Search Results for author: Shangqing Zhao

Found 6 papers, 1 papers with code

BIBench: Benchmarking Data Analysis Knowledge of Large Language Models

1 code implementation1 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).

Benchmarking

Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception

no code implementations26 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.

Adversarial Attack Speaker Recognition +2

LoMar: A Local Defense Against Poisoning Attack on Federated Learning

no code implementations8 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.

Density Estimation Edge-computing +2

Adversarial Machine Learning based Partial-model Attack in IoT

no code implementations25 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.

BIG-bench Machine Learning Decision Making

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

no code implementations4 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.

BIG-bench Machine Learning

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