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学者姓名：于娟
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Abstract ：
An intelligent evaluation method was proposed for the credibility of answers in online medical communities (OMCs). By applying the method to evaluate and classify the credibility of answers, this paper aimed to inspire users to adopt reliable health information, enhance the credibility of content, and support OMCs healthy development. The study constructed a content knowledge graph for answers in OMCs and a domain knowledge graph for diabetes. The concepts of entity regularity, relationship consistency coefficient, and relationship accuracy were introduced to calculate credibility scores for the triples in the community answers, which would be aggregated to evaluate the content credibility. Validation results from the xywy.com website show that our method effectively evaluated and classified the credibility of Q&A content, achieving intelligent identification and filtering of suspicious answers. The precision accuracy of credible answers is 92.5%, significantly improving efficiency and interpretability compared to manual scoring methods. This study optimized the current content review model in OMCs, enhanced content management efficiency and accuracy, and provided feasible tools and methods for monitoring the information quality in OMCs and delivering reliable medical knowledge services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Data accuracy Data accuracy Knowledge graph Knowledge graph
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| GB/T 7714 | Xi, Yunjiang , Geng, Jiaxiu , Deng, YuShan et al. Research on the Credibility Evaluation Method of Online Medical Community Answer Content Based on Domain Knowledge Graph [C] . 2025 : 256-276 . |
| MLA | Xi, Yunjiang et al. "Research on the Credibility Evaluation Method of Online Medical Community Answer Content Based on Domain Knowledge Graph" . (2025) : 256-276 . |
| APA | Xi, Yunjiang , Geng, Jiaxiu , Deng, YuShan , Liao, Xiao , Yu, Juan . Research on the Credibility Evaluation Method of Online Medical Community Answer Content Based on Domain Knowledge Graph . (2025) : 256-276 . |
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Recognizing the associations among entities in corporate reports accurately is crucial for market regulation and policy development. Nevertheless, confronted with massive corporate information, the traditional manual screening approach is cumbersome, struggling to match the demand. Consequently, we propose a multimodal network model incorporating conceptual semantic knowledge injection, CSKINet, for accurately extracting relations from Chinese corporate reports. The essential highlights in the design of the CSKINet model are the following: (1) Integrate the conceptual descriptions of corporations from external resources to construct the semantic knowledge repository of corporate concepts, which provides a solid semantic foundation for the model. (2) Multimodal features are extracted from the documents by various means and corporate conceptual knowledge is integrated into the model representation to enhance the representation capability of the model. (3) The multimodal self-attention mechanism that captures cross-modal associations and the biaffine classifier with Taylor polynomial loss function that optimizes training iterations further improve the learning efficiency and prediction accuracy. The results on the real corporate report dataset show that our proposed model can more accurately extract the relations from Chinese corporate reports compared to other baseline models, where the F1 score reaches 85.76%.
Keyword ：
Corporate report Corporate report Deep learning Deep learning Relation extraction Relation extraction Semantic knowledge injection Semantic knowledge injection
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| GB/T 7714 | Luo, Shun , Yu, Juan , Xi, Yunjiang . CSKINet: A multimodal network model integrating conceptual semantic knowledge injection for relation extraction of Chinese corporate reports [J]. | APPLIED SOFT COMPUTING , 2024 , 167 . |
| MLA | Luo, Shun et al. "CSKINet: A multimodal network model integrating conceptual semantic knowledge injection for relation extraction of Chinese corporate reports" . | APPLIED SOFT COMPUTING 167 (2024) . |
| APA | Luo, Shun , Yu, Juan , Xi, Yunjiang . CSKINet: A multimodal network model integrating conceptual semantic knowledge injection for relation extraction of Chinese corporate reports . | APPLIED SOFT COMPUTING , 2024 , 167 . |
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In the context of information overload,companies often struggle to effectively identify valuable ideas on their open innovation platforms.In this article,we propose an idea adoption strategy based on machine learning.We used data from a well-known open innovation platform,Salesforce,and extracted characteristic variables using the Information Adoption Model.Four classification models were then constructed based on AdaBoost,Random Forest,SVM and Logistic Regression models.Due to significant differences in the number of positive and negative samples in the OIP,we used the SMOTE method to address the problem of data imbalance.The results of the study showed that the ensemble learning models were more accurate in identifying valuable ideas than the individual machine learning models.When comparing the two ensemble learning models,AdaBoost outperformed Random Forest in predicting both positive and negative class samples.The SMOTE-AdaBoost model achieved a recall of 0.93,a precision of 0.92 and an impressive AUC of 0.98 in identifying adopted ideas,which could well identify valuable ideas and has implications for improving the efficiency and quality of idea adoption in OIP.The shortcoming of this work is that it only investigated a single platform.In the future,we will consider extending this method to different platforms and multiple classification problems.
