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arXiv 2024年

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Analysis on the causes of massive stranding of Yellow Sea green tide on Lianyungang and Rizhao coasts in 2022

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Journal of Oceanology and Limnology 2024年第3期42卷 816-830页

作者： Lingjuan WU Juan HUANG Yi DING Guiyan LIU Shuai HUANG Song GAO Chao YUAN Jiangling XU Ping WU Rui HUANG Ruobing WEN Jie MEI North China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesQingdao 266061China Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China East China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesShanghai 200136China North China Sea Ecological Center Ministry of Natural ResourcesQingdao 266033China

In 2022,Yellow Sea green tide caused by Ulva prolifera reached a historic minimum and the coastal areas of Shandong Peninsula were less ***,the largest amount of seaweed biomass has been washed ashore on Lianyungang and Rizhao coasts since *** studied the development pattern of Yellow Sea green tide in 2022,and analyzed the key environmental factors on the growth and drifting,then discussed the possible reasons that resulted in the massive stranding of green tide biomass in Lianyungang and *** show under the combined influence of the east to southeast winds and currents with shoreward anomalies,green tide drifted to the coastal waters between Shandong and Jiangsu provinces and the distribution areas located westward compared with previous years(2008–2021).Floating *** rafts from the coastal waters of Binhai and Sheyang drifted continuously into the coastal waters of Lianyungang and Rizhao,providing important supplements for Yellow Sea green *** green tide in 2022 distributed close to the coastal waters,the abundant nutrients might support their continuous high growth *** addition,the amount of rainfall around Shandong Peninsula from late June to early July were significantly higher than in previous years,which might promote the development of green tide to some extent.

关键词： Yellow Sea green tide stranding biomass development pattern environmental factor

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A Query Optimization Method Utilizing Large Language Models

arXiv

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arXiv 2025年

作者： Yao, Zhiming Li, Haoyang Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China

Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional approaches rely on heuristic search methods and cost predictions, but these often struggle with the complexity of the search space and inaccuracies in performance estimation, leading to suboptimal plan choices. This paper presents LLMOpt, a novel framework that leverages Large Language Models (LLMs) to address these challenges through two innovative components: (1) LLM for Plan Candidate Generation (LLMOpt(G)), which eliminates heuristic search by utilizing the reasoning abilities of LLMs to directly generate high-quality query plans, and (2) LLM for Plan Candidate Selection (LLMOpt(S)), a list-wise cost model that compares candidates globally to enhance selection accuracy. To adapt LLMs for query optimization, we propose fine-tuning pre-trained models using optimization data collected offline. Experimental results on the JOB, JOB-EXT, and Stack benchmarks show that LLMOpt(G) and LLMOpt(S) outperform state-of-the-art methods, including PostgreSQL, BAO, and HybridQO. Notably, LLMOpt(S) achieves the best practical performance, striking a balance between plan quality and inference efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Research on Production Condition Identification Method of Pumping Well Based on an Improved Pattern Recognition Algorithm 9

Research on Production Condition Identification Method of Pu...

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9th Annual International Conference on Network and Information Systems for Computers, ICNISC 2023

作者： Wu, Haili Zhang, Xiangyang Wu, Dan Li, Qun Yao, Gang Research Institute of Petroleum Exploration and Development-Northwest PetroChina Key Laboratory of Internet of Things Cnpc Petroleum and Petrochemical Research Center of National Engineering Laboratory of Big Data Collaborative Security Cnpc Lanzhou China Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China

ISBN: (纸本)9798350306682

In the process of oil and gas production, indicator diagram is an important basis for production management. Diagnosing the working condition of pumping unit based on indicator diagram can quickly grasp the working state of oil well, analyze and judge whether the production parameters of oil well are reasonable. The pattern recognition technology of indicator diagram generally includes two stages: feature extraction and classification. Classification methods mainly use a few common machine learning methods such as artificial neural network and SVM. In recent years, feature extraction methods mainly include geometric feature method, grid feature and moment invariant feature methods. Due to the limitations of these extraction methods, most of them can only extract some primary feature quantities. However, using these primary feature quantities to recognize the indicator diagram often has the technical problems of low recognition efficiency and low recognition accuracy. In this paper, an improved indicator diagram pattern recognition method based on binary gradient direction histogram feature is proposed. Compared with the previous feature extraction methods, this method has good robustness, fast calculation speed and high recognition accuracy. © 2023 IEEE.

