# api_taskdetail

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: 2025-09-17 14:17:15.038149
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIb2cydWN3cGk%3D

summary:
[cell repeats an earlier one in this file: 397 characters]

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author: 王旭阳;卢世红
date: 2025年1期
keywords: 多标签文本分类;CompGCN;Transformer;CorNet;标签相关性
quote: 0
source: 《贵州师范大学学报（自然科学版）》
site: 万方
title: 基于标签构建与特征融合的多标签文本分类研究方法
created_at: 2025-09-17 14:17:15.038635
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFGd6c2ZkeHhiLXpyMjAyNTAxMDExGghvZzJ1Y3dwaQ%3D%3D

summary:
  目前存在的多标签文本分类任务算法,对于标签的建模不是很成熟,其中对于标签的依赖性问题,以及标签特征和文本特征的融合程度问题,均缺乏有效的处理方法.为了更有效地利用标签间的依赖关系,以及整合标签特征与文本特征的融合,提出了一种名为CGTCN的多标签文本分类模型.该模型从标签构建和特征融合的角度出发,通过CompGCN建模标签依赖关系,先利用Transformer中的多头交叉注意力机制初步融合标签特征和文本特征,然后再通过CorNet网络进一步捕获标签特征与文本特征之间的相关性,从而得到最终的标签预测.实验结果显示,与基准模型相比,该方法能够有效的提升模型性能,在多标签文本分类任务中取得更好的分类效果.

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author: 余明峰;秦永彬;黄瑞章
date: 2025年6期
keywords: 多标签文本分类;对比学习;双注意力;标签注意力;自注意力
quote: 0
source: 《计算机应用》
site: 万方
title: 基于对比学习增强双注意力机制的多标签文本分类方法
created_at: 2025-09-17 14:17:15.039098
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzanl5MjAyNTA2MDA1GghvZzJ1Y3dwaQ%3D%3D

summary:
  针对现有的基于注意力机制的方法难以捕捉文本之间复杂的依赖关系的问题,提出一种基于对比学习增强双注意力机制的多标签文本分类方法.首先,分别学习基于自注意力和基于标签注意力的文本表示,并融合二者以获得更全面的文本表示捕捉文本的结构特征以及文本与标签之间的语义关联;其次,给定一个多标签对比学习目标,利用标签引导的文本相似度监督文本表示的学习,以捕捉文本之间在主题、内容和结构层面上复杂的依赖关系;最后,使用前馈神经网络作为分类器进行文本分类.实验结果表明,相较于LDGN(Label-specific Dual Graph neural Network),所提方法在EUR-Lex(European Union Law Document)数据集与Reuters-21578数据集上的排名第5处的归一化折现累积收益(nDCG@5)值分别提升了1.81和0.86个百分点,在AAPD(Arxiv Academic Paper Dataset)数据集与RCV1(Reuters Corpus Volume Ⅰ)数据集上也都取得了有竞争力的结果.可见,所提方法能有效捕捉文本之间在主题、内容和结构层面上复杂的依赖关系,从而在多标签文本分类任务上取得较优结果.

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author: 彭鹏;徐红姣
date: 2025年2期
keywords: 开源科技情报;文本分类;信息过滤;预训练语言模型
quote: 0
source: 《数字图书馆论坛》
site: 万方
title: 面向开源科技情报分析的智能文本分类方法研究
created_at: 2025-09-17 14:17:15.039597
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEHN6dHNnbHQyMDI1MDIwMDcaCG9nMnVjd3Bp

summary:
  随着网络信息的爆发式增长,从海量的网络文本信息中识别有价值的科技情报并对其进行智能分类成为开源科技情报分析的关键.针对开源科技情报文本的特点,构建了面向开源科技情报分析的文本智能去噪与分类一体化模型.结合大语言模型与提示工程的自动标注方法进行噪声数据标注及文本分类数据标注;基于预训练语言模型进行噪声识别与过滤,过滤非科技情报文本;利用多语言预训练模型及蒸馏技术,改进损失函数设计,解决类别分布不均和数据不足的问题,实现在一定程度上提升多标签科技情报文本分类的精度和稳定性的目标.实验结果表明,与TextCNN与BERT方法相比,所提出的方法具有较高的分类性能、更好的鲁棒性和适应性.

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author: 武子轩;王烨;于洪
date: 2025年2期
keywords: 层次多标签文本分类;多尺度特征提取;对比学习;层次Micro-F1;层次Macro-F1
quote: 1
source: 《郑州大学学报（理学版）》
site: 万方
title: 基于多尺度特征提取的层次多标签文本分类方法
created_at: 2025-09-17 14:17:15.040098
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwNBoIb2cydWN3cGk%3D

summary:
  针对现有的特征提取方法忽略文本局部和全局联系的问题,提出了基于多尺度特征提取的层次多标签文本分类方法.首先,设计了多尺度特征提取模块,对不同尺度特征进行捕捉,更好地表示文本语义.其次,将层次特征嵌入文本表示中,得到具有标签特征的文本语义表示.最后,在标签层次结构的指导下对输入文本构建正负样本,进行对比学习,提高分类效果.在WOS、RCV1-V2、NYT和AAPD数据集上进行对比实验,结果表明,所提模型在评价指标上表现出色,超过了其他主流模型.此外,针对层次分类提出层次Micro-F1和层次Macro-F1指标,并对模型效果进行了评价.

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author: 郑诚;李鹏飞
date: 2025年6期
keywords: 文本分类;超图;特征融合;SenticNet词库;自然语言处理
quote: 0
source: 《计算机工程》
site: 万方
title: 基于双超图神经网络特征融合的文本分类
created_at: 2025-09-17 14:17:15.040589
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamdjMjAyNTA2MDEzGghvZzJ1Y3dwaQ%3D%3D

summary:
  近年来,图神经网络(GNN)在文本分类任务中受到广泛应用.当前基于GNN的文本分类模型首先将文本建模为图,然后使用GNN对文本图进行特征传播与聚合,但是此类方法有两点不足:一是现有模型由于图结构的限制无法捕获单词之间的高阶语义关系;二是现有模型无法捕获文本中的关键语义信息.为了解决上述问题,提出一种基于双超图卷积网络特征融合的文本分类模型.一方面,使用原始文本建立文本超图;另一方面,为短文本引入外部知识,使用基于SenticNet词库的外部知识对文本进行语义增强,构建语义超图.经过超图卷积后通过注意力机制对双超图特征进行融合,实现短文本分类.在4个文本分类数据集上的实验结果表明,该模型优于基线模型,具有优越的文本分类性能.

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author: 熊亿坤;付雪峰;盛黄煜
date: 2025年1期
keywords: BERT模型;基于卷积神经网络的文本分类模型;注意力机制;新闻文本分类
quote: 0
source: 《江西师范大学学报（自然科学版）》
site: 万方
title: 一种融合BERT和注意力机制的新闻文本分类方法
created_at: 2025-09-17 14:17:15.041086
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWp4c2ZkeHhiMjAyNTAxMDA3GghvZzJ1Y3dwaQ%3D%3D

summary:
  文本分类任务是在自然语言处理中的一项重要任务,旨在将给定的文本分配到预定义的不同类别或标签中.针对近年来备受关注的注意力机制和一种基于Transformer结构的预训练模型BERT,该文提出一种基于BERT-TextRCNN-Attention混合神经网络的新闻文本分类方法.为了增强文本的特征表示和模型的分类效果,该分类方法首先使用BERT预训练模型对文本进行预训练,并作为TextRCNN的词向量嵌入,其次采用TextRCNN模型和注意力机制进一步对文本的上下文特征和局部关键特征进行提取,再对新闻文本进行分类;最后在THUCNews数据集上进行对比实验.实验结果表明:该文提出方法在准确率和F,值上比Transformer、TextRNN、TextCNN、DPCNN等文本分类模型均有所提升.

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author: 陈中涛;周亚同
date: 2025年4期
keywords: 弱监督;文本分类;自训练;种子词
quote: 0
source: 《计算机工程与应用》
site: 万方
title: 简单且有效的弱监督中文文本分类算法
created_at: 2025-09-17 14:17:15.041555
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWpzamdjeXl5MjAyNTA0MDE3GghvZzJ1Y3dwaQ%3D%3D

summary:
  目前基于种子词的弱监督文本分类算法大多需要从数据集中搜索所有种子词并以此扩展类别词典,出现频率较低的种子词的类别识别能力也较低.因此设计了一个简单且有效的弱监督中文文本分类算法(simple and effective weakly supervised Chinese text classification,SEWClass).该方法利用预训练语言模型初始权重生成对文本的抽象理解,并以此为基础继续生成抽象约束条件和具象约束条件,以构建初次训练的伪标签数据;根据类别数量联合构建降维模型与分类器,以适应弱监督文本分类需要预先指定类别和在自训练过程中需要增加训练数据的特点;通过两种约束条件,伪标签数据拥有较高精确率,并在自训练过程中仅训练降维模型以提升召回率和算法效率.SEWClass对每个类别只需要一个种子词,如类别名称,即可完成分类任务,且SEWClass的性能与种子词是否出现在数据集中无关.SEWClass在THUCNews与toutiao两个中文数据集上的性能均远高于其他弱监督算法.

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author: 张典;刘畅;陈雯柏;缪祎晟;吴华瑞
keywords: 注意力机制;农业文本分类;深度金字塔卷积网络;预训练模型;MacBERT模型
source: 中国农机化学报
site: 维普
title: 融合注意力机制的MacBERT-DPCNN农业文本分类模型
created_at: 2025-09-17 14:17:15.056551
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=1f132c2a6823971dcf0f28dcae01fb4bb8d1f36970b3bf2878ce5b944877b51c&expireTime=1789625695237&resourceId=3506615314

summary:
[cell repeats an earlier one in this file: 377 characters]

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author: 李晨;刘纳;郑国风;杨杰;道路
keywords: 特征融合;双通道;短文本分类;DPECNN;BiLSTM;医疗文本挖掘
source: 现代电子技术
site: 维普
title: 融合双通道特征信息的医疗短文本分类模型
created_at: 2025-09-17 14:17:15.057073
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=783c0a4700fbcc91ef6a9bdf117769e1c79c87474d6a5f3fe27b6a1660d4653f&expireTime=1789625695237&resourceId=3502600080

summary:
[cell repeats an earlier one in this file: 271 characters]

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author: 余明峰;秦永彬;黄瑞章;陈艳平;林川
keywords: 双注意力;多标签文本分类;自注意力;对比学习;标签注意力
source: 计算机应用
site: 维普
title: 基于对比学习增强双注意力机制的多标签文本分类方法
created_at: 2025-09-17 14:17:15.057550
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=a14cbdc8fa2e761b2fb43158252650fbbe826ff212ea69e2225bd61435c70328&expireTime=1789625695237&resourceId=3505889201

summary:
[cell repeats an earlier one in this file: 522 characters]

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author: 郑诚;李鹏飞
keywords: 特征融合;超图;SenticNet词库;自然语言处理;文本分类
source: 计算机工程
site: 维普
title: 基于双超图神经网络特征融合的文本分类
created_at: 2025-09-17 14:17:15.058053
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=41e44400aa3d1834d0c853f2dc39835480138fbb0cdda437050271d0871be67f&expireTime=1789625695237&resourceId=3501748678

summary:
[cell repeats an earlier one in this file: 315 characters]

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author: 张泽;王炯;王晓艺;聂凯峰;刘杰;周建设
keywords: 正则化更新;动态网络扩展;终身学习;文本分类
source: 小型微型计算机系统
site: 维普
title: 融合网络剪枝和动态扩展的终身文本分类方法
created_at: 2025-09-17 14:17:15.058539
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=af2e3ad82fe996169c245577013e1f692080114083a9e02db481c4b32b49168e&expireTime=1789625695237&resourceId=3500943458

summary:
[cell repeats an earlier one in this file: 357 characters]

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author: 李静娟;邢凯;聂挺
keywords: 数据分布;最优传输理论;预训练;文本分类
source: 计算机应用与软件
site: 维普
title: 基于最优传输理论和格兰杰因果关系检验的文本分类优化
created_at: 2025-09-17 14:17:15.059038
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=50732c8270c96c3314e8468f8d9fa44e853060369893450deace5df43befcc06&expireTime=1789625695237&resourceId=3502552353

summary:
[cell repeats an earlier one in this file: 285 characters]

parsed_summary:
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author: 杜润隆;谭柯鑫;高添;韩郅;徐云凤
keywords: TextRCNN;施工方案;Albert;段落文本
source: 水利水电技术(中英文)
site: 维普
title: 施工方案类别及文本分类模型实现研究分类
created_at: 2025-09-17 14:17:15.059527
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=6582cb88fe171ad559616404def49e42d4f648fdd33da2b09e9677c90c64ada4&expireTime=1789625695237&resourceId=3501769600

summary:
[cell repeats an earlier one in this file: 383 characters]

parsed_summary:
  {"background": "\u65bd\u5de5\u65b9\u6848\u662f\u65bd\u5de5\u7ec4\u7ec7\u8bbe\u8ba1\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u73b0\u6709\u65b9\u6cd5\u5728\u4ece\u975e\u7ed3\u6784\u5316\u6587\u672c\u4e2d\u63d0\u53d6\u548c\u5ba1\u67e5\u65bd\u5de5\u65b9\u6848\u65f6\u6548\u7387\u8f83\u4f4e\uff0c\u4e9f\u9700\u63d0\u5347\u81ea\u52a8\u5316\u548c\u667a\u80fd\u5316\u6c34\u5e73\uff0c\u4ee5\u63d0\u9ad8\u7f16\u5236\u8d28\u91cf\u5e76\u80fd\u53ca\u65f6\u53d1\u73b0\u5b89\u5168\u548c\u8d28\u91cf\u98ce\u9669\u3002", "contribution": "\u7814\u7a76\u5b9e\u73b0\u4e86\u65bd\u5de5\u7ec4\u7ec7\u8bbe\u8ba1\u6bb5\u843d\u5185\u5bb9\u7684\u9ad8\u6548\u81ea\u52a8\u5206\u7c7b\uff0c\u521b\u65b0\u6027\u5730\u5c06Albert\u4e0eTextRCNN\u7ed3\u5408\u5e94\u7528\u4e8e\u884c\u4e1a\u573a\u666f\uff0c\u4e3a\u540e\u7eed\u65bd\u5de5\u65b9\u6848\u63d0\u53d6\u3001\u81ea\u52a8\u5ba1\u67e5\u53ca\u667a\u80fd\u9884\u8b66\u6253\u4e0b\u57fa\u7840\u3002", "method": "\u57fa\u4e8e\u5bf9\u5de5\u7a0b\u65bd\u5de5\u65b9\u6848\u7c7b\u522b\u4e0e\u5185\u5bb9\u7ed3\u6784\u7684\u68b3\u7406\uff0c\u9009\u53d6\u57ce\u5e02\u7ba1\u7f51\u5de5\u7a0b\u65bd\u5de5\u7ec4\u7ec7\u8bbe\u8ba1\u6587\u672c\u4e3a\u6837\u672c\uff0c\u63d0\u51fa\u878d\u5408Albert\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u4e0eTextRCNN\u5206\u7c7b\u5668\u7684\u6a21\u578b\uff0c\u5b9e\u73b0\u6bb5\u843d\u5185\u5bb9\u7684\u81ea\u52a8\u5206\u7c7b\u3002", "objective": "\u65e8\u5728\u7814\u7a76\u975e\u7ed3\u6784\u5316\u65bd\u5de5\u65b9\u6848\u6587\u672c\u7684\u5185\u5bb9\u5206\u7c7b\u65b9\u6cd5\uff0c\u89e3\u51b3\u65bd\u5de5\u7ec4\u7ec7\u8bbe\u8ba1\u6bb5\u843d\u5185\u5bb9\u81ea\u52a8\u5f52\u7c7b\u95ee\u9898\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u65bd\u5de5\u65b9\u6848\u63d0\u53d6\u548c\u98ce\u9669\u9884\u8b66\u63d0\u4f9b\u652f\u6301\u3002", "results": "\u6240\u63d0\u51fa\u7684\u878d\u5408Albert\u548cTextRCNN\u7684\u5206\u7c7b\u6a21\u578b\uff0c\u5728\u65bd\u5de5\u7ec4\u7ec7\u8bbe\u8ba1\u6bb5\u843d\u5185\u5bb9\u5206\u7c7b\u4efb\u52a1\u4e2d\uff0c\u5c06\u51c6\u786e\u7387\u63d0\u5347\u4e860.79%\uff0c\u9a8c\u8bc1\u4e86\u5176\u5728\u8be5\u9886\u57df\u7684\u6709\u6548\u6027\u3002"}


author: 曾谁飞;孟瑶;刘静
keywords: 注意力机制;图神经网络;文本分类;图数据
source: 电信科学
site: 维普
title: 基于GNN与注意力机制的文本分类模型
created_at: 2025-09-17 14:17:15.060016
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=bb7d3ff306847f6eb9592f82abe657c0128f06e05cd08d900e8288d2bcb7cf86&expireTime=1789625695237&resourceId=3501556210

summary:
[cell repeats an earlier one in this file: 487 characters]

parsed_summary:
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author: 尹照煜;宋文爱;刘宏昊
keywords: 数据增强;低资源文本分类;多目标知识蒸馏;自然语言处理;GPT-4;大型语言模型Claude3;Haiku
source: 现代电子技术
site: 维普
title: 硬件有限环境中低资源文本分类新策略
created_at: 2025-09-17 14:17:15.060510
original_link:
  https://www.cqvip.com/doc/journal/3483736790?sign=b98fdb236ba729467ee5ab9ef22945c1610cdb966f8a2336593a929dc9211c1e&expireTime=1789625695237&resourceId=3483736790

summary:
[cell repeats an earlier one in this file: 398 characters]

parsed_summary:
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author: 韦莉;赵德春;秦璐;刘洋华子;沈宇辰;叶昌荣
keywords: 先验知识;医疗实体标签语义;医疗问句分类;自适应融合机制
source: 生物医学工程学杂志
site: 维普
title: 融合医疗实体标签语义的医疗文本分类模型
created_at: 2025-09-17 14:17:15.061035
original_link:
  https://www.cqvip.com/doc/journal/3483995749?sign=8bfadbf10fc1a5d6d945d3b955eae26c4faf281e2fec6d7a0aa045af7bfbec99&expireTime=1789625695237&resourceId=3483995749

summary:
[cell repeats an earlier one in this file: 397 characters]

parsed_summary:
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author: 廖列法;姜炫至
keywords: 知识蒸馏;预训练模型;文本分类
source: 计算机工程与应用
site: 维普
title: 融合预训练和双级元蒸馏的医学短文本分类方法
created_at: 2025-09-17 14:17:15.061529
original_link:
  https://www.cqvip.com/doc/journal/3483661962?sign=1c5fec4056a21192ccdd1b4825abc4d929b153963ae06410d1279287af245585&expireTime=1789625695237&resourceId=3483661962

summary:
  为解决医学文本使用预训练模型处理速度慢、对硬件的算力要求高、难以分辨少样本类别,以及使用传统小模型由于自身限制难以做到足够的准确度的问题,提出了一种融合预训练和元蒸馏的文本分类模型PTMD(fusion of pre-training and meta distillation model)。PTMD针对医学文本的多标签问题,通过对比训练对RoBERTa预训练方法进行微调,再由双向内置注意力简单循环单元充分获取语义信息。最后在传统蒸馏模型的基础上融合元学习和助教的思想,通过教学实验和双级模型等方法,提高模型的教学水平,最终在减少训练成本的基础上得到一个高性能医学文本分类模型。实验结果表明,教师模型在CHIP2019评测三数据集上的F1值达到了85.47%,同时学生模型在F1值损失1.45个百分点的情况下,将模型规模缩小到教师模型的近1/6,效果高于大多数传统预训练模型和知识蒸馏模型,证明了该模型具有良好的实用价值。