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| GB/T 7714 | Yunjiang XI , Futao HUANG , Lu HUANG et al. A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost:Taking Salesforce Platform as an Example [J]. | 系统科学与信息学报（英文版） , 2024 , 12 (4) : 476-490 . |
| MLA | Yunjiang XI et al. "A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost:Taking Salesforce Platform as an Example" . | 系统科学与信息学报（英文版） 12 . 4 (2024) : 476-490 . |
| APA | Yunjiang XI , Futao HUANG , Lu HUANG , Xiao LIAO , Juan YU . A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost:Taking Salesforce Platform as an Example . | 系统科学与信息学报（英文版） , 2024 , 12 (4) , 476-490 . |
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Corporations require screening critical information from numerous resumes with different formats and content for managerial decision-making. However, traditional manual screening methods have low accuracy to meet the demand. Therefore, we propose a multimodal network model incorporating entity semantic graphs, ESGNet, for accurately extracting critical informa-tion from Chinese resumes. Firstly, each resume is partitioned into distinct blocks according to content while constructing an entity semantic graph according to entity categories. Then we interact with associated features within image and text modalities to capture the latent semantic information. Furthermore, we employ Transformer containing multimodal self-attention to establish relationships among modalities and incorporate supervised comparative learning concepts into the loss function for categorizing feature information. The experimental results on the real Chinese resume dataset demonstrate that ESGNet achieves the best information extraction results on all three indicators compared with other models, with the comprehensive indicator F1 score reaching 91.65%.
Keyword ：
Deep learning Deep learning Entity semantic graphs Entity semantic graphs Multimodal network model Multimodal network model Resume information extraction Resume information extraction
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| GB/T 7714 | Luo, Shun , Yu, Juan . ESGNet: A multimodal network model incorporating entity semantic graphs for information extraction from Chinese resumes [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (1) . |
| MLA | Luo, Shun et al. "ESGNet: A multimodal network model incorporating entity semantic graphs for information extraction from Chinese resumes" . | INFORMATION PROCESSING & MANAGEMENT 61 . 1 (2024) . |
| APA | Luo, Shun , Yu, Juan . ESGNet: A multimodal network model incorporating entity semantic graphs for information extraction from Chinese resumes . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (1) . |
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针对现有虚假信息检测方法主要基于单模态数据分析,检测时忽视了信息之间相关性的问题,提出了结合社交网络图的多模态虚假信息检测模型.该模型使用预训练Transformer模型和图像描述模型分别从多角度提取各模态数据的语义,并通过融合信息传播过程中的社交网络图,在文本和图像模态中加入传播信息的特征,最后使用跨模态注意力机制分配各模态信息权重以进行虚假信息检测.在推特和微博两个真实数据集上进行对比实验,所提模型的虚假信息检测准确率稳定为约88％,高于EANN、PTCA等现有基线模型.实验结果表明所提模型能够有效融合多模态信息,从而提高虚假信息检测的准确率.
