# api_taskdetail

author: Srinivasarao U, Sharaff A.
original_link: https://pubmed.ncbi.nlm.nih.gov/37362691/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10107590/pdf/11042_2023_Article_15206.pdf
site: pubmed
created_at: 2025-09-11 06:25:42.921818
summary:
  Sentiment analysis using the inbox message polarity is a challenging task in text mining, this analysis is used to differentiate spam and ham messages in mail. Polarity estimation is mandatory for spam and ham identification, whereas developing a perfect architecture for such classification is the hot demanding topic. To fulfill that, fuzzy based Recurrent Neural network-based Harris Hawk optimization (FRNN-HHO) is introduced, which performs post-classification over the classified messages (spam and ham). Previously the authors tried to classify the spam and ham messages from the collection of SMSs. But sometimes, the spam messages may incorrectly be classified within the ham classes. This misclassification may reduce the accuracy. The sentiment analysis process is performed over the classified messages to improve such classification accuracy. The spam and ham messages from the available data are classified using a Kernel Extreme Learning Machine (KELM) classifier. The sentiment analysis and classification based experimental evaluation is carried out using accuracy, recall, f-measure, precision, RMSE, and MAE. The performance of the proposed architecture is evaluated using threedifferent datasets: SMS, Email, and spam-assassin. The Area under the curve (AUC) of the proposed approach is found to be 0.9699 (SMS dataset), 0.958 (Email dataset), and 0.95 (spam assassin).

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title:
  SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network.


author: AminiMotlagh M, Shahhoseini H, Fatehi N.
original_link: https://pubmed.ncbi.nlm.nih.gov/36532862/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9742011/pdf/13278_2022_Article_998.pdf
site: pubmed
title: A reliable sentiment analysis for classification of tweets in social networks.
created_at: 2025-09-11 06:25:42.922817
summary:
  In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions and determine whether a text is positive, negative, or neutral. In this article, we apply four widely used data mining classifiers, namely K-nearest neighbor, decision tree, support vector machine, and naive Bayes, to analyze the sentiment of the tweets. The analysis is performed on two datasets: first, a dataset with two classes (positive and negative) and then a three-class dataset (positive, negative and neutral). Furthermore, we utilize two ensemble methods to decrease variance and bias of the learning algorithms and subsequently increase the reliability. Also, we have divided the dataset into two parts: training set and testing set with different percentages of data to show the best train-test split ratio. Our results show that support vector machine demonstrates better outcomes compared to other algorithms, showing an improvement of 3.53% on dataset with two-class data and 7.41% on dataset with three-class data in accuracy rate compared to other algorithms. The experiments show that the accuracy of single classifiers slightly outperforms that of ensemble methods; however, they propose more reliable learning models. Results also demonstrate that using 50% of the dataset as training data has almost the same results as 70%, while using tenfold cross-validation can reach better results.

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author: Jiang X, Song C, Xu Y, Li Y, Peng Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/35721405/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9202631/pdf/peerj-cs-08-1005.pdf
site: pubmed
title: Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model.
created_at: 2025-09-11 06:25:42.924329
summary:
  Sentiment analysis of netizens' comments can accurately grasp the psychology of netizens and reduce the risks brought by online public opinion. However, there is currently no effective method to solve the problems of short text, open word range, and sometimes reversed word order in comments. To better solve the above problems, this article proposes a hybrid model of sentiment classification, which is based on bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) and a text convolution neural network (TextCNN) (BERT-BiLSTM-TextCNN). The experimental results show that (1) the hybrid model proposed in this article can better combine the advantages of BiLSTM and TextCNN; it not only captures local correlation while retaining context information but also has high accuracy and stability. (2) The BERT-BiLSTM-TextCNN model can extract important emotional information more flexibly in text and achieve multiclass classification tasks of emotions more accurately. The innovations of this study are as follows: (1) the use of BERT to generate word vectors has the advantages of more prior information and a full combination of contextual semantics; (2) the BiLSTM model, as a bidirectional context mechanism model, can obtain contextual information well; and (3) the TextCNN model can obtain important features well in the problem of text classification, and the combined effect of the three modules can significantly improve the accuracy of emotional multilabel classification.

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author: Zou H, Xiang K.
original_link: https://pubmed.ncbi.nlm.nih.gov/35327909/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8965825/pdf/entropy-24-00398.pdf
site: pubmed
title: Sentiment Classification Method Based on Blending of Emoticons and Short Texts.
created_at: 2025-09-11 06:25:42.924838
summary:
  With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

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author: 段宇锋;谢佳宏;柏萍;龚天阳
date: 2025-09-11 13:26
keywords: 提示工程;;大语言模型;;深度学习模型;;实体关系抽取;;中文医学文本;
source: 数据分析与知识发现
site: 知网
title: 大模型提示工程能够替代经典深度学习模型吗？——基于医学文本实体关系抽取任务的对比研究
created_at: 2025-09-11 15:03:19.265848
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-uPgd1L_msdCpclIRGwtLQB5Ol4DvFd9XWSr1Kl8NEOCx8LK8BAu6161zOskHz-KSm23SbQAfl3qQ_UtJFT_cZ9GQ4BWI42CtPzhtck11EIat7Bou3ZMVZx-4htTJIhDa_BC6mpypPD6iPdY9JFMQh6TTgIzMZ7FxzoqtAD9Wa_w==&uniplatform=NZKPT&language=CHS

summary:
  [目的]探讨在具有较高专业性和领域特色的中文医学文本实体关系抽取任务中，大模型提示工程方法能否替代经典深度学习模型。[方法]本研究使用GLM-4、ERNIE-4-Turbo、DeepSeek-R1三种大模型和CBLUE、CasRel、GPLinker三种经典深度学习模型，通过改变需要抽取的关系类型数量、大模型Prompt中的示例数量，以及经典深度学习模型的训练数据规模，系统比较基于提示工程的大模型与经典深度学习模型的性能差异。使用bert-base和roberta作为经典深度学习模型的编码器。[结果]在CMeIE-V2数据集上的实验结果表明：（1）roberta-CBLUE和roberta-GPLinker的抽取效果最好，在抽取1种关系类型时F1值达到0.5826和0.5853，抽取10种关系类型时F1值为0.5112和0.4934；（2）大模型不擅长同时抽取多种关系。GLM-4、ERNIE-4-Turbo和DeepSeek-R1抽取2种关系类型的F1值比抽取1种关系类型时分别下降0.1182、0.0885、0.1310；（3）在Prompt中添加示例可以提高大模型的抽取效果，但示例数...

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author: 李春辉;马腾腾;孙浩
date: 2025-09-10
source: 通信世界
site: 知网
title: 央（国）企大模型集约共享可行性与挑战分析
created_at: 2025-09-11 15:03:19.268361
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9TRDncUWKrQaol2TGVnhX5HP9R-UCTfmVRJwXGkBLF-IWDXxVVeSg4xRkTfTtZUyr7Xg4VnEjv2oRRz2Q99Id32a_R5DQPyp8W68Pp88gIqTZddFDsnPPmHCCZ_kY_OTB6eg61r9p9D5PnegGQMtU7X5uRJzeh6L80TqjdXkLO9A==&uniplatform=NZKPT&language=CHS

summary:
  <正>目前，国内AI大模型产业正从“百模大战”的“建起来”阶段，向“行业爆发”的“用起来”阶段过渡。权威机构发布的数据显示，2025年一季度国内大模型市场规模预计突破495亿元人民币，但AI大模型落地渗透率仍不足1%。因此，国内企业亟须提高大模型在其核心业务应用的渗透率，而实现央企大模型的集约共享将有效拓展应用生态，完善模型性能。文章重点介绍了国内大模型产业发展趋势与央（国）企布局现状，并分析了央（国）企集约共享大模型的可行性和挑战。国内大模型产业发展趋势与央（国）企布局现状国内大模型产业趋势分析国内大模型产业链分工日渐清晰，头部企业不断提升通用基础大模型的性能并开源；而中小企业由于缺乏资金和高质量数据集，其通用大模型与头部企业的差距不断加大，以致部分中小企业逐渐放弃。

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date: 2025-09-10
source: 交通企业管理
site: 知网
title: 多家单位联合发起交通大模型创新与产业联盟综合交通运输大模型落地再提速
created_at: 2025-09-11 15:03:19.269574
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9TRDncUWKrQaol2TGVnhX5HP9R-UCTfmV-VEdVPUw_ou3SLYDi1ztsRuzpo8D-9HAtjuDdL2x8BJMRoX6GUh9friOISjH9m1z0zaswcBppR4c995Le0PCgORF3_vYzseeG69GbmYYEZ9GNt8pqxB_P-GWcGovIF22A0w73bJLt2A==&uniplatform=NZKPT&language=CHS

summary:
  <正>2025年7月25日，“交通大模型创新与产业联盟”主要发起单位在北京召开发起会，研究部署相关成立事宜。交通运输部科技司、部规划研究院、部科学研究院，中交集团、招商局集团、中远海运集团、中国航信集团、中邮信科集团、山东港口集团、鹏城实验室、智谱华章科技等单位参加。

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author: 赵广立
date: 2025-09-10
source: 中国科学报
site: 知网
title: 无视“规模法则”!类脑脉冲大模型“瞬悉1.0”问世
created_at: 2025-09-11 15:03:19.269574
original_link:
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author: 王斐;曹建鑫;焦锐阳;朱修奇;高睿
date: 2025-09-09 10:48
keywords: 无人艇;;大模型;;智能体;;路径规划;;运动规划;
source: 中国舰船研究
site: 知网
title: 乏信息下基于大模型的无人艇路径规划智能体框架设计与研究
created_at: 2025-09-11 15:03:19.271924
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8FFHXXVBxpdAPyBjFr3X9AIoNvHpfUvmBDGT-XYCnQ41VnoNbgjmiHblz4Ai2UHuibRDPBDuuMJu1xjBnHNLOoopxdVPMif74gvhOoA53QFu6y18GwtWp-zPzseYPFtaftmGnC6j1pbbKOlC4g3Yv0-MiFkNcCnzGD53mUX1HeCg==&uniplatform=NZKPT&language=CHS

summary:
  [目的]针对现有无人艇路径规划算法在认知盲区、感知缺失和语义模糊等“乏信息”环境下高度依赖规则库和高精度传感输入，难以应对突发未知威胁的局限性，提出一种基于大语言模型的智能体框架，探索其在“乏信息”下进行无人艇路径规划的可行性与优势。[方法]通过集成多样化工具函数库（含各类传感器调用接口）及支持自然语言交互的人机交互接口，有效拓展大模型的感知与行动边界。引入长短期记忆机制，同时设计在线与离线相结合的演进策略，实现持续学习与经验积累，使智能体能够从持续交互与经验沉淀中完成自我优化。构建高度结构化的提示工程模板，将身份定位、环境信息、当前状态、传感器观测及置信度、任务目标、思维链提示、ReAct提示、先验常识、决策优先级和条件判断等信息系统整合为提示内容，以此高效引导并约束大模型开展环境感知、动态推理、人机交互及路径生成等工作。[结果]实验结果表明：在静态障碍物环境下，智能体在路径长度、转弯次数、平均转弯角度等关键指标上均优于或不明显逊于A*,RRT,PSO和Hybrid A*等代表性传统算法，综合得分为1，高于其他传统算法（分别为0.47,0.47,0.77,0.76）；在动态障碍物环境...

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author: 宋子钰;陈捷;姜琳琳;夏兆辰;丁海涛
date: 2025-09-09 09:55
keywords: 自动驾驶;;大模型;;决策和规划;
source: 汽车技术
site: 知网
title: 基于大模型的自动驾驶决策与规划研究进展综述
created_at: 2025-09-11 15:03:19.272921
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8FFHXXVBxpdAPyBjFr3X9AIoNvHpfUvmBgqF9PpmUUJYptdsusKwPgE9aieyRGZ7DSY6XpQH9IyPq3BpRV5H6FN3eaXUAFbXDmVY_mW6vOegxkBqA_WDd76EGfp1BF4VtcRld7pStdbdTiTpiGza1TCfNQM06eBgJl_KIlu5wFYw==&uniplatform=NZKPT&language=CHS

summary:
  现有的自动驾驶系统在可解释性、奖励设计与泛化性方面面临诸多挑战，系统地梳理了大语言模型与视觉语言模型在自动驾驶决策与规划中的多角色融合机制与工程实现路径。通过构建统一的角色框架，将大模型划分为决策与规划输出、特征与动作生成、奖励函数建模3类功能模块，并在此基础上分析其在强化学习与模仿学习中的典型融合方法，实现从信息处理到行为输出的全链路赋能机制，并对国内外代表性大模型方法在各环节的最新应用进展进行整合。结合大模型在推理延迟优化、奖励信号自动生成、端到端可解释性、协同推理等实际工程难题中的代表性解决方案，扩展了对奖励模型辅助及多模态特征生成等前沿主题的综述。同时，针对模型稳定性、推理延迟、表示鸿沟以及训练与验证等关键挑战，旨在为突破性的理论创新与工程解决方案提供系统性研究方向。

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author: 公雪;张婧楠;谢中取;刘秉新
date: 2025-09-09 09:52
keywords: 多智能体;;大模型;;教育数字化;;人机协同;;游戏化学习;;交互设计;
source: 现代远距离教育
site: 知网
title: 基于大模型的多智能体协同游戏化学习交互行为及机制研究
created_at: 2025-09-11 15:03:19.273917
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_GkprXS8inLjPGYDqqYETassxxT3yrBwT1Ecys5wBPdFSeRbqoLC_myHm_HOiaxYD8C6EwWqbdd-0z16-SDvVTzcUABlOIymNHklRFH0vSV6PX2LfJUc8wAkI0isD1avlaK8DvtLtKZa_3Sai_mvnw0nR5jEsVLFED0XQ2HeP81w==&uniplatform=NZKPT&language=CHS

summary:
  随着生成式人工智能技术快速迭新，多智能体渐以参与学习者非正式环境的游戏化学习，赋能学习者未来智能非正式教育。为发挥多智能体智能实时性优势，文章对多智能体多维协同学习者游戏化学习状态的进行技术构建与交互设计。针对学习者学习过程中的感官交互、认知交互及游戏交互三个维度的交互特征，挖掘了基于大数据的智导型、智伴型及智管型三类多智能体的技术架构模块、类型及交互机制框架。文章发现，引入大模型的多智能体在学习状态识别模块、多感官互动游戏模块及动态管理学习模块协同下对学习者学习的故事类、科普类、益智类、成长类四类主要学习类型进行游戏化交互，可有效提升学习者游戏化学习多感官体验。

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author: 陆成宽
date: 2025-09-09
keywords: ;;
source: 科技日报
site: 知网
title: 首个全流程国产化类脑脉冲大模型推出
created_at: 2025-09-11 15:03:19.274918
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_GkprXS8inLjPGYDqqYETa5ixLoDlxgiMaccy8iD6EHlzhRAIkKISOR736eSSLgDX2_e9nCDlJATC0dnNrSoQBnzNXSIuwIGjkRezVBXqC0y7sv-48c6UjG1BSYBsbySxGWTS_SWKDnDbkB__D7vzgreyoJLrrZtltJX6PFaWO3A==&uniplatform=NZKPT&language=CHS

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author: 匡亚林;蒙春雨
date: 2025-09-03
keywords: 人工智能;;大模型;;社会救助体系;;数字化转型;;被动响应;;预见性治理;
source: 内蒙古社会科学
site: 知网
title: 迈向智慧救助：AI大模型驱动社会救助数字化转型的实现路径
created_at: 2025-09-11 15:03:19.275872
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_GkprXS8inLjPGYDqqYETalxvnBY4um89IuJtPZ5O3rurobnrdcl1-7bnzj_cLn1xouh22oYuQdx8UCnVsaZqW4cuwycsYW8gWqjJORAK2PWVuoojicYYJL-rrC7H4snXR4W6a8FFnZYpEHo3TcA40nvyzBCwGcvdpqZqyvRkjCw==&uniplatform=NZKPT&language=CHS

summary:
  生成式人工智能引发的认知革命推动社会救助体系从“被动响应”向“预见性治理”跃迁。技术嵌入社会救助面临着数据碎片化、算法可解释性不足、智能系统偏差、技术理性僭越等现实困境。数智化治理工具的引入为破解困境提供了技术解决方案，通过数据重构与算法嵌入可重塑社会救助的治理范式，特别是以AI大模型为代表的新兴技术赋能社会救助具有巨大优势。因此，应构建技术赋能与制度进化的良性互动回路，建立数据治理、算法约束、服务创新、场景适应四重协同机制，以实现救助覆盖广度与精度的帕累托改进。

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author: 贺焱;盖耀辉;李海涛
date: 2025-09-03
keywords: AI大模型;;油田;;安全生产管理;;多模态大模型;;大语言模型;
source: 化工安全与环境
site: 知网
title: AI大模型在油田安全生产管理中应用探讨
created_at: 2025-09-11 15:03:19.275872
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9HGgsKI_MaTaKMtBxrXwuJp7j5hjbEGVM4miK3KNhVTA6CMpCgbbDKMhl5suazgg3Uibjiaz0jpQLzT4DNLrniwr6Sidjy7f473smPtMKY9zv6Uqpe7ZaFgrAyUsJwoDAlFAh5fD-XHsxUOGa_gXcdANrHuxwSAmD9p41Fo5-Taw==&uniplatform=NZKPT&language=CHS

summary:
  随着人工智能技术的飞速发展，大语言模型和多模态大模型在工业领域的应用正逐步深入，尤其在油田安全生产管理这一高风险、高复杂性的行业中展现出巨大潜力。文章系统性地梳理了AI大模型在油田安全生产管理中的关键技术路径、典型应用场景及面临的挑战，并对未来发展方向进行了展望。AI大模型可通过风险预测、智能巡检、应急决策支持等手段显著提升油田安全管理水平，但仍需解决数据孤岛、模型可解释性等行业适配问题。该研究可为油田安全生产管理的智能化转型提供理论参考与实践路径。

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author: 陈斌;李东;周致富
date: 2024年3期
keywords: 自然语言大模型;ChatGPT;新工科;信息化教学;教学改革实践
quote: 0
source: 《江西理工大学学报》
site: 万方
title: 类ChatGPT大模型
created_at: 2025-09-11 15:03:19.277422
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEW5meWp4eXhiMjAyNDAzMDEwGghpaXpyZTg1aA%3D%3D

summary:
  新工科建设是教育部主动应对新一轮科技革命和产业变革的战略行动,是新时代工程教育改革的新方向.信息化教学是新工科教育改革的必然趋势,以ChatGPT为代表的自然语言大模型给新工科信息化教学带来了新的机遇与挑战.本文在对类ChatGPT大模型在新工科信息化教学中的应用进行深入分析的基础上,以《流体机械能转化原理与技术》课程的信息化教学为例,详述了如何通过类ChatGPT大模型开展教学实践,以期为新工科信息化教学改革实践提供可借鉴的经验.

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author: 孙柏林
date: 2024年1期
keywords: 人工智能;大模型;深度学习
quote: 9
source: 《计算机仿真》
site: 万方
title: 大模型评述
created_at: 2025-09-11 15:03:19.279498
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamZ6MjAyNDAxMDAyGghpaXpyZTg1aA%3D%3D

summary:
  大模型是人工智能行业飞速前行的缩影.如今,以大模型、算力等为代表的前沿技术领域不断发展,正在塑造人工智能产业发展的新格局.随着人工智能技术的迅猛发展,AI大模型一直被视为推动人工智能领域提升的关键因素,大模型已成为了引领技术浪潮研究和应用方向.现从何为大模型,大模型的种类,大模型的优势,大模型案例与大模型的未来等方面对于大模型加以评述.