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author: 温浩;杨洋
keywords: 语义强化;深度学习;临床短文本分类;知识图谱;ERNIE
source: 计算机工程与应用
site: 维普
title: 融合ERNIE与知识增强的临床短文本分类研究
created_at: 2025-09-17 14:17:15.062015
original_link:
  https://www.cqvip.com/doc/journal/3483712439?sign=53da5082c1c4b91fdefdf34ab5c6516875d74cedcf217ebccaf7fdc021c24861&expireTime=1789625695237&resourceId=3483712439

summary:
  提出一种引入专业医疗知识与文本独特知识的ERNIE模型用于识别无规则的临床短文本。目前ERNIE模型具有一定的医疗领域知识,但是在处理下游任务时无法引入专业医疗知识与文本独特知识,因此为提高临床短文本分类的精确度与效率,提出KW-ERNIE-BiGRU模型。该模型通过引入医学知识与文本独特知识的ERNIE模型训练文本的特征向量,利用BiGRU强化上下文信息,最终在输出层进行文本分类。通过在真实的临床文本的验证与对比实验,KW-ERNIE-BiGRU模型的精确率、召回率、宏F1分别为93.4%、92.1%、92.7%,均优于其他模型。

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author: 孙海涛;林佳瑜;梁祖红;郭洁
keywords: 标签混淆;数据增强;中文新闻主题;预训练模型;文本分类
source: 计算机应用
site: 维普
title: 结合标签混淆的中文文本分类数据增强技术
created_at: 2025-09-17 14:17:15.062512
original_link:
  https://www.cqvip.com/doc/journal/3483695334?sign=d7abc1a6e75b545bbfd2eb2dc1aeb9aa79d6ddecab52dbae3e6750a652ac3efc&expireTime=1789625695237&resourceId=3483695334

summary:
  传统数据增强技术,如同义词替换、随机插入和随机删除等,可能改变文本的原始语义,甚至导致关键信息丢失。此外,在文本分类任务中,数据通常包含文本部分和标签部分,然而传统数据增强方法仅针对文本部分。为解决这些问题,提出一种结合标签混淆的数据增强(LCDA)技术,从文本和标签这2个基本方面入手,为数据提供全面的强化。在文本方面,通过对文本进行标点符号随机插入和替换以及句末标点符号补齐等增强,在保留全部文本信息和顺序的同时增加文本的多样性;在标签方面,采用标签混淆方法生成模拟标签分布替代传统的one-hot标签分布,以更好地反映实例和标签与标签之间的关系。在THUCNews(TsingHua University Chinese News)和Toutiao这2个中文新闻数据集构建的小样本数据集上分别结合TextCNN、TextRNN、BERT(Bidirectional Encoder Representations from Transformers)和RoBERTa-CNN(Robustly optimized BERT approach Convolutional Neural Network)文本分类模型的实验结果表明,与增强前相比,性能均得到显著提升。其中,在由THUCNews数据集构造的50-THU数据集上,4种模型结合LCDA技术后的准确率相较于增强前分别提高了1.19、6.87、3.21和2.89个百分点;相较于softEDA(Easy Data Augmentation with soft labels)方法增强的模型分别提高了0.78、7.62、1.75和1.28个百分点。通过在文本和标签这2个维度的处理结果可知,LCDA技术能显著提升模型的准确率,在数据量较少的应用场景中表现尤为突出。

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author: 武子轩;王烨;于洪
keywords: 1;层次多标签文本分类;层次Micro-F;层次Macro-F;多尺度特征提取;对比学习
source: 郑州大学学报（理学版）
site: 维普
title: 基于多尺度特征提取的层次多标签文本分类方法
created_at: 2025-09-17 14:17:15.062998
original_link:
  https://www.cqvip.com/doc/journal/3475024013?sign=952c9ed406b88dbda20bb9616404502990eced62e9c245cb72ce4e315fb10c06&expireTime=1789625695237&resourceId=3475024013

summary:
  针对现有的特征提取方法忽略文本局部和全局联系的问题,提出了基于多尺度特征提取的层次多标签文本分类方法。首先,设计了多尺度特征提取模块,对不同尺度特征进行捕捉,更好地表示文本语义。其次,将层次特征嵌入文本表示中,得到具有标签特征的文本语义表示。最后,在标签层次结构的指导下对输入文本构建正负样本,进行对比学习,提高分类效果。在WOS、RCV1-V2、NYT和AAPD数据集上进行对比实验,结果表明,所提模型在评价指标上表现出色,超过了其他主流模型。此外,针对层次分类提出层次Micro-F 1和层次Macro-F 1指标,并对模型效果进行了评价。

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author: 陈敏;王雷春;徐瑞;史含笑;徐渺
keywords: XLNet;自然语言处理;对比学习;文本分类;新闻主题
source: 郑州大学学报（理学版）
site: 维普
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: 2025-09-17 14:17:15.063480
original_link:
  https://www.cqvip.com/doc/journal/3474986946?sign=1380dc09ba064a0943060f94f8f88b8374cd1310e6dd39e7c1e164d8c03e4c57&expireTime=1789625695237&resourceId=3474986946

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F 1值上都有更好的表现,在三个数据集上的F 1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: 翟梦鑫;周艳玲;余杭
keywords: 小样本文本分类;问题导向式模板;提示调优
source: 计算机应用研究
site: 维普
title: 基于问题导向式提示调优小样本文本分类
created_at: 2025-09-17 14:17:15.063978
original_link:
  https://www.cqvip.com/doc/journal/3480290043?sign=3b65dfe67bdf286ef16bee1f48ca5f668f7285191ee2e3e4a8d8e6ad58d1c077&expireTime=1789625695237&resourceId=3480290043

summary:
  低资源场景下提示调优比带有额外分类器的通用微调方法(fine-tuning)分类性能好,但提示调优中设计一个较好的提示模板和标签词映射器需要花费大量的精力。针对该问题,提出问题导向式提示调优的小样本分类方法(question-oriented prompt-tuning,QPT)。首先,利用数据集标签和可训练的连续提示构建提问形式的模板,通过预训练模型学习到最优提示模板;然后,每条样本用模板进行填充,将文本分类任务转换成完形填空任务;最后,使用外部知识及两种细化方法构建标签词映射器,通过预测的标签词与分类标签的映射关系得出分类结果。在公开数据集AG’s News和IMDB上进行实验,结果表明,该方法在5-shot、10-shot和20-shot任务上性能均有所提升,在5-shot任务上准确率分别提高了0.81和1.36百分点,与基线模型相比,不仅易于实现且性能取得了最优。

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author: 胡婕;郑启扬;曹芝兰;刘梦赤
keywords: 极限多标签文本分类;链接策略;细粒度特征;感知文本
source: 中文信息学报
site: 维普
title: 基于链接策略和不同粒度特征融合的极限多标签文本分类模型
created_at: 2025-09-17 14:17:15.064465
original_link:
  https://www.cqvip.com/doc/journal/3484086182?sign=b3bc335b3e2fa53a2e6327069371f7e1de72ed02f926e2c2f056acde93c6b84a&expireTime=1789625695237&resourceId=3484086182

summary:
  现有基于Transformer的极限多标签文本分类模型尽管引入了标签语义,但利用标签语义来探索文本和标签之间的语义潜在关系仍存在不足。对此,该文将标签合并成序列,并使用链接策略在同一空间内联合学习文本和标签特征来捕获文本和标签的语义。然后,通过注意力机制将标签语义和文档内容相结合生成感知文本,有效地探索文本信息和标签语义的交互关系。此外,该文通过融合机制将粗粒度层次特征和细粒度特征相结合,帮助模型更好地学习不同层次粒度的文档语义信息。在三个公开的数据集Eurlex-4K、Wiki10-30K和Kan-Shan Cup上进行了模型验证,实验结果表明,该文所提模型P@k值优于对比模型,综合性能得到有效提升。

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author: Xiyuan Gao;Shekhar Nayak;Matt Coler
date: 4 September, 2025
original_link: https://arxiv.org/abs/2509.04605
pdf_url: https://arxiv.org/pdf/2509.04605
site: arxiv
created_at: 2025-09-17 14:25:15.722945
summary:
  Abstract:Sarcasm, a common feature of human communication, poses challenges in interpersonal interactions and human-machine interactions. Linguistic research has highlighted the importance of prosodic cues, such as variations in pitch, speaking rate, and intonation, in conveying sarcastic intent. Although previous work has focused on text-based sarcasm detection, the role of speech data in recognizing sarcasm has been underexplored. Recent advancements in speech technology emphasize the growing importance of leveraging speech data for automatic sarcasm recognition, which can enhance social interactions for individuals with neurodegenerative conditions and improve machine understanding of complex human language use, leading to more nuanced interactions. This systematic review is the first to focus on speech-based sarcasm recognition, charting the evolution from unimodal to multimodal approaches. It covers datasets, feature extraction, and classification methods, and aims to bridge gaps across diverse research domains. The findings include limitations in datasets for sarcasm recognition in speech, the evolution of feature extraction techniques from traditional acoustic features to deep learning-based representations, and the progression of classification methods from unimodal approaches to multimodal fusion techniques. In so doing, we identify the need for greater emphasis on cross-cultural and multilingual sarcasm recognition, as well as the importance of addressing sarcasm as a multimodal phenomenon, rather than a text-based challenge.

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title:
  Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm Recognition -- Multimodal Fusion, Challenges, and Future Prospects


date: 16 August, 2025
original_link: https://arxiv.org/abs/2508.11999
pdf_url: https://arxiv.org/pdf/2508.11999
site: arxiv
title: MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding
created_at: 2025-09-17 14:25:15.724028
author:
  Daoze Zhang;Zhanheng Nie;Jianyu Liu;Chenghan Fu;Wanxian Guan;Yuan Gao;Jun Song;Pengjie Wang;Jian Xu;Bo Zheng

summary:
  Abstract:With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, although existing discriminative dual-flow architectures drive progress in this field, they inherently struggle to model the many-to-one alignment between multiple images and texts of products. Therefore, we argue that generative Multimodal Large Language Models (MLLMs) hold significant potential for improving product representation learning. Nevertheless, achieving this goal still remains non-trivial due to several key challenges: the lack of multimodal and aspect-aware modeling modules in typical LLMs; the common presence of background noise in product images; and the absence of a standard benchmark for evaluation. To address these issues, we propose the first generative MLLM-based model named MOON for product representation learning. Our method (1) employs a guided Mixture-of-Experts (MoE) module for targeted modeling of multimodal and aspect-specific product content; (2) effectively detects core semantic regions in product images to mitigate the distraction and interference caused by background noise; and (3) introduces the specialized negative sampling strategy to increase the difficulty and diversity of negative samples. In addition, we release a large-scale multimodal benchmark MBE for various product understanding tasks. Experimentally, our model demonstrates competitive zero-shot performance on both our benchmark and the public dataset, showcasing strong generalization across various downstream tasks, including cross-modal retrieval, product classification, and attribute prediction. Furthermore, the case study and visualization illustrate the effectiveness of MOON for product understanding.

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author: Anju Rani;Daniel Ortiz-Arroyo;Petar Durdevic
date: 1 August, 2025
original_link: https://arxiv.org/abs/2508.00447
pdf_url: https://arxiv.org/pdf/2508.00447
site: arxiv
title: CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text
created_at: 2025-09-17 14:25:15.724571
summary:
  Abstract:Understanding the temporal dynamics of biological growth is critical across diverse fields such as microbiology, agriculture, and biodegradation research. Although vision-language models like Contrastive Language Image Pretraining (CLIP) have shown strong capabilities in joint visual-textual reasoning, their effectiveness in capturing temporal progression remains limited. To address this, we propose CLIPTime, a multimodal, multitask framework designed to predict both the developmental stage and the corresponding timestamp of fungal growth from image and text inputs. Built upon the CLIP architecture, our model learns joint visual-textual embeddings and enables time-aware inference without requiring explicit temporal input during testing. To facilitate training and evaluation, we introduce a synthetic fungal growth dataset annotated with aligned timestamps and categorical stage labels. CLIPTime jointly performs classification and regression, predicting discrete growth stages alongside continuous timestamps. We also propose custom evaluation metrics, including temporal accuracy and regression error, to assess the precision of time-aware predictions. Experimental results demonstrate that CLIPTime effectively models biological progression and produces interpretable, temporally grounded outputs, highlighting the potential of vision-language models in real-world biological monitoring applications.

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author: Keerthi Veeramachaneni;Praveen Tirupattur;Amrit Singh Bedi;Mubarak Shah
date: 17 July, 2025
original_link: https://arxiv.org/abs/2507.13224
pdf_url: https://arxiv.org/pdf/2507.13224
site: arxiv
title: Leveraging Pre-Trained Visual Models for AI-Generated Video Detection
created_at: 2025-09-17 14:25:15.725143
summary:
  Abstract:Recent advances in Generative AI (GenAI) have led to significant improvements in the quality of generated visual content. As AI-generated visual content becomes increasingly indistinguishable from real content, the challenge of detecting the generated content becomes critical in combating misinformation, ensuring privacy, and preventing security threats. Although there has been substantial progress in detecting AI-generated images, current methods for video detection are largely focused on deepfakes, which primarily involve human faces. However, the field of video generation has advanced beyond DeepFakes, creating an urgent need for methods capable of detecting AI-generated videos with generic content. To address this gap, we propose a novel approach that leverages pre-trained visual models to distinguish between real and generated videos. The features extracted from these pre-trained models, which have been trained on extensive real visual content, contain inherent signals that can help distinguish real from generated videos. Using these extracted features, we achieve high detection performance without requiring additional model training, and we further improve performance by training a simple linear classification layer on top of the extracted features. We validated our method on a dataset we compiled (VID-AID), which includes around 10,000 AI-generated videos produced by 9 different text-to-video models, along with 4,000 real videos, totaling over 7 hours of video content. Our evaluation shows that our approach achieves high detection accuracy, above 90% on average, underscoring its effectiveness. Upon acceptance, we plan to publicly release the code, the pre-trained models, and our dataset to support ongoing research in this critical area.

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author: Yinghao Ma;Siyou Li;Juntao Yu;Emmanouil Benetos;Akira Maezawa
date: 27 June, 2025
original_link: https://arxiv.org/abs/2506.12285
pdf_url: https://arxiv.org/pdf/2506.12285
site: arxiv
title: CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
created_at: 2025-09-17 14:25:15.725638
summary:
  Abstract:Recent advances in audio-text large language models (LLMs) have opened new possibilities for music understanding and generation. However, existing benchmarks are limited in scope, often relying on simplified tasks or multi-choice evaluations that fail to reflect the complexity of real-world music analysis. We reinterpret a broad range of traditional MIR annotations as instruction-following formats and introduce CMI-Bench, a comprehensive music instruction following benchmark designed to evaluate audio-text LLMs on a diverse set of music information retrieval (MIR) tasks. These include genre classification, emotion regression, emotion tagging, instrument classification, pitch estimation, key detection, lyrics transcription, melody extraction, vocal technique recognition, instrument performance technique detection, music tagging, music captioning, and (down)beat tracking: reflecting core challenges in MIR research. Unlike previous benchmarks, CMI-Bench adopts standardized evaluation metrics consistent with previous state-of-the-art MIR models, ensuring direct comparability with supervised approaches. We provide an evaluation toolkit supporting all open-source audio-textual LLMs, including LTU, Qwen-audio, SALMONN, MusiLingo, etc. Experiment results reveal significant performance gaps between LLMs and supervised models, along with their culture, chronological and gender bias, highlighting the potential and limitations of current models in addressing MIR tasks. CMI-Bench establishes a unified foundation for evaluating music instruction following, driving progress in music-aware LLMs.

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date: 22 July, 2025
original_link: https://arxiv.org/abs/2505.14311
pdf_url: https://arxiv.org/pdf/2505.14311
site: arxiv
title: HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing
created_at: 2025-09-17 14:25:15.726148
author:
  Shamsuddeen Hassan Muhammad;Ibrahim Said Ahmad;Idris Abdulmumin;Falalu Ibrahim Lawan;Babangida Sani;Sukairaj Hafiz Imam;Yusuf Aliyu;Sani Abdullahi Sani;Ali Usman Umar;Tajuddeen Gwadabe;Kenneth Church;Vukosi Marivate

summary:
  Abstract:Hausa Natural Language Processing (NLP) has gained increasing attention in recent years, yet remains understudied as a low-resource language despite having over 120 million first-language (L1) and 80 million second-language (L2) speakers worldwide. While significant advances have been made in high-resource languages, Hausa NLP faces persistent challenges, including limited open-source datasets and inadequate model representation. This paper presents an overview of the current state of Hausa NLP, systematically examining existing resources, research contributions, and gaps across fundamental NLP tasks: text classification, machine translation, named entity recognition, speech recognition, and question answering. We introduce HausaNLP (this https URL), a curated catalog that aggregates datasets, tools, and research works to enhance accessibility and drive further development. Furthermore, we discuss challenges in integrating Hausa into large language models (LLMs), addressing issues of suboptimal tokenization and dialectal variation. Finally, we propose strategic research directions emphasizing dataset expansion, improved language modeling approaches, and strengthened community collaboration to advance Hausa NLP. Our work provides both a foundation for accelerating Hausa NLP progress and valuable insights for broader multilingual NLP research.

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author: Xingyuan Lu;Yuxi Liu;Dongyu Zhang;Zhiyao Wu;Jing Ren;Feng Xia
date: 12 May, 2025
original_link: https://arxiv.org/abs/2505.13483
pdf_url: https://arxiv.org/pdf/2505.13483
site: arxiv
title: EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
created_at: 2025-09-17 14:25:15.726635
summary:
  Abstract:Metaphors play a pivotal role in expressing emotions, making them crucial for emotional intelligence. The advent of multimodal data and widespread communication has led to a proliferation of multimodal metaphors, amplifying the complexity of emotion classification compared to single-mode scenarios. However, the scarcity of research on constructing multimodal metaphorical fine-grained emotion datasets hampers progress in this domain. Moreover, existing studies predominantly focus on English, overlooking potential variations in emotional nuances across languages. To address these gaps, we introduce a multimodal dataset in Chinese comprising 5,000 text-image pairs of metaphorical advertisements. Each entry is meticulously annotated for metaphor occurrence, domain relations and fine-grained emotion classification encompassing joy, love, trust, fear, sadness, disgust, anger, surprise, anticipation, and neutral. Our dataset is publicly accessible (this https URL), facilitating further advancements in this burgeoning field.

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author: Yang Mu;Zhitong Xiong;Yi Wang;Muhammad Shahzad;Franz Essl;Mark van Kleunen;Xiao Xiang Zhu
date: 25 May, 2025
original_link: https://arxiv.org/abs/2505.12513
pdf_url: https://arxiv.org/pdf/2505.12513
site: arxiv
title: GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
created_at: 2025-09-17 14:25:15.727116
summary:
  Abstract:Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress in this field has been constrained by the scarcity of large-scale, labeled datasets. To address this, we introduce GlobalGeoTree, a comprehensive global dataset for tree species classification. GlobalGeoTree comprises 6.3 million geolocated tree occurrences, spanning 275 families, 2,734 genera, and 21,001 species across the hierarchical taxonomic levels. Each sample is paired with Sentinel-2 image time series and 27 auxiliary environmental variables, encompassing bioclimatic, geographic, and soil data. The dataset is partitioned into GlobalGeoTree-6M for model pretraining and curated evaluation subsets, primarily GlobalGeoTree-10kEval for zero-shot and few-shot benchmarking. To demonstrate the utility of the dataset, we introduce a baseline model, GeoTreeCLIP, which leverages paired remote sensing data and taxonomic text labels within a vision-language framework pretrained on GlobalGeoTree-6M. Experimental results show that GeoTreeCLIP achieves substantial improvements in zero- and few-shot classification on GlobalGeoTree-10kEval over existing advanced models. By making the dataset, models, and code publicly available, we aim to establish a benchmark to advance tree species classification and foster innovation in biodiversity research and ecological applications.

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author: Samaneh Hosseini Moghaddam;Kelly Lyons;Cheryl Regehr;Vivek Goel;Kaitlyn Regehr
date: 24 April, 2025
original_link: https://arxiv.org/abs/2504.17653
pdf_url: https://arxiv.org/pdf/2504.17653
site: arxiv
title: Towards a comprehensive taxonomy of online abusive language informed by machine leaning
created_at: 2025-09-17 14:25:15.727618
summary:
  Abstract:The proliferation of abusive language in online communications has posed significant risks to the health and wellbeing of individuals and communities. The growing concern regarding online abuse and its consequences necessitates methods for identifying and mitigating harmful content and facilitating continuous monitoring, moderation, and early intervention. This paper presents a taxonomy for distinguishing key characteristics of abusive language within online text. Our approach uses a systematic method for taxonomy development, integrating classification systems of 18 existing multi-label datasets to capture key characteristics relevant to online abusive language classification. The resulting taxonomy is hierarchical and faceted, comprising 5 categories and 17 dimensions. It classifies various facets of online abuse, including context, target, intensity, directness, and theme of abuse. This shared understanding can lead to more cohesive efforts, facilitate knowledge exchange, and accelerate progress in the field of online abuse detection and mitigation among researchers, policy makers, online platform owners, and other stakeholders.

