Zobrazeno 1 - 10
of 3 303
pro vyhledávání: '"SUN, Sheng"'
Autor:
Jiang, Xuefeng, Wu, Lvhua, Sun, Sheng, Li, Jia, Xue, Jingjing, Wang, Yuwei, Wu, Tingting, Liu, Min
Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based vulnerability detection methods rely on either fine-tuning medium-size
Externí odkaz:
http://arxiv.org/abs/2412.18260
Metaphor serves as an implicit approach to convey information, while enabling the generalized comprehension of complex subjects. However, metaphor can potentially be exploited to bypass the safety alignment mechanisms of Large Language Models (LLMs),
Externí odkaz:
http://arxiv.org/abs/2412.12145
To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system heterogeneity (i.e., the dive
Externí odkaz:
http://arxiv.org/abs/2412.07216
Autor:
Liu, Fang, Ji, Xiao-Bin, Sun, Sheng-Sen, Liu, Huai-Min, Fang, Shuang-Shi, Li, Xiao-Ling, Chen, Tong, Wang, Xin-Nan, Li, Ming-Run, Wang, Liang-Liang, Wu, Ling-Hui, Yuan, Ye, Zhang, Yao, Zhu, Wen-Jing
Using $(10087 \pm 44) \times 10^6$ $J/\psi$ events collected with the BESIII detector in 2009, 2012, 2018 and 2019, the tracking efficiency of charged pions is studied using the decay $J/\psi \rightarrow \pi^+ \pi^- \pi^0$. The systematic uncertainty
Externí odkaz:
http://arxiv.org/abs/2412.00469
Autor:
Liu, Qingxiang, Sun, Sheng, Liang, Yuxuan, Xu, Xiaolong, Liu, Min, Bilal, Muhammad, Wang, Yuwei, Li, Xujing, Zheng, Yu
Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offlin
Externí odkaz:
http://arxiv.org/abs/2411.14046
Autor:
Jiang, Xuefeng, Sun, Sheng, Li, Jia, Xue, Jingjing, Li, Runhan, Wu, Zhiyuan, Xu, Gang, Wang, Yuwei, Liu, Min
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corres
Externí odkaz:
http://arxiv.org/abs/2408.04301
Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to
Externí odkaz:
http://arxiv.org/abs/2407.18039
Autor:
Dai, Shengnan, Zhang, Shijie, Sheng, Ye, Dong, Erting, Sun, Sheng, Xi, Lili, Snyder, G. Jeffrey, Xi, Jinyang, Yang, Jiong
Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point defect (PD) scattering term to the phonon relaxation time, which has bee
Externí odkaz:
http://arxiv.org/abs/2407.00308
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges
Externí odkaz:
http://arxiv.org/abs/2405.13378
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers. However, training models for on-device deployment face significan
Externí odkaz:
http://arxiv.org/abs/2404.10255