Zobrazeno 1 - 10
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pro vyhledávání: '"Chen, Tianyu"'
Autor:
Lin, Jiayu, Chen, Guanrong, Jin, Bojun, Li, Chenyang, Jia, Shutong, Lin, Wancong, Sun, Yang, He, Yuhang, Yang, Caihua, Bao, Jianzhu, Wu, Jipeng, Su, Wen, Chen, Jinglu, Li, Xinyi, Chen, Tianyu, Han, Mingjie, Du, Shuaiwen, Wang, Zijian, Li, Jiyin, Suo, Fuzhong, Wang, Hao, Lin, Nuanchen, Huang, Xuanjing, Jiang, Changjian, Xu, RuiFeng, Zhang, Long, Cao, Jiuxin, Jin, Ting, Wei, Zhongyu
In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organize two tracks to handle the argumentative generation tasks in different
Externí odkaz:
http://arxiv.org/abs/2407.14829
Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. Ho
Externí odkaz:
http://arxiv.org/abs/2405.19690
In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictio
Externí odkaz:
http://arxiv.org/abs/2404.08681
Foundation models have revolutionized knowledge acquisition across domains, and our study introduces OmniArch, a paradigm-shifting approach designed for building foundation models in multi-physics scientific computing. OmniArch's pre-training involve
Externí odkaz:
http://arxiv.org/abs/2402.16014
A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are c
Externí odkaz:
http://arxiv.org/abs/2401.10547
Autor:
Chen, Tianyu, Siek, Jeremy G.
Languages with gradual information-flow control combine static and dynamic techniques to prevent security leaks. Gradual languages should satisfy the gradual guarantee: programs that only differ in the precision of their type annotations should behav
Externí odkaz:
http://arxiv.org/abs/2312.02359
Autor:
Chen, Tianyu, Li, Lin, Qian, Taotao, Liu, Jingyi, Yang, Wei, Li, Ding, Liang, Guangtai, Wang, Qianxiang, Xie, Tao
Applying security patches in open source software timely is critical for ensuring the security of downstream applications. However, it is challenging to apply these patches promptly because notifications of patches are often incomplete and delayed. T
Externí odkaz:
http://arxiv.org/abs/2310.02530
Autor:
Chen, Tianyu, Li, Lin, Zhu, Liuchuan, Li, Zongyang, Liu, Xueqing, Liang, Guangtai, Wang, Qianxiang, Xie, Tao
Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affe
Externí odkaz:
http://arxiv.org/abs/2308.04662
To address security vulnerabilities arising from third-party libraries, security researchers maintain databases monitoring and curating vulnerability reports. Application developers can identify vulnerable libraries by directly querying the databases
Externí odkaz:
http://arxiv.org/abs/2307.08206
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data remains a challe
Externí odkaz:
http://arxiv.org/abs/2306.17361