Keyword ：
多模态融合 多模态融合 社交网络图 社交网络图 网络舆情 网络舆情 虚假信息检测 虚假信息检测 跨模态注意力 跨模态注意力
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| GB/T 7714 | 叶舟波 , 罗舜 , 于娟 . 结合社交网络图的多模态虚假信息检测模型 [J]. | 计算机应用研究 , 2024 , 41 (7) : 1992-1998 . |
| MLA | 叶舟波 et al. "结合社交网络图的多模态虚假信息检测模型" . | 计算机应用研究 41 . 7 (2024) : 1992-1998 . |
| APA | 叶舟波 , 罗舜 , 于娟 . 结合社交网络图的多模态虚假信息检测模型 . | 计算机应用研究 , 2024 , 41 (7) , 1992-1998 . |
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跨语言文本主题发现是跨语言文本挖掘领域的重要研究方向，对跨语言文本分析和组织各种文本数据具有较高的应用价值。基于Bagging和跨语言词嵌入改进LDA主题模型，提出跨语言文本主题发现方法BCL-LDA(Bagging, Cross-lingual word embedding with LDA),从多语言文本中挖掘关键信息。该方法首先将Bagging集成学习思想与LDA主题模型结合生成混合语言子主题集；然后利用跨语言词嵌入和K-means算法对混合子主题进行聚类分组；最后使用TF-IDF算法对主题词进行过滤排序。汉语-德语、汉语-法语主题发现实验表明，该方法在主题连贯性和多样性方面均表现优异，能够提取出语义更加相关且主题更加连贯多样的双语主题。
Keyword ：
LDA LDA 主题发现 主题发现 主题聚类 主题聚类 德语 德语 法语 法语 跨语言 跨语言
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| GB/T 7714 | 李帅 , 于娟 , 巫邵诚 . 基于集成学习的跨语言文本主题发现方法研究 [J]. | 计算机科学 , 2024 , 51 (S1) : 194-201 . |
| MLA | 李帅 et al. "基于集成学习的跨语言文本主题发现方法研究" . | 计算机科学 51 . S1 (2024) : 194-201 . |
| APA | 李帅 , 于娟 , 巫邵诚 . 基于集成学习的跨语言文本主题发现方法研究 . | 计算机科学 , 2024 , 51 (S1) , 194-201 . |
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In the context of information overload, companies often struggle to effectively identify valuable ideas on their open innovation platforms. In this article, we propose an idea adoption strategy based on machine learning. We used data from a well-known open innovation platform, Salesforce, and extracted characteristic variables using the Information Adoption Model. Four classification models were then constructed based on AdaBoost, Random Forest, SVM and Logistic Regression models. Due to significant differences in the number of positive and negative samples in the OIP, we used the SMOTE method to address the problem of data imbalance. The results of the study showed that the ensemble learning models were more accurate in identifying valuable ideas than the individual machine learning models. When comparing the two ensemble learning models, AdaBoost outperformed Random Forest in predicting both positive and negative class samples. The SMOTE-AdaBoost model achieved a recall of 0.93, a precision of 0.92 and an impressive AUC of 0.98 in identifying adopted ideas, which could well identify valuable ideas and has implications for improving the efficiency and quality of idea adoption in OIP. The shortcoming of this work is that it only investigated a single platform. In the future, we will consider extending this method to different platforms and multiple classification problems. © 2024, Science China Press. All rights reserved.
Keyword ：
AdaBoost AdaBoost ensemble learning ensemble learning information adoption information adoption open innovation platform open innovation platform
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| GB/T 7714 | Xi, Y. , Huang, F. , Huang, L. et al. A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost: Taking Salesforce Platform as an Example [J]. | Journal of Systems Science and Information , 2024 , 12 (4) : 476-490 . |
| MLA | Xi, Y. et al. "A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost: Taking Salesforce Platform as an Example" . | Journal of Systems Science and Information 12 . 4 (2024) : 476-490 . |
| APA | Xi, Y. , Huang, F. , Huang, L. , Liao, X. , Yu, J. . A Study on Idea Adoption Prediction Model Based on SMOTE-AdaBoost: Taking Salesforce Platform as an Example . | Journal of Systems Science and Information , 2024 , 12 (4) , 476-490 . |
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Documents contain abundant information available for managerial decision-making. However, manual methods of screening document information lack accuracy due to the heterogeneity of documents. To address the above issue, we propose a multimodal network combining multivariate semantic association graphs, MMIE, for accurately extracting information from documents. Firstly, the multivariate semantic graphs between multimodal data within each modality are constructed based on the semantic association of text contents, followed by the semantic relationships in the graphs to lead the fusion and embedding of the extracted multimodal data and improve the feature representation capability. Subsequently, the semantically linked multimodal information is fed into the newly constructed multimodal self-attention module to better establish inter-modal associations. Finally, a supervised comparison learning loss function is employed to reduce further the information loss due to sample imbalance. The experimental results on three real datasets show that the proposed model can extract feature information of different modal data more accurately, and the F1 scores reach 87.28%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 82.53%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and 81.17%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} , respectively.
