# api_taskdetail

author: 马敬贺
date: 2025-08-15
keywords: 水土保持;;流域水文循环;;影响机制;;研究进展;
source: 农业科技创新
site: 知网
title: 水土保持对流域水文循环的影响研究进展
created_at: 2025-09-11 03:07:21.913785
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_hCV84MK5KK_wLI0T1h1iSIFibYNMAvQLGKfins0UmBTTi7BNgcG0HqITQnl64HzZy8JVQ5K9gmDllWsHoCM72O73RfX2XT6WoJL0qIn2XMXG_psF-YYJQ6ExTq2Njww6uGYAGmoPTfeeMcUqsvPwrzMUI7EapNEQQgThG3Ndn8g==&uniplatform=NZKPT&language=CHS

summary:
  在全球气候变化与人类活动加剧背景下，水土保持对流域水文循环调控作用受到广泛关注。文章梳理了生物、工程、农业等水土保持措施，通过改变下垫面条件影响降雨—径流、土壤入渗、地下水补给等过程的理论路径，并综述了相关研究进展。研究表明，植被恢复与工程措施协同作用，可显著削减洪峰(如黄土高原小流域洪峰降低40%—60%)，提高土壤入渗率(较裸地提升2—3倍)，优化水资源时空分配。区域案例分析显示，不同气候区水文响应存在差异，如东江流域水库调度使径流年内分配均匀，黄土高原人工林过度密植导致土壤干层等问题。当前，相关研究仍面临多尺度集成不足、气候变化交互作用机制不清等挑战，需要相关学者加强对“空天地”监测网络、耦合模型开发及社会—生态系统综合效益评估的研究。

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author: 曾希文;郭守立;刘小飞;杨涛;周财源
date: 2025-07-30 14:58
keywords: 绿原酸;;断奶仔猪;;育肥猪;;生长性能;;繁殖性能;;肉品质;
source: 饲料研究
site: 知网
title: 绿原酸的生物功能及其在猪生产中的应用研究进展
created_at: 2025-09-11 03:07:21.928858
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_hCV84MK5KK_wLI0T1h1iSIFibYNMAvQIKUsu345tIcysDOA2WNpttK38be1QIq_fiNlFumC-CTn16mCk3cO3X8oTiicsxWislebMlhQ0pWR4ygDjmzz3naFmu6hRZYg_42PVtXWCk0NMBMUSON7Jj28KQPNtW8Xa7p5dkc1cmYw==&uniplatform=NZKPT&language=CHS

summary:
  绿原酸（CGA）是一种天然水溶性多酚物质，广泛存在于高等双子叶、蕨类等植物中，具有抗炎、抗氧化、抗菌、抗病毒等多种生物学功能。在生猪养殖中，CGA能够减轻氧化应激、提高免疫力、改善肉品质、提高种猪繁殖性能及维护肠道健康等，是一种潜在的功能性饲料添加剂。文章对CGA的结构、来源等进行简单介绍，并对其主要生物活性功能及作用机制进行阐述，总结了CGA在种猪、断奶仔猪和育肥猪生产中的应用研究进展，以期为CGA作为猪饲料添加剂的应用提供参考。

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author: 韩成程;孙茂军;潘丹;陈红霞;黄晓梅
date: 2025-06-15
keywords: CT床;;复合材料;;力学性能;;X光射线;;研究进展;
source: 化工新型材料
site: 知网
summary: 介绍了医疗CT床用复合材料的制备技术、理论、实验和影响因素等方面的研究进展，以及测试标准等方面的进展，展望了碳纤维复合材料在医疗CT床制备领域的发展前景。
title: 医疗CT床用复合材料的研究进展
created_at: 2025-09-11 03:07:21.942437
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9qrlRaULUv8z-kiczQ2qJhgYu0ZmsWOIKNi5IFYjO0JcBn1Ad5ZUoJKw7KA6X6Bq7Srti4woh8UIMG4EPbsh6INsrzIq4CeidqdMvwAaugoA5_FLqMXqwEJsm0Cthckyf9JdbQyGFQFDmyCi1yNXIyOdnZQP70pCAK3ZmUIeN4Qg==&uniplatform=NZKPT&language=CHS