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author: 刘聚海;胡玥;姜喆
date: 2024年4期
keywords: 人工智能;AI大模型;机器学习;深度学习;自然资源;不动产登记;综述
quote: 3
source: 《自然资源信息化》
site: 万方
title: AI大模型综述
created_at: 2025-09-11 15:03:19.280493
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEGd0enl4eGgyMDI0MDQwMDEaCGlpenJlODVo

summary:
  本文通过文献调研方法梳理了人工智能(AI)的发展历史和AI大模型诞生的技术背景,归纳了AI大模型训练的技术方法;通过调研当前国内外主要的AI大模型基本情况,参考AI大模型的主流观点,提出AI大模型发展的基本态势.针对当前AI大模型的能力上限和边界、存在的问题和面临的挑战,借鉴AI大模型行业落地途径及部分应用案例,提出了AI赋能不动产登记的基本思路,即在自然资源行业大模型中外挂不动产向量知识库和不动产知识图谱,通过行业大模型、向量库和知识图谱的协同与优势互补构建不动产登记AI能力底座,再通过AI助手将AI能力嵌入不动产登记系统,实现AI赋能不动产登记全流程、各环节.

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author: 肖建力;邱雪;张扬
date: 2025年1期
keywords: 智能交通;通用大模型;交通大模型;交通安全;自动驾驶
quote: 4
source: 《交通运输工程学报》
site: 万方
title: 交通大模型综述
created_at: 2025-09-11 15:03:19.281493
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWp0eXNnY3hiMjAyNTAxMDAyGghpaXpyZTg1aA%3D%3D

summary:
  对大语言模型(LLMs)技术在交通领域的促进作用进行了深入探讨,展现了其在改善交通管理和控制、提高交通安全以及推动自动驾驶技术发展方面的巨大潜力;系统阐述了 LLMs、视觉大模型、多模态大模型的基本概念及发展历程.针对部分交通LLMs,总结了它们的模型架构和训练方法,探讨了 LLMs在交通领域,如交通管理和控制、交通安全和自动驾驶方面的主要应用.研究结果表明:在交通管理和控制方面,LLM s的应用可改善交通信号控制和交通状态预测等问题,并为城市交通管理带来新的可能性,这不仅能减少交通拥堵,还降低环境污染;在交通安全方面,相比于传统模型,LLMs的应用显著提高交通事故分析和预测能力,通过对历史事故数据的深入学习,模型能够识别出事故高发区域和时段,从而采取预防措施,提高交通安全指数;在自动驾驶领域,传统模型向多模态自动驾驶模型的转变不仅能提高自动驾驶系统的决策和环境适应能力,还会为用户提供更加安全、舒适的驾驶体验.本文挖掘了 LLMs在当今交通领域中的潜力和价值,同时为实现更加智能、高效的交通系统提供了有用的建议,如降低交通LLMs的计算成本,提升模型的实时性和可靠性等.

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author: 刘文军;徐四六
date: 2025年3期
keywords: 大模型;数学物理方法;教学方式;智能平台
quote: 0
source: 《湖北科技学院学报》
site: 万方
title: 大模型赋能高校教育
created_at: 2025-09-11 15:03:19.282493
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3huc3p4YjIwMjUwMzAxNBoIaWl6cmU4NWg%3D

summary:
  随着人工智能的飞速发展,基于生成式算法的大模型在教育实践的应用越发广泛,为大学数学物理方法课程的改革提供了契机.本研究基于北京邮电大学自主研发的"码上"智能教育平台,以数学物理方法课程为例,采取丰富教学内容,优化教学质量以及构造多指标评估体系的策略,完成教学模式改革,最终达到培养复合型人才,创新型人才的目的.

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author: 吴昀徽
date: 2025年12期
keywords: 大语言模型;财务大模型;金蝶;苍穹GPT;财务管理
quote: 0
source: 《中国集体经济》
site: 万方
title: 大模型赋能财务领域
created_at: 2025-09-11 15:03:19.283493
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3pnanRqajIwMjUxMjA1MxoIaWl6cmU4NWg%3D

summary:
  剖析金蝶财务大模型的部署和应用场景,探讨其对财务领域产生的影响.通过SWOT 分析法分析宏微观环境下基于财务大模型金蝶公司的内外部优势、劣势、机会和不足,并借鉴谷歌、微软等企业的技术方案,探讨苍穹GPT财务大模型未来的发展思路.随着大模型技术的引入,财务管理领域的数据处理能力得到了显著的拓展,该技术不仅覆盖了传统的结构化财务数据处理,更将其触角延伸至非结构化业务数据的处理范畴,极大地丰富了财务人员的数据管理职能.金蝶应加速出海战略,进行跨行业合作,随着人工智能领域大模型技术的不断演进,可以预见这一技术将在财务预测、预算编制、投资评估等多个财务管理环节发挥更加关键的作用,进一步推动财务管理向智能化、精准化方向发展.

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author: 张青龙;韩锐;刘驰
date: 2025年2期
keywords: 大模型;边缘侧动态环境;模型重训;缩放定律;云边大小模型协同训练
quote: 0
source: 《电子学报》
site: 万方
title: 云边协同大模型块粒度重训方法
created_at: 2025-09-11 15:03:19.284494
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWRpYW56aXhiMjAyNTAyMDAxGghpaXpyZTg1aA%3D%3D

summary:
  边缘侧大模型外部环境的不确定性(如路边摄像头画面中天气、光照、物体密度的变化),导致其输入数据分布持续改变,因此需进行重训以维持高精度.受限于设备可用资源和重训窗口,现有技术仅能训练固定压缩模型,其有限的泛化能力导致模型精度显著降低.本文提出云边协同大模型块粒度重训方法,引入模型重训缩放定律评估不同块对边缘侧当前数据的精度贡献,以此为依据生成有限资源下最优重训方案,将云平台大模型中精度最相关部分动态转换为边缘侧可重训小模型,构建大小模型协同训练系统.真实云边平台上对比实验表明,本文方法可以在相同资源消耗下提升大模型重训精度81.24%,并支持最大至330亿参数大模型重训.

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author: 钱波;李富江;郑常乐
date: 2025年3期
keywords: 医疗大模型;人工智能;预训练模型;辅助医疗
quote: 0
source: 《数据采集与处理》
site: 万方
title: 医疗大模型发展现状与展望
created_at: 2025-09-11 15:03:19.286494
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEHNqY2p5Y2wyMDI1MDMwMDIaCGlpenJlODVo

summary:
  医疗大模型是大规模预训练模型技术在医疗领域的重要应用成果,已成为智能辅助医疗的重要研究方向.通过在海量医学数据上进行预训练,这类模型展现出跨任务迁移、多模态理解和复杂推理等关键能力,突破了传统神经网络在医学应用中的多项限制.借助这些能力,医疗大模型正在重塑辅助诊断、病例报告生成和医学影像分析等核心任务的实现路径,对实现医疗"通用智能"具有深远意义.基于此,本文对医疗大模型的发展现状与未来趋势进行综述.首先,回顾了医疗人工智能模型在人工智能快速演进背景下的发展历程;其次,重点介绍了大模型在病理学、眼科和脑疾病等医学子领域的研究进展;最后探讨了当前医疗大模型面临的挑战,并展望其未来的发展方向.

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author: 包泽芃;钱铁云
date: 2025年1期
keywords: 红队;大模型安全;强化学习;语言模型;越狱
quote: 0
source: 《计算机科学》
site: 万方
title: 大模型红队测试研究综述
created_at: 2025-09-11 15:03:19.288008
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamt4MjAyNTAxMDAyGghpaXpyZTg1aA%3D%3D

summary:
  大模型红队测试(Large Model Red Teaming)旨在让大语言模型(Large Language Model,LLM)接收对抗测试,从而诱使模型输出有害的测试用例,进而发现模型中的漏洞并提高其鲁棒性.大模型红队测试是大模型领域的前沿课题,近年来受到学术界和工业界的广泛关注.研究者们针对大模型红队测试提出了众多解决方案,并在模型对齐上取得了一定进展.然而,受限于大模型红队数据的短缺和评价标准的模糊,现有研究大多局限于针对特定的场景进行评估.文中首先从与大模型安全相关的定义出发,对其所涉及的各种风险进行阐述;其次,针对大模型红队测试的重要性及其主要类别进行了阐述,综述和分析了相关红队技术的发展历程,并介绍了已有的数据集和评价指标;最后,对大模型红队测试的未来发展趋势进行了展望和总结.

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author: 高奇琦
date: 2025年1期
keywords: 大模型治理;共识性治理;主体间性;主体性
quote: 1
source: 《山西大学学报（哲学社会科学版）》
site: 万方
title: 主体间性视域下的大模型共识性治理
created_at: 2025-09-11 15:03:19.289020
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFHN4ZHh4Yi16eHNoMjAyNTAxMDA3GghpaXpyZTg1aA%3D%3D

summary:
  目前关于大模型治理的理解呈现出碎片化和主体性的特征,可以从企业治理、国家治理和全球治理三个层面来思考大模型的治理问题.其中,在企业层面,企业治理不足以形成有效的治理手段和结果;在国家层面,国家往往需要取悦领先企业,并且自身数字能力不足,应对变化相对缓慢;在全球层面,全球政府本身是缺位的.巨大的负外部性可能会外溢到发展中国家,同时各国竞速导致恶性竞争和全球共识缺乏.对于超大模型的全球治理可以在一定程度上借鉴核不扩散机制的经验.目前,大模型治理中不同的主体往往采取主体视角,而共识性治理的关键是,相关各方都要采取一种主体间性视角,并走向视域融合.共识性治理是要走向真正的共识性对齐,其中包括人类社会的对齐和人机对齐,而人向机器的对齐最终还要返回到人的主体性建设上来.

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author: Hugo Lourenço;Rui Eugénio
date: 2019
original_link: https://ieeexplore.ieee.org/document/8904623/
site: ieeeXplore
title: TrueChange (TM) Under the Hood: How We Check the Consistency of Large Models (Almost) Instantly
created_at: 2025-09-11 15:03:19.290648
summary:
  The OutSystems Platform is a visual model-driven development and delivery platform that allows developers to create enterprise-grade web and mobile applications. The models created with the platform are translated by its compiler into a set of standard-technology artifacts (C\#, JavaScript, SQL, etc). The model must be checked for consistency (i.e., that it is well-formed and well-typed) before compilation can proceed. Our Integrated Development Environment (IDE) does this in real-time: after each change made a developer, the IDE either automatically heals the other parts of the model that are impacted by the change, or provides immediate feedback on the errors that must be manually corrected. It is not uncommon for an OutSystems model to contain in excess of 200,000 individual elements. Handling large models efficiently is thus of paramount importance: consistency checks must run as fast as possible, otherwise the developer's experience is significantly impaired. In this paper we present the techniques we have developed to speed up consistency checks, and which resulted in the TrueChange engine. We use an incremental approached paired with automatically managed back pointers. We believe these techniques are of general application and not limited to our particular case.

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author: Qurat Ul Ain Ali
date: 2021
original_link: https://ieeexplore.ieee.org/document/9643677/
site: ieeeXplore
title: Heterogeneous Model Query Optimisation
created_at: 2025-09-11 15:03:19.291650
summary:
  With the growing size and complexity of software systems, the underlying models also grow in size proportionally. These large-scale models pose scalability issues for model-driven engineering technologies. These models can be persisted in various backend technologies (such as file systems, document and relational databases) and can be represented in different formats such as XMI and Flexmi. Several tailored high-level model management languages such as OCL and EOL enable developers to work on different backend technologies in a uniform way by shielding them from the complexities of different backends. On the contrary, performance with respect to execution time in tailored model management languages programs becomes one of the major scalability bottlenecks. In this work, we propose an architecture built on top of existing model query languages to facilitate query optimisation. The proposed approach will benefit from compile-time static analysis and automatic program rewriting to optimise queries operating over heterogeneous backend technologies. Optimisation strategies and performance will vary depending on the type of queries and the backend modelling technology. We expect to significantly improve performance (decrease in one order of magnitude of execution time) for model management programs, particularly over large-scale models.

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author: Manfred A. Jeusfeld;Ulrich Frank
date: 2021
original_link: https://ieeexplore.ieee.org/document/9643789/
site: ieeeXplore
title: Unifying multi-level modeling: A position paper
created_at: 2025-09-11 15:03:19.292649
summary:
  Multi-level modeling (MLM) as part of object-oriented modeling aims at fully utilizing the expressive power of multiple abstraction levels. While these levels where initially used to define domain-specific modeling languages, i.e. for linguistic purposes, the MLM community has long argued that there is much more to gain by tapping into ontological abstraction levels. While MLM is a rather specialized research field, there are now quite a number of different proposals. There is thus an opportunity to develop a uniform core of MLM that then possibly can become part of a standard and be taken up by the larger modeling community.

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author: David Delgado;Lola Burgueño;Javier Cámara;Javier Troya
date: 2023
original_link: https://ieeexplore.ieee.org/document/10350808/
site: ieeeXplore
title: Towards an Extensible Architecture and Tool Support for Model-Based Verification
created_at: 2025-09-11 15:03:19.293651
summary:
  Model-based software engineering (MBSE) brings models to the center of software and system design. Models are powerful abstractions used to support all phases of the software development life cycle of complex software. As these models grow larger and their complexity increases, they need to be verified and validated to preserve their correctness. One possible way to do so is by means of the use of formal methods. However, the availability of MBSE tools with support for validation and verification is limited, and they usually require the cumbersome deployment of software burdened by dependencies, preventing the adoption of these tools. This paper presents a web-based architecture designed to support the definition of domain models and provide translation capabilities to different verification formalisms. As a proof of concept for our architecture, we have developed a tool prototype that is light-weight, runs in the browser and supports: (i) definition of domain models represented as class diagrams and (ii) partial translation of class diagrams into the Alloy specification language, enabling verification of structural domain properties. We show how we have used this tool to verify properties for the public bus management system in the city of Málaga, Spain.

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author: Qurat Ul Ain Ali;Benedek Horváth;Dimitris Kolovos;Konstantinos Barmpis;Ákos Horváth
date: 2021
original_link: https://ieeexplore.ieee.org/document/9643785/
site: ieeeXplore
title: Towards Scalable Validation of Low-Code System Models: Mapping EVL to VIATRA Patterns
created_at: 2025-09-11 15:03:19.295649
summary:
  Adoption of low-code engineering in complex enterprise applications also increases the size of the underlying models. In such cases, the increasing complexity of the applications and the growing size of the underlying artefacts, various scalability challenges might arise for low-code platforms. Task-specific programming languages, such as OCL and EOL, are tailored to manage the underlying models. Existing model management languages have significant performance impact when it comes to complex queries operating over large-scale models reaching magnitudes of millions of elements in size. We propose an approach for automatically mapping expressions in Epsilon validation programs to VIATRA graph patterns to make the validation of large-scale low-code system models scalable by leveraging the incremental execution engine of VIATRA. Finally, we evaluate the performance of the proposed approach on large Java models of the Eclipse source code. Our results show performance speed-up up to 1481x compared to the sequential execution in Epsilon.

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author: Shuo Wang;Chihang Wang;Jia Gao;Zhen Qi;Hongye Zheng;Xiaoxuan Liao
date: 2025
original_link: https://ieeexplore.ieee.org/document/11063887/
site: ieeeXplore
title: Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models
created_at: 2025-09-11 15:03:19.298157
summary:
  This study proposes a knowledge distillation algorithm based on large language models and feature alignment, aiming to effectively transfer the knowledge of large pre-trained models into lightweight student models, thereby reducing computational costs while maintaining high model performance. performance. Different from the traditional soft label distillation method, this method introduces a multi-layer feature alignment strategy to deeply align the intermediate features and attention mechanisms of the teacher model and the student model, maximally retaining the semantic expression ability and context modeling ability of the teacher model. In terms of method design, a multi-task loss function is constructed, including feature matching loss, attention alignment loss, and output distribution matching loss, to ensure multi-level information transfer through joint optimization. The experiments were comprehensively evaluated on the GLUE data set and various natural language processing tasks. The results show that the proposed model performs very close to the state-of-the-art GPT-4 model in terms of evaluation indicators such as perplexity, BLEU, ROUGE, and CER. At the same time, it far exceeds baseline models such as DeBERTa, XLNet, and GPT-3, showing significant performance improvements and computing efficiency advantages. Research results show that the feature alignment distillation strategy is an effective model compression method that can significantly reduce computational overhead and storage requirements while maintaining model capabilities. Future research can be further expanded in the directions of self-supervised learning, crossmodal feature alignment, and multi-task transfer learning to provide more flexible and efficient solutions for the deployment and optimization of deep learning models.

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author: Bor-Sung Liang
date: 2023
original_link: https://ieeexplore.ieee.org/document/10134466/
site: ieeeXplore
created_at: 2025-09-11 15:03:19.301178
summary:
  The development of AI computing has reached a critical inflection point. The scale of large-scale AI neural network model parameters has grown rapidly to “pre-trillion-scale” level. The computing needs of training large-scale AI neural network models have reached “exa-scale” level. Besides, AI Foundation Model also affects the correctness of AI applications, and becoming a new information security issue. Future AI development will be pushed by progress of computing power (supercomputer), algorithm (neural network model and parameter scale), and application (foundation model and downstream fine tuning). In particular, the computational efficiency of AI will be a key factor in the commercialization and popularization of AI applications.

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title:
  AI Computing in Large-Scale Era: Pre-trillion-scale Neural Network Models and Exa-scale Supercomputing


author: Yuhong Zhang;Shilai Yang;Gert Cauwenberghs;Tzyy-Ping Jung
date: 2024
original_link: https://ieeexplore.ieee.org/document/10781627/
site: ieeeXplore
title: From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking
created_at: 2025-09-11 15:03:19.303177
summary:
  Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading. The code and data are available in github.

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author: Sathurshan Arulmohan;Marie-Jean Meurs;Sébastien Mosser
date: 2023
original_link: https://ieeexplore.ieee.org/document/10350787/
site: ieeeXplore
title: Extracting Domain Models from Textual Requirements in the Era of Large Language Models
created_at: 2025-09-11 15:03:19.304180
summary:
  Requirements Engineering is a critical part of the software lifecycle, describing what a given piece of software will do (functional) and how it will do it (non-functional). Requirements documents are often textual, and it is up to software engineers to extract the relevant domain models from the text, which is an error-prone and time-consuming task. Considering the recent attention gained by Large Language Models (LLMs), we explored how they could support this task. This paper investigates how such models can be used to extract domain models from agile product backlogs and compare them to (i) a state-of-practice tool as well as (ii) a dedicated Natural Language Processing (NLP) approach, on top of a reference dataset of 22 products and 1, 679 user stories. Based on these results, this paper is a first step towards using LLMs and/or tailored NLP to support automated requirements engineering thanks to model extraction using artificial intelligence.

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author: Chin-Po Chen;Jeng-Lin Li
date: 2024
original_link: https://ieeexplore.ieee.org/document/10782161/
site: ieeeXplore
created_at: 2025-09-11 15:03:19.305181
summary:
  Alzheimer’s disease (AD) stands as the predominant cause of dementia, characterized by a gradual decline in speech and language capabilities. Recent deeplearning advancements have facilitated automated AD detection through spontaneous speech. However, common transcript-based detection methods directly model text patterns in each utterance without a global view of the patient’s linguistic characteristics, resulting in limited discriminability and interpretability. Despite the enhanced reasoning abilities of large language models (LLMs), there remains a gap in fully harnessing the reasoning ability to facilitate AD detection and model interpretation. Therefore, we propose a patient-level transcript profiling framework leveraging LLM-based reasoning augmentation to systematically elicit linguistic deficit attributes. The summarized embeddings of the attributes are integrated into an Albert model for AD detection. The framework achieves 8.51% ACC and 8.34% F1 improvements on the ADReSS dataset compared to the baseline without reasoning augmentation. Our further analysis shows the effectiveness of our identified linguistic deficit attributes and the potential to use LLM for AD detection interpretation.

