no code implementations • 2 Mar 2024 • Li Cai, Xin Mao, Zhihong Wang, Shangqing Zhao, Yuhao Zhou, Changxu Wu, Man Lan
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion Temporal Knowledge Graph Completion
1 code implementation • 1 Jan 2024 • Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Qingquan Wu, Chong Yang, Aimin Zhou, Man Lan
To bridge this gap, we introduce BIBench, a comprehensive benchmark designed to evaluate the data analysis capabilities of LLMs within the context of Business Intelligence (BI).
no code implementations • 26 Jul 2022 • Rui Duan, Zhe Qu, Shangqing Zhao, Leah Ding, Yao Liu, Zhuo Lu
In this work, we formulate the adversarial attack against music signals as a new perception-aware attack framework, which integrates human study into adversarial attack design.
no code implementations • 8 Jan 2022 • Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, Yao Liu
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network.
no code implementations • 25 Jun 2020 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu
In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.
no code implementations • 4 May 2019 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu
In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.