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author: Siyuan Yan;Ming Hu;Yiwen Jiang;Xieji Li;Hao Fei;Philipp Tschandl;Harald Kittler;Zongyuan Ge
date: 13 April, 2025
original_link: https://arxiv.org/abs/2503.14911
pdf_url: https://arxiv.org/pdf/2503.14911
site: arxiv
created_at: 2025-09-17 14:25:15.728098
summary:
  Abstract:The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermatology has lagged behind other medical domains due to the lack of standard image-text pairs. Existing dermatological datasets are limited in both scale and depth, offering only single-label annotations across a narrow range of diseases instead of rich textual descriptions, and lacking the crucial clinical context needed for real-world applications. To address these limitations, we present Derm1M, the first large-scale vision-language dataset for dermatology, comprising 1,029,761 image-text pairs. Built from diverse educational resources and structured around a standard ontology collaboratively developed by experts, Derm1M provides comprehensive coverage for over 390 skin conditions across four hierarchical levels and 130 clinical concepts with rich contextual information such as medical history, symptoms, and skin tone. To demonstrate Derm1M potential in advancing both AI research and clinical application, we pretrained a series of CLIP-like models, collectively called DermLIP, on this dataset. The DermLIP family significantly outperforms state-of-the-art foundation models on eight diverse datasets across multiple tasks, including zero-shot skin disease classification, clinical and artifacts concept identification, few-shot/full-shot learning, and cross-modal retrieval. Our dataset and code will be publicly available at this https URL upon acceptance.

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title:
  Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology


author: Huangwei Chen;Yifei Chen;Zhenyu Yan;Mingyang Ding;Chenlei Li;Zhu Zhu;Feiwei Qin
date: 19 March, 2025
original_link: https://arxiv.org/abs/2503.12927
pdf_url: https://arxiv.org/pdf/2503.12927
site: arxiv
created_at: 2025-09-17 14:25:15.728575
summary:
  Abstract:Neuroblastoma (NB), a leading cause of childhood cancer mortality, exhibits significant histopathological variability, necessitating precise subtyping for accurate prognosis and treatment. Traditional diagnostic methods rely on subjective evaluations that are time-consuming and inconsistent. To address these challenges, we introduce MMLNB, a multi-modal learning (MML) model that integrates pathological images with generated textual descriptions to improve classification accuracy and interpretability. The approach follows a two-stage process. First, we fine-tune a Vision-Language Model (VLM) to enhance pathology-aware text generation. Second, the fine-tuned VLM generates textual descriptions, using a dual-branch architecture to independently extract visual and textual features. These features are fused via Progressive Robust Multi-Modal Fusion (PRMF) Block for stable training. Experimental results show that the MMLNB model is more accurate than the single modal model. Ablation studies demonstrate the importance of multi-modal fusion, fine-tuning, and the PRMF mechanism. This research creates a scalable AI-driven framework for digital pathology, enhancing reliability and interpretability in NB subtyping classification. Our source code is available at this https URL.

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title:
  MMLNB: Multi-Modal Learning for Neuroblastoma Subtyping Classification Assisted with Textual Description Generation


author: Arezo Shakeri;Mina Farmanbar;Krisztian Balog
date: 26 February, 2025
original_link: https://arxiv.org/abs/2502.19208
pdf_url: https://arxiv.org/pdf/2502.19208
site: arxiv
title: MultiConAD: A Unified Multilingual Conversational Dataset for Early Alzheimer's Detection
created_at: 2025-09-17 14:25:15.729104
summary:
  Abstract:Dementia is a progressive cognitive syndrome with Alzheimer's disease (AD) as the leading cause. Conversation-based AD detection offers a cost-effective alternative to clinical methods, as language dysfunction is an early biomarker of AD. However, most prior research has framed AD detection as a binary classification problem, limiting the ability to identify Mild Cognitive Impairment (MCI)-a crucial stage for early intervention. Also, studies primarily rely on single-language datasets, mainly in English, restricting cross-language generalizability. To address this gap, we make three key contributions. First, we introduce a novel, multilingual dataset for AD detection by unifying 16 publicly available dementia-related conversational datasets. This corpus spans English, Spanish, Chinese, and Greek and incorporates both audio and text data derived from a variety of cognitive assessment tasks. Second, we perform finer-grained classification, including MCI, and evaluate various classifiers using sparse and dense text representations. Third, we conduct experiments in monolingual and multilingual settings, finding that some languages benefit from multilingual training while others perform better independently. This study highlights the challenges in multilingual AD detection and enables future research on both language-specific approaches and techniques aimed at improving model generalization and robustness.

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author: Santhosh Kakarla;Gautama Shastry Bulusu Venkata;Aishwarya Gaddam
date: 6 February, 2025
original_link: https://arxiv.org/abs/2502.04269
pdf_url: https://arxiv.org/pdf/2502.04269
site: arxiv
title: How does a Multilingual LM Handle Multiple Languages?
created_at: 2025-09-17 14:25:15.729583
summary:
  Abstract:Multilingual language models have significantly advanced due to rapid progress in natural language processing. Models like BLOOM 1.7B, trained on diverse multilingual datasets, aim to bridge linguistic gaps. However, their effectiveness in capturing linguistic knowledge, particularly for low-resource languages, remains an open question. This study critically examines MLMs capabilities in multilingual understanding, semantic representation, and cross-lingual knowledge transfer. While these models perform well for high-resource languages, they struggle with less-represented ones. Additionally, traditional evaluation methods often overlook their internal syntactic and semantic encoding.

  This research addresses key limitations through three objectives. First, it assesses semantic similarity by analyzing multilingual word embeddings for consistency using cosine similarity. Second, it examines BLOOM-1.7B and Qwen2 through Named Entity Recognition and sentence similarity tasks to understand their linguistic structures. Third, it explores cross-lingual knowledge transfer by evaluating generalization from high-resource to low-resource languages in sentiment analysis and text classification.

  By leveraging linguistic probing, performance metrics, and visualizations, this study provides insights into the strengths and limitations of MLMs. The findings aim to enhance multilingual NLP models, ensuring better support for both high- and low-resource languages, thereby promoting inclusivity in language technologies.

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author: Gianluca Sperduti;Alejandro Moreo
date: 28 January, 2025
original_link: https://arxiv.org/abs/2501.16836
pdf_url: https://arxiv.org/pdf/2501.16836
site: arxiv
title: Misspellings in Natural Language Processing: A survey
created_at: 2025-09-17 14:25:15.730069
summary:
  Abstract:This survey provides an overview of the challenges of misspellings in natural language processing (NLP). While often unintentional, misspellings have become ubiquitous in digital communication, especially with the proliferation of Web 2.0, user-generated content, and informal text mediums such as social media, blogs, and forums. Even if humans can generally interpret misspelled text, NLP models frequently struggle to handle it: this causes a decline in performance in common tasks like text classification and machine translation. In this paper, we reconstruct a history of misspellings as a scientific problem. We then discuss the latest advancements to address the challenge of misspellings in NLP. Main strategies to mitigate the effect of misspellings include data augmentation, double step, character-order agnostic, and tuple-based methods, among others. This survey also examines dedicated data challenges and competitions to spur progress in the field. Critical safety and ethical concerns are also examined, for example, the voluntary use of misspellings to inject malicious messages and hate speech on social networks. Furthermore, the survey explores psycholinguistic perspectives on how humans process misspellings, potentially informing innovative computational techniques for text normalization and representation. Finally, the misspelling-related challenges and opportunities associated with modern large language models are also analyzed, including benchmarks, datasets, and performances of the most prominent language models against misspellings. This survey aims to be an exhaustive resource for researchers seeking to mitigate the impact of misspellings in the rapidly evolving landscape of NLP.

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author: Skala Kamaran Omer;Hossein Hassani
date: 15 August, 2025
original_link: https://arxiv.org/abs/2501.14528
pdf_url: https://arxiv.org/pdf/2501.14528
site: arxiv
title: Idiom Detection in Sorani Kurdish Texts
created_at: 2025-09-17 14:25:15.730583
summary:
  Abstract:Idiom detection using Natural Language Processing (NLP) is the computerized process of recognizing figurative expressions within a text that convey meanings beyond the literal interpretation of the words. While idiom detection has seen significant progress across various languages, the Kurdish language faces a considerable research gap in this area despite the importance of idioms in tasks like machine translation and sentiment analysis. This study addresses idiom detection in Sorani Kurdish by approaching it as a text classification task using deep learning techniques. To tackle this, we developed a dataset containing 10,580 sentences embedding 101 Sorani Kurdish idioms across diverse contexts. Using this dataset, we developed and evaluated three deep learning models: KuBERT-based transformer sequence classification, a Recurrent Convolutional Neural Network (RCNN), and a BiLSTM model with an attention mechanism. The evaluations revealed that the transformer model, the fine-tuned BERT, consistently outperformed the others, achieving nearly 99% accuracy while the RCNN achieved 96.5% and the BiLSTM 80%. These results highlight the effectiveness of Transformer-based architectures in low-resource languages like Kurdish. This research provides a dataset, three optimized models, and insights into idiom detection, laying a foundation for advancing Kurdish NLP.

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author: Burak Çelik;Ayhan Akbal
date: 24 January, 2025
original_link: https://arxiv.org/abs/2501.14390
pdf_url: https://arxiv.org/pdf/2501.14390
site: arxiv
title: Distinguishing Parkinson's Patients Using Voice-Based Feature Extraction and Classification
created_at: 2025-09-17 14:25:15.731081
summary:
  Abstract:Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts motor functions and speech characteristics This study focuses on differentiating individuals with Parkinson's disease from healthy controls through the extraction and classification of speech features. Patients were further divided into 2 groups. Med On represents the patient with medication, while Med Off represents the patient without medication. The dataset consisted of patients and healthy individuals who read a predefined text using the H1N Zoom microphone in a suitable recording environment at Fırat University Neurology Department. Speech recordings from PD patients and healthy controls were analyzed, and 19 key features were extracted, including jitter, luminance, zero-crossing rate (ZCR), root mean square (RMS) energy, entropy, skewness, and this http URL features were visualized in graphs and statistically evaluated to identify distinctive patterns in PD patients. Using MATLAB's Classification Learner toolbox, several machine learning classification algorithm models were applied to classify groups and significant accuracy rates were achieved. The accuracy of our 3-layer artificial neural network architecture was also compared with classical machine learning algorithms. This study highlights the potential of noninvasive voice analysis combined with machine learning for early detection and monitoring of PD patients. Future research can improve diagnostic accuracy by optimizing feature selection and exploring advanced classification techniques.

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date: 16 February, 2025
original_link: https://arxiv.org/abs/2501.13306
pdf_url: https://arxiv.org/pdf/2501.13306
site: arxiv
title: OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
created_at: 2025-09-17 14:25:15.731576
author:
  Xuelong Geng;Kun Wei;Qijie Shao;Shuiyun Liu;Zhennan Lin;Zhixian Zhao;Guojian Li;Wenjie Tian;Peikun Chen;Yangze Li;Pengcheng Guo;Mingchen Shao;Shuiyuan Wang;Yuang Cao;Chengyou Wang;Tianyi Xu;Yuhang Dai;Xinfa Zhu;Yue Li;Li Zhang;Lei Xie

summary:
  Abstract:Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable comprehensive speech-based interactions. However, most advanced SULMs are developed by the industry, leveraging large-scale datasets and computational resources that are not readily available to the academic community. Moreover, the lack of transparency in training details creates additional barriers to further innovation. In this study, we present OSUM, an Open Speech Understanding Model designed to explore the potential of training SLUMs under constrained academic resources. The OSUM model combines a Whisper encoder with a Qwen2 LLM and supports a wide range of speech tasks, including speech recognition (ASR), speech recognition with timestamps (SRWT), vocal event detection (VED), speech emotion recognition (SER), speaking style recognition (SSR), speaker gender classification (SGC), speaker age prediction (SAP), and speech-to-text chat (STTC). By employing an ASR+X training strategy, OSUM achieves efficient and stable multi-task training by simultaneously optimizing ASR alongside target tasks. Beyond delivering strong performance, OSUM emphasizes transparency by providing openly available data preparation and training methodologies, offering valuable insights and practical guidance for the academic community. By doing so, we aim to accelerate research and innovation in advanced SULM technologies.

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author: Aliabadi T, Saberi EA, Motameni Tabatabaei A, Tahmasebi E.
original_link: https://pubmed.ncbi.nlm.nih.gov/36268928/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9830410/pdf/ejtm-32-4-10813.pdf
site: pubmed
title: Antibiotic use in endodontic treatment during pregnancy: A narrative review.
created_at: 2025-09-17 14:25:15.732071
summary:
  More than half of pregnant women are usually affected by odontogenic pain affects. Pain often accompanies periapical or pulp infections and increases the risks to pregnant patients and their fetuses. The American Dental Association, in partnership with the American College of Obstetricians and Gynecologists, has offered a strong declaration reaffirming the significance of suitable and timely oral health care as an indispensable constituent of a healthy pregnancy. However, there is lack of knowledge about the use of antibiotics in endodontic treatment. Therefore, the present study would review the researches done in this area and tries to provide comprehensive and complete information about the use of antibiotics in endodontic treatment during pregnancy. Based on the results, it can be said that using antibiotics during pregnancy are allowed, and they can be used normally and safely by pregnant women.

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author: Doernberg E, Hollander E.
original_link: https://pubmed.ncbi.nlm.nih.gov/27364515/
site: pubmed
title: Neurodevelopmental Disorders (ASD and ADHD): DSM-5, ICD-10, and ICD-11.
created_at: 2025-09-17 14:25:15.732579
summary:
  Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

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author: Akinci B, Gular MC, Oral EA.
original_link: https://pubmed.ncbi.nlm.nih.gov/29989768/
site: pubmed
title: Lipodystrophy Syndromes: Presentation and Treatment.
created_at: 2025-09-17 14:25:15.733072
summary:
  Lipodystrophy syndromes are a heterogeneous group of diseases, characterized by selective absence of adipose tissue. In one sense, these diseases are lipid-partitioning disorders, where the primary defect is the loss of functional adipocytes, leading to ectopic steatosis, severe dyslipidemia, and insulin resistance. These syndromes have attracted significant attention since the mid-1990s as the understanding of adipose tissue biology grew, initially spurred by the discovery of the pathways leading to adipocyte differentiation and maturation, and then by the discovery of leptin. Although lipodystrophy syndromes are known since the beginning of the 20th century, significant progress in understanding these syndromes were made in the last two decades, placing these syndromes at the forefront of the translational metabolism field. Currently, more than 20 distinctive molecular etiologies have been attributed to cause human diseases most of which map to adipocyte differentiation or lipid droplet pathways. Seemingly acquired syndromes are recently reported to have a genetic basis, suggesting that our “pre-genome” understanding of the syndromes was inadequate and that we need to likely change our classification schemes. Regardless of the etiology, it is the selective absence of adipose tissue and its function, leading to the reduced ability to store long-term energy that perturbs insulin sensitivity and lipid metabolism. The treatment of these syndromes has also attracted considerable interest. The most successful example of the treatment of these syndromes came from the demonstration that leptin replacement strategy improved insulin resistance and dyslipidemia in the most severely affected forms of the disease, leading to an FDA approved therapy for generalized lipodystrophy syndromes. In the partial forms of the disease, the phenotypes are more complex, and the efficacy of leptin is not as uniform. Currently, standard treatment-resistant partial lipodystrophy is an EMA-approved indication, and numerous trials are in progress, either evaluating the efficacy of leptin in familial partial lipodystrophy or aiming to develop potential new treatments for the partial forms of the disease. These rare metabolic diseases are likely to continue to fuel novel breakthroughs in the field of metabolism in the foreseeable future. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG.

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original_link: https://pubmed.ncbi.nlm.nih.gov/21252716/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4513953/pdf/nihms708216.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.733562
author:
  Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, Beer DG, Powell CA, Riely GJ, Van Schil PE, Garg K, Austin JH, Asamura H, Rusch VW, Hirsch FR, Scagliotti G, Mitsudomi T, Huber RM, Ishikawa Y, Jett J, Sanchez-Cespedes M, Sculier JP, Takahashi T, Tsuboi M, Vansteenkiste J, Wistuba I, Yang PC, Aberle D, Brambilla C, Flieder D, Franklin W, Gazdar A, Gould M, Hasleton P, Henderson D, Johnson B, Johnson D, Kerr K, Kuriyama K, Lee JS, Miller VA, Petersen I, Roggli V, Rosell R, Saijo N, Thunnissen E, Tsao M, Yankelewitz D.

summary:
  Introduction: Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies.

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title:
  International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.


original_link: https://pubmed.ncbi.nlm.nih.gov/34564088/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8468915/pdf/medicines-08-00046.pdf
site: pubmed
title: The Effective Strategies to Avoid Medication Errors and Improving Reporting Systems.
created_at: 2025-09-17 14:25:15.734117
author:
  Mutair AA, Alhumaid S, Shamsan A, Zaidi ARZ, Mohaini MA, Al Mutairi A, Rabaan AA, Awad M, Al-Omari A.

summary:
  Background: Population-based studies from several countries have constantly shown excessively high rates of medication errors and avoidable deaths. An efficient medication error reporting system is the backbone of reliable practice and a measure of progress towards achieving safety. Improvement efforts and system changes of medication error reporting systems should be targeted towards reductions in the likelihood of injury to future patients. However, the aim of this review is to provide a summary of medication errors reporting culture, incidence reporting systems, creating effective reporting methods, analysis of medication error reports, and recommendations to improve medication errors reporting systems.

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author: Ganassi M, Zammit PS.
original_link: https://pubmed.ncbi.nlm.nih.gov/35302338/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8992676/pdf/ejtm-32-1-10064.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.734618
summary:
  Neuromuscular disorders are a heterogeneous group of acquired or hereditary conditions that affect striated muscle function. The resulting decrease in muscle strength and motility irreversibly impacts quality of life. In addition to directly affecting skeletal muscle, pathogenesis can also arise from dysfunctional crosstalk between nerves and muscles, and may include cardiac impairment. Muscular weakness is often progressive and paralleled by continuous decline in the ability of skeletal muscle to functionally adapt and regenerate. Normally, the skeletal muscle resident stem cells, named satellite cells, ensure tissue homeostasis by providing myoblasts for growth, maintenance, repair and regeneration. We recently defined 'Satellite Cell-opathies' as those inherited neuromuscular conditions presenting satellite cell dysfunction in muscular dystrophies and myopathies (doi:10.1016/j.yexcr.2021.112906). Here, we expand the portfolio of Satellite Cell-opathies by evaluating the potential impairment of satellite cell function across all 16 categories of neuromuscular disorders, including those with mainly neurogenic and cardiac involvement. We explore the expression dynamics of myopathogenes, genes whose mutation leads to skeletal muscle pathogenesis, using transcriptomic analysis. This revealed that 45% of myopathogenes are differentially expressed during early satellite cell activation (0 - 5 hours). Of these 271 myopathogenes, 83 respond to Pax7, a master regulator of satellite cells. Our analysis suggests possible perturbation of satellite cell function in many neuromuscular disorders across all categories, including those where skeletal muscle pathology is not predominant. This characterisation further aids understanding of pathomechanisms and informs on development of prognostic and diagnostic tools, and ultimately, new therapeutics.

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title:
  Involvement of muscle satellite cell dysfunction in neuromuscular disorders: Expanding the portfolio of satellite cell-opathies.


original_link: https://pubmed.ncbi.nlm.nih.gov/39110332/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11502568/pdf/12519_2024_Article_819.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.735173
author:
  Wang C, Liao Y, Wang S, Tian H, Huang M, Dong XY, Shi L, Li YQ, Sun JH, Du JB, Jin HF; Chinese Pediatric Cardiology Society, Chinese Pediatric Society, Chinese Medical Association; Committee of Pediatric Syncope, College of Pediatricians, Chinese Medical Doctor Association; Pediatric Cardiology Society, Beijing Pediatric Society, Beijing Medical Association; Committee of Pediatric Cardiology, College of Cardiovascular Physicians, Chinese Medical Doctor Association.

summary:
  Background: Significant progress has been made in the diagnosis and treatment of pediatric syncope since the publication of the "2018 Chinese Pediatric Cardiology Society (CPCS) guideline for diagnosis and treatment of syncope in children and adolescents" ("2018 Edition Guidelines"). Therefore, we have revised and updated it to assist pediatricians in effectively managing children with syncope.