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title:
  Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer’s Disease Detection


date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08827
pdf_url: https://arxiv.org/pdf/2509.08827
site: arxiv
title: A Survey of Reinforcement Learning for Large Reasoning Models
created_at: 2025-09-11 15:03:19.306181
author:
  Kaiyan Zhang;Yuxin Zuo;Bingxiang He;Youbang Sun;Runze Liu;Che Jiang;Yuchen Fan;Kai Tian;Guoli Jia;Pengfei Li;Yu Fu;Xingtai Lv;Yuchen Zhang;Sihang Zeng;Shang Qu;Haozhan Li;Shijie Wang;Yuru Wang;Xinwei Long;Fangfu Liu;Xiang Xu;Jiaze Ma;Xuekai Zhu;Ermo Hua;Yihao Liu

summary:
  Abstract:In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain, reassess its trajectory, and explore strategies to enhance the scalability of RL toward Artificial SuperIntelligence (ASI). In particular, we examine research applying RL to LLMs and LRMs for reasoning abilities, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area. We hope this review will promote future research on RL for broader reasoning models. Github: this https URL

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date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08826
pdf_url: https://arxiv.org/pdf/2509.08826
site: arxiv
title: RewardDance: Reward Scaling in Visual Generation
created_at: 2025-09-11 15:03:19.308204
author:
  Jie Wu;Yu Gao;Zilyu Ye;Ming Li;Liang Li;Hanzhong Guo;Jie Liu;Zeyue Xue;Xiaoxia Hou;Wei Liu;Yan Zeng;Weilin Huang

summary:
  Abstract:Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It primarily due to fundamental limitations in existing approaches: CLIP-based RMs suffer from architectural and input modality constraints, while prevalent Bradley-Terry losses are fundamentally misaligned with the next-token prediction mechanism of Vision-Language Models (VLMs), hindering effective scaling. More critically, the RLHF optimization process is plagued by Reward Hacking issue, where models exploit flaws in the reward signal without improving true quality. To address these challenges, we introduce RewardDance, a scalable reward modeling framework that overcomes these barriers through a novel generative reward paradigm. By reformulating the reward score as the model's probability of predicting a "yes" token, indicating that the generated image outperforms a reference image according to specific criteria, RewardDance intrinsically aligns reward objectives with VLM architectures. This alignment unlocks scaling across two dimensions: (1) Model Scaling: Systematic scaling of RMs up to 26 billion parameters; (2) Context Scaling: Integration of task-specific instructions, reference examples, and chain-of-thought (CoT) reasoning. Extensive experiments demonstrate that RewardDance significantly surpasses state-of-the-art methods in text-to-image, text-to-video, and image-to-video generation. Crucially, we resolve the persistent challenge of "reward hacking": Our large-scale RMs exhibit and maintain high reward variance during RL fine-tuning, proving their resistance to hacking and ability to produce diverse, high-quality outputs. It greatly relieves the mode collapse problem that plagues smaller models.

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date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08825
pdf_url: https://arxiv.org/pdf/2509.08825
site: arxiv
title: Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
created_at: 2025-09-11 15:03:19.309211
author:
  Joachim Baumann;Paul Röttger;Aleksandra Urman;Albert Wendsjö;Flor Miriam Plaza-del-Arco;Johannes B. Gruber;Dirk Hovy

summary:
  Abstract:Large language models (LLMs) are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outputs vary significantly depending on the implementation choices made by researchers (e.g., model selection, prompting strategy, or temperature settings). Such variation can introduce systematic biases and random errors, which propagate to downstream analyses and cause Type I, Type II, Type S, or Type M errors. We call this LLM hacking.

  We quantify the risk of LLM hacking by replicating 37 data annotation tasks from 21 published social science research studies with 18 different models. Analyzing 13 million LLM labels, we test 2,361 realistic hypotheses to measure how plausible researcher choices affect statistical conclusions. We find incorrect conclusions based on LLM-annotated data in approximately one in three hypotheses for state-of-the-art models, and in half the hypotheses for small language models. While our findings show that higher task performance and better general model capabilities reduce LLM hacking risk, even highly accurate models do not completely eliminate it. The risk of LLM hacking decreases as effect sizes increase, indicating the need for more rigorous verification of findings near significance thresholds. Our extensive analysis of LLM hacking mitigation techniques emphasizes the importance of human annotations in reducing false positive findings and improving model selection. Surprisingly, common regression estimator correction techniques are largely ineffective in reducing LLM hacking risk, as they heavily trade off Type I vs. Type II errors.

  Beyond accidental errors, we find that intentional LLM hacking is unacceptably simple. With few LLMs and just a handful of prompt paraphrases, anything can be presented as statistically significant.

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author: Thales Sales Almeida;Rodrigo Nogueira;Helio Pedrini
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08824
pdf_url: https://arxiv.org/pdf/2509.08824
site: arxiv
created_at: 2025-09-11 15:03:19.311212
summary:
  Abstract:The performance of large language models (LLMs) is deeply influenced by the quality and composition of their training data. While much of the existing work has centered on English, there remains a gap in understanding how to construct effective training corpora for other languages. We explore scalable methods for building web-based corpora for LLMs. We apply them to build a new 120B token corpus in Portuguese that achieves competitive results to an industrial-grade corpus. Using a continual pretraining setup, we study how different data selection and preprocessing strategies affect LLM performance when transitioning a model originally trained in English to another language. Our findings demonstrate the value of language-specific filtering pipelines, including classifiers for education, science, technology, engineering, and mathematics (STEM), as well as toxic content. We show that adapting a model to the target language leads to performance improvements, reinforcing the importance of high-quality, language-specific data. While our case study focuses on Portuguese, our methods are applicable to other languages, offering insights for multilingual LLM development.

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title:
  Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora


author: Jenna Kang;Maria Silva;Patsorn Sangkloy;Kenneth Chen;Niall Williams;Qi Sun
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08818
pdf_url: https://arxiv.org/pdf/2509.08818
site: arxiv
title: GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts
created_at: 2025-09-11 15:03:19.313731
summary:
  Abstract:Recent advances in probabilistic generative models have extended capabilities from static image synthesis to text-driven video generation. However, the inherent randomness of their generation process can lead to unpredictable artifacts, such as impossible physics and temporal inconsistency. Progress in addressing these challenges requires systematic benchmarks, yet existing datasets primarily focus on generative images due to the unique spatio-temporal complexities of videos. To bridge this gap, we introduce GeneVA, a large-scale artifact dataset with rich human annotations that focuses on spatio-temporal artifacts in videos generated from natural text prompts. We hope GeneVA can enable and assist critical applications, such as benchmarking model performance and improving generative video quality.

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author: Cheng Chen;Haiyan Yin;Ivor Tsang
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08809
pdf_url: https://arxiv.org/pdf/2509.08809
site: arxiv
created_at: 2025-09-11 15:03:19.314731
summary:
  Abstract:Large Language Models (LLMs), when paired with prompt-based tasks, have significantly reduced data annotation costs and reliance on human annotators. However, evaluating the quality of their annotations remains challenging in dynamic, unsupervised environments where oracle feedback is scarce and conventional methods fail. To address this challenge, we propose a novel agentic annotation paradigm, where a student model collaborates with a noisy teacher (the LLM) to assess and refine annotation quality without relying on oracle feedback. The student model, acting as an unsupervised feedback mechanism, employs a user preference-based majority voting strategy to evaluate the consistency of the LLM outputs. To systematically measure the reliability of LLM-generated annotations, we introduce the Consistent and Inconsistent (CAI) Ratio, a novel unsupervised evaluation metric. The CAI Ratio not only quantifies the annotation quality of the noisy teacher under limited user preferences but also plays a critical role in model selection, enabling the identification of robust LLMs in dynamic, unsupervised environments. Applied to ten open-domain NLP datasets across four LLMs, the CAI Ratio demonstrates a strong positive correlation with LLM accuracy, establishing it as an essential tool for unsupervised evaluation and model selection in real-world settings.

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title:
  Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals


date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08803
pdf_url: https://arxiv.org/pdf/2509.08803
site: arxiv
title: Scaling Truth: The Confidence Paradox in AI Fact-Checking
created_at: 2025-09-11 15:03:19.315732
author:
  Ihsan A. Qazi;Zohaib Khan;Abdullah Ghani;Agha A. Raza;Zafar A. Qazi;Wassay Sajjad;Ayesha Ali;Asher Javaid;Muhammad Abdullah Sohail;Abdul H. Azeemi

summary:
  Abstract:The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages. Our methodology tests model generalizability on claims postdating training cutoffs and four prompting strategies mirroring both citizen and professional fact-checker interactions, with over 240,000 human annotations as ground truth. Findings reveal a concerning pattern resembling the Dunning-Kruger effect: smaller, accessible models show high confidence despite lower accuracy, while larger models demonstrate higher accuracy but lower confidence. This risks systemic bias in information verification, as resource-constrained organizations typically use smaller models. Performance gaps are most pronounced for non-English languages and claims originating from the Global South, threatening to widen existing information inequalities. These results establish a multilingual benchmark for future research and provide an evidence base for policy aimed at ensuring equitable access to trustworthy, AI-assisted fact-checking.

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author: Hungjui Yu;Lander Ver Hoef;Kristen L. Rasmussen;Imme Ebert-Uphoff
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08802
pdf_url: https://arxiv.org/pdf/2509.08802
site: arxiv
created_at: 2025-09-11 15:03:19.317246
summary:
  Abstract:Global climate models (GCMs), typically run at ~100-km resolution, capture large-scale environmental conditions but cannot resolve convection and cloud processes at kilometer scales. Convection-permitting models offer higher-resolution simulations that explicitly simulate convection but are computationally expensive and impractical for large ensemble runs. This study explores machine learning (ML) as a bridge between these approaches. We train simple, pixel-based neural networks to predict convective storm frequency from environmental variables produced by a regional convection-permitting model. The ML models achieve promising results, with structural similarity index measure (SSIM) values exceeding 0.8, capturing the diurnal cycle and orographic convection without explicit temporal or spatial coordinates as input. Model performance declines when fewer input features are used or specific regions are excluded, underscoring the role of diverse physical mechanisms in convective activity. These findings highlight ML potential as a computationally efficient tool for representing convection and as a means of scientific discovery, offering insights into convective processes. Unlike convolutional neural networks, which depend on spatial structure and grid size, the pixel-based model treats each grid point independently, enabling value-to-value prediction without spatial context. This design enhances adaptability to resolution changes and supports generalization to unseen environmental regimes, making it particularly suited for linking environmental conditions to convective features and for application across diverse model grids or climate scenarios.

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title:
  Using machine learning to downscale coarse-resolution environmental variables for understanding the spatial frequency of convective storms


author: Byeonghee Lee;Joonsung Kang
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08788
pdf_url: https://arxiv.org/pdf/2509.08788
site: arxiv
title: Doubly robust average treatment effect estimation for survival data
created_at: 2025-09-11 15:03:19.319777
summary:
  Abstract:Considering censored outcomes in survival analysis can lead to quite complex results in the model setting of causal inference. Causal inference has attracted a lot of attention over the past few years, but little research has been done on survival analysis. Even for the only research conducted, the machine learning method was considered assuming a large sample, which is not suitable in that the actual data are high dimensional low sample size (HDLSS) method. Therefore, penalty is considered for numerous covariates, and the relationship between these covariates and treatment variables is reflected as a covariate balancing property score (CBPS). It also considers censored results. To this end, we will try to solve the above-mentioned problems by using penalized empirical likelihood, which considers both estimating equation and penalty. The proposed average treatment effect (ATE) estimator possesses the oracle property, exhibiting key characteristics such as double robustness for unbiasedness, sparsity in model selection, and asymptotic normality.

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author: Eric Slyman;Mehrab Tanjim;Kushal Kafle;Stefan Lee
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08777
pdf_url: https://arxiv.org/pdf/2509.08777
site: arxiv
title: Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensembles
created_at: 2025-09-11 15:03:19.321776
summary:
  Abstract:Multimodal large language models (MLLMs) are increasingly used to evaluate text-to-image (TTI) generation systems, providing automated judgments based on visual and textual context. However, these "judge" models often suffer from biases, overconfidence, and inconsistent performance across diverse image domains. While prompt ensembling has shown promise for mitigating these issues in unimodal, text-only settings, our experiments reveal that standard ensembling methods fail to generalize effectively for TTI tasks. To address these limitations, we propose a new multimodal-aware method called Multimodal Mixture-of-Bayesian Prompt Ensembles (MMB). Our method uses a Bayesian prompt ensemble approach augmented by image clustering, allowing the judge to dynamically assign prompt weights based on the visual characteristics of each sample. We show that MMB improves accuracy in pairwise preference judgments and greatly enhances calibration, making it easier to gauge the judge's true uncertainty. In evaluations on two TTI benchmarks, HPSv2 and MJBench, MMB outperforms existing baselines in alignment with human annotations and calibration across varied image content. Our findings highlight the importance of multimodal-specific strategies for judge calibration and suggest a promising path forward for reliable large-scale TTI evaluation.

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author: Bae HJ, Lozano-Durán A, Bose ST, Moin P.
original_link: https://pubmed.ncbi.nlm.nih.gov/31631905/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC6800713/pdf/nihms-1050971.pdf
site: pubmed
title: Dynamic slip wall model for large-eddy simulation.
created_at: 2025-09-11 15:03:19.323777
summary:
  Wall modelling in large-eddy simulation (LES) is necessary to overcome the prohibitive near-wall resolution requirements in high-Reynolds-number turbulent flows. Most existing wall models rely on assumptions about the state of the boundary layer and require a priori prescription of tunable coefficients. They also impose the predicted wall stress by replacing the no-slip boundary condition at the wall with a Neumann boundary condition in the wall-parallel directions while maintaining the no-transpiration condition in the wall-normal direction. In the present study, we first motivate and analyse the Robin (slip) boundary condition with transpiration (non-zero wall-normal velocity) in the context of wall-modelled LES. The effect of the slip boundary condition on the one-point statistics of the flow is investigated in LES of turbulent channel flow and a flat-plate turbulent boundary layer. It is shown that the slip condition provides a framework to compensate for the deficit or excess of mean momentum at the wall. Moreover, the resulting non-zero stress at the wall alleviates the well-known problem of the wall-stress under-estimation by current subgrid-scale (SGS) models (Jiménez & Moser, AIAA J., vol. 38 (4), 2000, pp. 605-612). Second, we discuss the requirements for the slip condition to be used in conjunction with wall models and derive the equation that connects the slip boundary condition with the stress at the wall. Finally, a dynamic procedure for the slip coefficients is formulated, providing a dynamic slip wall model free of a priori specified coefficients. The performance of the proposed dynamic wall model is tested in a series of LES of turbulent channel flow at varying Reynolds numbers, non-equilibrium three-dimensional transient channel flow and a zero-pressure-gradient flat-plate turbulent boundary layer. The results show that the dynamic wall model is able to accurately predict one-point turbulence statistics for various flow configurations, Reynolds numbers and grid resolutions.

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author: Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR.
original_link: https://pubmed.ncbi.nlm.nih.gov/34619769/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8769694/pdf/bbab387.pdf
site: pubmed
title: A protocol for dynamic model calibration.
created_at: 2025-09-11 15:03:19.325776
summary:
  Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.

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author: Krinner G, Flanner MG.
original_link: https://pubmed.ncbi.nlm.nih.gov/30181268/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC6156650/pdf/pnas.201807912.pdf
site: pubmed
title: Striking stationarity of large-scale climate model bias patterns under strong climate change.
created_at: 2025-09-11 15:03:19.327282
summary:
  Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very large climate change the bias patterns of key climate variables exhibit a striking degree of stationarity. Using only correlation with a model's preindustrial bias pattern, a model's 4xCO2 bias pattern is objectively and correctly identified among a large model ensemble in almost all cases. This outcome would be exceedingly improbable if bias patterns were independent of climate state. A similar result is also found for bias patterns in two historical periods. This provides compelling and heretofore missing justification for using such models to quantify climate perturbation patterns and for selecting well-performing models for regional downscaling. Furthermore, it opens the way to extending bias corrections to perturbed states, substantially broadening the range of justified applications of climate models.

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author: Palmisano A, Hoops S, Watson LT, Jones TC Jr, Tyson JJ, Shaffer CA.
original_link: https://pubmed.ncbi.nlm.nih.gov/26704692/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4690432/pdf/12918_2015_Article_237.pdf
site: pubmed
title: JigCell Run Manager (JC-RM): a tool for managing large sets of biochemical model parametrizations.
created_at: 2025-09-11 15:03:19.329508
summary:
  Background: Most biomolecular reaction modeling tools allow users to build models with a single list of parameter values. However, a common scenario involves different parameterizations of the model to account for the results of related experiments, for example, to define the phenotypes for a variety of mutations (gene knockout, over expression, etc.) of a specific biochemical network. This scenario is not well supported by existing model editors, forcing the user to manually generate, store, and maintain many variations of the same model.

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author: Kaplan D, Lee C.
original_link: https://pubmed.ncbi.nlm.nih.gov/29642717/
site: pubmed
created_at: 2025-09-11 15:03:19.332882
summary:
  This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

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title:
  Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.


author: Chen M.
original_link: https://pubmed.ncbi.nlm.nih.gov/40025223/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11873238/pdf/41598_2025_Article_91877.pdf
site: pubmed
created_at: 2025-09-11 15:03:19.334841
summary:
  Bridge life cycle management is a collaborative work process involving multiple groups, and finite element analysis is a popular method used in bridge management. The frequent establishment of finite element models by diverse groups directly reduces the efficiency and quality of bridge management. To address this issue, a large model of bridge model transformation was developed by integrating domain knowledge based on existing general large models, and data transformation and data recombination were proposed. Simulation investigation demonstrates that the proposed bridge modeling approach may generate finite element models that fulfill the various needs of participating organizations based on their specific criteria. The domain knowledge base supports the data transformation algorithm, which calls the Pangu Large Model's scientific computing model to enable autonomous data translation, minimizing the amount of time spent manually participating in the modeling process. The seismic study of the bridge reveals that the finite element model generated using data recombination differs by 5.93% from the analytical results of the manually established finite element model. Overall, data transformation and recombination modeling methods have greatly increased bridge modeling efficiency, reducing the need for duplicate modeling and improving bridge management efficiency.

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title:
  Data transformation and recombination based on large model: design of multi-group personalized Bridge models.


author: Blokpoel M.
original_link: https://pubmed.ncbi.nlm.nih.gov/28653371/
site: pubmed
title: Sculpting Computational-Level Models.
created_at: 2025-09-11 15:03:19.335837
summary:
  In this commentary, I advocate for strict relations between Marr's levels of analysis. Under a strict relationship, each level is exactly implemented by the subordinate level. This yields two benefits. First, it brings consistency for multilevel explanations. Second, similar to how a sculptor chisels away superfluous marble, a modeler can chisel a computational-level model by applying constraints. By sculpting the model, one restricts the (potentially infinitely large) set of possible algorithmic- and implementational-level theories.

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author: Cowan AE, Mendes P, Blinov ML.
original_link: https://pubmed.ncbi.nlm.nih.gov/31602314/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC6783478/pdf/41540_2019_Article_114.pdf
site: pubmed
title: ModelBricks-modules for reproducible modeling improving model annotation and provenance.
created_at: 2025-09-11 15:03:19.337389
summary:
  Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.

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author: Ozvoldik K, Stockner T, Krieger E.
original_link: https://pubmed.ncbi.nlm.nih.gov/37782001/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10598798/pdf/ci3c01136.pdf
site: pubmed
title: YASARA Model-Interactive Molecular Modeling from Two Dimensions to Virtual Realities.
created_at: 2025-09-11 15:03:19.338442
summary:
  The industry's transition from three-dimensional (3D) glasses to virtual reality (VR) headsets has left modelers stranded without hardware supply, since walking around and waving arms in a virtual world is a great experience but also very tiring when doing time-intensive modeling work. We present a novel software implementation that uses a VR headset while sitting at a desk in front of the normal screen, which is beamed into the virtual reality together with keyboard, mouse, and chair using the headset's cameras and an extra tracker attached to the seat-back. Compared to 3D glasses, this yields a comparably relaxing but much more immersive workplace and provides additional possibilities such as taking molecules into one's hands, standing up, and walking or teleporting through the models. This VR functionality has been combined with a molecular graphics engine based on Vulkan, a next-generation cross-platform application programming interface (API) for GPUs and the successor of the widely used Open Graphics Library (OpenGL). It is built into the YASARA Model program, which includes many features like small and large molecule builders, electron densities, partial surfaces, contact analysis, coordinate manipulation, and animations. Interactive tutorials are provided to guide modelers into VR and familiarize them with the molecular modeling features. YASARA Model is available for Linux, Windows, Android, and MacOS (the latter without VR) with an introductory video at www.YASARA.org/vr.

