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pro vyhledávání: '"MA Hua"'
Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weig
Externí odkaz:
http://arxiv.org/abs/2407.16477
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
Abstract The species of the genus Cerapanorpa Gao, Ma & Hua, 2016 (Mecoptera: Panorpidae) are characterized mainly by the presence of a finger-like anal horn on tergum VI of males and are distributed in the Oriental and eastern Palearctic regions. He
Externí odkaz:
https://doaj.org/article/227bd82c64ad463f80c0bf5e93ecfa99
Autor:
Ma, Hua, Wang, Shang, Gao, Yansong, Zhang, Zhi, Qiu, Huming, Xue, Minhui, Abuadbba, Alsharif, Fu, Anmin, Nepal, Surya, Abbott, Derek
All current backdoor attacks on deep learning (DL) models fall under the category of a vertical class backdoor (VCB) -- class-dependent. In VCB attacks, any sample from a class activates the implanted backdoor when the secret trigger is present. Exis
Externí odkaz:
http://arxiv.org/abs/2310.00542
Autor:
Gao, Yansong, Qiu, Huming, Zhang, Zhi, Wang, Binghui, Ma, Hua, Abuadbba, Alsharif, Xue, Minhui, Fu, Anmin, Nepal, Surya
Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services.To steal model architectures that are of valuable intellectual properties, a class of attacks has be
Externí odkaz:
http://arxiv.org/abs/2309.11894
Autor:
Wang, Guohong, Ma, Hua, Gao, Yansong, Abuadbba, Alsharif, Zhang, Zhi, Kang, Wei, Al-Sarawib, Said F., Zhang, Gongxuan, Abbott, Derek
Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly inc
Externí odkaz:
http://arxiv.org/abs/2309.04036
Akademický článek
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Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve perfor
Externí odkaz:
http://arxiv.org/abs/2305.05548
Autor:
Li, Qun, Thapa, Chandra, Ong, Lawrence, Zheng, Yifeng, Ma, Hua, Camtepe, Seyit A., Fu, Anmin, Gao, Yansong
Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for processing. Instead, local models are trained with local data; the mo
Externí odkaz:
http://arxiv.org/abs/2302.01550
Autor:
Ma, Hua, Li, Yinshan, Gao, Yansong, Zhang, Zhi, Abuadbba, Alsharif, Fu, Anmin, Al-Sarawi, Said F., Surya, Nepal, Abbott, Derek
Object detection is the foundation of various critical computer-vision tasks such as segmentation, object tracking, and event detection. To train an object detector with satisfactory accuracy, a large amount of data is required. However, due to the i
Externí odkaz:
http://arxiv.org/abs/2209.02339
Autor:
Ma, Hua, Li, Qun, Zheng, Yifeng, Zhang, Zhi, Liu, Xiaoning, Gao, Yansong, Al-Sarawi, Said F., Abbott, Derek
Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference attacks that can exploit shared-plaintext model updates to embed traces o
Externí odkaz:
http://arxiv.org/abs/2207.09080