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title:
  Guidelines for the diagnosis and treatment of neurally mediated syncope in children and adolescents (revised 2024).


original_link: https://pubmed.ncbi.nlm.nih.gov/38915908/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11195435/pdf/CPJ-2023-000383.pdf
site: pubmed
title: Spinal Muscular Atrophy Update in Best Practices: Recommendations for Diagnosis Considerations.
created_at: 2025-09-17 14:25:15.735668
author:
  Schroth M, Deans J, Arya K, Castro D, De Vivo DC, Gibbons MA, Ionita C, Kuntz NL, Lakhotia A, Neil Knierbein E, Scoto M, Sejersen T, Servais L, Tian C, Waldrop MA, Vázquez-Costa JF.

summary:
  Background and objectives: Spinal muscular atrophy (SMA) is an autosomal recessive progressive neurodegenerative primary motor neuron disorder caused by biallelic variants of the survival motor neuron 1 (SMN1) gene. The most recent SMA best practice recommendations were published in 2018 shortly after the approval of the first SMN-enhancing treatment. The availability of disease-modifying therapies for 5q SMA and implementation of SMA newborn screening (NBS) has led to urgency to update the SMA best practice recommendations for diagnosis and to reevaluate the current classification of SMA. In addition, the availability of disease-modifying therapies has opened the door to explore improved diagnosis of adult-onset SMA.

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original_link: https://pubmed.ncbi.nlm.nih.gov/36542815/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10419411/pdf/jco-41-1684.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.736184
author:
  Mutter JA, Alig SK, Esfahani MS, Lauer EM, Mitschke J, Kurtz DM, Kühn J, Bleul S, Olsen M, Liu CL, Jin MC, Macaulay CW, Neidert N, Volk T, Eisenblaetter M, Rauer S, Heiland DH, Finke J, Duyster J, Wehrle J, Prinz M, Illerhaus G, Reinacher PC, Schorb E, Diehn M, Alizadeh AA, Scherer F.

summary:
  Purpose: Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL.

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title:
  Circulating Tumor DNA Profiling for Detection, Risk Stratification, and Classification of Brain Lymphomas.


original_link: https://pubmed.ncbi.nlm.nih.gov/38941859/
site: pubmed
title: PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis.
created_at: 2025-09-17 14:25:15.736795
author:
  Song M, Wang J, Yu Z, Wang J, Yang L, Lu Y, Li B, Wang X, Wang X, Huang Q, Li Z, Kanellakis NI, Liu J, Wang J, Wang B, Yang J.

summary:
  The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers for vision-language alignment and diagnosis in a dialogic manner. Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data. In our work, we innovate by eliminating the text branch and substituting the dialogue head with a classification head. This approach presents a more effective method for harnessing LLMs in diagnosis with fewer learnable parameters. Furthermore, to balance the retention of detailed image information with progression towards accurate diagnosis, we introduce the contextual multi-token engine. This engine is specialized in adaptively generating diagnostic tokens. Additionally, we propose the information emitter module, which unidirectionally emits information from image tokens to diagnosis tokens. Comprehensive experiments validate the superiority of our methods.

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author: Turri-Silva N, Vale-Lira A, Verboven K, Quaglioti Durigan JL, Hansen D, Cipriano G Jr.
original_link: https://pubmed.ncbi.nlm.nih.gov/34597330/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8486136/pdf/pone.0257607.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.737325
summary:
  Introduction: Exercise training is strongly recommended as a therapeutic approach to treat individuals with heart failure. High-intensity exercise training modalities still controversial in this population. The study aims to preliminary assess the consequences of high-intensity exercise training modalities, aerobic interval training (HIIT) and progressive high circuit-resistance training (CRT), on primarily endothelial function and cardiorespiratory fitness, and secondly on muscle strength and physical performance in heart failure patients.

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title:
  High-intensity interval training versus progressive high-intensity circuit resistance training on endothelial function and cardiorespiratory fitness in heart failure: A preliminary randomized controlled trial.


author: Takada K, Nakane Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/9895188/
site: pubmed
title: Progress of ICD-10 (F) family in Japan: research, field trials and publications.
created_at: 2025-09-17 14:25:15.737835
summary:
  The Nagasaki Center, designated as a World Health Organization (WHO) Collaborating Center in 1979, has taken the lead in generalizing ICD-10 (F) in Japan. The progress of ICD-10 (F)-related research, trials and publications has been as follows. The field trials of ICD-10 Clinical Descriptions and Diagnostic Guidelines (CDDG) 1986 draft were carried out with 129 patients by 61 clinicians in five institutions coordinated by Tokyo Medical and Dental University and Nagasaki University in 1988. The Japanese version of ICD-10 CDDG was published by Tooru et al. in February 1993. The field trials of ICD-10 Diagnostic Criteria for Research (DCR) 1990 version were conducted with 847 patients by 169 clinicians at 28 institutions and 2299 assessments were obtained. The Japanese version of ICD-10 DCR final version was published by Nakane et al. in May 1994. The Japanese version of the Glossaries and Conversion Tables between ICD-9 and ICD-10 was published by Nakane et al. in October 1994. The field trials of ICD-10 Multiaxial Presentation were conducted with 34 psychiatrists at six institutions in 1993 and the total of 676 assessments was obtained, and the adequacy and usefulness of the text were examined.

parsed_summary:
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original_link: https://pubmed.ncbi.nlm.nih.gov/40199607/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11979595/pdf/jitc-13-4.pdf
site: pubmed
created_at: 2025-09-17 14:25:15.738324
author:
  Chang EL, Liu R, Keyhanian K, Huynh K, Berkenstock M, Bhatti MT, Chen JJ, Chodosh J, Costello F, Dalvin LA, DeLott LB, Dinkin M, Egan RA, Fraser CL, Freitag SK, Gangaputra S, Gordon LK, Guidon AC, Johnson DB, Kombo N, Kramer M, Lee AG, Levy M, Lobo-Chan AM, Mantopoulos D, Papaliodis G, Pless M, Pimkina J, Rubin KM, Sen HN, Shariff A, Subramanian PS, Tsui E, Yoon MK, McDunn J, Rine J, Reynolds KL, Sobrin L, Chwalisz BK.

summary:
  Ophthalmic immune-related adverse events (Eye-irAEs) from immune checkpoint inhibitors can cause visual morbidity. The absence of standardized definitions for Eye-irAEs not only impedes the development of evidence-based treatments but also progress in translational research. The objective of this study was to develop consensus guidance for an approach to Eye-irAEs.Four ophthalmic physicians (uveitis specialists and neuro-ophthalmologists) drafted Eye-irAE consensus guidance and definitions, which were reviewed by the multidisciplinary Eye-irAE definition panel. The panel was divided into Group A (Neuro-ophthalmology/Orbital Disease) and Group B (Uveitis/Ocular Surface Disease). A modified Delphi consensus process was used, with two rounds of anonymous ratings by panelists and two meetings to discuss areas of controversy. For each disorder, five diagnostic components were evaluated: symptoms, examination findings, laboratory studies/imaging findings, diagnostic criteria, and treatment. Panelists rated content for usability, appropriateness and accuracy on 9-point scales in electronic surveys and provided free-text comments. Aggregated survey responses were incorporated into revised definitions. Consensus was based on numeric ratings using the RAND Corporation/ University of California Los Angeles Health Services Utilization Study (RAND/UCLA) Appropriateness Method with prespecified definitions.29 panelists from 25 academic medical centers voted on 114 rating scales (66 neuro-ophthalmic/orbital disease components, 48 uveitis/ocular surface disease components); of these, 86.3% (57/66) in Group A and 89.6% (43/48) in Group B reached first-round consensus. After revisions, all items except 6.1% (4/66) in Group A and 1.6% (1/60) in Group B received second-round consensus. Consensus definitions were achieved for 10/11 neuro-ophthalmic/orbital disorders: optic neuritis, inflammatory optic disc edema, arteritic ischemic optic neuropathy, optic perineuritis, orbital inflammation, thyroid eye disease-like orbital inflammation, cavernous sinus syndrome, oculomotor mononeuritis, trochlear mononeuritis, and abducens mononeuritis. Consensus definitions were achieved for 9/10 uveitis/ocular surface disorders: anterior uveitis, intermediate uveitis, posterior uveitis, panuveitis, Vogt-Koyanagi-Harada-like syndrome, sarcoidosis-like syndrome, acute macular neuroretinopathy, dry eye disease, and scleritis.These disease definitions establish a standardized classification for Eye-irAE, highlighting differences between irAEs and other inflammatory disorders. Importantly, diagnostic certainty does not always align directly with the need to treat as an Eye-irAE. Given the consensus from this representative panel group, it is anticipated the definitions will be used broadly across clinical and research settings.

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title:
  Consensus disease definitions for ophthalmic immune-related adverse events of immune checkpoint inhibitors.


author: Alvarado SA, Nassiri M, Bahna SL.
original_link: https://pubmed.ncbi.nlm.nih.gov/30563587/
site: pubmed
title: Scoring systems for allergies and asthma in clinical research and practice.
created_at: 2025-09-17 14:25:15.738802
summary:
  Background: Scoring systems are increasingly being developed for various diseases, including asthma and allergic disorders, with the objective of improving the classification of disease severity and the assessment of efficacy of therapeutic modalities. Objective: This review provided concise summaries of published scoring systems used for allergic rhinitis, asthma, atopic dermatitis, urticaria, Stevens-Johnson syndrome/toxic epidermal necrolysis, eosinophilic esophagitis, and systemic allergic reactions (anaphylaxis). Methods: We searched the medical literature between 1985 and 2018 for published scoring systems that have been developed and used in clinical trials or in practice for assessment of asthma and a variety of allergic disorders. Results: The scoring systems for each of these diseases were briefly presented in the text in chronological order of publication, and selected information was presented in the tables for easy comparisons. For more details, the reader should refer to the original relevant publications. Conclusion: Such assessment methods are useful for sound designing of clinical trials, fair comparisons of findings of studies, and objective measurements of patients' progress in clinical practice. The choice of using one scoring system over another would depend on its proven degree of validity, the purpose, and applicability.

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author: Solis-Herrera C, Triplitt C, Reasner C, DeFronzo RA, Cersosimo E.
original_link: https://pubmed.ncbi.nlm.nih.gov/25905343/
site: pubmed
title: Classification of Diabetes Mellitus.
created_at: 2025-09-17 14:25:15.739296
summary:
  Diabetes is a heterogeneous, complex metabolic disorder characterized by elevated blood glucose concentrations secondary to either resistance to the action of insulin, insufficient insulin secretion, or both. The most common classifications include Type 1 diabetes mellitus, Type 2 diabetes mellitus, and gestational diabetes. Type 2 diabetes (T2DM) is characterized by insulin resistance and a relative deficiency of insulin secretion. The absolute plasma insulin concentration (both fasting and meal-stimulated) usually is increased, although "relative" to the severity of insulin resistance, the plasma insulin concentration is insufficient to maintain normal glucose homeostasis. Insulin secretion capacity progressively worsens over time in most patients with T2DM. Type 1 DM results in an absolute deficiency in beta-cell function in most. Autoimmune destruction of beta-cells is a common origin, though cases continue to be classified as idiopathic. Gestational diabetes mellitus (GDM) is defined as glucose intolerance which is first recognized during pregnancy. In most women who develop GDM, the disorder has its onset in the third trimester of pregnancy and patients with GDM have a high risk of developing T2DM later in life. Other causes of diabetes include genetic disorders, diseases that cause damage to the pancreas, as well as an excess of certain hormones such as growth hormone and glucocorticoids. Diabetes mellitus may also be due to drugs, chemicals, or infections. Proper classification of the type of diabetes often helps determine appropriate therapy. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG

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author: Cohen AM, Hersh WR.
original_link: https://pubmed.ncbi.nlm.nih.gov/15826357/
site: pubmed
title: A survey of current work in biomedical text mining.
created_at: 2025-09-17 14:25:15.739803
summary:
  The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5-10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.

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author: Zhang A, Zheng X, Yan G, Liu X, Xie D, Xu X, Sun M, Liu Z.
original_link: https://pubmed.ncbi.nlm.nih.gov/40739238/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12308987/pdf/12951_2025_Article_3583.pdf
site: pubmed
title: Sonodynamic biomimetic-nanomedicine fight cancers.
created_at: 2025-09-17 14:25:15.740319
summary:
  Sonodynamic therapy (SDT) represents an innovative oncological treatment modality that harnesses the synergistic effects of low-intensity ultrasound and sonosensitizers to kill tumor cells. However, this dynamic therapeutic strategy faces multiple obstacles, including physicochemical barriers in the tumor microenvironment (TME) and the potential toxicity of off-target sonosensitizers, which may accumulate in off-target sites and be non-specifically activated by ultrasound, leading to damage of normal tissues. Recently, biomimetic-nanomedicine platforms, specifically designed to target the TME challenges, have emerged as a potent solution, enabling the development of SDT that enhance therapeutic outcomes, which even derives the concept of the sonodynamic biomimetic-nanomedicine (SBNM). In light of the states, we herein aim to summarize a cutting-edge review on the rapid advances of SBNM. Firstly, tumor-killing mechanisms and development and classification of sonosensitizers are outlined. In addition, recent progresses in various biomimetic-nanomedicines, containing red blood cell membrane-coated, immune cell membrane-coated, cancer cell membrane-coated, hybrid membrane-coated, exosomal and microbial mimicking, are described in the development of preclinical research. Importantly, we offer an in-depth presentation for the superiority of SBNM designed to address physicochemical barriers of TME and abnormalization of the anti-tumor immunity. Last but not least, the challenges and perspectives related to SBNM are also discussed.

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author: 罗振华;唐寅
date: 2025-09-15
keywords: 政务数据共享;;区块链;;属性基加密;;访问控制;;星际文件系统;;数据存储;
source: 现代电子技术
site: 知网
title: 基于区块链的政务数据共享方案研究
created_at: 2025-09-18 10:26:15.184357
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTddDmQO8cevzPStS4SK9K-s1u7STZ-50ceohjK3COjNCCp43-02gKLluAr3rPvz684FpiZqJQ6sIc-JydK9FY7yJOAfGyLWTti6g41sc81wmq-Y_N4A_m31q_v3unPWfA_Gje_ll2grEjf5Lu2rLz7FS4-d8HzHQuQJadC_AROTg==&uniplatform=NZKPT&language=CHS

summary:
  随着国家数字政府的发展，政府部门间安全可靠的数据共享已成为研究热点。政务部门间的信息共享有助于强化政府机构的管理，提高政府机构的治理和服务水平，发掘政务数据中的经济价值和社会价值。在传统的政务数据管理体系中，数据往往通过集中式的存储方式进行保存，但这容易造成数据隐私泄露、单点故障、“数据孤岛”等问题。为解决该问题，提出一种基于区块链的政务数据共享方案。该方案用属性基加密方案加密政务数据，对数据请求者的属性进行了限制；改进了属性基加密方案，增加了访问控制中的细粒度和减少了计算开销；采用星际文件系统存储数据，缓解了区块链系统的存储压力。安全性分析和仿真实验结果表明，所提方案可满足政务数据安全共享的需求，为跨部门数据安全共享提供了一种可行方案。

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author: 姜峰;孟哲;陈江涛;何兴波
date: 2025-09-15
keywords: 多式联运;;区块链;;国产加密技术;;数据安全;;SM2;;SM4;
source: 集成技术
site: 知网
title: 基于国产加密算法实现技术的多式联运区块链数据安全加密研究
created_at: 2025-09-18 10:26:15.185111
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DSvOMkeFwLfrUTrRaNXDVMgIMMoWASg49HUP77TpiEm1t2a6kd6Kjwd4JyXmc1dwPUj6WEv08P15g73Am8jZbhYhYcdP7Wn0bDZimBlybvHSv8XKGkyH5OzTe5bJ-yqClEi-97mT2vrd1t95iMeK3NS699BKPX5Rfs583-D9JI1bQ==&uniplatform=NZKPT&language=CHS

summary:
  随着全球化进程的加快和电子交易的迅猛发展，对多式联运这种高效物流模式的需求也随之激增。多式联运过程涉及大量数据交换和实时通信，数据安全隐患日益凸显。在此背景下，本文研究构建了创新性的运用国产加密技术和区块链的数据安全加密方案，通过分析多式联运的业务流程和需求，结合具体业务领域，设计了区块链平台架构，使用SM2、SM3、SM4等国产加密算法实现技术，实现数据的加密传输和存储，保障数据的安全性和完整性。实验结果表明，该方案在数据防泄露、实时性和稳定性方面表现出色，满足了多式联运领域的实际应用需求。

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author: 景顺平;曹志晓;姜峰;潘亚兰;孟哲
date: 2025-09-15
keywords: 区块链;;乏燃料;;多式联运;;物联网;;数据存证;;防篡改;
source: 集成技术
site: 知网
title: 区块链赋能乏燃料多式联运：基于国产联盟链和国密算法的数据可信存证与防篡改技术应用
created_at: 2025-09-18 10:26:15.185586
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DSvOMkeFwLfrUTrRaNXDVMgIMMoWASg49HUP77TpiEm1t2a6kd6KjwdEpDF1m-j6dlGRYNFUwMTf1ea30_ELf9Non1Q4P5hKejmUsC8USPRWsi2koJxi6U-hxS6uC1HWKQ_18PWuPXtVvt3-43er6e-orZAAwqRCiDn3wwgkOC85g==&uniplatform=NZKPT&language=CHS

summary:
  乏燃料多式联运指通过多种运输方式(如公路、铁路、水路等)将核电站产生的乏燃料联合运输至后处理设施或贮存场所的过程。现有运输体系面临数据安全、信息孤岛和溯源困难等挑战。本研究基于区块链的去中心化、可溯源和防篡改等技术，采用国产联盟链架构和国产加密算法，构建“区块链+物联网”融合模型，提出了一种融合区块链技术的乏燃料多式联运数据可信存证与安全防护方案，实现了3个关键创新：在架构设计方面，采用多节点授权的联盟链架构结合分层国密算法动态加密，能有效抵御暴力破解、数据篡改和中间人攻击等安全威胁；在性能优化方面，优化“实用拜占庭容错共识+动态分片验证”机制，将异常数据验证时延缩短至2 s内，系统吞吐量达到1 200 TPS；在场景应用方面，开发了“北斗+物联网”毫秒级数据上链模型，实现了辐射、位置等关键数据的全生命周期可信存证。实证研究表明，该解决方案在安全性方面实现了100%的篡改检测率，将应急响应时效从83 min缩短至4.2 min，同时约降低了30%的运营成本。研究成果为高放射性物质运输提供了首个工程化落地的区块链解决方案，对提升乏燃料运输安全管理水平和推动能源行业数字化转型具有重要实践价...

