Zobrazeno 1 - 10
of 4 444
pro vyhledávání: '"He Kun"'
Autor:
Liu, Ao, Chen, Jing, He, Kun, Du, Ruiying, Xu, Jiahua, Wu, Cong, Feng, Yebo, Li, Teng, Ma, Jianfeng
Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions fac
Externí odkaz:
http://arxiv.org/abs/2411.06895
Sampling a random permutation with restricted positions, or equivalently approximating the permanent of a 0-1 matrix, is a fundamental problem in computer science, with several notable results attained through the years. In this paper, we first impro
Externí odkaz:
http://arxiv.org/abs/2411.02750
Although vision-language pre-training (VLP) models have achieved remarkable progress on cross-modal tasks, they remain vulnerable to adversarial attacks. Using data augmentation and cross-modal interactions to generate transferable adversarial exampl
Externí odkaz:
http://arxiv.org/abs/2409.06726
Autor:
Li, Qiao, Wu, Cong, Chen, Jing, Zhang, Zijun, He, Kun, Du, Ruiying, Wang, Xinxin, Zhao, Qingchuang, Liu, Yang
Deep neural networks (DNNs) are increasingly used in critical applications such as identity authentication and autonomous driving, where robustness against adversarial attacks is crucial. These attacks can exploit minor perturbations to cause signifi
Externí odkaz:
http://arxiv.org/abs/2408.10647
Autor:
Liang, Ruichao, Chen, Jing, Wu, Cong, He, Kun, Wu, Yueming, Cao, Ruochen, Du, Ruiying, Liu, Yang, Zhao, Ziming
Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in de
Externí odkaz:
http://arxiv.org/abs/2408.10116
Recent studies emphasize the crucial role of data augmentation in enhancing the performance of object detection models. However,existing methodologies often struggle to effectively harmonize dataset diversity with semantic coordination.To bridge this
Externí odkaz:
http://arxiv.org/abs/2408.02891
Humans exhibit remarkable proficiency in visual classification tasks, accurately recognizing and classifying new images with minimal examples. This ability is attributed to their capacity to focus on details and identify common features between previ
Externí odkaz:
http://arxiv.org/abs/2408.01427
For an integer $b\ge 0$, a $b$-matching in a graph $G=(V,E)$ is a set $S\subseteq E$ such that each vertex $v\in V$ is incident to at most $b$ edges in $S$. We design a fully polynomial-time approximation scheme (FPTAS) for counting the number of $b$
Externí odkaz:
http://arxiv.org/abs/2407.04989
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other n
Externí odkaz:
http://arxiv.org/abs/2406.19258
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the model input, enabling Transfo
Externí odkaz:
http://arxiv.org/abs/2406.19249