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author: Iffland-Stettner A, Okano H, Gralka M, Guessous G, Amarnath K, Cordero OX, Hwa T, Bonhoeffer S.
original_link: https://pubmed.ncbi.nlm.nih.gov/36853050/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10134806/pdf/msystems.00377-22.pdf
site: pubmed
title: A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01.
created_at: 2025-09-11 15:03:19.339613
summary:
  While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described. IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.

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author: 杨泽斌;洪玲儿;胡亦菲;吴先球
date: 2025-06-20
keywords: 中文文本分类;;SOLO分类理论;;物理实验;;教学评价;
source: 物理实验
site: 知网
title: 中文文本分类在物理实验教学评价中的应用——以物理实验课后练习题文字回答的自动评分为例
created_at: 2025-09-11 15:07:35.135825
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_kMnWtzcx6Wqs4UBHJ1XK54uDp8WG8NDuZpooUX452IKxfDoiFcpxpZ73fcejuIohkV2FVkIyHAX9bq8owHsZJ53MwZpebo_LAoHsydwLFsP1UrgPIH0rdWGvjUVmjRZC-rkUWtTrlMYICGAZZWUIMHjgacuDDcwJpyWPQ2nmLUw==&uniplatform=NZKPT&language=CHS

summary:
  以物理实验课后练习题文字回答的自动评分为例，探讨了基于中文文本分类技术在大学物理实验教学评价中的应用.依据SOLO分类理论制定了大学物理实验课后练习题文字回答的分类规则，在比较不同分类器性能指标后选择出最优分类器，实现对学生文字回答的自动分类和评价.结果表明，利用中文文本分类技术可实现学生物理实验课后练习题的文本自动分类，能够帮助教师对学生进行高效评价与分类统计，为教师教学提供有效的改进依据.

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author: 于清
date: 2025-06-11
keywords: 文本情感分类;;文本多分类;;动态词向量;;深度学习;;注意力机制;
source: 烟台大学
site: 知网
title: 基于动态语义建模和特征提取的文本分类方法研究
created_at: 2025-09-11 15:07:35.138782
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_kMnWtzcx6Wqs4UBHJ1XK56JNm2KjiUUS6LCPYrJvFgBtqMhSikwEQObX3RTdFpt1ugdnhQdaDpQQtQEBaz8ViJzAGksHaCZRVexob6ekyCjX0esVI66-LgfrdJr3jDFpgR1_VrM5624JvjRa-58qCu-R1u7F-dkdCSn7bHrVe6A==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网和通信技术的不断发展,社交媒体平台每天生成着大量的文本数据,这些数据不仅包含丰富的情感表达,还涉及多种文本类别。如何准确地进行情感分类以及对文本主题多分类,对社会生活具有重要的现实意义和价值。文本分类作为自然语言处理领域的重要内容,分类模型的性能一直深受该领域学者的研究。基于词典和规则的文本分类方法,可以通过预定义的词汇表或规则来直接判断文本的情感倾向或主题类别。基于机器学习的文本分类方法,能够通过学习标注文本中的语义特征,进而构建分类器对目标文本进行预测。基于深度学习的文本分类方法,利用多层非线性变换能够从文本数据中学习并提取到更深层次的语义特征,从而更准确地对文本类别或情感倾向进行分类。目前文本分类面临着更加复杂且细粒度化的情感表达,以及更为多样化的文本类别,为了应对这些挑战,本文提出了三种基于动态语义建模和特征提取的文本分类方法。具体工作内容如下: (1)针对句子级情感分类过程中句子独立于文档存在导致缺乏完整上下文语义的问题,提出一种通过权重分配策略增强局部情感特征的方法进行句子级情感分类。该方法先使用动态预训练模型对文本进行向量化表示,得到文本序列的动态词向量矩阵。再利...

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author: 黄锦
date: 2025-06-07
keywords: 文本分类;;图卷积网络;;多任务学习;;SBERT;
source: 大连交通大学
site: 知网
title: 基于图卷积网络的文本分类方法研究
created_at: 2025-09-11 15:07:35.138782
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_kMnWtzcx6Wqs4UBHJ1XK56JNm2KjiUUS6LCPYrJvFgKGUKwqhNJFpCByxaYXjqPeQ92nfQh3EcIY4zNJ5EIL21rMjoyfj0pdYJ_iMtk9p9Es9NIRDrwPmMApw6__6QpjudurZPvyPeZRt-UegQTTb7hgh4Vu7byIFuguydYRzfRunrGHI8YPH&uniplatform=NZKPT&language=CHS

summary:
  文本分类作为自然语言处理领域的核心任务,其主要目标在于通过语义理解和信息归纳来优化用户的信息管理效率。在文本分类预测任务中,图卷积网络将输入文本构建为词-文档图结构,极大地提高了分类结果的精度,因此该模型被广泛应用于该领域。然而,其现有方法仅依赖词-文档图中的文档节点对文档类别进行预测,忽视了词节点在分类任务中的潜在辅助价值,存在语义利用不足的问题。此外,传统方法的词-文档图节点仅以独热编码作为初始化,存在特征表达能力不足以及文本语义提取不够深层的问题。针对以上问题,本文提出了两种基于图卷积网络的文本分类模型,研究了图卷积网络方法在文本分类中的应用,以进一步提高文本分类任务的准确率。论文的主要研究工作如下: (1)针对目前基于图卷积网络的文本分类方法对语义信息利用不够充分的问题,本文以经典的Text GCN为基础进行优化改进。首先,为了减小梯度消失问题并增强信息传递,本文采用Mish函数替换Text GCN中的Re LU激活函数。Mish函数具有平滑的梯度曲线,能够在深层网络中更好地传递梯度,从而提升模型的训练效果。其次,本文引入了词性标注和命名实体识别作为文本分类的辅助任务,结合多任务...

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author: 邢义昀
date: 2025-06-06
keywords: 新闻文本分类;;特征融合;;层次标签;;预训练模型;
source: 齐鲁工业大学
site: 知网
title: 基于多元特征融合的新闻文本分类方法研究
created_at: 2025-09-11 15:07:35.140804
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8O9Yfj3xXHQTBtSMUDvGZ03exzPNfNYbJnGTjRvNPdWiGuqXwfGDF85XARkvDDq0tjoncltPtf-S_9mu_QtbZHUgGMIdK1O_xxI2EVjkcj_CAJ1OxdYrslZwrZXz6BfTj4zvR450X7bSDdiRzrTybX98yy7a6vvWwZymsh67_Rxw==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着互联网新闻数据量的指数级增长和传播渠道的碎片化趋势,新闻文本分类技术在信息管理、个性化推荐及舆情监测等领域的重要性日益凸显。然而,现有方法在单标签新闻文本分类中面临上下文信息提取困难、语义特征模糊等问题,在层次标签分类中则存在标签层次信息利用不足、文本与结构交互效率低下的挑战。为克服这些局限,本文针对新闻文本分类的两种模式,分别提出了基于特征融合的单标签分类模型CBDN和基于动态层次标签融合的层次分类模型DHFPE,并通过系统实验验证了其性能与应用价值。 在单标签分类任务中,CBDN模型使用经过全词掩码策略改进的BERT-wwm做为嵌入层,通过全词掩码方法优化中文新闻文本的语义表示,生成高质量的词向量;引入长链接双向长短期记忆网络和多头注意力机制,分别捕捉时序依赖和多视角语义关联;采用改进的金字塔卷积神经网络提取多尺度局部特征,并通过加权融合与门控机制实现全局、时序及局部特征的动态交互。在层次分类任务中,DHFPE模型以Ro BERTa为基础,通过提示嵌入机制将层次标签路径融入文本输入,利用动态层次特征融合生成更具区分度的表示;引入基于注意力机制的对比学习策略,通过生成增强正...

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author: 顾启梁
date: 2025-06-06
keywords: 多标签文本分类;;司法文本;;标签语义嵌入;;特征融合;;深度学习;
source: 齐鲁工业大学
site: 知网
title: 面向司法文本的多标签文本分类方法研究
created_at: 2025-09-11 15:07:35.141815
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8O9Yfj3xXHQTBtSMUDvGZ03exzPNfNYbJnGTjRvNPdWrji9WhaGCrywSSyzDp3rZHb45bjtZfwdhREZk_I6LPE8y_m0Sv8Va0t0XpMwTdG2EfNiLRV3BQksLEC996Ul4Q3L1ciXCGsGk5WZ4uzxxNVN1UVKurKXrdlZy5ZTe1rOQ==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着数字技术的快速发展,司法领域积累了大量法律文档和判例文本。多标签文本分类作为一种重要的自然语言处理技术,能够将单个文档归入多个相关类别,在司法文本处理方面展现出巨大潜力。随着深度学习的发展,基于深度学习的多标签文本分类方法成为当前研究的主流方向。现有的司法文本多标签分类任务主要基于案件事实描述,对罪名、法律条文和刑期进行预测。多标签文本分类技术能够显著提升法律文档的检索效率,使法官、律师等法律从业者更快速地找到相关案例和条款,同时减少人工干预与误差,确保相似案件的分类和处理一致性,从而节省大量时间和人力资源。然而,现有模型仍然存在以下问题:特征提取与利用不足、忽视标签与文本之间的关联、循环神经网络在提取全局信息时可能导致信息过度压缩或丢失、未充分建模标签之间的多重相关性,以及多头注意力机制在标签选择过程中未有效融合标签的局部与全局信息。针对上述问题,本文提出了两种基于深度学习的司法文本多标签分类模型: (1)针对不同层次特征提取与利用不足、标签与文本之间语义关联建模能力弱,以及循环神经网络在全局信息建模过程中可能导致信息丢失的问题,本文设计了一种基于多特征融合和标签嵌入的神经...

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author: 曹文宇
date: 2025-06-05
keywords: 海量文本分类;;图卷积神经网络;;BERT;;多层次注意力机制;;特征融合;
source: 内蒙古科技大学
site: 知网
title: 基于深度学习的海量文本分类技术研究
created_at: 2025-09-11 15:07:35.142823
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-J-N-Uo4DxYYquZGtyg-9qnAkKbRosa-boDlIsUjLKCN6iqjJV1VTw_96cOwYAbTtzknouXny6m-fNUZ_1xrfthXd4hkdpkzSTMUCGfYq55nD2fLH0zN9_zO1LGPTA0_hUPkeZHcf-Gh31pZkjt-80rLu5sQ1qDZOIl0J52g-4zw==&uniplatform=NZKPT&language=CHS

summary:
  海量文本分类是自然语言处理中的核心任务,在信息检索、舆情分析、自动推荐等多个领域具有广泛的应用价值。随着互联网和社交媒体的快速发展,面对高维、非结构和动态变化的文本数据,传统的基于规则和机器学习的方法已难以满足高效分类的需求。近年来,深度学习方法尤其是图神经网络和预训练语言模型,为文本分类提供了新的技术路径。其中,图卷积神经网络和BERT预训练语言模型分别在文本的全局拓扑信息建模和上下文语义表示方面具有优势,但各自仍存在局限性。图卷积神经网络依赖固定的静态邻接矩阵,难以适应不同文本样本的特征变化,影响模型的泛化能力;BERT预训练模型采用的掩码语言模型具有随机性,受限于固定的掩码策略,未能充分捕捉全局拓扑结构信息,限制了上下文语义建模能力,从而影响分类性能。针对上述问题,本文分别提出两种改进策略,以提升分类的准确性和泛化能力,主要研究内容如下: (1)针对传统图卷积神经网络在文本分类任务中受限于固定静态邻接矩阵,难以适应不同文本样本的特征差异,导致模型准确率受限的问题,本文提出了一种基于变分结构和动态边权重学习的图卷积神经网络改进模型(VSD-GCN),方法利用变分结构生成稳定的文本表示...

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author: 李胜南
date: 2025-06-01
keywords: 医疗文本;;异构图卷积网络;;图扩散;;文本异构图;;文本分类;
source: 长春工业大学
site: 知网
title: 基于异构图扩散网络的医疗文本分类方法研究
created_at: 2025-09-11 15:07:35.143821
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-J-N-Uo4DxYYquZGtyg-9qnAkKbRosa-boDlIsUjLKCA4FGmGbSw3kwQlz-6VL12REAlT6cSpYxrEdhTh35gi2a1O1v1ypR-kXW5rg9hTqb6y0sP1twh37kYceD9rTzUReCbNrfM5Tdao6m4LZHnDl-qQcGeABnBTlPa2Xil9Yrw==&uniplatform=NZKPT&language=CHS

summary:
  医疗文本分类可以自动识别和归类海量医疗文本,方便医生快速检索相关信息,提高诊断效率和准确率。医疗文本分类是智慧医疗的核心技术之一,其发展将推动医疗信息化、智能化和个性化,最终惠及广大患者。然而医疗文本专业性强、术语密集,聚焦于医学知识和健康信息,对文本分类任务的准确性和严谨性要求极高。普通文本语言较通俗,更注重情感表达,分类任务相对灵活,数据处理和模型应用难度较低。针对现有方法常忽略文本结构信息和无法利用高阶邻居联系的缺陷,本文提出基于异构图扩散网络的医疗文本分类方法(Medical Graph Diffusion,MGD),用于医疗文本分类。MGD构建文本异构图以捕捉更多结构关系,并采用图扩散方法寻找长程依赖,再结合异构图卷积网络和多层感知器完成分类任务。主要内容如下: (1)设计并构建文本异构图。为解决现有方法常忽略文本信息、无法有效提取结构关系的问题,本文构建文本异构图以捕捉全局信息。该异构图涵盖单词-单词、句子-句子及单词-句子间的关系。特别地,添加句子-句子关系,弥补远程依赖捕捉难题,丰富信息维度。消融实验结果表明,文本异构图的构建显著提升了分类任务的性能,验证了其在本研究中的...

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author: 刘克爽
date: 2025-06-01
keywords: 文本分类;;图卷积神经网络;;癌症文本;;多视图;
source: 长春工业大学
site: 知网
title: 融合多视图和图卷积神经网络的癌症文本分类方法研究
created_at: 2025-09-11 15:07:35.144821
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-J-N-Uo4DxYYquZGtyg-9qnAkKbRosa-boDlIsUjLKCMmh9HDjtkQ_iQd8IK1YZ6F4cVza_U_WPIriTGSDuyLrerflSD8NTL46oBltqQ75h5r-OjFSi0Fqf6Eib0FT2hcMD1ciYK7YPB50Joz0T4AZ1ITM02kb1H7kMiPOmTTWeQ==&uniplatform=NZKPT&language=CHS

summary:
  随着医疗信息化进程的加速推进,癌症相关的医疗文本数据呈现指数级增长趋势,如何高效处理和分析这些专业文本数据成为医学信息学领域的重要课题。与普通文本相比,癌症相关文本在词汇和结构层面具有显著差异:一方面包含大量专业术语,另一方面呈现独特的文本组织形式。这种专业性和结构特殊性导致传统文本分类方法在癌症文本分类任务中往往表现欠佳。 本文系统性地梳理癌症文本分类技术在医学领域的应用现状,深入分析关键技术,并提出一种融合多视图和图卷积的癌症文本分类方法。研究表明,该方法在癌症文本分类任务当中优于其他现有方法,为后续癌症文本分类应用提供参考价值。主要研究内容包括: (1)通过文本表示学习方法,对文本进行表示学习,构建单词节点特征和句子节点特征。并根据两种节点表示构建两种视图,以确保每个单词节点的独特属性和句子节点的语义完整性得到保留。 (2)对于不同的视图分别使用特定的图卷积模块对其进行特征表示学习,通过该方法提取到单词节点视图和句子节点视图的整体特征表示,并对两种视图特征表示进行融合,得到文本的完整表示。通过该方法有效解决了癌症文本中存在较多专业术语,以及其独特结构带来的问题。 (3)通过在癌症文...

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author: 刘琪
date: 2025-06-01
keywords: 文本分类;;预训练模型;;图神经网络;;特征融合;
source: 北京印刷学院
site: 知网
title: 基于预训练模型的图神经网络文本分类方法研究
created_at: 2025-09-11 15:07:35.146823
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_B3vL4SlTe26DBvdMQ4jiVfIeL5A-Uo9iIOz2BcipX7A88Jz5l0IQWJpdXuK6MBKDu7tPELYygdgEzVjS11gPrAoELXqsmuRKbrEFyMK99pOyiyp-2TtHXgFJb9Wn0DPUuOF6YjmOwezLDc5wtkAGA7dKhvVSpcY3z4-4zRWSUE5pY4P1IOw8S&uniplatform=NZKPT&language=CHS

summary:
  文本分类是自然语言处理领域的一项重要任务,主要目的是将文本数据归类到特定的类别中。图神经网络将文本之间的复杂关系通过某种方法关联起来,提升了文本分类的准确性,预训练模型依靠本身强大的语义表达力和迁移学习的优点,在各种文本分类相关的任务里都取得了明显成效。因此,有学者提出了融合两种方法的文本分类模型,通过预训练模型捕捉文本的深层语义信息,借助图神经网络构建文本之间的结构关系,提高了模型的分类效果。BertGCN通过联合训练BERT和图卷积网络(Graph Convolutional Networks,GCN)模块,在许多文本分类数据集上取得了优异的表现,虽然BERT模块能够有效地捕捉文本的语义信息,GCN模块能准确构建词与词之间的依赖关系,但是BertGCN仍然存在一些局限性,为了解决这些问题,本文做了以下工作: (1)BERT模块中的[MASK]标记会导致预训练与下游任务目标表征不一致的问题,因而模型缺乏对文本结构的表征能力,无法捕捉长距离依赖关系。针对这个问题,本文提出了一种基于隐层空间混合增强的文本分类方法(XLNet with Hidden State Mixing,XLNet M...

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author: 黎鉴斌
date: 2025-05-29
keywords: 图自监督对比学习;;长文本分类;;文本事件;;多关系;;负采样;
source: 中南林业科技大学
site: 知网
title: 基于图自监督对比学习的长文本分类方法研究
created_at: 2025-09-11 15:07:35.147822
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_B3vL4SlTe26DBvdMQ4jiVfIeL5A-Uo9iIOz2BcipX7O3vYE7DntXuQP4o1w0PRfF7VZ2xiCXX1z5Ld3zyLvJCQnT8Camq8Z7k7AAYNKmLoW850gdZBq2Zr6wndfjs12qcD9TeD2wnG18jr-g9NJn_vtDUG1Otz6GdhmFEWDg0WA==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网的快速发展,长文本逐渐成为使用最广泛的信息载体。长文本分类作为有效的文本挖掘技术,普遍应用于新闻分类和垃圾邮件过滤。随着海量文本不断涌现,而现有的文本分类方法通常需要60%以上的标注数据才能达到最优效果,这使得数据标注成为繁琐的工作。因此,研究人员尝试将图自监督对比学习用于长文本分类任务,然而这仍然面临着巨大的挑战。第一个挑战,长文本是包含丰富语义信息的长序列,而现有方法难以识别文本中的核心特征。第二个挑战,目前的方法没有对负样本的选取进行区分,存在负采样偏差,会导致文本过度聚类问题。针对以上挑战,本文结合图自监督对比学习展开对长文本分类任务的方法研究,主要创新点如下: (1)提出了基于聚类负采样优化的图自监督对比方法ClusterText。该方法首先提取事件并通过计算其相似度来构建文本间的关系。由此构建的文本图包含了多种关系,有效整合了文本结构和语义信息。接着,提出了聚类优化策略以优化图对比阶段的负采样过程。具体来说,把文本聚类产生的伪标签作为自监督信号,从不同的聚类中选取节点作为锚节点的负样本集合,提高了负采样的准确率。此外,还设计了自我校正机制,利用节点间的距离长短来扩充...

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author: 宋东桓;胡懋地;丁洁兰
date: 2025年2期
keywords: 多任务学习;跨类型文本分类;迁移学习;集成学习
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于多任务学习的跨类型文本分类技术研究
created_at: 2025-09-11 15:07:35.148822
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGgg2aHZtcHFlNA%3D%3D

summary:
  [目的]解决常规文本分类任务中由于领域训练数据稀缺、类型间差异大等因素导致的分类准确率低的问题.[方法]引入深层金字塔卷积网络与多门控制单元机制,构建基于BERT-DPCNN-MMOE模型框架的分类模型,通过设计多任务实验和迁移学习实验,对标8种基线模型,验证本文分类模型的有效性.[结果]自主构建多任务跨类型的数据作为训练测试基础,发现本文模型在多任务实验和迁移学习实验中的分类效果均优于8种基线模型,F1值的提升幅度均超过4.7个百分点.[局限]模型在其他领域的适应性需进一步研究.[结论]基于BERT-DPCNN-MMOE分类模型在多任务、跨类型文本分类任务上能够表现出更优的效果,对未来专题情报分类任务具有重要意义.