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author: 许多;张弛;胡昕
date: 2025-09-11
keywords: 区块链;;气象水文数据;;可信流转技术;
source: 科学技术创新
site: 知网
title: 基于区块链的气象水文数据可信流转技术研究
created_at: 2025-09-18 10:26:15.186093
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQ29yOo67eyTuvIAH997QedlYCRowmDOl0EQrtU9gR-Bg00v9QXxxaZILESfGRDGpadZfBDp4PqRCCPCQ-wSJP0L29KyxSVOni9FWrqB9iy7RP5Ggn037s6IyLmzxaeA-zwDhjXXWlqlBzQGfYmW_cOKgo-AaDy7AaTuEVYhBgyAA==&uniplatform=NZKPT&language=CHS

summary:
  气象水文数据的可信流通与安全管控对智慧防灾和资源管理意义重大。本文基于区块链的气象水文数据可信流转技术，采用多层架构，遵循可行性、合理性、创新性原则。引入跨链技术以及结合IPFS星际文件系统的混合存储方案，保障原始数据不可篡改性，将高频气象数据检索响应时间压缩至毫秒级。该技术的数据流转处理流程涵盖数据上链与存储、授权与流转、使用与追溯。其关键特性包括去中心化与数据透明性、不可篡改与可追溯性、安全性与隐私保护、高效流转与跨链技术。在实际应用中，有效提升了气象水文数据管理水平，为智慧防灾提供准确及时的数据支持，大幅提升防灾效率，同时为数字孪生流域构建奠定坚实的数据基础。

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author: 朱旭华;雷雅如;赵喜洋
date: 2025-09-10
keywords: 区块链技术;;智慧物流;;协同协作;;智能合约;;投资策略;
source: 交通企业管理
site: 知网
title: 区块链技术驱动下的供应商智慧物流投资策略研究
created_at: 2025-09-18 10:26:15.186585
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQ29yOo67eyTuvIAH997QedlYCRowmDOl3mQGJ1oPFRhgwXXkw9_aOJqZ9uMKbDdYZ0mUljIZPRYakBooc_5pFU0KntbLVEsWTjKYR9BJRzG8zQpa4PP6E82vzDdsxT8TwSePwk8M1_HciW1Iubq5oRACqbyGi3OGrJLtLgzfc37w==&uniplatform=NZKPT&language=CHS

summary:
  <正>随着第四次工业革命的深入推进，互联网和信息技术的发展为物流行业带来了前所未有的机遇与挑战。物流作为国民经济发展的动脉，承担着社会生产与消费之间的桥梁作用，是连接产业链、价值链的关键纽带。近年来，物流业逐渐从追求速度和规模增长向追求效益质量增长转变，智慧物流成为现代化快递物流业的主要运营模式。然而，智慧物流的发展仍面临一系列复杂而紧迫的挑战。

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author: 郭浩达
date: 2025-09-02
keywords: 区块链;;虚拟货币诈骗;;网络犯罪;;治理对策;
source: 网络安全技术与应用
site: 知网
title: 区块链安全视角下虚拟货币诈骗治理困境与应对策略
created_at: 2025-09-18 10:26:15.187042
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQ29yOo67eyTuvIAH997QedlYCRowmDOl11pguxgcyFK6LUb7wDC7ZFSb7BGWyfCPAaRIBkAjo5p2F86-XnrayzT7AqwLYTp3BGilbcucPlK8QfjYKGvtrtS37wUX8_aptj-linkZnR71J1xKySV8TZrcIXx14ZnOf11OBtbxSZXg==&uniplatform=NZKPT&language=CHS

summary:
  在区块链安全视角下，探讨了区块链技术在识别和预防这些新型诈骗中的应用潜力，为政策制定者、执法机构人员提供治理参考，有效地应对日益猖獗隐晦的虚拟货币诈骗问题。通过对加密货币追踪公司Chainalysis(Chainalysis是区块链数据平台，为70多个国家/地区的政府机构、交易所、金融机构以及保险和网络安全公司提供数据、软件、服务和研究)发布的2024年《加密犯罪报告》的解读，针对虚拟货币诈骗犯罪的特征，分析其治理难点和治理对策。区块链技术的发展，促进了全球数字经济发展，但是也带来了网络安全、个人财产安全等问题。针对我国当前虚拟货币诈骗治理面临的现实困境，需要在区块链安全视角下完善各个层面的安全防护，以维护财产安全和网络安全。

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author: 郑惺怡
date: 2025-09-01
keywords: 区块链教育;;Python模拟系统;;实验教学法;
source: 福建电脑
site: 知网
title: 面向基础教育的区块链Python实验课堂
created_at: 2025-09-18 10:26:15.187540
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DShdpXI1OlJn59SA5_Pvb2JuMAYAh4-jSjXF_I0YEZ6mKO7JRvyLgoMkPmCBq3kfZsKLyBNbT52ZX5E4-cX5hNQT7GSpZB95wtbSIP59ZFfh5B6JrelrL_iLtcvo04ZhLaXBVlu8iSuXIbkvYr6JR2vcwsJZbemTm5emGYJgVY_HA==&uniplatform=NZKPT&language=CHS

summary:
  为探索在小学基础教育中开展区块链的实验教学法，本文设计了区块链模拟实验教学方法。能过将区块链核心特征拆解为独立模块，并利用Python的可视化库动态展示区块链数据流动，以增强学生对抽象概念的可视化理解。实践教学的结果表明，90%的学生对教学实验的课程内容表示满意，区块链模拟实验教学方法能够增强学生的学习兴趣和参与感。

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author: 张煜;王昊;叶昊
date: 2025-09-01
keywords: 区块链技术;;网络安全;;可扩展性;;性能瓶颈;;隐私保护;
source: 网络安全技术与应用
site: 知网
title: 区块链技术在网络安全中的应用与挑战
created_at: 2025-09-18 10:26:15.188088
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DShdpXI1OlJn59SA5_Pvb2JuMAYAh4-jSg-AkqDumxLf1DzDpt7CfftvGqvM2yIM_rKrCaJKHPuWCqvhF9I_qnwSWE3jeLlTV_kWih6YEo5AWBw7R6LcNmwPYoTQiGA1wG7L9Y4rP8C-WTceM2_gHowl9Y0zlB0emqKbEnY66XsfA==&uniplatform=NZKPT&language=CHS

summary:
  本文探讨了区块链技术在网络安全领域的应用及其面临的挑战。区块链以其去中心化、不可篡改和透明性等特点，在增强网络安全、保护用户隐私、提高数据完整性等方面展现出巨大潜力。文中详细分析了区块链在网络安全监测与预警、身份认证与访问控制、数据保护与隐私加密等方面的应用实例。然而，区块链技术也面临着可扩展性、性能瓶颈、隐私保护难题、智能合约安全性、法律法规与监管问题以及资源消耗与环境影响等挑战。文章最后展望了区块链技术在网络安全领域的未来发展趋势，包括跨链技术的研发与应用、标准化与互操作性的提升、人才培养与技术创新等，以期为区块链技术在网络安全中的广泛应用提供参考。

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author: 张新龙;王建林;光奇;丁雪乾;焦阳
date: 2025-09-01
keywords: 区块链;;可信安全;;存证;;科研数据;
source: 网络安全技术与应用
site: 知网
title: 基于区块链的医疗科研数据备案平台构建与应用
created_at: 2025-09-18 10:26:15.188555
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DShdpXI1OlJn59SA5_Pvb2JuMAYAh4-jSg-AkqDumxLf1DzDpt7CfftyXBcqqw-GVOaG27I7ptzWupA8tN2Rz2MEuROV7U9J5nuecI0WXT6UA33jPX6HmjgJ0hYsu1FMLg49y-xx88TU0y7og_fRqZI8nTr5fOB_jGAkPkrF3R3xQ==&uniplatform=NZKPT&language=CHS

summary:
  [目的]为解决当前医疗机构科研数据管理难度大、实验室数据易丢失、篡改、追溯求证难等问题，研究构建基于区块链技术的医疗科研数据备案平台。[方法]通过使用信息摘要算法提取科研数据文件的唯一编码，生成信息摘要算法值上传至区块链应用平台，创建唯一的科研存证交易记录。结果 此平台建设使用以来，访问用户数量4857人，上传备案科研数据附件36份，各项功能能够满足医疗机构科研数据存证备案的需求。[结论]此平台具有分布式存储、不可篡改、安全保密及可溯源等特点，可为原创研究、隐私信息等知识产权信息提供高效快捷的存证服务，推动知识产权保护和科研诚信的进步发展。

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author: 吕乃基
date: 2019年1期
keywords: 区块链;通证;人工智能
quote: 20
source: 《系统科学学报》
site: 万方
title: 区块链4.0
created_at: 2025-09-18 10:26:15.189024
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEHh0Ynp4eGIyMDE5MDEwMTMaCHM4emFzdzFv

summary:
  区块链是当今炙手可热的技术前沿.本文从尚未成型的“通证经济学”入手,梳理关于中心化与去中心化之争.区块链将推进良币驱逐劣币.区块链4.0可能是区块链与人工智能的结合.现实世界中的国家之争,现实世界中的权力与未来世界之争,可能会影响人类社会走向未来的途径和未来世界的建构.

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author: 张孝;秦春玲;王文收
date: 2025年7期
keywords: 区块链;测试基准;性能评估;可扩展性;安全性
quote: 0
source: 《软件学报》
site: 万方
title: 区块链测试基准综述
created_at: 2025-09-18 10:26:15.189470
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTEaCHM4emFzdzFv

summary:
  近年来,区块链技术已经广泛应用到数据要素流通、金融、物流、政务以及司法等领域.随之也出现了若干区块链测试基准,以评测不同区块链系统的性能.然而,现有区块链测试基准内容差异较大,缺少统一的区块链测试基准框架来规范基准的内容,也缺失统一的指标体系来明确区块链系统在性能和安全性方面应具备的能力.从数据库发展历程来看,统一的、可重复的、公平的测试基准规范可以更好地引导行业发展.区块链本身也是一种特殊的分布式数据库管理系统,应该借鉴数据库技术在发展过程中积累的宝贵经验.参考数据库测试基准的内容,针对区块链系统所特有的去中心化、不可篡改、可信等特点,提出一个区块链测试基准参考框架UFBCB.该参考框架定义了区块链测试基准的 5 个核心要素:应用模型、数据模型、负载、指标和执行规则,并阐明 5 个要素相互之间的关系,为区块链性能评测提供了一个统一的参考标准.提出一个全面衡量区块链系统各项关键能力的测试指标体系,包含性能、能耗、扩展性和安全性这 4 个方面的指标,基本覆盖了已有的区块链测试指标.在此基础上,将UFBCB框架与现有区块链测试基准进行详尽的对比分析,指出目前区块链测试基准存在的普遍问题.最后,对区块链测试基准未来的发展方向进行了讨论.

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author: 程亚玲
date: 2020年7期
keywords: 区块链;链式结构;区块链网络;区块链共识
quote: 2
source: 《发明与创新·职业教育》
site: 万方
title: 区块链探析
created_at: 2025-09-18 10:26:15.189911
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFGZteWN4LWp5eHhoMjAyMDA3MTAyGghzOHphc3cxbw%3D%3D

summary:
  区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式.区块链(Block chain),是比特币的一个重要概念,它本质上是一个去中心化的数据库,同时作为比特币的底层技术,是一串使用密码学方法相关联产生的数据块,每一个数据块中包含了一批次比特币网络交易的信息,用于验证其信息的有效性(防伪)和生成下一个区块.

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author: 吕丽华
date: 2019年9期
keywords: 区块链;价值;风险
quote: 0
source: 《广西质量监督导报》
site: 万方
summary: 区块链作为一项崭新的跨时代的伟大技术,正在不断的刷新着人们的想像,它存在着巨大的使用价值和发展前景.笔者从区块链的含义、 价值和风险挑战几个方面较为详细的阐述了区块链.
title: 浅议区块链
created_at: 2025-09-18 10:26:15.190369
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWd4emxqZGRiMjAxOTA5MDQ0GghzOHphc3cxbw%3D%3D

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author: 司冰茹;肖江;刘存扬
date: 2024年2期
keywords: 区块链;拓扑结构;传输协议;跨链网络;仿真工具
quote: 22
source: 《软件学报》
site: 万方
title: 区块链网络综述
created_at: 2025-09-18 10:26:15.190813
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI0MDIwMTAaCHM4emFzdzFv

summary:
  区块链是典型的分布式系统,底层网络的性能和安全性至关重要.区块链网络的本质是P2P网络,然而在安全模型、传输协议和性能指标等方面与传统P2P网络存在明显差异.首先,针对区块链网络的传输流程进行全面、深入地分析,阐明区块链网络所面临的瓶颈挑战.其次,针对区块链网络拓扑结构和传输协议的最新研究工作,从节点异构性、编码方案、广播算法和中继网络等方面系统性地分类梳理,并归纳总结跨链网络实现和网络仿真工具.最后,探讨区块链网络的未来研究趋势.

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author: 王远
date: 2018年4期
keywords: 区块;分布式数据库;链;科技领域;算法设计;安全可靠;产业升级;网络交易
quote: 0
source: 《昆钢科技》
site: 万方
title: 区块链
created_at: 2025-09-18 10:26:15.191248
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISF1FLVjIwMTgyMDE4MTEyMDAwMDM2OTAwGghzOHphc3cxbw%3D%3D

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author: 孙善勇;张玉清
date: 2020年2期
keywords: 比特币;区块链;共识机制;智能合约;以太坊;超级账本
quote: 30
source: 《首都师范大学学报（自然科学版）》
site: 万方
title: 区块链技术
created_at: 2025-09-18 10:26:15.191695
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFHNkc2ZkeHhiLXpyMjAyMDAyMDE1GghzOHphc3cxbw%3D%3D

summary:
  区块链技术源于比特币系统的底层框架,是具备去中心化、去信任化、集体维护、时序数据、可编程和不可篡改等特点的分布式存储方案,对于金融领域乃至整个宏观社会系统具有重大历史意义.区块链是由一些已经成熟的技术整合而成,数据层、网络层、共识层、激励层、合约层和应用层构成了区块链底层基础架构.区块链目前主要有公有链、联盟链及私有链3类,主流开发平台有以太坊和超级账本.本文从区块链技术定义概述出发,介绍了区块链底层基础架构,阐述了各层架构中应用的核心技术,归纳了区块链主要类别以及相应类型下的区块链应用底层开发平台,最后列举了区块链技术当下面临的主要问题,希望能够为相关研究和学习提供参考.

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author: 田君
date: 2021年5期
keywords: 区块链;数据安全;虚拟资产;可信数据;区块链应用场景
quote: 2
source: 《企业管理》
site: 万方
title: 区块链全解码
created_at: 2025-09-18 10:26:15.192147
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXF5Z2wyMDIxMDUwMDQaCHM4emFzdzFv

summary:
  目前,各国都在加快布局区块链技术的应用.我国在区块链领域拥有良好的基础,很多地方政府都建立了区块链产业园,各地也出现了许多区块链研究机构,然而区块链技术的整体应用落地仍然进展缓慢.有如下原因:一是区块链底层技术还处于发展阶段,二是区块链的应用场景还有待摸索,三是还需要继续探讨对区块链的边界认知.我国要加快区块链技术和产业创新发展,积极推进区块链和经济社会融合发展.

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author: 高昊昱;曹春杰;白伊瑞
date: 2025年4期
keywords: 区块链;区块链安全;区块链监管
quote: 0
source: 《通信学报》
site: 万方
title: 区块链安全监管研究综述
created_at: 2025-09-18 10:26:15.192584
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXR4eGIyMDI1MDQwMDQaCHM4emFzdzFv

summary:
  区块链是一种安全和可信的新型分布式计算范式,在众多领域得到了广泛应用,但安全问题日渐凸显,监管需求日益迫切.简要介绍区块链生态现状和主要国家的监管政策背景,结合区块链技术和应用架构将相关文献进行划分,从链内、链间和链外3个方面分析现有的监管技术及方案的特点.首先将链内监管进一步划分为基础设施层监管、核心功能层监管和用户层监管3个层次,并分别详细探讨了各个层次下不同监管技术的优势和不足.随后将链间监管划分为基于"以链治链"思想的监管和跨链安全监管两类,分别简要讨论相关研究的特点,并简要介绍了链外监管的一些代表性案例.最后分析了当前区块链安全监管的共性问题并指出了可能的改进方向以及待监管的新领域,填补区块链监管综述方面的空白,为区块链监管方案设计提供了参考.

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date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.13393
pdf_url: https://arxiv.org/pdf/2509.13393
site: arxiv
created_at: 2025-09-18 10:26:15.193052
author:
  Bilal Ahmad;Jianguo Ding;Tayyab Ali;Doreen Sebastain Sarwatt;Ramsha Arshad;Adamu Gaston Philipo;Huansheng Ning

summary:
  Abstract:The increasing adoption of electric vehicles has spurred significant interest in Vehicle-to-Grid technology as a transformative approach to modern energy systems. This paper presents a systematic review of V2G systems, focusing on their integration challenges and potential solutions. First, the current state of V2G development is examined, highlighting its growing importance in mitigating peak demand, enhancing voltage and frequency regulation, and reinforcing grid resilience. The study underscores the pivotal role of artificial intelligence and machine learning in optimizing energy management, load forecasting, and real-time grid control. A critical analysis of cybersecurity risks reveals heightened vulnerabilities stemming from V2G's dependence on interconnected networks and real-time data exchange, prompting an exploration of advanced mitigation strategies, including federated learning, blockchain, and quantum-resistant cryptography. Furthermore, the paper reviews economic and market aspects, including business models (V2G as an aggregator or due to self-consumption), regulation (as flexibility service provider) and factors influencing user acceptance shaping V2G adoption. Data from global case studies and pilot programs offer a snapshot of how V2G has been implemented at different paces across regions. Finally, the study suggests a multi-layered framework that incorporates grid stability resilience, cybersecurity resiliency, and energy market dynamics and provides strategic recommendations to enable scalable, secure, and economically viable V2G deployment.

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title:
  Vehicle-to-Grid Integration: Ensuring Grid Stability, Strengthening Cybersecurity, and Advancing Energy Market Dynamics


author: Henrik Axelsen;Jan Damsgaard
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.13156
pdf_url: https://arxiv.org/pdf/2509.13156
site: arxiv
created_at: 2025-09-18 10:26:15.193504
summary:
  Abstract:Blockchain has been promoted as a remedy for coordination in fragmented, multi-stakeholder ecosystems, yet many projects stall at pilot stage. Using a design-science approach, we develop the Hybrid Cooperative (HC), a digitally native governance architecture that combines smart-contract coordination with a minimal, code-deferent legal interface and jurisdictional modules. This selective decentralization decentralizes rules where programmability lowers agency and verification costs, and centralizes only what is needed for enforceability. A post-case evaluation against two traceability initiatives in supply chains illustrates how the HC improves distributed task management, verifiable information, incentive alignment, institutional interoperability, and scalable, contestable governance. The paper contributes to Information Systems by specifying a socio-technical model for scalable, multi-stakeholder coordination across regulatory and organizational boundaries.

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title:
  Designing the Hybrid Cooperative: A Socio-Technical Architecture for Scalable, Global Coordination Using Blockchain


author: Yihao Guo;Haoming Zhu;Minghui Xu;Xiuzhen Cheng;Bin Xiao
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.12957
pdf_url: https://arxiv.org/pdf/2509.12957
site: arxiv
title: xRWA: A Cross-Chain Framework for Interoperability of Real-World Assets
created_at: 2025-09-18 10:26:15.193942
summary:
  Abstract:Real-World Assets (RWAs) have recently attracted increasing attention as a means of bridging traditional financial instruments with decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can enhance liquidity, broaden accessibility, and extend the scope of decentralized finance. Industry forecasts further suggest rapid growth of tokenized RWAs in the coming years, underscoring their potential role in the evolution of digital financial markets. However, when deployed across multiple blockchains, RWAs face challenges such as repeated authentication on different chains and inefficiency caused by multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, we design a cross-chain channel that enables the settlement of RWAs without requiring channel closure, thereby improving operational efficiency. We implement the framework and evaluate it through simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.

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author: Michael Senn;Christian Cachin
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12942
pdf_url: https://arxiv.org/pdf/2509.12942
site: arxiv
title: Asymmetric Grid Quorum Systems for Heterogeneous Processes
created_at: 2025-09-18 10:26:15.194404
summary:
  Abstract:Quorum systems are a common way to formalize failure assumptions in distributed systems. Traditionally, these assumptions are shared by all involved processes. More recently, systems have emerged which allow processes some freedom in choosing their own, subjective or asymmetric, failure assumptions. For such a system to work, individual processes' assumptions must be compatible. However, this leads to a Catch-22-style scenario: How can processes collaborate to agree on compatible failure assumptions when they have no compatible failure assumptions to start with?

  We introduce asymmetric grid quorum systems that allow a group of processes to specify heterogeneous trust assumptions independently of each other and without coordination. They are based on qualitative attributes describing how the processes differ. Each process may select a quorum system from this class that aligns best with its subjective view. The available choices are designed to be compatible by definition, thereby breaking the cycling dependency. Asymmetric grid quorum systems have many applications that range from cloud platforms to blockchain networks.

