Zobrazeno 1 - 10
of 752
pro vyhledávání: '"GUO, Songtao"'
Autor:
Zhou, Pengzhan, He, Yuepeng, Zhai, Yijun, Gao, Kaixin, Chen, Chao, Qin, Zhida, Zhang, Chong, Guo, Songtao
Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve pe
Externí odkaz:
http://arxiv.org/abs/2412.01295
Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictio
Externí odkaz:
http://arxiv.org/abs/2412.01281
Autor:
Zhai, Yijun, Zhou, Pengzhan, He, Yuepeng, Qu, Fang, Qin, Zhida, Jiao, Xianlong, Liu, Guiyan, Guo, Songtao
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. Howev
Externí odkaz:
http://arxiv.org/abs/2411.13979
Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentication. However, deep learning models are not trustworthy enough because
Externí odkaz:
http://arxiv.org/abs/2310.00633
There are a prohibitively large number of floating-point time series data generated at an unprecedentedly high rate. An efficient, compact and lossless compression for time series data is of great importance for a wide range of scenarios. Most existi
Externí odkaz:
http://arxiv.org/abs/2306.16053
Publikováno v:
Journal of Systems Architecture 134 (2023) 102780
Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular appl
Externí odkaz:
http://arxiv.org/abs/2209.12749
2D face recognition has been proven insecure for physical adversarial attacks. However, few studies have investigated the possibility of attacking real-world 3D face recognition systems. 3D-printed attacks recently proposed cannot generate adversaria
Externí odkaz:
http://arxiv.org/abs/2205.13412
Publikováno v:
In Journal of Colloid And Interface Science November 2024 673:616-627
Autor:
Cui, Shuang, Yu, Wei, Han, XingZhi, Hu, Tianhua, Yu, Mengqi, Liang, Yongliang, Guo, Songtao, Ma, Jinlian, Teng, Liwei, Liu, Zhensheng
Publikováno v:
In Journal of Hazardous Materials 5 September 2024 476