Keqin Bao

University of Science and Technology of China

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i-10 index

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Co-Authors

Fuli Feng

University of Science and Technology of China

Jizhi Zhang

University of Science and Technology of China

Xiangnan He

University of Science and Technology of China

Wenjie Wang

National University of Singapore

Yang Zhang

University of Science and Technology of China

Zhengyi Yang

University of Science and Technology of China

Ziwen Qian

Shanghai University of Electric Power

Dayiheng Liu

Sichuan University

Jun Xie

Lenovo (China)

Yu Wan

Harbin Institute of Technology

Cooperative Institutions

Chinese Academy of Sciences

University of Chinese Academy of Sciences

University of Science and Technology of China

Tsinghua University

Central South University

North China Electric Power University

Sun Yat-sen University

Peking University

State Grid Corporation of China (China)

Shanghai Jiao Tong University

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Improving transmission line stability: Feasibility study for low wind resistance conductors

2020 7th International Forum on Electrical Engineering and Automation (IFEEA) (2023)

Ziyu Yin Keqin Bao Dezhao Qin Wenbin Zhao

The purpose of this study is to investigate the effect of different section shapes of low resistance wire on the performance of reducing wind resistance in wind tunnel experiments. Transmission wire is the key component of power system, its design is very important to improve the transmission efficiency of electric energy and the stable operation of power grid. In order to deeply understand the influence of different section shapes on the wind resistance performance of the conductor, we conducted a series of wind tunnel experiments, and adopted reasonable measurement techniques and data analysis methods to evaluate the effectiveness of low wind resistance conductor design. The different section shapes used in this study include the traditional circular section, the section with semicircular groove and the unconventional section shape with narrow arc groove. By comparing the wind resistance coefficients of these wires, we can gain insight into the influence of different shapes on the wind resistance performance of the wires, and how to minimize the wind resistance and improve the stability of power transmission. The experimental results show that the wind resistance coefficient of the non-conventional wire is lower than that of the traditional circular wire at high wind speed, and the wind resistance performance is improved. In addition, we also analyze the difference of wind resistance performance of different shapes of low wind resistance wires under different wind speed conditions, which provides an important reference for the design of low wind resistance wires in practical power systems.

Line (geometry)

10.1109/ifeea60725.2023.10429237

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Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task

arXiv (Cornell University) (2022)

Keqin Bao Yu Wan Dayiheng Liu Baosong Yang Wenqiang Lei

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our systems employ the framework of UniTE, which combined three types of input formats during training with a pre-trained language model. First, we apply the pseudo-labeled data examples for the continuously pre-training phase. Notably, to reduce the gap between pre-training and fine-tuning, we use data pruning and a ranking-based score normalization strategy. For the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions. Finally, we collect the source-only evaluation results, and ensemble the predictions generated by two UniTE models, whose backbones are XLM-R and InfoXLM, respectively. Results show that our models reach 1st overall ranking in the Multilingual and English-Russian settings, and 2nd overall ranking in English-German and Chinese-English settings, showing relatively strong performances in this year's quality estimation competition.

Normalization

Benchmark (surveying)

10.48550/arxiv.2210.10049

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Item-side Fairness of Large Language Model-based Recommendation System

arXiv (Cornell University) (2024)

Meng Jiang Keqin Bao Jizhi Zhang Wenjie Wang Zhengyi Yang

Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the unique characteristics of LRS compared to conventional recommendation systems. To bridge this gap, this study examines the property of LRS with respect to item-side fairness and reveals the influencing factors of both historical users' interactions and inherent semantic biases of LLMs, shedding light on the need to extend conventional item-side fairness methods for LRS. Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS. IFairLRS covers the main stages of building an LRS with specifically adapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS to fine-tune LLaMA, a representative LLM, on \textit{MovieLens} and \textit{Steam} datasets, and observe significant item-side fairness improvements. The code can be found in https://github.com/JiangM-C/IFairLRS.git.

