://doaj.org/article/ce71dda0f5b94f0e9c317c5c11bff513
Autor:
Hong MEI, Xiaoyong DU, Hai JIN, Xueqi CHENG, Yunpeng CHAI, Xuanhua SHI, Xiaolong JIN, Yasha WANG, Chi LIU
Publikováno v:
大数据, Vol 9, Pp 1-20 (2023)
Major countries in the world attach great importance to the development of big data technology.China also puts big data as a national strategy, of great significance to develop in the long run.Big data technologies include data collection, transmissi
Externí odkaz:
https://doaj.org/article/89229e8048764b6085300339d23af4bf
Publikováno v:
IEEE Access, Vol 8, Pp 50485-50496 (2020)
Big data processing frameworks (e.g., Spark, Storm) have been extensively used for massive data processing in the industry. To improve the performance and robustness of these frameworks, developers provide users with highly-configurable parameters. D
Externí odkaz:
https://doaj.org/article/47ccd89dd1464569923b95031def32d2
Publikováno v:
IEEE Access, Vol 6, Pp 46584-46594 (2018)
Kernels of operating systems are written in low-level unsafe languages, which make them inevitably vulnerable to memory corruption attacks. Most existing kernel defense mechanisms focus on preventing control-data attacks. Recently, attackers have tur
Externí odkaz:
https://doaj.org/article/e36c825e97664c56bc4d96cc5f274c16
Publikováno v:
大数据, Vol 6, Pp 2020033-1 (2020)
In recent years,deep learning has achieved great success in many fields.As the deep neural network develops towards a deeper and wider direction,the training and inference of a deep neural network face huge memory pressure.The limited memory space of
Externí odkaz:
https://doaj.org/article/186bec219fa04ea88a8e278436c52db1
Publikováno v:
IEEE Transactions on Big Data. 9:677-687
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5828b25287cefdc1561cb8f289dda8ff
https://doi.org/10.1109/tbdata.2022.3180360
https://doi.org/10.1109/tbdata.2022.3180360
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2969f7f65fefe8669c5d789846acffc8
https://doi.org/10.1109/tpds.2023.3267943
https://doi.org/10.1109/tpds.2023.3267943
Publikováno v:
IEEE Transactions on Computers. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5b16652579f86dd03f69dfc5068a906
https://doi.org/10.1109/tc.2023.3257507
https://doi.org/10.1109/tc.2023.3257507
Autor:
Guoren Wang, Yue Zeng, Rong-Hua Li, Hongchao Qin, Xuanhua Shi, Yubin Xia, Xuequn Shang, Liang Hong
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-15
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cb90e456ac9451743be0d09ae42c5ef
https://doi.org/10.1109/tkde.2023.3270460
https://doi.org/10.1109/tkde.2023.3270460
Publikováno v:
IEEE Transactions on Computers. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::256019f1a12289ae918c2979642ff97d
https://doi.org/10.1109/tc.2023.3278530
https://doi.org/10.1109/tc.2023.3278530