Zobrazeno 1 - 10
of 711
pro vyhledávání: '"Cheng, Xiuzhen"'
Autor:
Li, Kun, Zhuang, Shichao, Zhang, Yue, Xu, Minghui, Wang, Ruoxi, Xu, Kaidi, Fu, Xinwen, Cheng, Xiuzhen
Large Language Models (LLMs) excel in diverse tasks such as text generation, data analysis, and software development, making them indispensable across domains like education, business, and creative industries. However, the rapid proliferation of LLMs
Externí odkaz:
http://arxiv.org/abs/2411.10683
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos
Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for
Externí odkaz:
http://arxiv.org/abs/2411.10032
The rapid expansion of software systems and the growing number of reported vulnerabilities have emphasized the importance of accurately identifying vulnerable code segments. Traditional methods for vulnerability localization, such as manual code audi
Externí odkaz:
http://arxiv.org/abs/2410.15288
Autor:
Hu, Pengfei, Qian, Yuhang, Zheng, Tianyue, Li, Ang, Chen, Zhe, Gao, Yue, Cheng, Xiuzhen, Luo, Jun
Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarel
Externí odkaz:
http://arxiv.org/abs/2410.09747
The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most popular m
Externí odkaz:
http://arxiv.org/abs/2408.15688
Backdoor attacks on deep neural networks have emerged as significant security threats, especially as DNNs are increasingly deployed in security-critical applications. However, most existing works assume that the attacker has access to the original tr
Externí odkaz:
http://arxiv.org/abs/2408.11444
With the widespread adoption of blockchain technology, the transaction fee mechanism (TFM) in blockchain systems has become a prominent research topic. An ideal TFM should satisfy user incentive compatibility (UIC), miner incentive compatibility (MIC
Externí odkaz:
http://arxiv.org/abs/2406.18957
The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federated framework named Fed-Grow for multiple participants to cooperativel
Externí odkaz:
http://arxiv.org/abs/2406.13450
The paper studies a fundamental federated learning (FL) problem involving multiple clients with heterogeneous constrained resources. Compared with the numerous training parameters, the computing and communication resources of clients are insufficient
Externí odkaz:
http://arxiv.org/abs/2406.13351
Autor:
Xue, Xiaolong, Shang, Guangyong, Ma, Zhen, Xu, Minghui, Guo, Hechuan, Li, Kun, Cheng, Xiuzhen
Digital watermarking methods are commonly used to safeguard digital media copyrights by confirming ownership and deterring unauthorized use. However, without reliable third-party oversight, these methods risk security vulnerabilities during watermark
Externí odkaz:
http://arxiv.org/abs/2405.19099