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author: 赵霞;高刚强;吴连海;夏永欣;张家恒
date: 2025-05-25
keywords: 怀牛膝;;育种;;栽培;;品质;;研究进展;
source: 乡村科技
site: 知网
title: 怀牛膝栽培育种与品质研究进展
created_at: 2025-09-11 03:07:21.957042
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9qrlRaULUv8z-kiczQ2qJhgYu0ZmsWOIJ4cQmM6DSCYqQXs55pxFw8-kxIiovloEb8-4DerxpMUtSgWJw6w4tbsPLq8gTsl1jWzrMkjWt5LbzVxwUGQ2-w19-tUqoozIqvV6QyCDatc7skY15eE5vAQJMhxhdJrWiYZfFBH4Bg3g==&uniplatform=NZKPT&language=CHS

summary:
  怀牛膝作为四大怀药之一，具有重要的药用价值。文章采用文献分析法，主要从生长发育、组织培养、连作、系统选育、栽培品质和病虫害防治等方面，对怀牛膝栽培育种与品质研究进行系统性综述。为提升怀牛膝品质和产量，建议研发小麦-玉米/牛膝轮作技术，重视种子种苗生产的标准化，加强栽培和品种选育研究，以促进怀牛膝产业的可持续发展。

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author: 本刊编辑部
date: 2025-04-30
source: 中国航天
site: 知网
title: 2025 宇航领域科学问题和技术难题重要研究进展发布
created_at: 2025-09-11 03:07:21.987546
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K9qrlRaULUv8z-kiczQ2qJhgYu0ZmsWOILh07DO2ycZJ-6-qIDKyUou5QjWNPRXMWdWFZTPDc3Q9m1A8VrwwKUifCHZW7sNwFFhPUD41pWPp4a0yGG9B5QZEnEgE7se9h2UCI2cRBSlWckiPx3IGfo84lLyy6MSVhotA58KeuaHCA==&uniplatform=NZKPT&language=CHS

summary:
  2025年4月24日，2025宇航领域科学问题和技术难题重要研究进展在中国航天大会主论坛上发布。此次发布是继2020年中国航天大会发布宇航领域科学问题和技术难题以来的第六次发布活动，并且是首次发布问题难题的重要研究进展。当前，世界百年未有之大变局正在加速演进，科技创新进入前所未有的密集活跃期，各国都把塑造宇航领域竞争优势作为重要战略选择，积极抢占未来航天科技制高点。科学问题和技术难题是科学发现、技术创新的出发点和动力源，在向科技创新“无人区”迈进的征程中，提出问题、解决问题具有重要的价值和意义。

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author: 肖灵辉;骆俊
date: 2025-04-11
keywords: 血清生物标志物;;肝细胞癌;;早期诊断;;研究进展;;诊断准确性;
source: 2025年CACA肿瘤标志物学术大会暨肿瘤标志物产业创新大会暨肿瘤标志物青年科学家大会暨整合肿瘤学学术会议论文摘要集
site: 知网
title: 血清生物标志物在肝细胞癌早期诊断中的研究进展
created_at: 2025-09-11 03:07:22.003498
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-04C1MK1JGElDzKlhXM1nCJrVPCXmZRRSVTzeOfkR7GseHy-70ppZEAfhQEMlTfKKSGDNM3GKlpFCv-wYZm7qW9owL6RHz1NKOBVuOEIi8efORFQftdNKx5XAEiQ9zPw_xZ8jcBSONJ6XE3-8JcXfwCiYr9xdwQiBkXAVfgkFh6y-EFiYpWZ4G&uniplatform=NZKPT&language=CHS

summary:
  目的:本文旨在综述血清生物标志物在肝细胞癌早期诊断中的最新研究进展,分析现有生物标志物的应用价值、敏感性和特异性,并探讨新发现的生物标志物在提高诊断准确性方面的潜力。方法:本研究通过文献回顾和综合分析的方法,对近年来关于血清生物标志物在肝细胞癌早期诊断中的研究进行了系统梳理。首先,收集了相关领域的文献资料,并对其进行了详细阅读和总结。其次,对文献中提到的血清生物标志物进行了分类和归纳,并分析了它们在肝细胞癌早期诊断中的应用价值、敏感性和特异性。最后,结合最新研究成果,探讨了新发现的生物标志物在提高诊断准确性方面的潜力。结果:1.现有血清生物标志物:目前,已有多种血清生物标志物被用于肝细胞癌的早期诊断,如甲胎蛋白(AFP)、α-L-岩藻糖苷酶(AFU)、脱γ-羧基凝血酶原(DCP)等。这些生物标志物在肝细胞癌的早期诊断中显示出一定的应用价值,但敏感性和特异性有待提高。2.新发现的生物标志物:近年来,随着分子生物学和蛋白质组学等技术的快速发展,研究者们发现了多种新的血清生物标志物,如MicroRNA、外泌体等。这些新发现的生物标志物在肝细胞癌的早期诊断中显示出更高的敏感性和特异性,为肝细胞癌...