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author: 于娟;赵慧云;巫邵诚
date: 2025年2期
keywords: 跨语言;文本分类;句向量加权;文本相似度
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于句向量加权的跨语言文本分类方法
created_at: 2025-09-11 15:07:35.148822
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0Ggg2aHZtcHFlNA%3D%3D

summary:
  [目的]减少文本分类过程中因语言差异和文本特征选择所导致的语义偏离和丢失,保留更多的文本信息.[方法]首先,采用SBERT的预训练模型进行句子表示;其次,使用句向量旋转相似度方法计算不同文本中句子的相似度并对文本内的句子加权形成文本向量;最后,集成机器学习和神经网络的分类方法完成跨语言文本分类.[结果]在中、英、俄、法、西班牙文等跨语言文本数据集和多语言公开数据集Reuters的分类任务上进行实验的结果表明,所提方法的准确率约为96％,较已有方法有显著提升,且在召回率、精确率和F1值等评价指标上均有所提升.[局限]未考虑句子在文本中的出现位置对其权重的影响.[结论]句向量加权的文本表征方法能够减少语义偏离和语义丢失,从而提升跨语言文本分类的性能.

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author: 张静;高子信;丁伟杰
date: 2025年2期
keywords: BERT;DPCNN;警情文本分类;激活函数
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 基于BERT-DPCNN的警情文本分类研究
created_at: 2025-09-11 15:07:35.150843
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1Ggg2aHZtcHFlNA%3D%3D

summary:
  [目的]对海量警情文本数据进行智能化分类.[方法]针对警情文本分类任务,提出一个基于BERT-DPCNN 的文本分类模型.采用BERT预训练模型生成文本词向量,通过优化DPCNN模型中的激活函数和改进动态学习率提高分类性能.[结果]将BERT-DPCNN与BERT、BERT-CNN、BERT-RCNN、BERT-RNN、BERT-LSTM、ERNIE等6类模型进行对比实验,结果表明BERT-DPCNN的准确率、召回率和精准率最佳.在二分类任务中,BERT-DPCNN的准确率达到98％以上,十一分类任务中,其准确率达到82％以上.[局限]模型参数较多,实验次数有限,有待进一步测试.[结论]基于BERT-DPCNN的文本分类模型能有效提升警情文本分类的准确率,为公安部门进行警情分析和研判提供数据支撑.

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author: 韩普;王志伟;杜文文
date: 2025年4期
keywords: 法律文本分类;深度图神经网络;细粒度分类;解耦合;注意力扩散
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于深度注意力扩散图神经网络的法律文本细粒度分类方法
created_at: 2025-09-11 15:07:35.151849
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGgg2aHZtcHFlNA%3D%3D

summary:
  [目的]利用解耦技术缓解过度平滑并构建深度图网络学习文本隐藏特征,同时采用注意力扩散机制增强图网络的长距离交互能力,以提升法律文本细粒度分类效果.[方法]提出基于深度注意力扩散图神经网络的法律文本细粒度分类模型FLGNN.首先使用预训练模型BERT作为嵌入层获取长距离语义特征,接着构建文本有向图通过深度图网络捕获文本全局图信息和隐藏特征,最后利用特征融合和节点级注意力机制优化文本特征并进行分类任务.[结果]模型在来自北大法宝数据库的PKULawData数据集上Acc达94.85％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.15、3.44和1.72个百分点;在法律合同文本数据集JSCLawData上Acc达90.91％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.35、4.19和4.10个百分点.[局限]模型在其他领域的适用性需要进一步探究.[结论]FLGNN模型能捕获法律文本的全局图信息并挖掘深层语义信息,进一步提升了法律文本细粒度分类效果,可为法律领域智能化管理和人工智能提供有效支撑.

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author: 高瑜蔚;王兆钧;胡良霖
date: 2025年2期
keywords: 科学数据集;门控机制;词性特征;长文本;文本分类
quote: 0
source: 《北京邮电大学学报》
site: 万方
title: 面向科学数据集决策影响力评价的长文本分类方法
created_at: 2025-09-11 15:07:35.152602
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoINmh2bXBxZTQ%3D

summary:
  数据要素时代,对决策的影响力成为衡量开放科学数据集质量与价值的重要维度,自动、客观地从政府公文中提取和评价开放科学数据集的决策影响力成为可行的手段.政府公告文件通常是一种长文本,从其中判断和科学数据集相关度作为关键环节,可转化成长文本的分类任务.针对该问题,面向科学数据集决策影响力评价场景,提出了一种基于词性特征融合的长文本分类方法(LTC-WF).首先,将文本中的词语根据不同词性进行分类,将相同词性的短语进行整合.然后,将归类后的短语组和原文本分别进行嵌入表示.最后,为了验证融合词性信息的效果,分别设计了拼接融合单元和门控融合单元,其中门控融合单元将短语组嵌入向量和原文文本嵌入向量分别赋予不同权重进行聚合,生成文本的最终嵌入表示进行分类.通过在构建的科学数据政策数据集上的实验结果表明,该方法比现有主流方法取得了更好的性能,为实现评价科学数据集决策影响力提供了有效技术方案.

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author: 黄靖;陶竹林;杜晓宇
date: 2025年7期
keywords: 多标签文本分类;图卷积网络;注意力机制;动态图
quote: 0
source: 《软件学报》
site: 万方
title: 基于特征融合动态图网络的多标签文本分类算法
created_at: 2025-09-11 15:07:35.153608
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCDZodm1wcWU0

summary:
  多标签文本分类旨在为文本分配若干预定义的标签或类别.为了充分发掘标签间的关联,目前的方法通常使用标签关系图并结合图神经网络获取标签特征表示.然而,这类方法过度依赖初始建图策略,忽视了当前文本中固有的标签相关性,使得分类结果更依赖于数据集统计信息,而容易忽视当前文本段中的标签相关信息.因此,提出一种基于特征融合动态图网络的多标签文本分类算法,设计动态图来建模当前文本中的标签相关性,并结合特征融合与图神经网络,形成基于当前文本的标签表示,并由此形成更为准确的多标签文本结果.随后,设计实验进行验证,在3个数据集实验结果表明,所提出的模型在多标签分类任务中取得优秀的性能,验证其有效性和可行性.

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author: 李倩;赵彦云;燕雪倩
date: 2025年3期
keywords: 新能源;产业政策;实施效果;文本分类;面板分位数回归模型
quote: 0
source: 《干旱区资源与环境》
site: 万方
title: 新能源产业政策文本分类及实施效果差异性研究
created_at: 2025-09-11 15:07:35.155609
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGgg2aHZtcHFlNA%3D%3D

summary:
  科学评估不同类型新能源产业政策实施效果的差异性,对于优化产业政策、推动产业高质量发展具有重要现实意义.基于2005-2022年新能源产业政策及相关数据,利用ERNIE-DPCNN对新能源产业政策进行多标签分类,并建立面板分位数回归模型,实证检验不同类型政策在不同分位数下对产业发展影响的差异性.研究表明:ERNIE-DPCNN模型可以有效提升新能源产业政策的分类效率,政策分类结果中环境型政策最多,尤其目标规划型政策,需求型政策的应用略显不足.供给型政策对新能源产业发展产生正向影响,并且在新能源发电量低、中、高地区对产业发展的影响力度逐渐降低;需求型政策在新能源发电量高的地区对产业发展产生正向影响.因此,在新能源产业发展模式成熟的地区,更加需要推广应用、政府采购等需求型政策推动产业高质量发展.

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author: 谌文佳;杨琳;李金林
date: 2025年2期
keywords: 意图识别;医疗问答;语义相似度分类;深度学习
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 嵌入意图识别的医疗健康问答文本语义分类模型
created_at: 2025-09-11 15:07:35.156609
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGgg2aHZtcHFlNA%3D%3D

summary:
  [目的]基于意图识别构建更准确的语义相似度分类模型,为中文医疗健康查询服务提供更精准的答案匹配结果.[方法]融合BERT和卷积神经网络(CNN)构建意图识别模型,然后将其作为模型嵌入层构建嵌入意图识别的孪生BERT(ITBERT)语义分类模型.[结果]在CHIP-STS数据集上,相较于BERT和TextCNN单一模型,融合模型在意图识别的Top-1结果准确率分别提高了 1.5和8.2个百分点,达到了 73.6％;Top-3结果准确率分别提高了 3.2和7.6个百分点,达到了 91.2％,证明了融合模型对于意图识别效果的提升.对于语义相似度分类结果,相较于基准模型,ITBERT模型AUC值提高了 0.015～0.087,证明了意图知识的嵌入对医疗语义相似度分类效果的提升.[局限]人工标注的意图信息存在一定的偏差,可能会影响语义相似度的分类结果.[结论]融合模型可以改善医疗健康查询服务中的意图识别效果,嵌入识别的意图知识可以提高语义相似度分类模型的准确率,有利于提供更准确的医疗健康自动问答服务.

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author: 王友卫;刘奥;凤丽洲
date: 2025年5期
keywords: 计算机应用;情感分类;知识蒸馏;数据增强;历史评论
quote: 0
source: 《吉林大学学报（工学版）》
site: 万方
title: 基于知识蒸馏和评论时间的文本情感分类新方法
created_at: 2025-09-11 15:07:35.157610
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoINmh2bXBxZTQ%3D

summary:
  针对现有的情感分类方法普遍未能充分考虑用户个性化特征且忽略时间因素对情感分类结果的影响的问题,提出一种基于知识蒸馏和评论时间的文本情感分类新方法.首先,为解决数据集中高质量标注数据较少的问题,采用RoFormer-Sim生成模型对训练文本数据增强;然后,引入评论时间属性,从用户历史评论中提取用户的个性化信息,提出基于多特征融合的评论文本情感得分预测模型;最后,为提高针对冷启动用户的泛化性能,引入知识蒸馏理论,利用SKEP模型对基于多特征融合的情感分类模型进行通用性增强.在从中文股吧爬取的真实数据集上的实验结果表明:与SKEP、ELECTRA等典型方法相比,本文方法在准确率上分别提高了 3.1%和 0.9%,在F1 值上分别提高了2.7%和 1.0%,验证了其在改善情感分类表现方面的有效性.

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author: 陈敏;王雷春;徐瑞
date: 2025年2期
keywords: 自然语言处理;文本分类;新闻主题;XLNet;对比学习
quote: 1
source: 《郑州大学学报（理学版）》
site: 万方
title: 基于XLNet和多粒度对比学习的新闻主题文本分类方法
created_at: 2025-09-11 15:07:35.158613
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoINmh2bXBxZTQ%3D

summary:
  新闻主题文本内容简短却含义丰富,传统方法通常只考虑词粒度或句粒度向量中的一种进行研究,未能充分利用新闻主题文本不同粒度向量之间的关联信息.为深入挖掘文本的词向量和句向量间的依赖关系,提出一种基于XLNet和多粒度特征对比学习的新闻主题分类方法.首先,利用XLNet对新闻主题文本进行特征提取获得文本中词、句粒度的特征表示和潜在空间关系;然后,通过对比学习R-Drop策略生成不同粒度特征的正负样本对,以一定权重对文本的词向量-词向量、词向量-句向量和句向量-句向量进行特征相似度学习,使模型深入挖掘出字符属性和语句属性之间的关联信息,提升模型的表达能力.在THUCNews、Toutiao和SHNews数据集上进行实验,实验结果表明,与基准模型相比,所提方法在准确率和F1值上都有更好的表现,在三个数据集上的F1值分别达到了93.88％、90.08％、87.35％,验证了方法的有效性和合理性.

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author: Elijah Tomin;Marina Solnyshkina;Elzara Gafiyatova;Albina Galiakhmetova
date: 2023
original_link: https://ieeexplore.ieee.org/document/10387935/
site: ieeeXplore
title: Automatic Text Classification as Relevance Measure for Russian School Physics Texts
created_at: 2025-09-11 15:07:35.160120
summary:
  The research tests two text classification methods applied to lexical features of Russian physics schoolbook texts. The research findings indicate that text classification methods can be implemented effectively to identify if a text relevant for a specific topic or a year of studying. We use and describe results via classical methods to validate the classification models.

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author: Shoujin Wang;Chengyu Duan;Yuanjiao Yang
date: 2022
original_link: https://ieeexplore.ieee.org/document/10047503/
site: ieeeXplore
title: Weakly Supervised Chinese Short Text Classification Algorithm Based on ConWea Model
created_at: 2025-09-11 15:07:35.161127
summary:
  The version sort algorithm based on full supervision needs to use a large amount of label data, and the labeling task of text data is time-consuming and difficult to label. Finally, use high-quality pseudo-label data to train a Chinese brief version systematics model. Experiment on the THUCNews news headlines dataset. The test outcome illustrate the performance of this algorithm is slightly stronger than the mainstream semi-supervised classification algorithm when only a small amount of label data is used, and it is not inferior to the general fully supervised classification algorithm. which provides a better solution for unlabeled data classification tasks.

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author: Gang Jin
date: 2022
original_link: https://ieeexplore.ieee.org/document/10036794/
site: ieeeXplore
created_at: 2025-09-11 15:07:35.161633
summary:
  How to use advanced deep learning technology to build an effective and powerful text classification model, extract text semantic attributes and achieve good classification results on large-scale test datasets has always been the main problem and severe challenge of text classification tasks. The purpose of this paper is to study the application optimization of NLP system in text semantics and text classification based on deep learning technology. A WC-GCN classification algorithm combining GCN and Attention mechanism is proposed. The related tasks of text classification are discussed, including three typical text classification tasks, including sentiment analysis, topic analysis, and news classification. The algorithm model proposed in this paper is verified from the experimental point of view. Compared with other deep learning-based text classification methods, the proposed algorithm model performs better on the public datasets of three text classification tasks. The news text also achieved a high accuracy rate.

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title:
  Application Optimization of NLP System under Deep Learning Technology in Text Semantics and Text Classification


author: Bihui Yu;Chen Deng;Liping Bu
date: 2022
original_link: https://ieeexplore.ieee.org/document/9849586/
site: ieeeXplore
title: Policy Text Classification Algorithm Based on Bert
created_at: 2025-09-11 15:07:35.162639
summary:
  With the development of the Internet, the policy text classification model of deep learning is used to improve the effect of policy text classification, and to play and use the huge value contained in the policy text. In order to more accurately determine the policy field described by the text, a BERT-based policy text classification algorithm is proposed. First, the algorithm uses the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model to vectorize the sentence-level feature of the policy field text, and then the obtained feature vector is input into the classifier for classification, and finally the homology policy field is used. The text data set is verified. The experimental results show that the classification of the trained model on the test set recorded the highest F1 value of 93.25%. It is nearly 6% higher than the classification task of the BERT model for the MRPC task. Therefore, the proposed policy domain text classification algorithm can more accurately and efficiently judge the domain of the policy text, which is helpful for further analysis of the text data in the policy domain and extract more valuable information.

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author: Fatih Gürcan
date: 2018
original_link: https://ieeexplore.ieee.org/document/8567307/
site: ieeeXplore
title: Multi-Class Classification of Turkish Texts with Machine Learning Algorithms
created_at: 2025-09-11 15:07:35.163639
summary:
  The problem of text classification is the process of supervised assignment of text documents to one or more predefined categories or classes according to the content of the processed texts with natural language processing methods. Text classification applications are actively used in various fields such as categorization of social interactions, web pages and news texts, optimization of search engines, extracting information, and automatically processing e-mails. In this context, it is aimed to classify Turkish texts with methods based on supervised machine learning. In this context, the classification success of supervised learning models on Turkish texts was analyzed with different parameters. These models have been tested for classification of news texts on five predefined classes (economy, politics, sport, health, and technology) and the system was trained with different number of training documents and the classification process was carried out. In this context, the classification performances of Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, and Decision Trees algorithms on Turkish news texts are compared and interpreted in the light of the results obtained with different parameters. As a result of the study, the procedure with the best classification success was the Multinomial Naïve Bayes algorithm with a classification success of about 90%. These results show that the Naïve Bayes probability model can be used as an effective classifier method in classifying Turkish texts compared to other methods. In this context, it is envisaged that the proposed methodology could be applied to Turkish texts on different web platforms (social networks, forums, communication networks, etc.) for different purposes.

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author: Zhenzhong Li;Wenqian Shang;Menghan Yan
date: 2016
original_link: https://ieeexplore.ieee.org/document/7550929/
site: ieeeXplore
title: News text classification model based on topic model
created_at: 2025-09-11 15:07:35.164639
summary:
  In modern society, some famous news websites such as Sina and Times server to provide information every day for millions of users. But with the continuous development of information technology, the amount of disorder data is increasing. How to organize the text and make automatically text classification has become a challenge. The traditional manual classification of news text not only consumes a lot of human and financial resources, but also hardly achieved classification task quickly. In this paper, the paper mainly makes a research about the news text classification. It proposes a news text classification model based on Latent Dirichlet Allocation (LDA). Due to the dimension of the news texts is too high, this model uses topic model to make text dimension reduced and get features. At the same time, the paper also makes a research on Softmax regression algorithm to solve multi-class of text problems in our life and make it as model's classifier. The paper evaluates proposed model on a real news dataset and the result of the experiment shows the improved model performs relatively well. The model can effectively reduce the features dimension of the news text and get good classification results.

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author: Zhou Faguo;Zhang Fan;Yang Bingru;Yu Xingang
date: 2010
original_link: https://ieeexplore.ieee.org/document/5557448/
site: ieeeXplore
title: Research on Short Text Classification Algorithm Based on Statistics and Rules
created_at: 2025-09-11 15:07:35.165639
summary:
  In this paper, we introduced the overview of short text research and the short text classification firstly. On the foundation of several common used classic text classification algorithms, mainly according to the major feature extraction methods, the short text classification based on statistics and rules is proposed. Experiments show that this algorithm has better performance than other algorithms. In order to improve the recall rate of short text classification, two-steps classification method is put forward.

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author: Yanming Cheng;Zhigang Yu;Je Hu;Mingchuan Yang
date: 2022
original_link: https://ieeexplore.ieee.org/document/10009824/
site: ieeeXplore
title: A Chinese Short Text Classification Method Based on TF-IDF and Gradient Boosting Decision Tree
created_at: 2025-09-11 15:07:35.166639
summary:
  To solve the problem of feature extraction and semantic sparsity in Chinese short text classification, this paper uses TF-IDF algorithm to extract category keywords and uses the set of category keywords as the feature set of short text classification. Next, the weight of keyword features is obtained by calculating the maximum similarity between the category keywords and each word in the essay. Based on the weighted keyword feature vector set, the short text is represented by vectors. Finally, we use the GBDT algorithm to train the classifier for short text classification and carry out experiments to verify the effectiveness of this method in improving the classification effect.

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author: Xiao-Dan Zhang
date: 2010
original_link: https://ieeexplore.ieee.org/document/5529774/
site: ieeeXplore
title: A general decision layer text classification fusion model
created_at: 2025-09-11 15:07:35.167640
summary:
  An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centre having different pre-processing, their classification results input into the decision layer fusion centre separately. And the final classification result output from decision layer fusion centre. KNN, SVM and BP Net are used in feature layer, and D-S Theory is used in decision layer. The model is realized in the experiment. From the experiment and contrast, the text classification fusion model can improve the classification precision effectively.

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author: Fang Miao;Pu Zhang;Libiao Jin;Hongda Wu
date: 2018
original_link: https://ieeexplore.ieee.org/document/8530178/
site: ieeeXplore
title: Chinese News Text Classification Based on Machine Learning Algorithm
created_at: 2025-09-11 15:07:35.168640
summary:
  Text classification is the key technology for mining and organizing text information, which is the process of determining the text types automatically according to the content. Based on machine learning algorithm, text classification system includes four processes, namely text pretreatment, text representation, classifier training and classification. In this paper, a Chinese news text classification system model is designed. And in the classifier training part, we separately chose and compared K-nearest Neighbor, Naive Bayes, and Support Vector Machine as our classification algorithm. Then, we tested and analyzed these classifiers with each other and finally got a conclusion. The experimental conclusion shows that the Chinese news text classification system can get satisfied results based on the machine learning algorithm.

