Zobrazeno 1 - 10
of 994
pro vyhledávání: '"Wang Yongwei"'
Publikováno v:
Fenmo yejin jishu, Vol 42, Iss 3, Pp 264-274, 296 (2024)
The research progress of oxides in the high performance steels by additive manufacturing was reviewed in this paper, including the characteristics and formation of oxides, the influence of oxides on the molten pool, the mechanism of oxide destruction
Externí odkaz:
https://doaj.org/article/fbdd4fc223b64e6f85a97e9da06be0df
Software defect prediction (SDP) aims to identify high-risk defect modules in software development, optimizing resource allocation. While previous studies show that dependency network metrics improve defect prediction, most methods focus on code-base
Externí odkaz:
http://arxiv.org/abs/2410.19550
Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. However, the potential misuse of LLMs has raised significant concerns, underscoring the urgent need for reliable detection
Externí odkaz:
http://arxiv.org/abs/2410.06072
Autor:
Cao, Huangsen, Wang, Yongwei, Liu, Yinfeng, Zheng, Sixian, Lv, Kangtao, Zhang, Zhimeng, Zhang, Bo, Ding, Xin, Wu, Fei
The emergence of diverse generative vision models has recently enabled the synthesis of visually realistic images, underscoring the critical need for effectively detecting these generated images from real photos. Despite advances in this field, exist
Externí odkaz:
http://arxiv.org/abs/2410.06044
Large vision models have been found vulnerable to adversarial examples, emphasizing the need for enhancing their adversarial robustness. While adversarial training is an effective defense for deep convolutional models, it often faces scalability issu
Externí odkaz:
http://arxiv.org/abs/2410.05951
Vulnerability detection is garnering increasing attention in software engineering, since code vulnerabilities possibly pose significant security. Recently, reusing various code pre-trained models has become common for code embedding without providing
Externí odkaz:
http://arxiv.org/abs/2408.04863
Autor:
Xia, Renqiu, Mao, Song, Yan, Xiangchao, Zhou, Hongbin, Zhang, Bo, Peng, Haoyang, Pi, Jiahao, Fu, Daocheng, Wu, Wenjie, Ye, Hancheng, Feng, Shiyang, Wang, Bin, Xu, Chao, He, Conghui, Cai, Pinlong, Dou, Min, Shi, Botian, Zhou, Sheng, Wang, Yongwei, Yan, Junchi, Wu, Fei, Qiao, Yu
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these documents and assessing large models' abilities to handle scientific docume
Externí odkaz:
http://arxiv.org/abs/2406.11633
Continuous Conditional Generative Modeling (CCGM) aims to estimate the distribution of high-dimensional data, typically images, conditioned on scalar continuous variables known as regression labels. While Continuous conditional Generative Adversarial
Externí odkaz:
http://arxiv.org/abs/2405.03546
Backdoor attacks have been shown to impose severe threats to real security-critical scenarios. Although previous works can achieve high attack success rates, they either require access to victim models which may significantly reduce their threats in
Externí odkaz:
http://arxiv.org/abs/2403.13017
Publikováno v:
E3S Web of Conferences, Vol 162, p 01005 (2020)
In this paper, a recuperator model is established to simulate the real working state of the recuperator in the micro turbine. The relative error between simulated and experimental data doesn't exceed 5%, which indicates that the model can better refl
Externí odkaz:
https://doaj.org/article/da9e325d12af42de97bd59ba0a8f0230