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author: 梁桂莲;徐君南;李晓睿;马艺文;孙涛
date: 2025-04-11
keywords: 白桦脂酸;;乳腺癌;;作用机制;;研究进展;
source: 2025年CACA肿瘤标志物学术大会暨肿瘤标志物产业创新大会暨肿瘤标志物青年科学家大会暨整合肿瘤学学术会议论文摘要集
site: 知网
title: 白桦脂酸在乳腺癌治疗中的作用机制及研究进展
created_at: 2025-09-11 03:07:22.017352
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-04C1MK1JGElDzKlhXM1nCJrVPCXmZRRSVTzeOfkR7GseHy-70ppZEFTby7YBkSWL99PLBc6YCbDze02KRCSwEFIrSoNmyK0UjvmFht9M8klmY8scn0YsPmHoRhRCiZa29nVXNqqMWrDvWnutFUTl6ipVF1jyxDrp3PIY0kXscaDWa47qGVYrZ&uniplatform=NZKPT&language=CHS

summary:
  白桦脂酸(Betulinic acid,BA)是一种广泛存在于植物界的五环三萜类化合物。近年来,因其优良的生物学活性,BA受到了科学界的广泛关注。研究表明,BA具有多种药理作用,包括抗肿瘤、抗病毒、抗糖尿病等。其中,其抗肿瘤作用机制尤为突出,主要包括诱导细胞凋亡、阻滞细胞周期、抑制细胞增殖及生长、抑制细胞转移及侵袭、通过抑制VEGF和VEGFR发挥抗血管生成作用,以及与其他治疗手段的协同效应等。本文旨在系统探讨BA在乳腺癌治疗中的作用机制及研究进展,以期为乳腺癌治疗策略的优化和新药研发提供理论依据和潜在方向。

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author: 崔璇
date: 2025-03-20
keywords: 海洋多糖;;壳聚糖;;纳米颗粒;;研究进展;
source: 食品工业
site: 知网
title: 壳聚糖纳米颗粒的生物活性及应用研究进展
created_at: 2025-09-11 03:07:22.040060
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-04C1MK1JGElDzKlhXM1nCU2L8qculfM9koGNjubsL4TKBB6t4LMoOsYDEwr_O1FoSDyb3SZSj01G2bYhc2vjehXJRD8a9RJadB04zYtkPD7thXTMJBir8Ol4Ud9fU0xL-sO3AZThT4WCB2IpCHdVJNmwh1chikSMeos1Q8dek6g==&uniplatform=NZKPT&language=CHS

summary:
  近年来,海洋多糖因其种类丰富、稳定性强、生物相容性好等因素,不仅受到学术界的广泛关注,也在医学和制药领域应用广泛。壳聚糖是海洋甲壳动物中开发最广泛的多糖之一,已成为生物医学医药、功能性食品开发、材料等多个领域的研究热点,其在纳米递送载体方面的研究突飞猛进。文章综述了壳聚糖作为纳米递送载体在食品、医学、材料等领域的研究和应用,为后续壳聚糖更为广泛的应用奠定基础。

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author: 徐晴;魏兵
date: 2025-01-26
keywords: 咳嗽变异性哮喘;;网络药理学;;作用机制;;研究进展;
quote: 1
source: 光明中医
site: 知网
title: 中药治疗咳嗽变异性哮喘的网络药理学研究进展
created_at: 2025-09-11 03:07:22.053733
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K910SJiO0RYAWId5shChXA8HtaAGhqyOkCyIRdk2R2HriTq5xhf-pXLm4eNvRVKxVoeUSubatcn2__MME6zpFGnclFsnuN66Y0lqNRDaI61n720dXKw035qlf6jLRdyO2-fQkJKUVMdjbJ1XClQuLs7LQlfXvf347Ia3wZuRrlLSw==&uniplatform=NZKPT&language=CHS

summary:
  咳嗽变异性哮喘(CVA)是一种以慢性咳嗽为主要症状的特殊类型哮喘。中药治疗CVA有丰富的临床经验和确切疗效。随着多组学技术的兴起，基于网络药理学探讨中药治疗CVA的潜在机制已成为研究焦点。文章通过对相关文献进行总结分析，从单味中药、中药药对、中药复方及中成药等角度对治疗CVA的作用机制进行分类阐述，旨在为今后中医药治疗CVA的临床应用研究及相关机制研究提供更多的依据与参考。

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author: 周艳丹;肖忠春;章莉;高迪;马蕊
date: 2025-01-10
keywords: 甘蓝型油菜;;耐旱性;;研究进展;
source: 科技创新与生产力
site: 知网
title: 甘蓝型油菜耐旱性研究进展
created_at: 2025-09-11 03:07:22.067798
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K910SJiO0RYAWId5shChXA8HtaAGhqyOkCYOK2s4bW8R1AtcoWWqRDZB-v_UCNJSbQjrq9BtHftwsBcz229OJfXHhTVJJXRR4xvHNsy_KGTXGrBFNO4eYqowY8IMR8lw8dqRIh1hgH2fh7wScfdsVblbfBI0x3g5HeLSFZvy1Rdeg==&uniplatform=NZKPT&language=CHS

summary:
  文章指出近年来干旱现象频发，甘蓝型油菜是3种油菜类型中综合性状最为优良的一类，但是存在抗旱性相对较差的不足；国内外的很多研究人员尝试采用各种生物学手段去解析甘蓝型油菜的抗旱遗传机制，培育耐旱甘蓝型油菜品种。文章主要对甘蓝型油菜耐旱性鉴定、相关生理生化、QTL定位、全基因组关联分析（GWAS）、相关基因功能等方面的研究进展进行总结，以期为后期甘蓝型油菜耐旱性的研究提供一定的参考依据。