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date: 5 September, 2025
original_link: https://arxiv.org/abs/2509.12223
pdf_url: https://arxiv.org/pdf/2509.12223
site: arxiv
title: Ratio1 -- AI meta-OS
created_at: 2025-09-18 10:26:15.194847
author:
  Andrei Damian;Petrica Butusina;Alessandro De Franceschi;Vitalii Toderian;Marius Grigoras;Cristian Bleotiu

summary:
  Abstract:We propose the Ratio1 AI meta-operating system (meta-OS), a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices. Its key innovation is an integrated blockchain-based framework that transforms idle computing resources (laptops, smartphones, cloud VMs) into a trustless global supercomputer. The architecture includes novel components: a decentralized authentication layer (dAuth), an in-memory state database (CSTORE), a distributed storage system (R1FS), homomorphic encrypted federated learning (EDIL), decentralized container orchestration (Deeploy) and an oracle network (OracleSync), which collectively ensure secure, resilient execution of AI pipelines and other container based apps at scale. The protocol enforces a formal circular token-economic model combining Proof-of-Availability (PoA) and Proof-of-AI (PoAI) consensus. Compared to centralized heterogeneous cloud MLOps and existing decentralized compute platforms, which often lack integrated AI toolchains or trusted Ratio1 node operators (R1OP) mechanics, Ratio1's holistic design lowers barriers for AI deployment and improves cost-efficiency. We provide mathematical formulations of its secure licensing and reward protocols, and include descriptive information for the system architecture and protocol flow. We argue that our proposed fully functional ecosystem proposes and demonstrates significant improvements in accessibility, scalability, and security over existing alternatives.

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author: Weihao Zhu;Long Shi;Kang Wei;Zhen Mei;Zhe Wang;Jiaheng Wang;Jun Li
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12141
site: arxiv
title: When MoE Meets Blockchain: A Trustworthy Distributed Framework of Large Models
created_at: 2025-09-18 10:26:15.195286
summary:
  Abstract:As an enabling architecture of Large Models (LMs), Mixture of Experts (MoE) has become prevalent thanks to its sparsely-gated mechanism, which lowers computational overhead while maintaining learning performance comparable to dense LMs. The essence of MoE lies in utilizing a group of neural networks (called experts) with each specializing in different types of tasks, along with a trainable gating network that selectively activates a subset of these experts to handle specific tasks. Traditional cloud-based MoE encounters challenges such as prolonged response latency, high bandwidth consumption, and data privacy leakage. To address these issues, researchers have proposed to deploy MoE over distributed edge networks. However, a key concern of distributed MoE frameworks is the lack of trust in data interactions among distributed experts without the surveillance of any trusted authority, and thereby prone to potential attacks such as data manipulation. In response to the security issues of traditional distributed MoE, we propose a blockchain-aided trustworthy MoE (B-MoE) framework that consists of three layers: the edge layer, the blockchain layer, and the storage layer. In this framework, the edge layer employs the activated experts downloaded from the storage layer to process the learning tasks, while the blockchain layer functions as a decentralized trustworthy network to trace, verify, and record the computational results of the experts from the edge layer. The experimental results demonstrate that B-MoE is more robust to data manipulation attacks than traditional distributed MoE during both the training and inference processes.

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author: Zhantong Xue;Pingchuan Ma;Zhaoyu Wang;Shuai Wang
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.11708
pdf_url: https://arxiv.org/pdf/2509.11708
site: arxiv
title: From Evaluation to Enhancement: Large Language Models for Zero-Knowledge Proof Code Generation
created_at: 2025-09-18 10:26:15.195749
summary:
  Abstract:Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, blockchain scalability, and secure finance. However, authoring ZK programs remains challenging: unlike mainstream programming, ZK development requires reasoning about finite field arithmetic, constraint systems, and gadgets, making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages, their effectiveness for ZK programming, where correctness hinges on both language mastery and gadget-level reasoning, remains unexplored. To address this gap, we propose \textsc{ZK-Eval}, a domain-specific evaluation pipeline that probes LLM capabilities at three levels: language knowledge, gadget competence, and end-to-end program generation. Our evaluation of four state-of-the-art LLMs reveals that models excel at surface-level syntax but struggle with gadget usage and semantic correctness, often yielding incorrect programs. Based on these insights, we introduce \textsc{ZK-Coder}, an agentic framework that augments LLMs with constraint sketching, guided retrieval, and interactive repair. Experiments on Circom and Noir show substantial gains, with success rates improving from 17.35\% to 83.38\% and from 32.21\% to 90.05\%, respectively. With \textsc{ZK-Eval} and \textsc{ZK-Coder}, we establish a foundation for systematically measuring and augmenting LLMs in ZK code generation to lower barriers for practitioners and advance trustworthy computation.

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author: Sina Aeeneh;Nikola Zlatanov;Jiangshan Yu
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11294
pdf_url: https://arxiv.org/pdf/2509.11294
site: arxiv
created_at: 2025-09-18 10:26:15.196189
summary:
  Abstract:Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.

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title:
  An Incentive-Compatible Reward Sharing Mechanism for Mitigating Mirroring Attacks in Decentralized Data-Feed Systems


author: M. Z. Haider;M. Dias de Assuncao;Kaiwen Zhang
date: 13 September, 2025
original_link: https://arxiv.org/abs/2509.11006
pdf_url: https://arxiv.org/pdf/2509.11006
site: arxiv
title: A Range-Based Sharding (RBS) Protocol for Scalable Enterprise Blockchain
created_at: 2025-09-18 10:26:15.196621
summary:
  Abstract:Blockchain technology offers decentralization and security but struggles with scalability, particularly in enterprise settings where efficiency and controlled access are paramount. Sharding is a promising solution for private blockchains, yet existing approaches face challenges in coordinating shards, ensuring fault tolerance with limited nodes, and minimizing the high overhead of consensus mechanisms like PBFT. This paper proposes the Range-Based Sharding (RBS) Protocol, a novel sharding mechanism tailored for enterprise blockchains, implemented on Quorum. Unlike traditional sharding models such as OmniLedger and non-sharding Corda framework, RBS employs a commit-reveal scheme for secure and unbiased shard allocation, ensuring fair validator distribution while reducing cross-shard transaction delays. Our approach enhances scalability by balancing computational loads across shards, reducing consensus overhead, and improving parallel transaction execution. Experimental evaluations demonstrate that RBS achieves significantly higher throughput and lower latency compared to existing enterprise sharding frameworks, making it a viable and efficient solution for largescale blockchain deployments.

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author: Sivasankari B, Varalakshmi P.
original_link: https://pubmed.ncbi.nlm.nih.gov/35612074/
site: pubmed
title: Blockchain and IoT Technology in Healthcare: A Review.
created_at: 2025-09-18 10:26:15.197078
summary:
  The tremendous shift in technology has led to many unconnected things getting interconnected via IoT. IoT is one of the major modes of collecting data from various networked resources and other connected devices. The broad range of IoT, with its huge heterogeneity in handling data, addresses many challenges in the realm of healthcare. Blockchain technology has elevated the use of distributed storage in a positive way. The recent emergence of this technology has paved way for potentially enormous utilization in various fields. Blockchain technology in the fields of IT, finance, industries, government, healthcare, media, and law enforcement has altered the service quality levels to an ethical ideal. Blockchain, in conjunction with IoT, facilitates decentralized collection and storage of data. Integrating blockchain with IoT has emerged as a cutting-edge tool for the decentralized sharing of medical records, monitoring of patients, ensuring the privacy of patient records, predicting the quantum of insurance, and managing supply chains.

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author: Zhou X, Kraft M.
original_link: https://pubmed.ncbi.nlm.nih.gov/35363506/
site: pubmed
title: Blockchain Technology in the Chemical Industry.
created_at: 2025-09-18 10:26:15.197525
summary:
  This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.

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author: Fang HSA, Tan TH, Tan YFC, Tan CJM.
original_link: https://pubmed.ncbi.nlm.nih.gov/33847591/
site: pubmed
title: Blockchain Personal Health Records: Systematic Review.
created_at: 2025-09-18 10:26:15.197961
summary:
  Background: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing.

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author: Khanna A, Sah A, Bolshev V, Burgio A, Panchenko V, Jasiński M.
original_link: https://pubmed.ncbi.nlm.nih.gov/35890918/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9320072/pdf/sensors-22-05238.pdf
site: pubmed
title: Blockchain-Cloud Integration: A Survey.
created_at: 2025-09-18 10:26:15.198420
summary:
  Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain-cloud services being offered by existing Cloud Service providers.

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author: Uddin M, Salah K, Jayaraman R, Pesic S, Ellahham S.
original_link: https://pubmed.ncbi.nlm.nih.gov/33899576/
site: pubmed
title: Blockchain for drug traceability: Architectures and open challenges.
created_at: 2025-09-18 10:26:15.198869
summary:
  Pharmaceutical supply chain (PSC) consists of multiple stakeholders including raw material suppliers, manufacturers, distributors, regulatory authorities, pharmacies, hospitals, and patients. The complexity of product and transaction flows in PSC requires an effective traceability system to determine the current and all previous product ownerships. In addition, digitizing track and trace process provides significant benefit for regulatory oversight and ensures product safety. Blockchain-based drug traceability offers a potential solution to create a distributed shared data platform for an immutable, trustworthy, accountable and transparent system in the PSC. In this paper, we present an overview of product traceability issues in the PSC and envisage how blockchain technology can provide effective provenance, track and trace solution to mitigate counterfeit medications. We propose two potential blockchain based decentralized architectures, Hyperledger Fabric and Besu to meet critical requirements for drug traceability such as privacy, trust, transparency, security, authorization and authentication, and scalability. We propose, discuss, and compare two potential blockchain architectures for drug traceability. We identify and discuss several open research challenges related to the application of blockchain technology for drug traceability. The proposed blockchain architectures provide a valuable roadmap for Health Informatics researchers to build and deploy an end-to-end solution for the pharmaceutical industry.

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author: Rathod T, Jadav NK, Alshehri MD, Tanwar S, Sharma R, Felseghi RA, Raboaca MS.
original_link: https://pubmed.ncbi.nlm.nih.gov/35684802/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9185230/pdf/sensors-22-04182.pdf
site: pubmed
title: Blockchain for Future Wireless Networks: A Decade Survey.
created_at: 2025-09-18 10:26:15.199310
summary:
  The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.

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author: Hasselgren A, Kralevska K, Gligoroski D, Pedersen SA, Faxvaag A.
original_link: https://pubmed.ncbi.nlm.nih.gov/31865055/
site: pubmed
title: Blockchain in healthcare and health sciences-A scoping review.
created_at: 2025-09-18 10:26:15.199777
summary:
  Background: Blockchain can be described as an immutable ledger, logging data entries in a decentralized manner. This new technology has been suggested to disrupt a wide range of data-driven domains, including the health domain.

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author: Oktian YE, Lee SG.
original_link: https://pubmed.ncbi.nlm.nih.gov/37420824/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10302079/pdf/sensors-23-05658.pdf
site: pubmed
title: Blockchain-Based Federated Learning System: A Survey on Design Choices.
created_at: 2025-09-18 10:26:15.200223
summary:
  The vanilla federated learning is made for a trusted environment, while in contrast, its actual use cases require collaborations in an untrusted setting. For this reason, using blockchain as a trusted platform to run federated learning algorithms has gained traction lately and has become a significant research interest. This paper performs a literature survey on state-of-the-art blockchain-based federated learning systems and analyzes several design patterns researchers often take to solve existing issues through blockchain. We find about 31 design item variations throughout the whole system. Each design is further analyzed to find pros and cons, considering fundamental metrics such as robustness, efficiency, privacy, and fairness. The result shows a linear relationship between fairness and robustness in which, if we focus on improving fairness, it will indirectly become more robust. Furthermore, improving all those metrics altogether is not viable because of the efficiency trade-off. Finally, we classify the surveyed papers to spot which designs are popular among researchers and determine which areas require immediate improvements. Our investigation shows that future blockchain-based federated learning systems require more effort regarding model compression, asynchronous aggregation, system efficiency evaluation, and the application for cross-device settings.

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author: Krichen M, Ammi M, Mihoub A, Almutiq M.
original_link: https://pubmed.ncbi.nlm.nih.gov/35890953/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9317832/pdf/sensors-22-05274.pdf
site: pubmed
title: Blockchain for Modern Applications: A Survey.
created_at: 2025-09-18 10:26:15.200679
summary:
  Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

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author: 刘炜;李淑培;田钊;佘维
keywords: 区块链;数据共享;智能合约;访问控制;属性加密
source: 郑州大学学报(理学版)
site: 维普
title: 基于区块链的去中心化多授权机构访问控制方法
created_at: 2025-09-18 10:26:15.201137
original_link:
  https://www.cqvip.com/doc/journal/3507467139?sign=d5a0a05fb7b3845c5be32dd1d6016710485fe3b9ed04dbf7454c2df3f6fdc186&expireTime=1789698297405&resourceId=3507467139

summary:
  传统的基于单一授权机构的访问控制方案存在单点故障、效率低下等问题,为此提出一种基于区块链的去中心化多授权机构访问控制方法。首先,采用基于联盟链的多授权机构取代传统访问控制方法中的中心化实体,提供可靠、细粒度的访问控制;其次,智能合约允许自动化访问判决,为了解决多授权机构带来的访问效率问题,提出一种基于智能合约的数据映射算法,利用数据关键信息构建映射表实现数据快速访问;最后,实验表明,所提出的方案能够有效降低用户访问时延,实现数据访问过程中的安全共享。

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author: 李先东;陈玉洁;赵兰兰;韩瑛
keywords: 区块链;结构方程模型;消费者;生态畜产品;质量安全
source: 中国农机化学报
site: 维普
title: 区块链技术与可追溯生态畜产品购买意愿研究——基于新疆消费者的调查实证
created_at: 2025-09-18 10:26:15.201580
original_link:
  https://www.cqvip.com/doc/journal/3508540424?sign=8cd8b98bf8d67ac894a5eb97d302b794f1bb9d9ac56c0b9816ee71c3d1ee2767&expireTime=1789698297405&resourceId=3508540424

summary:
  探究区块链溯源食品的消费者接受度有助于完善食品溯源体系、提高溯源食品公信力,进而保障农畜产品质量安全。以生态畜产品为例,基于新疆558名消费者的微观调研数据,利用结构方程模型分析消费者对区块链溯源生态畜产品的认知情况、心理感知及购买意愿。结果表明:区块链认知和自我效能均在1%的显著水平上正向影响购买意愿,感知有用性在5%的显著水平上正向影响购买意愿,感知易用性对购买意愿的影响不显著;感知易用性、感知有用性分别在区块链认知和自我效能对购买意愿的影响过程中发挥部分中介作用(间接效应和直接效应的95%置信区间均不含0);生产商信任、监管信任在感知有用性对购买意愿的影响过程中发挥正向调节作用(p值<0.05)。据此,需要增强公众食品安全意识和区块链溯源食品的认知水平,加强溯源系统信息内容建设,并继续完善可追溯食品监督管理体系,以达到优化区块链溯源系统服务质量、助力我国食品安全水平提升的目标。

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author: 周雄;王峥;程哲凡
keywords: 区块链;可搜索加密;隐藏向量加密;车联网;访问控制
source: 小型微型计算机系统
site: 维普
title: 车联网中的区块链和可搜索加密数据共享方案
created_at: 2025-09-18 10:26:15.202020
original_link:
  https://www.cqvip.com/doc/journal/3509792626?sign=0f41a57105cec2268f2c002bac09caa1c677f616c96e155d48dfc06f808f509f&expireTime=1789698297405&resourceId=3509792626

summary:
  云计算为车联网数据存储和资源共享带来了极大的便利.然而,云存储的集中化对数据安全共享构成了潜在的威胁,可能导致云服务提供商和非法用户未经授权访问和滥用车辆数据.为此,本文提出一种基于区块链和可搜索加密的车联网数据共享方案.首先,该方案采用隐藏向量加密技术为文件设置轻量级的访问控制策略,实现对加密数据的细粒度访问控制,以满足多用户密文搜索请求;其次,使用可搜索加密技术和智能合约实现加密数据在区块链上的公平搜索.此外,该方案支持多关键词搜索和结果的完整性验证.最后,安全性分析与实验表明,该方案在安全性和效率方面是可行的.

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author: 姚荷英
keywords: 档案管理;电子文件;数字档案馆;区块链;全生命周期;企业集团;真实性保障
source: 档案管理
site: 维普
title: 区块链驱动档案全生命周期真实性保障探讨——以企业集团数字档案馆(室)建设为场景
created_at: 2025-09-18 10:26:15.202485
original_link:
  https://www.cqvip.com/doc/journal/3508551027?sign=a1693a3b4d195238e603ef3347044ba392297bccaf1a7a13b78bb1b7006fa6d0&expireTime=1789698297405&resourceId=3508551027

summary:
  企业集团数字档案馆(室)是基于信息技术发展趋势和企业档案工作数字化转型战略需求而提出的企业数字档案馆(室)的新形态。区块链有可信验证、去中心化等独特的技术优势,能在企业集团数字档案馆(室)建设中为档案元数据管理和档案管理协作环境搭建提供可靠的技术保障。文章从需求分析入手,探究区块链技术在企业数字档案管理中的应用场景,提出基于区块链的企业档案全生命周期真实性保障体系,最后给出实例分析,为企业集团数字档案馆(室)建设提供新的视角和发展参考。

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author: 房永浩;姚中原;李敏;斯雪明
keywords: 区块链;共识算法;分布式能源交易;密钥分享;零知识证明
source: 计算机工程
site: 维普
title: 一种适用于分布式电力交易的安全高效区块链共识算法
created_at: 2025-09-18 10:26:15.202938
original_link:
  https://www.cqvip.com/doc/journal/3508561717?sign=7e2f84e1d8e8f93649de06337fee43b5b880d45307069eac169b51c2e5294248&expireTime=1789698297405&resourceId=3508561717

summary:
  分布式电力交易成为电力能源交易的未来发展趋势,区块链凭借其技术特性为分布式电力交易中缺乏监管机制、交易成本过高、信息规则不明等问题提供解决思路。然而,随着分布式电力交易规模逐渐增大,区块链系统吞吐量降低,这也间接限制了分布式电力交易的成交速度。为此,提出一种适用于分布式电力交易的高效安全的区块链共识算法。基于分布式电力交易网络内节点的历史交易特性,使用聚类算法将共识网络规划为多共识集双层网络结构,并采用双层共识流程提升共识的并行性。同时,设计高效的单共识集内领导节点选举策略,可以快速确定表现良好、性能较优的领导节点。最后,引入结合零知识证明与密钥分享的认证方法,进一步降低恶意节点参与共识的可能性。实验结果表明,所提共识算法的抗拜占庭节点数量可以抵御双花攻击等多种区块链攻击,能够显著降低共识通信开销与时延,有效提高系统吞吐量。

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author: 张硕;孙国凯;庄园;冯小雨;王敬之
keywords: 区块链网络层;eclipse攻击检测;深度学习;图注意力网络;多层感知机
source: 计算机应用
site: 维普
title: 面向区块链节点分析的eclipse攻击动态检测方法
created_at: 2025-09-18 10:26:15.203385
original_link:
  https://www.cqvip.com/doc/journal/3508553854?sign=0955658792f1ed54940aa0cdc042edb00c6d5de2e861cbba7013d074c544a3d6&expireTime=1789698297405&resourceId=3508553854

summary:
  eclipse攻击作为针对区块链网络层的一种显著威胁,通过控制节点的网络连接,可导致被攻击节点与整个网络的隔离,进而影响该节点接收区块和交易信息的能力。攻击者还可以在此基础上发起双重支付等攻击,这会对区块链系统造成巨大破坏。针对该问题,结合深度学习模型,提出一种面向区块链节点分析的eclipse攻击动态检测方法。首先,利用节点综合韧性指标(NCRI)表达节点的多维属性特征,并引入图注意力神经网络(GAT)动态更新网络拓扑结构的节点特征;然后,使用卷积神经网络(CNN)融合节点的多维特征;最后,结合多层感知机(MLP)来预测整体网络的脆弱性。实验结果表明,所提方法在不同的eclipse攻击强度下的准确率最高可以达到89.80%,并且能在不断变化的区块链网络中保持稳定的性能。