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date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08624
pdf_url: https://arxiv.org/pdf/2509.08624
site: arxiv
title: UOPSL: Unpaired OCT Predilection Sites Learning for Fundus Image Diagnosis Augmentation
created_at: 2025-09-11 15:07:35.171662
author:
  Zhihao Zhao;Yinzheng Zhao;Junjie Yang;Xiangtong Yao;Quanmin Liang;Daniel Zapp;Kai Huang;Nassir Navab;M. Ali Nasseri

summary:
  Abstract:Significant advancements in AI-driven multimodal medical image diagnosis have led to substantial improvements in ophthalmic disease identification in recent years. However, acquiring paired multimodal ophthalmic images remains prohibitively expensive. While fundus photography is simple and cost-effective, the limited availability of OCT data and inherent modality imbalance hinder further progress. Conventional approaches that rely solely on fundus or textual features often fail to capture fine-grained spatial information, as each imaging modality provides distinct cues about lesion predilection sites. In this study, we propose a novel unpaired multimodal framework \UOPSL that utilizes extensive OCT-derived spatial priors to dynamically identify predilection sites, enhancing fundus image-based disease recognition. Our approach bridges unpaired fundus and OCTs via extended disease text descriptions. Initially, we employ contrastive learning on a large corpus of unpaired OCT and fundus images while simultaneously learning the predilection sites matrix in the OCT latent space. Through extensive optimization, this matrix captures lesion localization patterns within the OCT feature space. During the fine-tuning or inference phase of the downstream classification task based solely on fundus images, where paired OCT data is unavailable, we eliminate OCT input and utilize the predilection sites matrix to assist in fundus image classification learning. Extensive experiments conducted on 9 diverse datasets across 28 critical categories demonstrate that our framework outperforms existing benchmarks.

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author: Samuel Lockman
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08477
pdf_url: https://arxiv.org/pdf/2509.08477
site: arxiv
created_at: 2025-09-11 15:07:35.173670
summary:
  Abstract:We classify Riemannian \text{spin}^c manifolds carrying a type I imaginary generalized Killing spinor, by explicitly constructing a parallel spinor on each leaf of the canonical foliation given by the Dirac current. We also provide a class of Riemannian \text{spin}^c manifolds carrying a type II imaginary generalized Killing spinor, by considering spacelike hypersurfaces of Lorentzian \text{spin}^c manifolds. We carry out much of the work in the setting of semi-Riemannian \text{spin}^c-manifolds carrying generalized Killing spinors, allowing us to draw conclusions in this setting as well. In this context, the Dirac current is not always a closed vector field. We circumvent this in even dimensions, by considering a modified Dirac current, which is closed in the cases when the original Dirac current is not. On the path to these results, we also study semi-Riemannian manifolds carrying closed and conformal vector fields.

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title:
  Semi-Riemannian \text{spin}^c
  manifolds carrying generalized Killing spinors and the classification of Riemannian \text{spin}^c
  manifolds admitting a type I imaginary generalized Killing spinor


author: Lata Pangtey;Omkar Kabde;Shahid Shafi Dar;Nagendra Kumar
date: 9 September, 2025
original_link: https://arxiv.org/abs/2509.08024
pdf_url: https://arxiv.org/pdf/2509.08024
site: arxiv
created_at: 2025-09-11 15:07:35.175674
summary:
  Abstract:With the rapid proliferation of information across digital platforms, stance detection has emerged as a pivotal challenge in social media analysis. While most of the existing approaches focus solely on textual data, real-world social media content increasingly combines text with visual elements creating a need for advanced multimodal methods. To address this gap, we propose a multimodal stance detection framework that integrates textual and visual information through a hierarchical fusion approach. Our method first employs a Large Language Model to retrieve stance-relevant summaries from source text, while a domain-aware image caption generator interprets visual content in the context of the target topic. These modalities are then jointly modeled along with the reply text, through a specialized transformer module that captures interactions between the texts and images. The proposed modality fusion framework integrates diverse modalities to facilitate robust stance classification. We evaluate our approach on the MultiClimate dataset, a benchmark for climate change-related stance detection containing aligned video frames and transcripts. We achieve accuracy of 76.2%, precision of 76.3%, recall of 76.2% and F1-score of 76.2%, respectively, outperforming existing state-of-the-art approaches.

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title:
  Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Change


author: Bingjian Yang;Danni Xu;Kaipeng Niu;Wenxuan Liu;Zheng Wang;Mohan Kankanhalli
date: 8 September, 2025
original_link: https://arxiv.org/abs/2509.08008
pdf_url: https://arxiv.org/pdf/2509.08008
site: arxiv
title: A New Dataset and Benchmark for Grounding Multimodal Misinformation
created_at: 2025-09-11 15:07:35.177670
summary:
  Abstract:The proliferation of online misinformation videos poses serious societal risks. Current datasets and detection methods primarily target binary classification or single-modality localization based on post-processed data, lacking the interpretability needed to counter persuasive misinformation. In this paper, we introduce the task of Grounding Multimodal Misinformation (GroundMM), which verifies multimodal content and localizes misleading segments across modalities. We present the first real-world dataset for this task, GroundLie360, featuring a taxonomy of misinformation types, fine-grained annotations across text, speech, and visuals, and validation with Snopes evidence and annotator reasoning. We also propose a VLM-based, QA-driven baseline, FakeMark, using single- and cross-modal cues for effective detection and grounding. Our experiments highlight the challenges of this task and lay a foundation for explainable multimodal misinformation detection.

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author: Jiahui Li;Sean Papay;Roman Klinger
date: 9 September, 2025
original_link: https://arxiv.org/abs/2509.07869
pdf_url: https://arxiv.org/pdf/2509.07869
site: arxiv
title: Are Humans as Brittle as Large Language Models?
created_at: 2025-09-11 15:07:35.178675
summary:
  Abstract:The output of large language models (LLM) is unstable, due to both non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distributional shifts in outputs, it is largely assumed, yet unexplored, that the prompt brittleness effect is unique to LLMs. This raises the question: do human annotators show similar sensitivity to instruction changes? If so, should prompt brittleness in LLMs be considered problematic? One may alternatively hypothesize that prompt brittleness correctly reflects human annotation variances. To fill this research gap, we systematically compare the effects of prompt modifications on LLMs and identical instruction modifications for human annotators, focusing on the question of whether humans are similarly sensitive to prompt perturbations. To study this, we prompt both humans and LLMs for a set of text classification tasks conditioned on prompt variations. Our findings indicate that both humans and LLMs exhibit increased brittleness in response to specific types of prompt modifications, particularly those involving the substitution of alternative label sets or label formats. However, the distribution of human judgments is less affected by typographical errors and reversed label order than that of LLMs.

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author: Zexuan Li;Hongliang Dai;Piji Li
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.07730
pdf_url: https://arxiv.org/pdf/2509.07730
site: arxiv
created_at: 2025-09-11 15:07:35.180185
summary:
  Abstract:For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their advances. However, when leveraging LLMs for RE with predefined relation categories, two key challenges arise. First, in a multi-class classification setting, LLMs often struggle to comprehensively capture the semantics of every relation, leading to suboptimal results. Second, although employing binary classification for each relation individually can mitigate this issue, it introduces significant computational overhead, resulting in impractical time complexity for real-world applications. Therefore, this paper proposes a framework called M-BRe to extract training instances from unlabeled texts for RE. It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision. Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.

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title:
  M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models


author: Raja Mallina;Bryar Shareef
date: 8 September, 2025
original_link: https://arxiv.org/abs/2509.07213
pdf_url: https://arxiv.org/pdf/2509.07213
site: arxiv
title: XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning
created_at: 2025-09-11 15:07:35.181196
summary:
  Abstract:Background: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification, yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text prompts can add clinical context, but directly applying weakly localized text-image cues (e.g., CAM/CLIP-derived signals) tends to produce coarse, blob-like responses that smear boundaries unless additional mechanisms recover fine edges. Methods: We propose XBusNet, a novel dual-prompt, dual-branch multimodal model that combines image features with clinically grounded text. A global pathway based on a CLIP Vision Transformer encodes whole-image semantics conditioned on lesion size and location, while a local U-Net pathway emphasizes precise boundaries and is modulated by prompts that describe shape, margin, and Breast Imaging Reporting and Data System (BI-RADS) terms. Prompts are assembled automatically from structured metadata, requiring no manual clicks. We evaluate on the Breast Lesions USG (BLU) dataset using five-fold cross-validation. Primary metrics are Dice and Intersection over Union (IoU); we also conduct size-stratified analyses and ablations to assess the roles of the global and local paths and the text-driven modulation. Results: XBusNet achieves state-of-the-art performance on BLU, with mean Dice of 0.8765 and IoU of 0.8149, outperforming six strong baselines. Small lesions show the largest gains, with fewer missed regions and fewer spurious activations. Ablation studies show complementary contributions of global context, local boundary modeling, and prompt-based modulation. Conclusions: A dual-prompt, dual-branch multimodal design that merges global semantics with local precision yields accurate BUS segmentation masks and improves robustness for small, low-contrast lesions.

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author: Kun Zhai;Siheng Chen;Xingjun Ma;Yu-Gang Jiang
date: 2 September, 2025
original_link: https://arxiv.org/abs/2509.06992
pdf_url: https://arxiv.org/pdf/2509.06992
site: arxiv
title: FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
created_at: 2025-09-11 15:07:35.181708
summary:
  Abstract:Federated Prompt Tuning (FPT) is an efficient method for cross-client collaborative fine-tuning of large Vision-Language Models (VLMs). However, models tuned using FPT are vulnerable to adversarial attacks, leading to misclassification in downstream tasks. In this work, we introduce Federated Adversarial Prompt Tuning (\textbf{FedAPT}), a novel method designed to enhance the adversarial robustness of FPT. We identify a key issue in FedAPT under non-independent and identically distributed (non-IID) settings: a \textit{class information gap} between clients and the global model. Clients rely solely on limited local label information to generate adversarial samples for training, while the global model must defend against adversarial attacks from global labels. To address this issue, we propose a \textbf{class-aware prompt generator} that generates visual prompts from text prompts. This generator is guided by a \emph{Global Label Embedding} (serving as a ``beacon") which encodes cross-client label information to create more globally-aligned visual prompts. Additionally, we propose a \textbf{cross-layer generator sharing} strategy to enhance prompt coupling across different layers of the model, further boosting adversarial robustness. Extensive experiments on multiple image classification datasets demonstrate the superiority of FedAPT in improving adversarial robustness, outperforming existing methods by a large margin. FedAPT also exhibits exceptional generalization in cross-domain and cross-dataset scenarios, indicating its effectiveness in real-world applications.

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author: Yuqing Wen;Hebei Li;Kefan Gu;Yucheng Zhao;Tiancai Wang;Xiaoyan Sun
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.06932
pdf_url: https://arxiv.org/pdf/2509.06932
site: arxiv
title: LLaDA-VLA: Vision Language Diffusion Action Models
created_at: 2025-09-11 15:07:35.182713
summary:
  Abstract:The rapid progress of auto-regressive vision-language models (VLMs) has inspired growing interest in vision-language-action models (VLA) for robotic manipulation. Recently, masked diffusion models, a paradigm distinct from autoregressive models, have begun to demonstrate competitive performance in text generation and multimodal applications, leading to the development of a series of diffusion-based VLMs (d-VLMs). However, leveraging such models for robot policy learning remains largely unexplored. In this work, we present LLaDA-VLA, the first Vision-Language-Diffusion-Action model built upon pretrained d-VLMs for robotic manipulation. To effectively adapt d-VLMs to robotic domain, we introduce two key designs: (1) a localized special-token classification strategy that replaces full-vocabulary classification with special action token classification, reducing adaptation difficulty; (2) a hierarchical action-structured decoding strategy that decodes action sequences hierarchically considering the dependencies within and across actions. Extensive experiments demonstrate that LLaDA-VLA significantly outperforms state-of-the-art VLAs on both simulation and real-world robots.

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author: Marc Marone;Orion Weller;William Fleshman;Eugene Yang;Dawn Lawrie;Benjamin Van Durme
date: 8 September, 2025
original_link: https://arxiv.org/abs/2509.06888
pdf_url: https://arxiv.org/pdf/2509.06888
site: arxiv
title: mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
created_at: 2025-09-11 15:07:35.183713
summary:
  Abstract:Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature sampling ratio. We add over 1700 low-resource languages to the data mix only during the decay phase, showing that it boosts performance dramatically and maximizes the gains from the relatively small amount of training data. Despite only including these low-resource languages in the short decay phase we achieve similar classification performance to models like OpenAI's o3 and Google's Gemini 2.5 Pro. Overall, we show that mmBERT significantly outperforms the previous generation of models on classification and retrieval tasks -- on both high and low-resource languages.

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author: Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P.
original_link: https://pubmed.ncbi.nlm.nih.gov/34242279/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8270201/pdf/pone.0254007.pdf
site: pubmed
title: Text classification to streamline online wildlife trade analyses.
created_at: 2025-09-11 15:07:35.184712
summary:
  Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

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author: Shen K, Hao P, Li R.
original_link: https://pubmed.ncbi.nlm.nih.gov/32831821/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7428956/pdf/CIN2020-8879795.pdf
site: pubmed
title: A Compressive Sensing Model for Speeding Up Text Classification.
created_at: 2025-09-11 15:07:35.186708
summary:
  Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss.

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author: Baghdadi Y, Bourrée A, Robert A, Rey G, Gallay A, Zweigenbaum P, Grouin C, Fouillet A.
original_link: https://pubmed.ncbi.nlm.nih.gov/31522022/
site: pubmed
created_at: 2025-09-11 15:07:35.187708
summary:
  Background: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveillance based on medical causes of death in free-text format. Reactive mortality surveillance is based on the monitoring of mortality syndromic groups (MSGs). An MSG is a cluster of medical causes of death (pathologies, syndromes or symptoms) that meets the objectives of early detection and impact assessment of public health events. The aim of this study is to implement and measure the performance of a rule-based method and two supervised models for automatic free-text cause of death classification from death certificates in order to implement them for routine surveillance.

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title:
  Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.


author: Knights D, Costello EK, Knight R.
original_link: https://pubmed.ncbi.nlm.nih.gov/21039646/
site: pubmed
title: Supervised classification of human microbiota.
created_at: 2025-09-11 15:07:35.188708
summary:
  Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale. A major goal of these studies is the identification of important groups of microorganisms that vary according to physiological or disease states in the host, but the incidence of rare taxa and the large numbers of taxa observed make that goal difficult to obtain using traditional approaches. Fortunately, similar problems have been addressed by the machine learning community in other fields of study such as microarray analysis and text classification. In this review, we demonstrate that several existing supervised classifiers can be applied effectively to microbiota classification, both for selecting subsets of taxa that are highly discriminative of the type of community, and for building models that can accurately classify unlabeled data. To encourage the development of new approaches to supervised classification of microbiota, we discuss several structures inherent in microbial community data that may be available for exploitation in novel approaches, and we include as supplemental information several benchmark classification tasks for use by the community.

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original_link: https://pubmed.ncbi.nlm.nih.gov/37559119/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10413517/pdf/12916_2023_Article_2975.pdf
site: pubmed
created_at: 2025-09-11 15:07:35.188708
author:
  Li P, Shuai P, Shen S, Zheng H, Sun P, Zhang R, Lan S, Lan Z, Jayawardana T, Yang Y, Zhao J, Liu Y, Chen X, El-Omar EM, Wan Z.

summary:
  Background: The results of human observational studies on the correlation between gut microbiota perturbations and polycystic ovary syndrome (PCOS) have been contradictory. This study aimed to perform the first systematic review and meta-analysis to evaluate the specificity of the gut microbiota in PCOS patients compared to healthy women.

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title:
  Perturbations in gut microbiota composition in patients with polycystic ovary syndrome: a systematic review and meta-analysis.


author: Zhang D, Li J, Xie Y, Wulamu A.
original_link: https://pubmed.ncbi.nlm.nih.gov/37824464/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10569603/pdf/pone.0292582.pdf
site: pubmed
created_at: 2025-09-11 15:07:35.190729
summary:
  Text pre-processing is an important component of a Chinese text classification. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.

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title:
  Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification.


author: HaCohen-Kerner Y, Miller D, Yigal Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/32357164/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7194364/pdf/pone.0232525.pdf
site: pubmed
title: The influence of preprocessing on text classification using a bag-of-words representation.
created_at: 2025-09-11 15:07:35.192444
summary:
  Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.

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author: Huh J, Yetisgen-Yildiz M, Pratt W.
original_link: https://pubmed.ncbi.nlm.nih.gov/24025513/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC3874858/pdf/nihms523972.pdf
site: pubmed
title: Text classification for assisting moderators in online health communities.
created_at: 2025-09-11 15:07:35.193450
summary:
  Objectives: Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators' help.

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author: Wang Y, Li R, Zhou Y, Ling Z, Guo X, Xie L, Liu L.
original_link: https://pubmed.ncbi.nlm.nih.gov/27057545/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4769744/pdf/BMRI2016-6598307.pdf
site: pubmed
created_at: 2025-09-11 15:07:35.194450
summary:
  Background: Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty.

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title:
  Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.


author: Koopman B, Karimi S, Nguyen A, McGuire R, Muscatello D, Kemp M, Truran D, Zhang M, Thackway S.
original_link: https://pubmed.ncbi.nlm.nih.gov/26174442/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4502908/pdf/12911_2015_Article_174.pdf
site: pubmed
title: Automatic classification of diseases from free-text death certificates for real-time surveillance.
created_at: 2025-09-11 15:07:35.195449
summary:
  Background: Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV.

