Zobrazeno 1 - 10
of 5 892
pro vyhledávání: '"Xin, CHENG"'
Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial t
Externí odkaz:
http://arxiv.org/abs/2412.08068
We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utiliz
Externí odkaz:
http://arxiv.org/abs/2411.10889
Autor:
Zan, Xiaozhou, Gong, Ming, Pan, Zitian, Liu, Haiwen, Dong, Jingwei, Zhu, Jundong, Liu, Le, Chu, Yanbang, Watanabe, Kenji, Taniguchi, Takashi, Shi, Dongxia, Yang, Wei, Du, Luojun, Xie, Xin-Cheng, Zhang, Guangyu
High-harmonic generation (HHG), an extreme nonlinear effect, introduces an unprecedented paradigm to detect emergent quantum phases and electron dynamics inconceivable in the framework of linear and low-order nonlinear processes. As an important mani
Externí odkaz:
http://arxiv.org/abs/2408.09741
End-to-end topological learning using 1-parameter persistence is well-known. We show that the framework can be enhanced using 2-parameter persistence by adopting a recently introduced 2-parameter persistence based vectorization technique called GRIL.
Externí odkaz:
http://arxiv.org/abs/2406.07100
We consider a cooperative learning scenario where a collection of networked agents with individually owned classifiers dynamically update their predictions, for the same classification task, through communication or observations of each other's predi
Externí odkaz:
http://arxiv.org/abs/2405.20808
Deep Learning (DL)-based methods have proven to be effective for software vulnerability detection, with a potential for substantial productivity enhancements for detecting vulnerabilities. Current methods mainly focus on detecting single functions (i
Externí odkaz:
http://arxiv.org/abs/2404.15596
Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However,
Externí odkaz:
http://arxiv.org/abs/2403.19096
Autor:
Dey, Tamal K., Xin, Cheng
For a $P$-indexed persistence module ${\sf M}$, the (generalized) rank of ${\sf M}$ is defined as the rank of the limit-to-colimit map for the diagram of vector spaces of ${\sf M}$ over the poset $P$. For $2$-parameter persistence modules, recently a
Externí odkaz:
http://arxiv.org/abs/2403.08110
Publikováno v:
Published in Transactions on Machine Learning Research (TMLR), 2024
A hypergraph consists of a set of nodes along with a collection of subsets of the nodes called hyperedges. Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph. A hyperedge representation learned
Externí odkaz:
http://arxiv.org/abs/2402.11339
Open-Source Software (OSS) vulnerabilities bring great challenges to the software security and pose potential risks to our society. Enormous efforts have been devoted into automated vulnerability detection, among which deep learning (DL)-based approa
Externí odkaz:
http://arxiv.org/abs/2401.13169