Zobrazeno 1 - 10
of 741
pro vyhledávání: '"Du, Junping"'
With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at find
Externí odkaz:
http://arxiv.org/abs/2403.02723
Autor:
Su, Zhongbo, Ma, Yaoming, Chen, Xuelong, Peng, Xiaohua, Du, Junping, Han, Cunbo, He, Yanbo, Hofste, Jan G., Li, Maoshan, Li, Mengna, Lv, Shaoning, Ma, Weiqiang, Polo, María J., Peng, Jian, Qian, Hui, Sobrino, Jose, van der Velde, Rogier, Wen, Jun, Wang, Binbin, Wang, Xin, Yu, Lianyu, Zhang, Pei, Zhao, Hong, Zheng, Han, Zheng, Donghai, Zhong, Lei, Zeng, Yijian
A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A85365
https://ul.qucosa.de/api/qucosa%3A85365/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A85365/attachment/ATT-0/
Relational extraction is one of the basic tasks related to information extraction in the field of natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and informa
Externí odkaz:
http://arxiv.org/abs/2311.02564
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets. Our approach, named CWUTM (Topic model based on co-occurrence word networks for unbalanced short text datasets), Our approach addresses the challenge
Externí odkaz:
http://arxiv.org/abs/2311.02566
Epidemic decision-making can effectively help the government to comprehensively consider public security and economic development to respond to public health and safety emergencies. Epidemic decision-making can effectively help the government to comp
Externí odkaz:
http://arxiv.org/abs/2311.01749
Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. However, the heterogeneity of data in federated learning can lead to unfair representation of the globa
Externí odkaz:
http://arxiv.org/abs/2311.00959
The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems. Existing HIN-based recommender systems operate under the assumption o
Externí odkaz:
http://arxiv.org/abs/2310.11730
Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed
Externí odkaz:
http://arxiv.org/abs/2306.12859
Publikováno v:
ACM Transactions on Knowledge Discovery from Data (TKDD). 2023
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based
Externí odkaz:
http://arxiv.org/abs/2304.00485
Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solve
Externí odkaz:
http://arxiv.org/abs/2210.05243