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author: 张伟
date: 2025-09-09
keywords: 人工智能图像识别功能;;图像描述;;盲校;;物理;;探究式教学;
source: 现代特殊教育
site: 知网
title: 利用人工智能图像识别功能赋能盲校物理探究式教学
created_at: 2025-09-11 16:23:03.783801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9EhX9mG_dZP8XrJK2D-zi70C0HaN8cb4dJBVnjDDisY-LAWlZ31eCrAsk162pXz0YCDI3Rl_9-fc8gvT3x5A2V-vPSV8YXLBgyB9YylwtChlQS6AGrpKeKpO6LVyt_VBGZY6lNDq1dswAsxSj3laMhXv25W5_HVkguX-oIuflzHQ==&uniplatform=NZKPT&language=CHS

summary:
  人工智能图像识别功能可以快速生成图像的文字描述。在盲校物理探究式教学中，教师可合理应用人工智能图像识别功能，提高图像描述的撰写效率，让描述内容更加全面具体，同时引导盲生利用人工智能图像识别功能开展自主探究学习，培养其自主学习能力。

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author: 钟绍辉
date: 2025-09-08
keywords: 太阳能储能设备;;深度学习;;图像识别;;问题检测;
source: 储能科学与技术
site: 知网
title: 基于深度学习与图像识别的太阳能储能设备问题检测
created_at: 2025-09-11 16:23:03.786801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_4f47LncJtx4aSfUSEpY57spM3lG0eNC0fVZHLDPXDEmihY0OglI_vsv5nH_RjOwtIy96-RNFgDCtNzelHJVtl-SimtFIydiAe4NriKuJKGuLyEBMtqVJrZJLkborptmawSY_u24a3SbfzL1SbHXyo0eSmHutmiQ_cW_wRXd44cw==&uniplatform=NZKPT&language=CHS

summary:
  随着太阳能储能设备的广泛应用，对其运行状态的实时监测和问题检测变得至关重要。本文结合深度学习与图像识别技术的相关技术以及理论基础提出了一种太阳能储能设备问题检测方法。通过采集太阳能储能设备的图像数据，利用深度学习算法对图像进行处理和分析，实现对设备潜在问题的准确识别和定位。该方法具有较高的检测准确率和效率，能够为太阳能储能设备的维护和管理提供有力支持。

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author: 杨丽萍
date: 2025-09-01
keywords: 图像识别;;桥梁;;裂缝检测;
source: 信息记录材料
site: 知网
title: 基于图像识别技术的桥梁裂缝检测方法研究
created_at: 2025-09-11 16:23:03.787803
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_Ge9PJxR_fkgFP4XwKqPpfYbwAn72Zkp6bZrPlC_NFCQg22iUinTwFaMe03ERJakiQOCj98t96BoKj1dbLs6vYOtHPo8URY2Znr65LLfzl_lVW-c-S9Js7fBClM_Puz1p0aBcFSByE3_Fa91c71-unOhjhwONfjpjoqMZEuFR4Tw==&uniplatform=NZKPT&language=CHS

summary:
  针对传统桥梁裂缝检测方法存在的效率低、精度不稳定等问题，本研究提出了一种基于图像识别技术的桥梁裂缝自动检测方法。通过图像采集与预处理、裂缝特征提取、深度学习(DL)模型构建与训练等步骤，构建了完整的裂缝识别系统，并将其与传统人工检测方法进行了对比测试。结果表明：该方法在裂缝长度与宽度检测方面与人工实测结果高度吻合，具备较高的识别精度与良好的稳定性，且检测效率显著提升。该技术适用于大范围、高难度环境下的桥梁裂缝监测，具有广阔的工程应用前景。

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author: 李嘉卉;佟冰;赵腾;李伟峰;许多
date: 2025-08-25
keywords: 图像识别;;RH精炼;;底吹氩;;卷积神经网络;;强化学习;
source: 工业控制计算机
site: 知网
title: 基于图像识别的RH底吹氩控制系统
created_at: 2025-09-11 16:23:03.788800
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9ygF4W8Yw6FSxJcqyGWIh15_Pw8pw3GOBY247O3_JkVmxrC4SdwQZNLuHcS5VuAW-7V45fS-NTYysVxE-tRNz1EFzv-UigOY9z4hUyk0PbGoY1dF5ZUx_qPWdWUl0Y9vk9tL9OcdCWryZwWH-NCv3Che6H6TuQ7aAVSjOb_vrbNA==&uniplatform=NZKPT&language=CHS

summary:
  随着钢铁冶炼技术的发展，RH(Ruhrstahl Heraeus)精炼工艺成为提高钢材质量的重要手段。在RH精炼过程中，氩气的底吹对钢液的脱气、均匀化、脱硫等环节至关重要。然而，传统的氩气吹入控制方法难以精确把握钢液的复杂动态变化，影响了炼钢的稳定性和成品质量。提出了一种基于图像识别技术的RH底吹氩控制系统，通过卷积神经网络（CNN）处理钢液表面图像，结合强化学习算法优化氩气吹入策略，从而实现智能化、精确化的氩气控制。实验结果表明，该系统有效提升了钢液的均匀性与脱硫效率，显著改善了钢材的整体质量。

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author: 张宏;刘钊;赵洪波;张海滨;唐佳民
date: 2025-08-25
keywords: 焊缝缺陷;;图像识别;;石油工业;;焊缝检测;;神经网络;
source: 自动化应用
site: 知网
title: 焊缝缺陷图像识别技术研究综述
created_at: 2025-09-11 16:23:03.789800
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_mDdn9Xf_UR1ip_L5ntFwAPLHhjoemQIwqAxgU8YDQyIUTiBYmuJstmJh_gF-o3FUPXxoZOCRIJBxpJSEGZ1KPQmi6bD0Tr61jRaTbx-39PjggSGMINJvQm6E8NFGqoLrKYZGw6FqAwBbOiMKZLNs92T_D2mm_nN44G4fi2C8V-A==&uniplatform=NZKPT&language=CHS

summary:
  焊缝缺陷图像识别技术作为焊缝缺陷检测中重要的技术手段，具有广阔的应用前景。焊接技术作为一种关键的制造工艺，在石油工业领域中占据着重要地位，然而，焊缝的质量控制和缺陷检测一直是焊接工艺中的关键挑战之一。旨在对焊缝缺陷图像识别技术进行分析，以便更深层次地理解该技术，从而更好地应用。首先，回顾了传统焊缝检测方法，指出了传统方法的局限与不足，并对现有的焊缝缺陷检测技术（如超声相控阵、超声无损检测、漏磁检测和射线检测）进行了探讨，归纳了图像识别技术在焊缝缺陷检测中的应用情况。然后，收集并分析了焊缝缺陷图像识别方面的资料，对未来焊缝缺陷图像识别技术的发展方向和可能的应用领域作出了展望。该成果对于了解和评估焊缝缺陷图像识别技术的研究现状和未来发展具有重要参考价值。

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author: 王建伟
date: 2025-08-15
keywords: 卷积神经网络;;图像识别;;特征融合;;深度监督学习;;正则化;
source: 网络安全技术与应用
site: 知网
title: 基于卷积神经网络的图像识别技术优化
created_at: 2025-09-11 16:23:03.791801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_mDdn9Xf_UR1ip_L5ntFwAPLHhjoemQIxYiVRbgGmnOGR4AadwUVXTBW45IjQ5r7f3hSGjvotN66sKy2apa9pAa68zHJUP2KzhH2Dkl5qxTIBVBJVuAovCRp_u2faTFaUga3qWs9T-ggDlOGq99p7cOPcLzbyddUcjoySgUjVuSA==&uniplatform=NZKPT&language=CHS

summary:
  本文针对传统卷积神经网络（Convolutional Neural Network,CNN）在图像识别任务中存在的过拟合和训练效率低等问题，提出了一种基于特征融合、深度监督学习和正则化技术的优化方案。通过在CIFAR-100数据集上的实验验证，改进后的CNN模型在准确率、F1得分和训练时间方面均优于传统模型，显著提升了图像识别的精度和泛化能力。该方法有效增强了模型对细节和全局特征的捕捉能力，为图像识别技术的优化提供了新的解决思路。

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author: 唐泽
date: 2025-08-15
keywords: 具身认知;;基本概念;;图像识别;
source: 教育传播与技术
site: 知网
title: 具身认知理论视角下小学人工智能基本概念教学策略——以四年级上册“探秘图像识别”一课为例
created_at: 2025-09-11 16:23:03.792801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_PDo8hEFRfFXUr5vp0ibvvT0BlAPKffkCJRgBSEJze2OqR9hZmB1Xy-i2ef2yg5RzoNNOj4GmrJ_etM3QlRpNjm02f53fkAEs7l0S7lMr6rm89UTDRYrLpplcNrxYl5X7hIqmGaZsaaOw87D2Oyfjf2LikiUQB9kRrGFoiqFhT3g==&uniplatform=NZKPT&language=CHS

summary:
  为解决小学人工智能基础课程中存在的“基本概念抽象、学生兴趣衰减”的难题，文章基于具身认知理论，探索适用于人工智能课程中基本概念的教学策略，具体包括游戏模拟、生活映射、实验探究。对上海市中小学人工智能基础课程小学四年级“探秘图像识别”这一课例展开研究，在实践中发现这些教学策略能显著降低学生学习人工智能基本概念时的认知负荷，使得大部分学生能够准确描述图像识别的一般过程，并能迁移分析图像识别的应用场景，进而感受技术的价值。

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author: 林超;张世杰;张子龙
date: 2025-08-15
keywords: 高速铁路;;钢轨伸缩调节器;;智能监测;;图像识别;;嵌入式开发;
source: 中国铁路
site: 知网
title: 基于图像识别的高速铁路钢轨伸缩调节器变形识别系统设计与应用
created_at: 2025-09-11 16:23:03.793801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_PDo8hEFRfFXUr5vp0ibvveQ1_Nid4a29s2KeyO21y8xqrAapNwOc0dXix6QUh-htgpMNJh7e8ll_lyoUumtTFWwRI9ZrYdXzRJk7ko1cjpLAhW-j7TDvNjGgjcvGstUkIrS4zJIptE8LYtMWNU0-vySzVtOZc5MIEpCVEdDVYtA==&uniplatform=NZKPT&language=CHS

summary:
  钢轨伸缩调节器作为高速铁路轨道结构的薄弱环节之一，是高铁养护维修的重难点。高速铁路钢轨伸缩调节器运行状态主要采用人工巡检+轨检车定期检测的方式确定，存在夜间天窗时间短，白天无法实时掌握调节器状态等问题。因此提出高速铁路钢轨伸缩调节器变形实时监测方案，应用于有砟和无砟轨道钢轨伸缩调节器的监测工作。基于图像识别技术自动化获取钢轨伸缩调节器尖轨伸缩位移、基本轨伸缩位移、伸缩装置纵向位移以及钢枕间距等变形情况，通过位移识别算法实现钢轨伸缩调节器变形精准识别；采用嵌入式开发实现图像数据流现场处理，保证系统高频数据采集与高效数据处理和传输。成果已经在福厦高铁4座大跨度桥梁钢轨伸缩调节器监测工作中应用，变形识别精度达到了±1 mm，为高铁运营安全提供了保障。

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author: 孟田祥;虞凤萍;佟兵;李刚
date: 2025-08-15
source: 中国科技信息
site: 知网
title: 智能驾驶领域图像识别技术专利分析
created_at: 2025-09-11 16:23:03.794801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-SieOVTFvL9TPrx-A8-rAOZlsRfD04ZZZz9lthuKPh-Jvas2EVAMsnD9G4pwjia0l3idaiGK2pA711qS-rtKqZ9ooKUza52jp25hcA77zXr46mzeEJSSKXP3nq-D_PHusFM5Aqym6oDklNjp6bDFalFxJ8xMC3Md4Q6kDDQ0cDvA==&uniplatform=NZKPT&language=CHS

summary:
  <正>1背景随着人工智能与物联网技术的飞速发展，智能驾驶已成为全球交通出行领域的核心研究方向之一。作为智能驾驶系统的核心技术，环境感知通过图像识别手段实时获取道路、行人、车辆及交通标识等信息，为决策与控制提供关键依据。然而，复杂的动态场景（如光照变化、行人姿态多样性、遮挡问题等）对图像识别的实时性与鲁棒性提出了严峻挑战，推动相关技术持续迭代创新。在此背景下，专利分析成为洞察技术演进趋势、挖掘竞争格局的重要工具，其研究成果可为产业技术布局与政策制定提供科学支撑。本研究基于专利分析视角，系统梳理智能驾驶领域图像识别技术的全球发展脉络，结合技术生命周期理论，通过挖掘重点专利的技术特征与演进路径，揭示创新主体的布局策略，为中国智能驾驶产业的技术突破与知识产权保护提供参考。

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author: 李志强;田少波;王伟;李红育;陈帅宇
date: 2025-08-13
keywords: 生成对抗网络;;图像识别;;图像增强;;数据增强;
source: 科技与创新
site: 知网
title: 基于生成对抗网络的图像识别与增强技术研究
created_at: 2025-09-11 16:23:03.795801
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-PcM677gd0yUK1Mt3V_zaJGEOJIjA7DYWar7p4fy7XUmhogVhSapyGOupUew5mibpZ8e-zhprNee73emDDPESGN1WXS3uv2e_UGNaTPLRR_vYqUzw07fyrkY4g1WwZ04PTfHoqtc1iC8HgBWql76DU3RpN5o8eGSaaXHIj6Hwzcg==&uniplatform=NZKPT&language=CHS

summary:
  概述了生成对抗网络(GANs)的基本原理及其在图像处理领域的独特优势,深入分析了GANs在图像识别中的应用,涵盖了利用生成逼真图像来扩展训练数据集、在图像特征提取中的应用,以及生成对抗样本以增强识别模型的泛化能力。同时,还探讨了GANs在图像增强方面的作用,如图像超分辨率、去噪、去模糊等,并详细讨论了条件生成对抗网络在图像增强中的具体应用。通过对GANs在图像识别与增强中的技术研究,旨在为相关领域提供新的思路和方法,以推动图像处理技术的进一步发展。

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author: 汤斌;贺渝龙;唐欢
date: 2025年1期
keywords: 高光谱数据预处理;霉斑识别;纸质文物;注意力机制;图像分割
quote: 0
source: 《光谱学与光谱分析》
site: 万方
title: 基于注意力机制的高光谱图像降维在纸质文物霉斑识别的研究
created_at: 2025-09-11 16:23:03.797802
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdweHlncGZ4MjAyNTAxMDM5GggzOW40NnpqNA%3D%3D

summary:
  纸质文物作为文物传承的重要工具,用于记录不同时期人类历史及人文风貌,其在保存过程中极易受到霉菌等微生物的侵害.霉菌会加速纤维素的降解,在纸张表面生成霉斑,并且散落的孢子会随空气流动大范围传播,增加其他纸质文物发生霉变的风险.因此,定期对纸质文物进行霉斑检测对了解纸质文物现状和纸质文物修复至关重要.高光谱成像技术是一种非接触性、非破坏性的检测技术,能同时获得空间数据和光谱数据,与计算机技术结合可以实现纸质文物的大批次实时无损检测.针对黑曲霉这一广泛出现的霉菌,提出一种基于注意力机制的高光谱数据降维方法,通过采集其高光谱数据,实现了高光谱冗余数据的自适应预处理.采集了来自重庆中国三峡博物馆提供的20份纸质文物黑曲霉霉斑样本,使用ENVI软件分析得出在413～855 nm波段范围内,黑曲霉霉斑感染区域和健康区域的平均光谱曲线,平均反射率差异明显;在855～1 021 nm波段范围内,黑曲霉霉斑感染区域和墨迹区域的平均光谱曲线,平均反射率差异明显.文中将所提出方法与传统主成分分析和独立成分分析预处理方法分别处理原始高光谱数据,并将结果在经典U-Net、SegNet、DeepLabV3+和PSPNet四个语义分割网络上进行了对比.结果表明,该算法预处理的数据在U-Net和SegNet经典网络中有明显优势,相较于主成分分析法和独立成分分析法,霉斑识别精度取得了较大提升达到89.49％和88.46％,验证了本文所提出算法的有效性,为文物保护领域提供有效的支撑和新的思路.

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author: 张洪;蒋小刚;朱志伟
date: 2025年1期
keywords: 斜拉索;表观病害;图像识别;深度学习
quote: 1
source: 《西南交通大学学报》
site: 万方
title: 斜拉索表观病害图像智能识别综述
created_at: 2025-09-11 16:23:03.798801
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhuanRkeHhiMjAyNTAxMDAyGggzOW40NnpqNA%3D%3D

summary:
  斜拉索是斜拉桥的主要承重构件之一,其外层护套病害容易渗透影响索内钢丝健康,采用视频图像法智能识别出拉索表观病害意义重大.从传统图像检测和深度学习两方面系统地综述了基于图像识别的斜拉索表观损伤识别方法,以及各个方法的基本原理和应用效果,对目前的检测实例进行深入分析;介绍一些前沿深度学习方法,为拉索表观检测提供参考;归纳各类方法的主要特点,对目前检测中存在的问题进行了探讨和展望.以深度学习模型为主的图像识别方法具有较好的识别准确率和算法鲁棒性、较强的学习能力和适应性,综合图像缺陷识别效果最优,但仍存在检测精度和速度难平衡、图像数据需求大和标注成本高等难点.为此,可以采取提高图像质量、构建更多半监督和无监督相关的深度学习模型、提升检测模型学习能力等手段改善检测方法.

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author: 杨育婷;李玲玲;刘旭
date: 2025年2期
keywords: Transformer;多尺度;多方向;特征表示;图像识别
quote: 1
source: 《计算机学报》
site: 万方
title: 基于多尺度-多方向Transformer的图像识别
created_at: 2025-09-11 16:23:03.800849
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzanhiMjAyNTAyMDAxGggzOW40NnpqNA%3D%3D

summary:
  有效的特征表示对提升深度学习模型的表征能力和图像识别性能至关重要.例如,多尺度特征表示方法能够捕捉不同尺度的丰富信息,有助于提高深度学习模型的图像识别性能.然而,当前的多尺度深度学习方法仍存在对图像方向特征建模不明确的局限,导致对具有方向性目标的误识别.为了更好地表示图像中蕴含的多方向特征,本文提出了一种基于多尺度-多方向Transformer的网络框架(MSMDFormer).首先,该框架中设计了一种能够捕获并增强多个方向特征的多方向特征编码器.在此基础上,本文联合了不同尺度的Gabor表征与多头注意力机制,设计了一种多尺度多方向Transformer编码器,以有效地聚合图像的多尺度和多方向特征.最后,该框架对卷积特征和多尺度-多方向特征进行融合,然后将融合特征用于图像识别.实验结果表明,MSMDFormer 在CIFAR10、CIFAR100和SVHN数据集上分别取得了95.65%、77.46%和96.87%的整体准确率,在与19种基准方法的对比中显示出具有竞争力的图像分类性能.与11种图像分割基准方法相比,MSMDFormer在ADE20K数据集上展现出0.33%至6.58%mIoU的性能增益.综上所述,本文提出的MSMDFormer在深度学习图像识别任务中展现了卓越的特征表示能力,具有广泛的应用前景.另外,探索更有效的方向特征表示方法将成为未来研究的重要方向.

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author: 周宇;姚心弦;姚凯洲
date: 2025年1期
keywords: 轨道交通;钢轨;表面伤损;目标检测;图像识别
quote: 1
source: 《同济大学学报（自然科学版）》
site: 万方
title: 钢轨表面伤损的细粒度图像识别
created_at: 2025-09-11 16:23:03.802846
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3RqZHh4YjIwMjUwMTAxMhoIMzluNDZ6ajQ%3D

summary:
  基于智慧工务对钢轨轨面状态和伤损的精准识别、定量化修理等需求,结合深度学习和机器视觉,提出了钢轨轨面伤损细粒度图像识别与量化方法.通过采集轨面状态和伤损图像并实现伤损细粒度标注,建立轨面伤损RD-1094数据集,其中的目标密度达到了每图22.9个.建立轨面伤损深度学习目标检测算法,通过对RD-1094数据集的训练和学习,实现了对0.5～30mm的剥离掉块、波长20～200mm的波磨等轨面伤损及其各自发展阶段特征的识别,达到毫米级细粒度.算法对单双排波磨、细小密集和轻重伤剥离掉块、单支和成片疲劳裂纹等能较好的兼容性,可以实现轨面光带形位、伤损尺寸、轻重伤总数、分布面积、波磨波长等状态和伤损的量化评估.

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author: 谷瑞;宋翠玲;李元昊
date: 2025年6期
keywords: 中药饮片;BIM-Res2Net-ICA;多尺度特征;改进的坐标注意力;图像识别
quote: 0
source: 《时珍国医国药》
site: 万方
title: 基于BIM-Res2Net-ICA的中药饮片图像识别模型
created_at: 2025-09-11 16:23:03.804846
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3N6Z3lneTIwMjUwNjAzNhoIMzluNDZ6ajQ%3D

summary:
  中药饮片是中医药文化的重要载体,针对现有中药饮片识别模型准确率和泛化能力不足的问题,文章从增强多尺度特征适应性和突出重要特征区域两方面入手,提出一种新的中药饮片图像识别模型 BIM-Res2Net-ICA.首先,在Res2Net内部构建双向融合策略,以提升不同尺度特征融合的有效性,获取更丰富的特征信息;其次,嵌入改进的坐标注意力机制,在空间和通道两个方向上增强模型对目标的方向感知和坐标信息的捕获能力;最后,结合Focal Loss损失函数有效克服样本分布不均的问题,进一步提高分类的准确性.实验表明,该模型在构建的20 类中药饮片数据集上的准确率、精确率、召回率、F1-Score分别为97.48%、96.32%、96.92%和97.59%,与其他先进算法相比,具有更低的计算复杂度和更高的准确率,且模型具有较强的泛化能力,能为各种中药识别场景提供准确的算法支持.