Keyword ：
Deep learning Deep learning Document information extraction Document information extraction Multimodal fusion Multimodal fusion Multivariate semantic association Multivariate semantic association
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| GB/T 7714 | Luo, Shun , Yu, Juan , Xi, Yunjiang . Incorporating multivariate semantic association graphs into multimodal networks for information extraction from documents [J]. | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (13) : 18705-18727 . |
| MLA | Luo, Shun et al. "Incorporating multivariate semantic association graphs into multimodal networks for information extraction from documents" . | JOURNAL OF SUPERCOMPUTING 80 . 13 (2024) : 18705-18727 . |
| APA | Luo, Shun , Yu, Juan , Xi, Yunjiang . Incorporating multivariate semantic association graphs into multimodal networks for information extraction from documents . | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (13) , 18705-18727 . |
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To meet the demand for a large amount of invoice entry work in the financial industry and improve the low accuracy of traditional manual entry, we construct SGFNet, a financial invoice information extraction network that integrates semantic graph associations and multimodal modeling. First, we construct a graph of strong and weak semantic associations between data within each modality based on the correlation of text content. Subsequently, we model the multimodal data in a unified structure, extract the text modal information of invoices along with corresponding image and layout modal information, and guide the fusion and embedding of multimodal data through semantic associations in the graph to produce a richer feature representation. Furthermore, semantically linked multimodal information is fed into an aggregated multimodal self-attention mechanism to establish effective connection between modalities. Finally, with the combination of supervised contrastive learning and smoothed Kullback–Leibler divergence in terms of loss functions, the accuracy degradation problem incurred by sample imbalance and convergence instability is reduced. In our experiments, we achieved F1 scores of 93.71% for the English financial invoice dataset and 96.27% for the Chinese dataset, indicating that the proposed method successfully extracts feature information from different data modalities, thereby achieving satisfactory information extraction results. © 2024
Keyword ：
Deep learning Deep learning Invoice information extraction Invoice information extraction Multimodal modeling Multimodal modeling Semantic graph Semantic graph
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| GB/T 7714 | Luo, S. , Yu, J. . SGFNet: A semantic graph-based multimodal network for financial invoice information extraction [J]. | Expert Systems with Applications , 2024 , 258 . |
| MLA | Luo, S. et al. "SGFNet: A semantic graph-based multimodal network for financial invoice information extraction" . | Expert Systems with Applications 258 (2024) . |
| APA | Luo, S. , Yu, J. . SGFNet: A semantic graph-based multimodal network for financial invoice information extraction . | Expert Systems with Applications , 2024 , 258 . |
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Dealing with the insufficient detection accuracy and speed of aircraft targets in remote sensing images under complex background, this paper proposes a new detection method, YOLOv5-Aircraft, based on the YOLOv5 network. The YOLOv5-Aircraft model is improved in 3 ways: (1) At the beginning and end of original batch normalization module, centering and scaling calibration are added to enhance the effective features and form a more stable feature distribution, which strengthens the feature extraction ability of network model. (2) The cross-entropy loss function in the confidence of the original loss function is improved to the loss function based on smoothed Kullback-Leibler divergence. (3) For reducing information loss, the CSandGlass module is designed on the backbone feature extraction network of YOLOv5 to replace the residual module. Meanwhile, low-resolution feature layers are eliminated to reduce semantic loss. Experiment results demonstrate that the YOLOv5-Aircraft model can enhance the accuracy and speed of aircraft target detection in remote sensing images while achieving easier convergence.
Keyword ：
Aircraft Aircraft aircraft detection aircraft detection batch normalization batch normalization Calibration Calibration Convolutional neural networks Convolutional neural networks Feature extraction Feature extraction loss function loss function Object detection Object detection Real-time systems Real-time systems Remote sensing Remote sensing Remote sensing image Remote sensing image YOLOv5 YOLOv5
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| GB/T 7714 | Luo, Shun , Yu, Juan , Xi, Yunjiang et al. Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv5 [J]. | IEEE ACCESS , 2022 , 10 : 5184-5192 . |
| MLA | Luo, Shun et al. "Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv5" . | IEEE ACCESS 10 (2022) : 5184-5192 . |
| APA | Luo, Shun , Yu, Juan , Xi, Yunjiang , Liao, Xiao . Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv5 . | IEEE ACCESS , 2022 , 10 , 5184-5192 . |
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