10.48550/arxiv.2402.15215

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Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation

arXiv (Cornell University) (2024)

Yang Zhang Juntao You Yimeng Bai Jizhi Zhang Keqin Bao

Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequences, resulting in suboptimal preference modeling for personalized recommendations. In this study, we propose a novel Counterfactual Fine-Tuning (CFT) method to address this issue by explicitly emphasizing the role of behavior sequences when generating recommendations. Specifically, we employ counterfactual reasoning to identify the causal effects of behavior sequences on model output and introduce a task that directly fits the ground-truth labels based on these effects, achieving the goal of explicit emphasis. Additionally, we develop a token-level weighting mechanism to adjust the emphasis strength for different item tokens, reflecting the diminishing influence of behavior sequences from earlier to later tokens during predicting an item. Extensive experiments on real-world datasets demonstrate that CFT effectively improves behavior sequence modeling. Our codes are available at https://github.com/itsmeyjt/CFT.

Causality

Sequence (biology)

10.48550/arxiv.2410.22809

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Photovoltaic Power Prediction Optimization of Neural Network Based on Tent Chaotic Mapping Sparrow Search Algorithm

Buqi Chen Ziwen Qian Keqin Bao

Photovoltaic power generation is affected by climate and environmental factors, and its shortcomings such as intermittency and high volatility have a great influence on the safe and stable operation of power grid. Therefore, accurate and reasonable prediction of photovoltaic power generation is of great importance. This paper takes the photovoltaic system of the Smart Energy Project of Lingang Campus of Shanghai Electric Power University as the research object, proposes a neural network model based on Tent chaotic mapping, improves SSA (sparrow search algorithm), expands the initial population of sparrows through Tent chaotic mapping, and selects the sparrow individual with the best fitness as the initial population. Then the weight and threshold of neural network are optimized. Eventually, the simulation test conducted by unoptimized neural network. The simulation results demonstrate that the optimized algorithm has better fitting effect than the unoptimized neural network.

Optimization algorithm

10.1109/aees59800.2023.10468783

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Supervision engineer and sub-contract management

Shanxi Architecture (2008)

Keqin Bao

According to the important roles of supervision engineer acted in sub-contract management as well as construction characteristics of sub-contracted project present situations of supervision engineers in sub-contract management are analyzed in the round.In view of the questions existed corresponding countermeasures are proposed,in order to ensure smooth running of project.

Construction contract

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Realizing the real time monitoring of remote motion data

East China Electric Power (2009)

Keqin Bao

Communication of the remote motion data is the key part to realize automatic dispatching.Currently effective technical approach to monitor this part is still lacked,which caused blindness in terms of analysis of abnormal remote motion signals,and affected the troubleshooting as a result.The technical scheme and application experience are introduced,which could be useful to solve the problems mentioned before.

Trouble shooting

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Research on Voltage Oscillation Optimization in Offshore Wind Power Access Area

Haoyu Zhu Keqin Bao Guangqing Zhang

Oscillation (cell signaling)

10.1109/icnepe64067.2024.10860398

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Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

Jizhi Zhang Keqin Bao Yang Zhang Wenjie Wang Fuli Feng

The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm — Recommendation via LLM (RecLLM). Nevertheless, it is important to note that LLMs may contain social prejudices, and therefore, the fairness of recommendations made by RecLLM requires further investigation. To avoid the potential risks of RecLLM, it is imperative to evaluate the fairness of RecLLM with respect to various sensitive attributes on the user side. Due to the differences between the RecLLM paradigm and the traditional recommendation paradigm, it is problematic to directly use the fairness benchmark of traditional recommendation. To address the dilemma, we propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes1 in two recommendation scenarios: music and movies. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations. Our code and dataset can be found at https://github.com/jizhi-zhang/FaiRLLM.

Benchmark (surveying)

Code (set theory)

10.1145/3604915.3608860

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Citations (50)

The Design and Application for Test System of Communicative Code

Journal of Shanghai University of Electric Power (2007)

Keqin Bao

Correspondence equipment of data transmission follows a particular code,and it is difficult to make a definite diagnosis for the equipment.This design successfully works out this problem.It can serve as a tool applied in automation process and conduct the third side supervisory and analysis report system.

Code (set theory)

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