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author: 孙继平;李小伟
date: 2025年1期
keywords: 矿井火灾;图像顺滑度;图像边界;火灾监测;图像识别
quote: 1
source: 《中国矿业大学学报》
site: 万方
title: 基于图像顺滑度的矿井外因火灾识别及抗干扰方法研究
created_at: 2025-09-11 16:23:03.806849
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpna3lkeHhiMjAyNTAxMDE2GggzOW40NnpqNA%3D%3D

summary:
  图像监测是矿井火灾火焰主要感知方法,但受矿井光源干扰.图像圆形度和矩形度方法受摄像机安装位置、拍摄目标图像角度影响大,难以排除矿井光源的干扰.本文揭示了圆形灯、正方形灯和长方形灯等矿井光源图像实际边界周长近似等于其等面积矩形图像边界周长,火焰图像实际边界周长明显大于其等面积矩形图像边界周长的特点.提出图像顺滑度计算方法,用形状与外接矩形图像相似、面积与目标图像面积相等的相似矩形周长,除以图像实际边界周长的比值,表示图像顺滑度.目标图像顺滑度数值越小,说明图像边界越不顺滑、凸凹越严重.提出基于图像顺滑度的矿井外因火灾识别及抗干扰方法,计算目标图像顺滑度,根据矿井光源图像顺滑度数值较大,而火焰图像顺滑度数值较小,区分火焰与矿井光源.本方法不受矿井光源形状、摄像机距检测目标距离和图像大小、摄像机安装位置及其拍摄检测目标的角度等影响,适应范围广,识别准确率高.试验研究表明,顺滑度识别火灾火焰图像准确率为98.1％,召回率为97.9％;矩形度识别准确率为81％,召回率为78.9％;圆形度识别准确率仅为33.3％,召回率为28.9％.

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author: 李珊;林鹏;许振浩
date: 2025年8期
keywords: 高光谱成像;岩性识别;迁移学习;三维残差神经网络;光谱特征
quote: 0
source: 《光谱学与光谱分析》
site: 万方
title: 基于岩石高光谱图像迁移学习的岩性智能识别
created_at: 2025-09-11 16:23:03.807850
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdweHlncGZ4MjAyNTA4MDMyGggzOW40NnpqNA%3D%3D

summary:
  岩性快速识别具有重要的基础地质研究意义与工程应用价值.传统岩性识别主要依赖于岩石图像特征,但在识别外观相似的岩石时易出现混淆.为此,相关研究进一步利用光谱特征以反映岩石组成信息,但光谱测试通常需要制样,且属于有损测试.为此,凭借成像高光谱技术图谱合一、无损非接触的优势,提出了一种基于岩石高光谱图像迁移学习的岩性智能识别方法.首先,对岩石感兴趣区域的高光谱数据进行归一化和降维处理,降低光谱数据的冗余度.然后,结合三维残差网络(3D-ResNet)构建岩石高光谱图像迁移学习模型,利用残差网络提取三维信息.再利用迁移学习的方法,通过加载预训练权重对模型进行训练,从而实现岩性智能识别.采用混淆矩阵、准确率(ACC)、精确率(P)、召回率(R)、F1值(F1)作为模型准确率的评价指标,针对基于ResNet-101和ResNet-18/34/50的模型开展对比分析.结果表明,ResNet-101迁移模型在测试集的准确率最高,可达98.29％.P最高可达98.32％,R最高可达98.29％,F1最高可达98.31％.ResNet-101对岩石光谱数据识别的准确率基本达到90％以上(除了绿泥石片岩),大部分结果甚至可以达到100％.相较于ResNet-18/34/50,ResNet-101识别精度更高且对每类岩石识别的稳定性更好.此外,利用本方法对隧道现场采样岩石进行了逐个像素点的岩性预测,验证了本文提出的岩性智能识别方法具有良好的鲁棒性和泛化性,可用于地质、测井、交通、水利等工程中的岩性快速智能识别.

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author: 刘传洋;吴一全
date: 2025年6期
keywords: 电力设备识别;发热故障诊断;红外图像;传统图像处理;深度学习;视觉检测
quote: 6
source: 《中国电机工程学报》
site: 万方
title: 基于红外图像的电力设备识别及发热故障诊断方法研究进展
created_at: 2025-09-11 16:23:03.809847
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpnZGpnY3hiMjAyNTA2MDExGggzOW40NnpqNA%3D%3D

summary:
  在电力大数据背景下,依托机器视觉和深度学习技术,从海量的红外图像数据中实现电力设备识别及发热故障诊断,已经成为该领域运维工作亟待解决的问题.该文以红外图像为研究对象,综述了基于传统图像处理和基于深度学习两类方法的红外图像中电力设备识别及发热故障诊断研究进展.首先,概述电力设备红外图像识别及发热故障诊断的发展历程及技术流程;然后,阐明基于传统图像处理的电力设备识别及发热故障诊断方法,从图像预处理、图像配准、图像分割、特征提取与分类、发热故障诊断5个方面进行归纳总结;阐述基于深度学习的变电站设备和输电线路设备识别及发热故障诊断方法,与传统图像处理方法相比,深度学习方法能够快速准确地识别电力设备发热故障;最后,指出基于深度学习的视觉技术在电力设备识别及发热故障诊断应用中存在的问题,基于现有的深度学习技术和最近的研究思路,对未来研究工作进行展望.

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author: 孙继平;李小伟
date: 2025年1期
keywords: 矿井火灾;图像凹陷度;图像边界;凹陷区域;火灾监测;图像识别
quote: 0
source: 《煤炭科学技术》
site: 万方
title: 矿井外因火灾图像凹陷度识别方法
created_at: 2025-09-11 16:23:03.810851
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD210a3hqczIwMjUwMTAyNxoIMzluNDZ6ajQ%3D

summary:
  图像监测是矿井火灾火焰主要感知方法,但受矿井光源影响.内凹度能减小摄像机的安装位置、拍摄距离和拍摄角度的影响,排除矿井光源干扰,快速识别并排除矩形光源等非弧形类光源正视图及其变形图像;但对于圆形光源和类圆形光源等弧形类干扰光源,计算量较大,识别时间较长.圆形度能够排除圆形光源的干扰,但难以排除非圆形光源的干扰.矩形度能排除矩形光源的干扰,但难以排除非矩形光源的干扰.受摄像机安装位置和拍摄角度等影响,圆形和矩形光源图像会出现变形,无法呈现理想的规则形状.因此,圆形度和矩形度算法难以排除矿井光源的干扰.揭示了火焰图像边界上存在多个凹陷区域,总凹陷区域面积较大;圆形灯、长方形灯和正方形灯等矿井实际光源图像边界上不存在凹陷区域等特点.提出矿井外因火灾图像凹陷度识别方法,计算目标图像边界总凹陷区域面积与其目标图像实际面积的比值(即图像凹陷度),根据火焰图像凹陷度数值较大,而矿井光源图像凹陷度数值较小,区分矿井光源与火焰.研究提出的凹陷度方法不受摄像机安装位置、拍摄距离和拍摄角度、矿井光源形状和大小等影响,适应性强,识别准确率高.凹陷度识别方法计算得到的矿井干扰光源图像与火焰图像凹陷度平均差值绝对值最大,波动较小,区分度最好.内凹度识别方法计算得到的矿井干扰光源图像与火焰图像内凹度平均差值绝对值较大,波动较小,区分度较好.矩形度识别方法计算得到的矿井干扰光源减去火焰图像矩形度平均差值绝对值较小,波动较大,区分度较差.圆形度识别方法计算得到的矿井干扰光源减去火焰图像圆形度平均差值绝对值最小,波动最大,区分度最差.结果表明:凹陷度识别火灾火焰图像准确率为 98.2%,召回率为 98.4%,准确率和召回率最好;内凹度识别火灾火焰图像准确率为 92.8%,召回率为 92.4%,准确率和召回率较好;矩形度识别准确率为 86.5%,召回率为 86.5%,准确率和召回率较差;圆形度识别准确率仅为35.9%,召回率为31.9%,准确率和召回率最差.

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author: 于美玲;周云成;侯玉涵
date: 2025年2期
keywords: 日光温室;场景识别;特征聚合;视觉Transformer;图像处理
quote: 0
source: 《农业机械学报》
site: 万方
title: 基于局部图像特征聚合的温室场景识别方法
created_at: 2025-09-11 16:23:03.811846
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD255anh4YjIwMjUwMjA0NRoIMzluNDZ6ajQ%3D

summary:
  场景识别可作为温室环境空间定位的替代方案,也是智能农机装备视觉系统的重要功能之一.针对以特征聚类为基础的场景识别范式无法适应高动态变化且高度相似的温室场景识别的问题,提出一种基于深度特征聚合的温室场景识别方法,以预训练的视觉Transformer网络为基础,提取场景图像局部特征,应用多层感知机全局感受野特性,考虑局部特征空间关系,融合图像局部特征,生成场景图像全局描述子,以多重相似性损失最小化为优化目标,构建温室场景识别模型.试验结果表明,模型场景识别R@1(top-1召回率)、R@5和R@10分别达到78.43％、89.21％和92.47％,具有较高的场景识别精度.所提出的基于多层感知机的特征混合方法是有效的,与采用池化操作进行特征聚合相比,R@1提高8.01个百分点.模型对光照条件变化具有一定的鲁棒性,与正常的中等光照条件相比,强光及弱光条件下,R@1下降未超过4.00个百分点.相机视角及采样距离的变化也会影响模型识别性能,20°以内的视角变化,R@1下降6.61个百分点,2倍以内的距离变化,R@1下降17.87个百分点.与现有场景识别基准方法 NetVLAD、GeM、Patch-NetVLAD、MultiRes-NetVLAD 和 MixVPR 相比,R@1分别提高 7.82、6.59、3.56、4.14、1.88个百分点,在温室场景识别任务上模型性能有较大提升.该研究构建的基于多层感知机的图像全局特征聚合方法,能够生成可靠的全局描述子,用于温室场景识别,且具有一定的光照、视角、距离及时间变化的鲁棒性,研究结果可为智能农机视觉系统设计提供技术参考.

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author: 王思敏;张德洲;赵静;王超群;李琨;陈杰;白雪;赵海龙;张少杰;马渊;郝韵腾;杨洋
keywords: 临床应用;工程化组织构建;颈椎;深度学习;人工智能;人工智能模型;医学图像;研究进展
source: 中国组织工程研究
site: 维普
title: 人工智能与颈椎图像识别:应用前景与挑战
created_at: 2025-09-11 16:23:03.812847
original_link:
  https://www.cqvip.com/doc/journal/3477167963?sign=85593b98b20c6bf433c4476d9b28002eac7b2a2133b0ada10222623579c5335b&expireTime=1789114710544&resourceId=3477167963

summary:
  背景:颈椎病是一种慢性退行性疾病,已经成为威胁人类健康的常见病和多发病之一。目前对颈椎及其周围结构病变的初步诊断主要倚赖于放射科医师对医学影像的解读,这不仅对操作人员技术要求较高,而且存在主观性较强、劳动强度高、效率低等缺点。随着人工智能技术的快速发展,其强大的数据处理和图像识别能力使其在医疗领域展现出广阔的应用前景,深度学习也在脊柱疾病的研究中取得了一定的进展。目的:综述近年来人工智能技术在颈椎影像图像中的应用现状和研究进展,评估人工智能模型的表现以及未来的发展趋势及需要克服的挑战。方法:由第一作者在2024年6月以“人工智能,深度学习,颈椎”为中文检索词,以“Artificial Intelligence,AI,Cervical Vertebrae,Cervical”为英文检索词分别在万方数据库、中国知网和PubMed数据库进行检索,最终纳入101篇文章进行综述分析。结果与结论:①人工智能技术可通过对医学图像部位进行分割、分类、关键点识别等技术实现对颈椎椎体的自动分割及曲度改变的测量,检测颈椎骨折、神经根和脊髓型颈椎病,识别颈椎后纵韧带骨化,预测手术后相关危险因素以及颈椎成熟度分类等;②尽管人工智能技术在颈椎研究领域已展现出巨大潜力,但其仍处于初期探索与快速发展阶段,有无限的发展与创新空间。

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author: 彭炜峰;罗江华;张洲亚
source: 农机化研究
site: 维普
title: 基于注意力胶囊网络的梨树叶部病害图像识别方法
created_at: 2025-09-11 16:23:03.813800
original_link:
  https://www.cqvip.com/doc/journal/3485146955?sign=e09552a1d09af6f862dc6e5dc7abc8547a136b4ffc5bf65d2321931d5ef1c9f9&expireTime=1789114710544&resourceId=3485146955

summary:
  针对梨树叶部病害人工识别防治工作中存在主观性强、耗时费工、效率低下、误判率高,以及当前部分网络算法和信息技术对梨树叶部病害图像识别准确率较低的问题,以重庆市永川区黄瓜山梨种植基地为研究区域,以胶囊网络为基础,提出了一种基于注意力胶囊网络的梨树叶部病害图像识别方法。首先,融入优化的注意力机制和残差密集单元,提高模型对病害图像特征提取能力;其次,在不改变网络结构的基础上,验证了不同参数、不同迭代次数对病害图像识别效果的影响;最后,在基本参数相同的情况下,验证了不同算法对病害图像识别的有效性。通过与传统胶囊网络、卷积神经网络、CNN等多种算法在准确率、有效性方面的对比试验,基于注意力胶囊网络的梨树叶部病害识别方法准确率达到93.74%,能够有效抑制病害图像中不相关特征,并很好地提取梨树叶部病害图像有效关键特征,具有较强的病害图像识别能力。研究结果可为梨树叶部病害智能识别诊断鉴定提供新思路、新方法,对推进智慧农业发展和农业农村现代化建设提供理论参考。

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author: 戈秀龙;戴文华;李积武;赵云;沈佳健
source: 农机化研究
site: 维普
title: 基于OpenMV模组和MobileNet V2模型的葡萄图像识别
created_at: 2025-09-11 16:23:03.814801
original_link:
  https://www.cqvip.com/doc/journal/3485157052?sign=27672978d325957d05ca90bb69a4a62f02072b432bc8b49019f8a36519806032&expireTime=1789114710544&resourceId=3485157052

summary:
  为了提高葡萄种植棚内葡萄自动采摘的准确度和效率,在葡萄的图像识别中必须依靠性能优越的嵌入式计算机和轻量级低延迟的神经网络模型。为此,基于OpenMV4 H7 Plus和OpenMV IDE开发环境,在葡萄种植棚中现场采集葡萄图片,利用PyCharm的Python编程,经过旋转、平移、缩放和噪声添加等操作后形成扩展的深度学习葡萄图像数据集;应用Edge Impulse搭建模型规模小、检测速度快的MobileNet V2神经网络,以像素96×96作为输入并采用迁移学习的方式训练,得到反映模型质量的Epoch Loss、Train Loss、Recall和F 1 Score等指标,显示在经过一定的学习循环后Epoch Loss、Train Loss均收敛到较小值且Recall和F 1 Score逐步趋于稳定,其验证集准确度为92.4%;同时,搭建了一个试验装置,将所得模型部署到OpenMV4 H7 Plus模块,模拟试验摄像头相对葡萄横向移动时的识别效果。研究结果表明:配置了神经网络学习算法的OpenMV4 H7 Plus模块对葡萄识别准确度较高且识别速度较快,具有一定的应用价值。

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author: 王海光
source: 植物病理学报
site: 维普
title: 植物病害图像识别及其发展前景
created_at: 2025-09-11 16:23:03.816803
original_link:
  https://www.cqvip.com/doc/journal/3507521266?sign=c434e574495c15dc02ec5a5995190c7c173b3e669068f5cffd1686b34f8f815c&expireTime=1789114710544&resourceId=3507521266

summary:
  植物病害可对农业生产造成严重危害。及时、准确识别植物病害是开展有效病害管理的基础和前提。随着信息技术的迅速发展,利用图像处理技术进行植物病害识别的研究和应用日益增多,提高了植物病害监测和管理水平,为保障农业安全生产提供了有力支撑。本文从植物病害图像识别、病害图像获取和预处理、病害图像分割、病害图像特征提取和选择、病害图像识别模型和应用等方面对植物病害图像识别研究和应用中存在的问题进行讨论,提出相关的解决方案,并从病害图像获取和管理、病害图像识别关键技术、多平台病害图像识别等方面对未来植物病害图像识别研究和应用前景进行展望,旨在为植物病害图像识别研究和应用提供参考,促进植保信息化和智慧植保的发展。

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author: 刘思远;张文东;卢润;牛森;马梦楠
keywords: 特征提取;卷积神经网络;并行注意力;多尺度特征;金字塔卷积;图像识别
source: 现代电子技术
site: 维普
title: RPPM-Net:基于多尺度特征并行融合的图像识别方法
created_at: 2025-09-11 16:23:03.817804
original_link:
  https://www.cqvip.com/doc/journal/3506678128?sign=90eaf2c3c32734531a8b1c52cd3053ff9e8e204768e833f08fade734a195def2&expireTime=1789114710544&resourceId=3506678128

summary:
  针对传统深度神经网络在图像识别任务中存在的无法有效关注重点区域与特征、感受野较小导致信息退化以及模型泛化能力不足的问题,文中提出一种基于多尺度并行融合的图像识别网络,即残差并行金字塔多尺度注意力网络(RPPM-Net)。首先,设计并行注意力网络,通过多头并行的混合注意力机制捕获多样化的特征信息,在保持模型效率的同时,增强网络在识别任务中对重要区域和关键特征的聚焦能力,解决对重点特征关注不足的问题;其次,设计多尺度金字塔卷积注意力模块,通过在不同层级有效集成多尺度特征,解决感受野较小导致的信息退化问题;最后,采用增强型全连接层及正则化技术,有效缓解过拟合问题,提升模型在不同数据集上的泛化能力。实验结果表明,RPPM-Net在CIFAR-10、CIFAR-100、Caltech-256数据集上的准确率分别达到97.02%、85.04%、89.69%,在不显著增加计算成本的前提下,充分结合结构正则化和特征整合,有效地增强了网络的性能。

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author: 姜宁;陈静;余昌霞;苏海辉;董浩然;李巧珍;于海龙;李正鹏
keywords: YOLOv5;菌盖斑点;深度学习;斑玉蕈;图像识别
source: 食用菌学报
site: 维普
title: 基于深度学习和图像识别技术构建斑玉蕈菌盖斑点表型识别分析系统
created_at: 2025-09-11 16:23:03.818802
original_link:
  https://www.cqvip.com/doc/journal/3508566161?sign=f05f21019ba314bdd61257c09a0e5da0392278f494ef49c63e04de8a5b472c51&expireTime=1789114710544&resourceId=3508566161

summary:
  为提高斑玉蕈(Hypsizygus marmoreus)生产过程中质量监测能力,基于深度学习和图像识别技术构建菌盖斑点表型识别分析系统。采用MV-CA013-20GC工业相机采集斑玉蕈菌盖图像,并通过图像颜色校正(标准色块提取、白平衡校正和Lab色域转换与色差优化)消除环境光干扰,确保图像色彩还原准确,采用X-AnyLabeling 2.5.3对500张图像进行边界框标注,构建训练数据集,基于YOLOv5检测模型开展模型训练,训练完成后选用树莓派4B部署平台实现菌盖自动分割与菌盖斑点特征分析,采用OpenCV的findContours算法提取斑点数量、大小、分布状态和颜色。该系统可有效完成菌盖斑点表型识别与分析。

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author: 胡艳;赵宇红;张洪明;吕波;尹相辉;李晓星;胡奎;范玉;符佳;王福地;曾超;孙波
keywords: 控制系统;延迟时间;FPGA;实时控制;线阵相机;图像识别;小麦分选
source: 现代电子技术
site: 维普
title: 一种基于图像识别的小麦分选系统
created_at: 2025-09-11 16:23:03.819802
original_link:
  https://www.cqvip.com/doc/journal/3506560845?sign=ec15a3e7b6d14e21491b1ef9e00867c9df9dec4e7e3b27218c85472d310c773d&expireTime=1789114710544&resourceId=3506560845

summary:
  在深度学习技术日益推动小麦不完善粒检测精度提升的背景下,为了实现从识别到筛选的有效控制对接,文中开发一套完善的分选控制系统。系统主要运用融合图像识别与FPGA实时控制的策略,对图像分割和识别过程中产生的时间差进行处理,完成精确分选控制。该系统基于全自动控制流程,在保证时间精确控制的前提下,成功实现对小麦的高效分选操作。通过对5种性状各100个麦粒进行测试,发现种子类型的识别率达到94%以上,种子分选率达到97%以上。实验结果表明,该系统能够快速准确地完成分选任务,为实现小麦分选的自动化和智能化提供了有效的技术支持。

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