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author: 梁喜;魏光何;阳成虎
keywords: 区块链;CSR;闭环供应链;定价;减排
source: 管理工程学报
site: 维普
title: 考虑区块链和CSR投入的闭环供应链定价与减排决策
created_at: 2025-09-18 10:26:15.203859
original_link:
  https://www.cqvip.com/doc/journal/3506278295?sign=e39f403ec2f7ad25c5b69d6223d63d65763f9fa7b530a9ee7749156612a1b7e8&expireTime=1789698297405&resourceId=3506278295

summary:
  在由单个制造商、单个零售商和单个第三方组成的闭环供应链系统中,根据是否投入区块链技术和不同企业社会责任(CSR)投入主体,本文将闭环供应链区分为四种模式,运用Stakelberg博弈对比分析了消费者对产品来源不信任程度、区块链技术投入成本、消费者低碳偏好系数、碳减排成本系数等参数对闭环供应链定价与减排决策的影响。研究表明,不投入区块链技术时,若消费者对产品不信任程度较低,则制造商投入CSR能同时实现制造商、零售商及系统社会福利更高;投入区块链技术时,若区块链技术投入成本、消费者低碳偏好系数和回收废旧品验证费用较低,则零售商投入CSR能同时实现制造商、零售商及系统社会福利更高;若消费者对产品来源不信任程度较高,则投入区块链技术有利于实现更高的碳减排水平;只有消费者对产品来源不信任程度或碳减排水平正向影响需求的折扣程度较高或回收废旧品的验证费用较低时,投入区块链技术后各成员利润与系统社会福利更高;无论是否投入区块链技术,零售商投入CSR时的碳减排水平始终更高,第三方利润在各成员投入CSR时均相等;随着区块链技术投入成本的增加,若碳减排成本系数较小,则零售商投入CSR能增加制造商、零售商与系统社会福利;若初次检测的回收检验成本较小,无论哪一方投入CSR,投入区块链技术时制造商利润与系统社会福利均更高。

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author: 刘文豪;戴欢;王陆平;唐毅
keywords: 区块链;分片技术;全局信誉;吞吐量;可验证随机函数;隐私保护;安全路由
source: 计算机工程与设计
site: 维普
title: 基于动态混合分片机制的区块链系统优化方案
created_at: 2025-09-18 10:26:15.204322
original_link:
  https://www.cqvip.com/doc/journal/3508563638?sign=3424291f30b6e25820b898d90f11dd902a947d74e6af0b361cd2328e278196af&expireTime=1789698297405&resourceId=3508563638

summary:
  针对基于随机分片的区块链系统往往易受协同攻击,存在性能低、数据丢包等问题,提出一种基于动态混合分片机制的区块链系统优化方案。在系统中采用VRF_DPOS算法划分节点类型,混合分配至各个分片,充分利用分片的并行处理能力减轻系统负载;通过将包含交易信息的subMerkleRoot哈希值划分给各个分片中周期性更新的可信委员会节点处理交易,降低协同攻击对分片的影响;提出一种基于节点行为的信誉评估机制,该机制在遴选出恶意节点的同时,计算出可靠路径,保障区块的安全转发。实验结果表明,该方案能够有效抑制不同行为模式下恶意节点带来的威胁,提升了分片的安全性,降低了交易处理延迟,提高了网络的吞吐量。

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author: 李亚红;李哲玮;李强;王彩芬;张学军
keywords: 电子病历;区块链;可搜索加密;代理重加密
source: 计算机工程与科学
site: 维普
title: 区块链中可搜索加密电子病历数据共享方案
created_at: 2025-09-18 10:26:15.204767
original_link:
  https://www.cqvip.com/doc/journal/3509792375?sign=1b57549310c1c0793152832cd189e54c5677b3210ab3d2108f0b4b7315b009c6&expireTime=1789698297405&resourceId=3509792375

summary:
  针对电子病历存在数据安全和存储、数据共享等方面的问题,提出区块链中可搜索加密电子病历数据共享方案。首先,所提方案利用云服务器实现电子病历的存储,并对相应密文进行重加密,以保证不同医疗机构之间的数据共享。其次,使用区块链存储索引,搜索阶段在联盟链上调用智能合约执行关键词密文搜索,实现索引安全存储的同时,降低半诚实的第三方不实搜索风险。同时,该方案将条件隐藏在重加密密钥中来保证数据的机密性,保证代理无法了解任何有关条件的信息。最后,通过实验分析可知,该方案是轻量级的,在计算和通信开销方面具有很大优势。

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author: 黄琰;黄熙婷;陶绮
date: 2025-09-10
keywords: 深度学习;;同伴互评;;学习进阶;;ICAP理论;;认知网络分析;
source: 中国考试
site: 知网
title: 促进深度学习进阶的同伴互评模型的实证研究
created_at: 2025-09-18 11:28:45.365009
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQAdgUcX928j2_VMzf9EhtQxkYMragQgP1_OGKcuZd0g-KgpRYnElcHHKr4jJbCaobOc32oSDUA8v3dh51wKwKTgHDsD9ekMaWP3jainYnARK-wKJbHC0OZSWhq3n0OtFB7b140EwWLsg7SCOcCZUE4hdPEOrOE6wSNswwrsiOS5w==&uniplatform=NZKPT&language=CHS

summary:
  同伴互评作为教育评价改革的一种创新性探索，对深度学习的促进效果常受限于学生被动参与、单向交互及形式化等因素。本研究依托ICAP认知参与理论构建同伴互评模型，主要包括同伴互评流程、深度学习进阶、多方协同支持三个核心环节；并以“微格教学训练”课程为例，采用认知网络分析法可视化分析应用前、中、后期三个阶段以及不同组别学习者的认知差异。研究发现，教学模式应用后学生的深度学习能力呈整体上升趋势，但不同阶段的认知差异显著，高分组转化速度更快且高阶学习特征更突出。研究据此提出促进深度学习进阶的策略建议，为推动高校教育评价改革提供实践参考。

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author: 罗能;刘才学;朱军;周正平;罗婷
date: 2025-09-01 16:04
keywords: 深度学习;;设备状态评估;;核领域;
source: 核动力工程
site: 知网
title: 深度学习在设备状态评估中的研究现状及其在核领域的应用展望
created_at: 2025-09-18 11:28:45.365893
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQAdgUcX928j2_VMzf9EhtQ5AENmPne8Lj-ynysfptxbVXTfchAhYPoWuDdx8ICnwxT61X0IdqVHEpzCBYiO-c6Q9kYfMRSuI_8eYYnMbU6mdTqt2gM9mXLUX7vSyZXIxkPB33nBy7aKjRQI4mfkE3rIPXbnD7hxIiQcpk8_JmlvA==&uniplatform=NZKPT&language=CHS

summary:
  随着核能在全球各行业的广泛应用，反应堆机电设备的状态评估在覆盖范围、实时性、经济性和准确性方面面临更高要求。传统基于物理仿真、专家知识或数据驱动的方法难以满足这些新挑战。本文从深度学习在设备状态评估中的研究思路入手，围绕设备状态甄别、故障诊断、故障预测以及状态评估模型的部署应用4个核心任务，系统梳理了当前研究的现状与不足，并对核领域设备状态评估中的深度学习应用进行了回顾与展望。在此基础上，提出了一种基于知识和数据融合迭代的设备状态评估技术体系，明确了未来技术研究的方向，为核能设备智能化运维提供了新的思路和方法。

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author: 李娟;阳正旺
date: 2025-08-28
keywords: 深度学习;;融合式教学;;教学评价;
source: 邵阳学院学报(社会科学版)
site: 知网
title: 面向深度学习的高校融合式教学质量评价指标体系的研究
created_at: 2025-09-18 11:28:45.366403
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTiEFNLxYjqhkH6FrbHsE6K9bvY6wSdftuXOzEbB7qYh39WlXtZZVZ3malUYCPTyJwCszyhgffKBZ0K9oViyOgAhBUMu2hZzu8dzO1nroKYSgUQhxHTUfJHMOanAf5qAarCjRCaEvU7Y37tXlDm-QLIiE6GOnB3TktVgQThgtXvRA==&uniplatform=NZKPT&language=CHS

summary:
  数智技术发展使社会人才需求结构发生深刻变化，推动高校依据“金课”标准积极深化课程改革，以促进学生深度学习为目标重构教学体系。这一转型使得传统教学质量评价体系的适用性面临严峻挑战。为此，文章结合深度学习理论、融合式教学质量特征，采用德尔菲法和层次分析法，从深度学习的视角，构建了高校融合式教学质量评价指标体系。该体系包含“准备”“实施”“评价”3个一级指标项，下设“教学理念”“学情分析”等10个二级指标项，并细化形成25个三级指标项。实证分析表明，该指标体系具有较高的效度与信度，权重分配合理，能够为高校融合式教学质量评估提供有效工具。

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author: 胡玲
date: 2025-08-25
keywords: 深度学习;;共词分析;;点度中心性;;二模矩阵;
source: 内江师范学院学报
site: 知网
title: 基于共词分析方法的国内深度学习研究趋势可视化分析
created_at: 2025-09-18 11:28:45.366873
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTiEFNLxYjqhkH6FrbHsE6K9bvY6wSdfturQ7NyJY9-Ycc3fLJVaQVU-NKjaihVj014l4z9_0kTdFC2bg8MyoP31VdTsePeYOHQal9kf0U_2i83qZIorx_3iNgXXkgejq5C0JYhRyCBGP18WrqK18r2Q7XAvm_JMRSNWaVElE7QTg==&uniplatform=NZKPT&language=CHS

summary:
  采用文献计量学共词分析方法，以中国知网北大核心及CSSCI学术期刊中收录的1 365篇“深度学习”相关文献为研究对象，利用COOC 14.9和VOSviewer 1.6.19工具，构建高频关键词相异矩阵进行系统聚类分析；通过点度中心性权重绘制关键词网络层级图，探析研究热点；通过构建的核心作者与高频关键词二模矩阵，展示核心作者的主要研究领域和作者之间的关联性.研究结果表明：我国当前的深度学习研究主要集中在理论研究、混合式教学、人工智能应用、深度教学和教学改革研究五个方面，并根据可视化分析、突变检测结果提出了深度学习研究领域在技术整合、教学设计、实证研究和教学评价等方面的研究展望.

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author: 汪雅霜;陈志娟
date: 2025-08-20 17:27
keywords: 深度学习;;教学方式;;初中生;;随机森林;
source: 湖南师范大学教育科学学报
site: 知网
title: 何种教学方式更能促进初中生深度学习
created_at: 2025-09-18 11:28:45.367344
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTiEFNLxYjqhkH6FrbHsE6K_ZIjBqXT6kYokv_1wLTFzFYzU1j3lObcUkz-zh1C8poprpouATok6fJHB9KtHADXlcaVoR3vxxQE8K9l3Iulr0GbnlfwjJ4K2BOQ4ScQlbA84lRoWtpD3AinXOWTeX7hD6AyFTbPnnPtYbVWVn3ejg==&uniplatform=NZKPT&language=CHS

summary:
  深度学习是一种适应当下中国基础教育课程改革需要的学习方式。研究基于江苏省S市12所学校1277份初中生调查数据,使用回归分析、随机森林等方法,探究教师教学方式对初中生深度学习的影响。研究发现,探究式教学对初中生深度学习的影响最大,但存在性别异质性;差异化教学使用频率最高,且对深度学习产生正向影响;讲授式教学对高阶性学习和整合性学习的影响最小;合作式教学对整合性学习产生正向影响,但对反思性学习的影响最小。建议大力提倡探究式教学,破除性别刻板印象;深化差异化教学应用,精准赋能深度学习;明确定位讲授功能,融合互动提升效能;优化合作学习设计,促进整合与反思的平衡。

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author: 李志高;徐冉;王欣;陈志伟;索海龙
date: 2025-07-28
keywords: 剩余寿命预测;;深度学习;;大型设备;
source: 机床与液压
site: 知网
title: 基于深度学习的装备系统剩余寿命预测研究综述
created_at: 2025-09-18 11:28:45.367841
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DRThQyNWBphMVp3DoyTzBb9chs1kvybNfKpV-TbdTBQx3CZ5sp8stfDm8EI2l20hcsk_hiwb8cDMg0DEF_G-vV7JVVb7D7fP4EOqjTzpe709Xpb0xaKMzjqhnq_MLfWQrRtCShTbVx_wMOE8IZZ-G-9YtfamhOzDacTNt0Pm1mREA==&uniplatform=NZKPT&language=CHS

summary:
  剩余寿命预测是一种通过复杂多维数据前向拓展映射到设备或零件使用情况预测的技术。可靠和准确的剩余寿命预测可以为设备提供有效的预测性维修，从而避免资源浪费、灾难性故障以及人员伤亡。基于深度学习的剩余寿命预测方法能实现模型参数的自主学习，并自动提取原始数据的深度特征，在部件基础上对系统建模以实现更高精度的剩余寿命预测。因此，基于深度学习的方法非常适合复杂退化系统的建模分析。近5年来，大量研究学者在深度学习寿命预测领域取得了诸多突破性进展。总结分析基于深度学习的剩余寿命预测研究，对深度学习剩余寿命预测在装备系统中的应用进行梳理，并对深度学习在装备系统剩余寿命预测方面的未来研究方向进行预测与评估。

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author: 刘晓艳
date: 2025-07-28
keywords: 深度学习;;混合式教学;;核心素养;;教学模式;
source: 南方农机
site: 知网
title: 基于深度学习理论的混合式教学模式设计
created_at: 2025-09-18 11:28:45.368311
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DRThQyNWBphMVp3DoyTzBb9chs1kvybNfJrI19k2k9mraDfR7otyhYtCShQ3VyNpj4SIOwXZyi_y2ghZYpHBYILk0Xi8mrmKCs8_s3rL8fjSNX6s3hsPVS5tW7zWz6BbpEiibAtqeFwMGMdS4JVW7MHJ1jc3HZ6PnDCkI06vQTI_g==&uniplatform=NZKPT&language=CHS

summary:
  【目的】解决目前混合式教学中存在的资源设计系统性不够、教学组织中心性不足、教学成效引领性欠缺及教学有效性低等问题。【方法】以深度学习融入混合式教学的切入点分析为基础，提出基于深度学习理论的混合式教学模式设计。将深度学习的核心要素融入课程教学目标，以教学目标为指引，拓展数字化教学场景，优化资源配置；实施过程中坚持以学生为主体的交互式教学活动设计，以聚焦深度学习要素的教学评价为支撑，提升教学有效性。并以江苏电子信息职业学院电气自动化技术专业群为例，实施了基于深度学习理论的混合式教学改革。【结果】通过基于深度学习理论的混合式教学改革实践，学生课堂抬头率、获得感显著提升，教学效果得到师生一致好评，实施成果被主流媒体宣传报道。【结论】基于深度学习理论的混合式教学具有实践性、职业性、交互式特征，有助于学生实现深度学习，并助力高职复合型技术技能人才的培养。

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author: 张家军;黄儒军
date: 2025-07-25 16:56
keywords: 数字教材;;深度学习;;技术限度;
source: 西南大学学报(社会科学版)
site: 知网
title: 数字教材赋能深度学习：内在机理、可能困境与突破路径
created_at: 2025-09-18 11:28:45.368750
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTHMs4QjgduEGvVnsH-hvRTY2OI0SCpJ9vdnglu4IvUkW6cABZbWx41CccDLJpSWtAFusSBMN6MH1zC7ZYmupSLRIGpUukWEr66DihQuv3A-eMNXm6hzzn10Ns8UUp0PKkfS0Z7j2_1Vljv72LXeyGHRjxfwp-RwEa1imofOT7sEA==&uniplatform=NZKPT&language=CHS

summary:
  数字教材赋能深度学习具有清晰的内在机理，可通过具身化存储方式、差异化学习资源、智能化交互活动、形象化生发过程推动深度学习的协同性、个性化、对话性、境脉性。现代技术具备的解蔽与遮蔽的双重性和深度学习的复杂性，意味着数字教材对深度学习的赋能也面临着挑战，体现为教师角色转变不畅对深度学习课堂设计与实践的制约、信息呈现方式粒化对深度学习高阶思维能力养成的弱化、技术依赖与崇拜对深度学习情感与价值观培养的遮蔽、数据驱动式评估对深度学习隐性指标测度的难为。为更好地实现数字教材对深度学习的赋能，应引导教师成为深度学习课堂的积极策划者和忠实执行者，推动数字教材内容编排的主体模块化和生活情境化，注重数字教材使用过程中价值理性与工具理性的契合，形塑多元化的评价方式。

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author: 王汀;赫静雅
date: 2025-07-25
keywords: 智慧课堂;;环境设计专业;;灵活深度学习;;设计路径;
source: 赤峰学院学报(自然科学版)
site: 知网
title: 基于智慧课堂构建的环境设计专业灵活深度学习设计路径
created_at: 2025-09-18 11:28:45.369229
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTHMs4QjgduEGvVnsH-hvRTY2OI0SCpJ9tQKct9S0wRU-1nLD6mHM5b8hEaAT6ayJm6J43rSvtA5onIXsptkE3_zLxB_EcRHqhMaKH1QVcXQ0TeJQMAQ1BDTET6SxiykJjJaVclcdXtFzuhRFibhWRX-oU-Iuq9ApbOyLMZ7rE5fw==&uniplatform=NZKPT&language=CHS

summary:
  在智慧课堂理念下，结合环境设计专业的性质及特点，依托先进的物联网、深度学习及数据挖掘等技术，促进智慧课堂与深度学习的有效融合，制定灵活深度学习设计方案，能够促进学生高阶思维能力的发展，强化智能化、数字化信息技术对环境设计专业教学的赋能，全面提高环境设计专业教学质量和效率。本研究主要对智慧课堂和灵活深度学习进行了概述，分析了基于智慧课堂构建的环境设计专业灵活深度学习设计路径，并总结了环境设计专业灵活深度学习策略，以期构建科学、合理的环境设计专业灵活深度学习框架，培育学生的环境专业设计思维和技能。

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author: 谢欢;汪李娟
date: 2025-07-25
keywords: 深度学习;;外语教育;;CiteSpace;;可视化分析;
source: 宜春学院学报
site: 知网
title: 我国外语教育领域深度学习研究述评——基于CiteSpace的可视化分析
created_at: 2025-09-18 11:28:45.369690
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTHMs4QjgduEGvVnsH-hvRTY2OI0SCpJ9tD003eYrRPvAOiF1445SOraWc01NxbC6f-TvyQ8jXIr4Zo-J_BxuUvN62LK8JEbURkJacX6KGxvkNuouZJMR4KWIqAhkGvu9U3FM3BigkKpb00qW-jIDac_-kVgRwjX22NhQqJud4YlQ==&uniplatform=NZKPT&language=CHS

summary:
  对中国知网中主题为“深度学习”、学科分类为“外国语言文字”的相关文献进行检索，得到574篇文献；借助可视化分析软件CiteSpace,采用定量与定性相结合的方法述评我国外语教育领域深度学习的研究情况。我国外语教育领域深度学习研究已进入稳定发展期，具有较明晰的内涵特征与基本理论框架，未来应扩大研究范围，涵盖教学评价研究、场域研究、听说能力研究等更多领域，同时还应加大对基础教育方面的研究力度，完善相关理论；一线教师需结合已有的理论成果，积极开展实证研究，推动我国外语教育领域深度学习理论和实践的纵深发展。

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author: 方颖;张延伟;利晞;颜培栋;毕苗
keywords: Transformer;检测精度;深度学习;YOLOv8;目标检测;膝骨关节炎
source: 中国组织工程研究
site: 维普
title: 基于深度学习改进的膝骨关节炎自动诊断方法
created_at: 2025-09-18 11:28:45.370162
original_link:
  https://www.cqvip.com/doc/journal/3483672308?sign=92ad56786770c8296ec8d85ab6414141980c3c8c9b8e0e3c54f32062fa58fea7&expireTime=1789701808871&resourceId=3483672308

summary:
  背景:膝骨关节炎是一种常见的退行性疾病,不仅严重影响患者的生活质量,同时增加社会医疗负担。早期准确诊断膝骨关节炎对于患者的治疗和预后至关重要,传统的诊断方法不仅主观且耗时,还不能保证稳定的高准确率。目的:开发一种基于深度学习的膝骨关节炎自动诊断方法,利用深度学习网络提高诊断的准确性和效率。方法:在YOLOv8n网络基础上采用Efficient-ViT网络替换YOLOv8n的骨干网络以及增加注意力机制的方法,提出了一种新的网络模型YOLOV8-ViT模型,用于自动识别和分类膝骨关节炎的X射线片图像。实验数据集来自广州中医药大学第三附属医院的5078张膝骨关节炎患者的X射线片图像,由3个影像医师根据Kellgren-Lawrence分级标准采用labelme软件来标注膝关节炎部位并进行分类,采用并集结果。评价指标包括Precision、F1分数、mean average precision(mAP)、Recall、val/box_loss、val/cls_loss和val/dfl_loss。结果与结论:实验结果表明,与YOLOv5n、YOLOv8n、YOLOv9n模型比较,YOLOV8-ViT模型的准确率、IoU阈值为0.5的平均精度(mAP50)、IoU阈值为0.5-0.95的平均精度(mAP50-95)、F1分数和Recall均有所提高,val/box_loss、val/cls_loss和val/dfl_loss分别降低了0.496、0.45和0.523,1.037、0.305和0.728,0.267、0654和0.854,验证了该模型具有较高的检测精度。