关键词： Extraction

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Explicit and Implicit Consumer Electronics Information Integration via Automatic Large Language Model Construction

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IEEE Transactions on Consumer Electronics 2025年

作者： Xue, Xingsi Li, Xingwang the Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou China the School of Physics and Electronic Information Engineering Henan Polytechnic University China

—Integrating diverse Consumer Electronics (CE) information is essential to enhance and optimize user experiences, but CE Information Integration (CEII) faces challenges arising from differences in entity descriptions. In recent years, Large Language Models (LLMs) have emerged as valuable tools for analyzing heterogeneous CE data due to their deep contextual embeddings. However, different LLMs capture various nuances and complexity levels within the CE data, and none of them can ensure their effectiveness in all heterogeneous scenarios. To address this issue, this paper proposes an automatic LLM Construction for CEII, which can determine both explicit and implicit CE entity mappings. First, a hybrid Genetic Programming is developed to determine explicit entity mappings using a new individual representation, an innovative fitness function, and a Probability-based Incremental Learning algorithm. Then, a novel Rough Set based matching method is presented to efficiently induce implicit CE entity mappings, which uses the entity feature relevance metric and the dominance-based matching approach. The experiment tests the performance of our approach with the OAEI’s KG and common KG datasets, and the practical CE datasets in the smart home context. The experimental results show the effectiveness of the developed approach, which significantly outperforms the state-of-the-art entity matching methods. © 2025 IEEE.

关键词： Genetic programming

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Preprocessing technology of consuming big data based on user interest with internet of location mining

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International Journal of Computers and Applications 2021年第2期43卷 147-152页

作者： Dagao, Duan Yang, Gao Department of Computer Science and Information Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

The current group discovery research mainly uses the communication relationship to mine user groups and fails to make full use of the user social relationships implied in the network, so that the mining groups can not truly reflect the user’s social relations in the social life. In this paper, the consumer big data preprocessing technology based on user interest location mining is proposed. The sequence pattern mining algorithm is used to mine user’s positional regularity sequence, and the position sequence correlation metrics are established. The positional regularity correlation is used to reveal the social relationship of the user’s social activities. Second, this paper combines the local similarity measure method to calculate the user’s communication distance index, which reflects the degree of understanding between users. Finally, using the communication distance index to weight the position correlation to calculate the user group correlation and then use the split clustering algorithm to mine user groups with communication relationship and social relationship. The experimental results showed that this method can effectively mine the communication relevance and position correlation in the user social activities and reflect the user’s group relations in real social activities. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： data mining

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A Brief Survey of Distribution Robust Graph Neural Networks 11

A Brief Survey of Distribution Robust Graph Neural Networks

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Zheng, Lei Quan, Pei Shi, Yong Niu, Lingfeng The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China The College of Economics and Management Beijing University of Technology Beijing100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing1001090 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction. © 2024 The Authors.

关键词： Distribution generalization Graph neural networks Shift robust

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows

Task Offloading Optimization Based on Position Prediction in...

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International Conference on Computer and Communications (ICCC)

作者： Dawen Zheng Lusheng Wang Caihong Kai Min Peng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay.

关键词： Service robots Simulation Force Predictive models Prediction algorithms Delays Task analysis

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Construction and Application of Subject knowledge Graph for Basic Education 7

Construction and Application of Subject Knowledge Graph for ...

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7th International Conference on Computer and Information Processing Technology, ISCIPT 2022

作者： Xu, Tao Ma, Xiaqing Wang, Fengsi Liu, Chun Zhang, Zixiang Lu, Peiming Han, Daojun Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

In view of the problem that the distribution of knowledge points in the current knowledge system in the form of chapters can no longer meet the needs of students and teachers. This paper proposes to use SVM to identify knowledge points of tests, build a subject knowledge Graph based on the identified knowledge points, and design and implement a subject knowledge Graph system. This paper takes the mechanical movement of the first chapter of the eighth grade (upper) physics course as an example to conduct empirical research. By 10-fold cross-validation, the average F1 value of the algorithm used in this paper is 89.66%. © 2022 Copyright for this paper by its authors.

关键词： knowledge graph

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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