Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Zecheng He"'
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
BackgroundThe status of sentinel lymph nodes is crucial for prognosis and treatment decisions in breast cancer patients. This study aimed to evaluate the predictive value of combined methylene blue and indocyanine green for sentinel lymph node metast
Externí odkaz:
https://doaj.org/article/1b7783a299c042a3b3186702d53dda86
Autor:
Juntian Lu, Qiqi Dong, Guohu Lan, Zecheng He, Dongying Zhou, He Zhang, Xiaoguang Wang, Xibo Liu, Chunji Jiang, Zheng Zhang, Shubo Wan, Xinhua Zhao, Haiqiu Yu
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Changes in the canopy microclimate in intercropping systems, particularly in the light environment, have important effects on the physiological characteristics of photosynthesis and yield of crops. Although different row ratio configurations and stri
Externí odkaz:
https://doaj.org/article/db65cc2b5c7d46ed84144aa97dd9ab47
Publikováno v:
Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eecff9dc83a773968357a162ff8a2fe5
https://doi.org/10.1145/3577923.3583639
https://doi.org/10.1145/3577923.3583639
Speculative execution attacks leverage the speculative and out-of-order execution features in modern computer processors to access secret data or execute code that should not be executed. Secret information can then be leaked through a covert channel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73f12118740e95b78340970bf0028766
http://arxiv.org/abs/2301.03724
http://arxiv.org/abs/2301.03724
Publikováno v:
IEEE Internet of Things Journal. 8:9706-9716
Benefiting from the advance of deep learning (DL) technology, Internet-of-Things (IoT) devices and systems are becoming more intelligent and multifunctional. They are expected to run various DL inference tasks with high efficiency and performance. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20740c5fe5c56ff3c735be4aab2ba310
https://doi.org/10.1109/jiot.2020.3022358
https://doi.org/10.1109/jiot.2020.3022358
Autor:
Zecheng He, Srinivas Sunkara, Xiaoxue Zang, Ying Xu, Lijuan Liu, Nevan Wichers, Gabriel Schubiner, Ruby Lee, Jindong Chen
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:5931-5938
As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety of setting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61e191fbb3994779738e67effbefbf4a
https://doi.org/10.1609/aaai.v35i7.16741
https://doi.org/10.1609/aaai.v35i7.16741
Publikováno v:
HPCA
Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df55b1c815e5ef3f5914b2b13dc473cc
https://doi.org/10.1109/hpca51647.2021.00014
https://doi.org/10.1109/hpca51647.2021.00014
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
MICCAI (3)
Deep neural networks for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision-making. Recent findings have shown that existing medical AEs are easy to detect in feature space. To be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4498d3370c7f0a468a7a1a6ccdcb6ca2
https://doi.org/10.1007/978-3-030-87199-4_4
https://doi.org/10.1007/978-3-030-87199-4_4
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597184
MICCAI (4)
MICCAI (4)
Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnerability of CNNs to adversarial-example attacks can be easily exploited
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff5f437d27ab1aef4bdcffc3bfc710af
https://doi.org/10.1007/978-3-030-59719-1_67
https://doi.org/10.1007/978-3-030-59719-1_67
Publikováno v:
ACSAC
The prevalence of deep learning has drawn attention to the privacy protection of sensitive data. Various privacy threats have been presented, where an adversary can steal model owners' private data. Meanwhile, countermeasures have also been introduce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5933d386b12040cdffec110a25b402f6
https://doi.org/10.1145/3359789.3359824
https://doi.org/10.1145/3359789.3359824