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author: 周小洁;侯本祥
source: 国际口腔医学杂志
site: 维普
title: 基于深度学习技术诊断龋病方法的研究进展
created_at: 2025-09-18 11:28:45.370654
original_link:
  https://www.cqvip.com/doc/journal/3509822358?sign=e8e932764f083cdb661f42ff85bece1a1ce81dd6d1865dd6cf851b2b9c147c22&expireTime=1789701808871&resourceId=3509822358

summary:
  龋病发病率高,及时诊断是临床治疗的基础。随着深度学习技术在自然图像处理领域的突破,基于深度学习利用口腔医学影像进行龋病自动诊断的方法受到了广泛关注。基于深度学习的龋病诊断方法在龋病识别、检测与分割两大类典型任务中取得了进展,本文从多个角度对两类任务的深度学习方法进行综述与对比,对龋病诊断数据集以及面临的挑战进行分析,有助于龋病智能诊断的研究。

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author: 张晶;周凯;吴文涛
source: 中国农机化学报
site: 维普
title: 基于深度学习的农作物病害检测算法研究
created_at: 2025-09-18 11:28:45.371110
original_link:
  https://www.cqvip.com/doc/journal/3509798040?sign=8c911430c0a626e132608bb3783679294fea5b36e14b124de69e9c33d43c3b5a&expireTime=1789701808871&resourceId=3509798040

summary:
  针对目前无法高效地检测农作物病害的问题,基于深度学习算法YOLOv5进行改进,利用K-means算法对标签锚框进行调整,以便更好地适应训练数据集中的病害区域面积的大小。将Ghostnet模块引入YOLOv5的backbone网络中,在不影响特征提取的前提下减小计算量和参数量。同时,基于CBAM注意力机制中的通道注意力和空间注意力,提出并行连接结构和残差连接提高网络的特征提取能力,引入到算法的Neck部位。改进后的YOLOv5算法模型大小由原来的13.8 MB下降到11.1 MB,平均精度均值mAP@0.5可达到90.7%,相比于YOLOv5提升3.2%,F1分数提升2.1%。为高效检测农作物病害提供一种实际可行的方法,能够以较高的精确率和检测速度对病害进行分类和定位。

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author: 刘凯;汪佳琴;李汉涛
source: 郑州大学学报(工学版)
site: 维普
title: 基于深度学习的车辆轨迹预测研究综述
created_at: 2025-09-18 11:28:45.371558
original_link:
  https://www.cqvip.com/doc/journal/3508569253?sign=3d76b41d2f397795f6b9dba2d8c86dcc3337f431bcd220c9349797fd50c6d8eb&expireTime=1789701808871&resourceId=3508569253

summary:
  车辆轨迹预测(VTP)是交通技术领域中的重要研究对象。传统VTP方法需要大量特征工程,且难以实时适应复杂变化的环境。深度学习(DL)通过多层神经网络实现高效数据表达,克服了传统方法的局限性。对基于DL的VTP方法进行了综述,探讨了其在VTP中的应用及性能表现。首先,回顾了传统VTP方法和基于DL的VTP方法,介绍了VTP主要考虑的问题和问题的表述;其次,分析并比较了各类VTP方案,包括输入数据、输出结果和预测方法;再次,介绍了常用的评估指标,比较了这些VTP方案的实验结果,分析了VTP的应用,并展示了DL在VTP中表现出的优异性能;最后,展望了VTP未来在数据集、建模和计算效率方面的研究方向,指出车辆交互协同建模、模型的泛化以及多模态融合将是未来的挑战和研究方向。

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author: 付强;关海鸥;李嘉琪
source: 光谱学与光谱分析
site: 维普
title: 基于改进E-DWT算法和深度学习模型的红小豆锈病诊断方法
created_at: 2025-09-18 11:28:45.372029
original_link:
  https://www.cqvip.com/doc/journal/3509807533?sign=45c0ab49ae5591f1a1d5ed8e881350339a0279cff3eb406a6fb1f7aeb44e4e91&expireTime=1789701808871&resourceId=3509807533

summary:
  红小豆锈病是一种由真菌引起的常见植物病害,主要通过感染叶片影响光合作用,导致作物产量显著下降。本文提出了一种基于改进经验模态分解-小波变换(E-DWT)算法和深度学习模型的新型红小豆锈病诊断方法。选用“宝清红”红小豆作为实验对象,使用手持可见/近红外光谱仪对960例红小豆叶片进行为期10天的连续光谱数据采集,获取波长范围为326~1075 nm的红小豆叶片反射率数据。首先,采用改进的E-DWT算法对采集的光谱数据进行去噪处理。该算法结合了经验模态分解(EMD)和小波阈值去噪技术,能够在去除噪声的同时最大限度保留信号的有效信息。通过对比RMSE和SNR指标确定了最佳的小波基函数(sym5)和分解层数(4层)。为了进一步降低高维数据中的冗余信息,采用连续投影算法(SPA)从750个初始波长中筛选出了12个具有代表性的特征波长,实现了数据降维,将特征波长数量减少了98.4%。接着,结合格拉姆角场(GAF)方法,将一维波长序列转换为二维光谱图像,增强了不同波段之间的相关性,便于后续的模型训练。在模型设计上,采用了结合卷积神经网络(CNN)和卷积块注意力机制(CBAM)的深度学习模型。CBAM模块通过引入通道和空间注意力机制,能够有效区分光谱数据中不同特征波长和时间节点的权重,使模型更加关注影响红小豆锈病识别的关键特征。实验结果表明,基于CBAM的CNN模型在训练集中的识别率为99.31%,而在测试集中的识别率为98.33%,召回率达到98.89%,明显高于传统CNN模型的表现。与现有的其他方法相比,本文提出的模型在识别准确性、稳定性以及训练收敛速度上均具有显著优势。总体而言,本文所提出的基于改进E-DWT算法与CBAM-CNN模型的红小豆锈病诊断方法,不仅实现了高效、精准的病害检测,还为未来数据驱动型作物病害诊断系统的构建提供了理论依据与技术支持。

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author: 赵昭阳;赵建国;欧阳芳;肖增佳;马铭;闫博鸿
source: 地球物理学报
site: 维普
title: 基于岩石物理模型正演的标签制作方法在深度学习波阻抗预测中的应用
created_at: 2025-09-18 11:28:45.372491
original_link:
  https://www.cqvip.com/doc/journal/3509794736?sign=d7894bd16049b83e5b1612b91317aa2aad7931f65d53f3f52423d9011d318544&expireTime=1789701808871&resourceId=3509794736

summary:
  深度学习波阻抗预测已成为油气勘探领域的重要课题,因测井数据有限,导致可用于神经网络训练的标签数量明显不足.为此,本文采用理论岩石物理模型道集正演的方法来获得训练标签,该方法使得利用单口井数据进行深度学习储层参数反演成为可能.通过理论岩石物理模型流体替换和波动方程正演,获得与实际工区岩石物理参数一致的变孔隙度和饱和度的训练标签.为了提高模型的泛化度和精确性,实验使用逐步回归法在20多种地震属性中优选出9种最优的地震属性组合.将这9种地震属性组合和对应的测井波阻抗数据带入到深度神经网络(DNN)中进行训练,准确地预测出波阻抗,预测波阻抗与实际波阻抗的互相关系数达到了0.992,平均误差为304(m·s)·(g·cm).在三维实际地震工区的应用中,使用该方法预测了研究工区的纵、横波阻抗和密度结果,通过建立地层模型,反演地层模型参数,反演结果显示,深度学习反演方法的预测精度比传统反演方法预测精度高,因此更有利于对薄层的识别.

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author: 成洪壮;宁欣如;郭楚云;钟捷
source: 眼科新进展
site: 维普
title: 深度学习在视网膜疾病研究中的现状与发展
created_at: 2025-09-18 11:28:45.373002
original_link:
  https://www.cqvip.com/doc/journal/3509801770?sign=2eeb12fd4a2eb309866744b320bf3b81ef94409675f79cd19219b49ac61b21e6&expireTime=1789701808871&resourceId=3509801770

summary:
  深度学习为视网膜疾病的早期诊断、病变分割和治疗预测等提供强有力的技术支持,显著提高诊断的效率和准确性。但也面临模型适用范围不同和性能差异等方面的挑战,其主要原因在于不同的网络结构在特征提取能力、计算复杂度和临床适应性方面存在差异,使得它们在不同的应用场景下具有不同的优势和局限。通过对近5年内PubMed和Web of Science数据库中的相关文献进行系统检索,本文整理了常见玻璃体视网膜疾病中最常用的深度学习网络架构,总结其不同的优势与局限,并分析各架构在眼科领域的最佳应用方向,为未来的研究提供参考与借鉴。

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author: 王喜龙;杨志斌;周勇
source: 小型微型计算机系统
site: 维普
title: 基于深度学习的自然语言需求到时序逻辑的生成方法
created_at: 2025-09-18 11:28:45.373506
original_link:
  https://www.cqvip.com/doc/journal/3509816587?sign=951a700e2f5d2694b5a91e50c3dd211d1119346a149685fb2243bf7d46b14295&expireTime=1789701808871&resourceId=3509816587

summary:
  在嵌入式实时系统当中,时序逻辑是形式化规约的重要组成部分.现有自然语言需求到时序逻辑的生成方法往往局限于特定领域,缺乏泛化性.本文提出的基于深度学习的方法DeepTL能够完成不同领域自然语言需求到多种时序逻辑的生成任务.DeepTL包含原子命题识别、时序逻辑结构翻译、原子命题翻译3个模块.通过拆分为多个模块,DeepTL不仅显著减少了所需的模型训练数据量,还使得时序逻辑结构翻译不再受限于特定领域.本文在3个领域进行了实验,结果表明,DeepTL在具备较高翻译准确率的同时,减少了对于大规模训练数据和计算资源的依赖.

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author: 周立;吴洋洋;苗晓晔
source: 计算机研究与发展
site: 维普
title: 基于深度学习的不完整时序数据补全方法综述
created_at: 2025-09-18 11:28:45.373962
original_link:
  https://www.cqvip.com/doc/journal/3509797033?sign=6de320692997c3a3e58e535e36c0b713edb4fe37c5bc6cbef9dc94eb283c113c&expireTime=1789701808871&resourceId=3509797033

summary:
  时序数据广泛存在于工业、金融、交通、气象及医疗等领域,具有重要的分析应用价值.然而由于人为或偶发因素导致的缺失,时序数据的完整性经常遭到破坏,进而削弱基于其开展的分析与决策的准确性和可靠性.不完整时序数据通常包含复杂的时序依赖和变量间关系,为缺失值的有效补全带来较大挑战.深度学习方法具有强大的建模能力,是应对该挑战的有效技术,并日益成为研究热点.故系统综述基于深度学习的不完整时序数据补全方法的研究现状,首先介绍其相关概念和定义,进而对现有方法进行分类,归纳各类代表性方法的核心思想与特点.随后整理常用的开源数据集与评价指标,并设计全面系统的实验方案.实验从缺失场景、缺失率、变量数量、序列长度以及对下游任务的提升效果等多个角度,评估10种主流深度学习方法的补全质量与效率,最终总结实验结果并依据场景推荐合适的补全方法.最后,结合当前研究进展对该领域的未来发展趋势进行展望,并总结全文.

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author: 杜岳峰;侯思余;李国润
keywords: 含杂检测;田间试验;深度学习;DeepLab v3+;玉米收获
source: 农机化研究
site: 维普
title: 基于深度学习的玉米籽粒含杂检测方法研究
created_at: 2025-09-18 11:28:45.374418
original_link:
  https://www.cqvip.com/doc/journal/3499929388?sign=9591486e051b99e28d18ad84ab0dc8872cffc03607bd4ea9f12389aeb5eab9d4&expireTime=1789701808871&resourceId=3499929388

summary:
  玉米籽粒含杂率是籽粒收获机的关键指标之一,但目前我国玉米籽粒收获过程无法实现在线实时检测。为此,首先设计了一种玉米籽粒含杂检测装置,并建立了籽粒含杂计算模型;然后,收集了玉米籽粒-含杂图像数据集,并进行了图像增强和标签制作;再者,提出了一种结合压缩-激励注意力模块的改进DeepLab v3+模型;最后,进行了不同模型的对比试验和田间试验。试验结果表明:改进DeepLab v3+深度学习模型的MIoU和PA分别达到92.17%和96.45%,其分割效果优于其他对比模型。采用Xception为主干网络的改进DeepLab v3+模型比ResNet50和ResNet101的效果更好。在田间试验中,装置检测与人工检测的结果相近,两者的绝对误差和相对误差较小。所提出的含杂率检测装置和算法能够实时在线检测含杂率,提高了玉米籽粒收获效率和质量,推动了我国玉米籽粒收获机的自动化进程。

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author: Kyu Beom Lee;Hyu Soung Shin
date: 2019
original_link: https://ieeexplore.ieee.org/document/8876906/
site: ieeeXplore
created_at: 2025-09-18 11:28:45.374883
summary:
  In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object Detection and compares the BBoxs of the current and previous video frames to assign a unique ID number to each moving and detected object. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks. A deep learning model in ODTS was trained with a dataset of event images in tunnels to Average Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire, respectively. Then, based on a trained deep learning model, the ODTS based Tunnel CCTV Accident Detection System was tested using four accident videos which including each accident. As a result, the system can detect all accidents within 10 seconds. The more important point is that the detection capacity of ODTS could be enhanced automatically without any changes in the program codes as the training dataset becomes rich.

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title:
  An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents Under Bad CCTV Monitoring Conditions in Tunnels


author: Dionysis Goularas;Sani Kamis
date: 2019
original_link: https://ieeexplore.ieee.org/document/8876896/
site: ieeeXplore
title: Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data
created_at: 2025-09-18 11:28:45.375328
summary:
  This study presents a comparison of different deep learning methods used for sentiment analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at the same time to the solution of a wide range of problems, gained popularity among researchers. Particularly, two categories of neural networks are utilized, convolutional neural networks(CNN), which are especially performant in the area of image processing and recurrent neural networks (RNN) which are applied with success in natural language processing (NLP) tasks. In this work we evaluate and compare ensembles and combinations of CNN and a category of RNN the long short-term memory (LSTM) networks. Additionally, we compare different word embedding systems such as the Word2Vec and the global vectors for word representation (GloVe) models. For the evaluation of those methods we used data provided by the international workshop on semantic evaluation (SemEval), which is one of the most popular international workshops on the area. Various tests and combinations are applied and best scoring values for each model are compared in terms of their performance. This study contributes to the field of sentiment analysis by analyzing the performances, advantages and limitations of the above methods with an evaluation procedure under a single testing framework with the same dataset and computing environment.

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date: 2020
original_link: https://ieeexplore.ieee.org/document/10280427/
site: ieeeXplore
title: Deep Learning with JavaScript: Neural networks in TensorFlow.js
created_at: 2025-09-18 11:28:45.375789
summary:
  Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. <i>Deep Learning with JavaScript</i> shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

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date: 2020
original_link: https://ieeexplore.ieee.org/document/10280268/
site: ieeeXplore
title: Deep Reinforcement Learning in Action
created_at: 2025-09-18 11:28:45.376231
summary:
  Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. <i>Deep Reinforcement Learning in Action</i> teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.

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date: 2020
original_link: https://ieeexplore.ieee.org/document/10280272/
site: ieeeXplore
title: Deep Learning with PyTorch
created_at: 2025-09-18 11:28:45.376666
summary:
  Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. <i>Deep Learning with PyTorch</i> will make that journey engaging and fun.

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date: 2021
original_link: https://ieeexplore.ieee.org/document/10953839/
site: ieeeXplore
title: Deep Self‐taught Learning in Remote Sensing
created_at: 2025-09-18 11:28:45.377113
summary:
  Summary <p>Deep self&#x2010;taught learning (DSTL) is a deep learning&#x2010;based machine learning paradigm which originates from self&#x2010;taught learning (STL, Raina et al. ()). It is a valuable procedure to a combined exploitation of unlabeled and labeled data for classification, without the constraint that both need to contain the same classes or follow the same data distribution. Therefore, it can utilize unlabeled data from further scenes and acquisition times. The core of self&#x2010;taught learning is a representation learning method such as sparse representation which learns a rich data representation which can be used, for example, as input into a classification algorithm. This chapter introduces the basic concept of STL and DSTL utilizing sparse representation and illustrates how it can be used for the classification of remote sensing images. In contrast to similar deep learning methods like autoencoders, DSTL has a higher interpretability, because the representation consists of an easily comprehensible (non)&#x2010;linear combination of real data samples. This chapter explains connections to other procedures by drawing parallels and analyzing advantages and disadvantages.</p>

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date: 2020
original_link: https://ieeexplore.ieee.org/document/10280270/
site: ieeeXplore
title: Grokking Deep Reinforcement Learning
created_at: 2025-09-18 11:28:45.377568
summary:
  <div class="manual-productpage-video-container"> <iframe allow="autoplay; encrypted-media" allowfullscreen="" frameborder="0" src="https://www.youtube.com/embed/tz2VGGo2GXM" type="text/html"/> </div><br/><br/> We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. <i>Grokking Deep Reinforcement Learning</i> introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

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author: Betül Ay;Galip Aydın;Zeynep Koyun;Mehmet Demir
date: 2019
original_link: https://ieeexplore.ieee.org/document/8876932/
site: ieeeXplore
title: A Visual Similarity Recommendation System using Generative Adversarial Networks
created_at: 2025-09-18 11:28:45.378003
summary:
  The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

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author: Luo Yun
date: 2024
original_link: https://ieeexplore.ieee.org/document/10873253/
site: ieeeXplore
title: Research on dynamic adaptation of English translation based on deep reinforcement learning
created_at: 2025-09-18 11:28:45.378468
summary:
  This research is dedicated to exploring the application of deep reinforcement learning (DRL) in dynamic adaptability of English translation. This study compared traditional sequence-to-sequence models, models using attention mechanisms, and advanced models integrating deep reinforcement learning to assess their impact on improving machine translation quality. The research found the deep reinforcement learning model significantly outperforms other models across key performance indicators like BLEU score, accuracy, and recall, particularly for complex translation tasks and maintaining contextual coherence. While deep reinforcement learning models perform well, their high computational demands and potential generalization issues also emerged as limitations. Overall, the study highlights the potential of deep reinforcement learning to adapt in dynamic environments and its promising impact on machine translation. The conclusions of this study provide strong evidence for the application of deep reinforcement learning technology in improving the performance of machine translation systems, and provide suggestions for future research directions and model optimization.

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author: Haiyan Guo
date: 2024
original_link: https://ieeexplore.ieee.org/document/10873245/
site: ieeeXplore
created_at: 2025-09-18 11:28:45.378911
summary:
  The popularization of AI technology, yes the emerging technology represented by deep learning and neural network has gradually become an important tool for solving complex problems. Specifically, in the application field of mobile application development, the cutting-edge deep learning technology is integrated into the JAVA mobile application development course by utilizing the teaching mode of industry-teaching fusion to cultivate students' innovative ability and practical ability. This paper first analyzes the current market demand and technology trends in the mobile application development industry, and points out the great potential of deep learning and neural network technology in enhancing application intelligence and personalization. It describes how to introduce deep learning framework and neural network model in JAVA mobile application development course, and analyzes how to realize deep learning applications such as image recognition, emotion analysis and speech recognition in mobile applications. Under the background of the integration of industry and education, students can be provided with learning resources and internship opportunities that are closer to reality through the deep cooperation with industry enterprises. The research in this paper can provide useful practical reference for the teaching reform of mobile application development courses in colleges and universities.

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title:
  JAVA Mobile Application Development Course Under the Framework of Industry-Education Integration: Practice of Deep Learning and Neural Network Integration