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author: 张冉;闫向波;石献华
date: 2025年2期
keywords: 铝佐剂;疫苗;免疫;研究进展
quote: 0
source: 《中国医药工业杂志》
site: 万方
title: 铝佐剂研究进展
created_at: 2025-09-11 03:07:22.082947
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3pneXlneTIwMjUwMjAwMxoIenp3cDVtaTQ%3D

summary:
  铝佐剂具有高安全性、耐受性和成本低等优点,是疫苗佐剂的首选方案.但是,目前铝佐剂促进机体免疫的具体机制仍不明确.文章总结了铝佐剂的特点、不同铝佐剂的差异和应用,整理了近些年来关于铝佐剂如何促进机体免疫反应的各种机制研究,以帮助研究者深入了解铝佐剂,为新型佐剂的开发提供参考.

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author: 王艳娇;仝红娟;刘斌
date: 2024年10期
keywords: Voxelotor;合成;研究进展
quote: 0
source: 《化学研究与应用》
site: 万方
title: Voxelotor合成研究进展
created_at: 2025-09-11 03:07:22.098085
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEGh4eWp5eXkyMDI0MTAwMDIaCHp6d3A1bWk0

summary:
  Voxelotor(GBT440)是一款用于治疗镰状细胞疾病的一线药物,于 2019 年经美国食品药品管理局批准上市.作为一种有效的镰状血红蛋白聚合抑制剂,Voxelotor的临床应用前景非常广泛.近年来,关于Vox-elotor合成路线的开发及工艺研究受到了研究人员的广泛关注.本文按照Voxelotor的合成策略不同,综述其合成研究进展,期望为Voxelotor的合成提供一定的借鉴和参考.

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author: 董碧莲;高传;梅婷
date: 2025年10期
keywords: 荷叶炭;炮制方法;化学成分;药理作用;研究进展
quote: 0
source: 《广东化工》
site: 万方
title: 荷叶炭研究进展
created_at: 2025-09-11 03:07:22.111125
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDWdkaGcyMDI1MTAwMTMaCHp6d3A1bWk0

summary:
  荷叶炭为荷叶的炮制品,具有收涩化瘀止血的作用,用于多种出血症及产后血晕.目前相关研究多以荷叶为主而关于荷叶炭研究报道较少,荷叶炭是一味常用的中药材,故作者通过查阅相关研究文献,分别对荷叶炭的炮制方法、成分含量变化、药理作用进行整理分析,对其研究进展进行综述,以期为荷叶炭炮制标准的制定、临床运用及后续研究等提供参考.

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author: 艾飞;苏晓菁;韦亚一
date: 2025年1期
keywords: 先进集成电路技术;反演光刻技术;光学临近效应修正;掩模优化;研究进展
quote: 1
source: 《电子与信息学报》
site: 万方
title: 反演光刻技术的研究进展
created_at: 2025-09-11 03:07:22.125269
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2R6a3h4azIwMjUwMTAwMhoIenp3cDVtaTQ%3D

summary:
  反演光刻技术(ILT)相比传统的光学临近效应修正(OPC),生成的掩模具有成像效果更好,工艺窗口更大等优点,在当前芯片制造的工艺尺寸不断减小的背景下,逐渐成为主流的光刻掩模修正技术.该文首先介绍了反演光刻算法的基本原理和几种主流实现方法;其次,调研了当前反演光刻技术应用在光刻掩模优化问题上的研究进展,分析了反演光刻技术的优势和存在的问题.以希望为计算光刻及相关研究领域的研究人员提供参考,为我国先进集成电路产业的发展提供技术支持.

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author: 林安
date: 2025年1期
keywords: 薯蓣丸;临床应用;虚劳;研究进展
quote: 0
source: 《广西中医药大学学报》
site: 万方
title: 薯蓣丸研究进展
created_at: 2025-09-11 03:07:22.139443
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWd4enl4eXhiMjAyNTAxMDE4Ggh6endwNW1pNA%3D%3D

summary:
  薯蓣丸出自医圣张仲景所著的《金匮要略》,全方配伍得当,功专补虚祛风,善治因虚产生的风邪或体虚受风所引起的各种病症,是治虚劳的经典名方,疗效确切.近年来,围绕薯蓣丸的临床研究及药理学研究得到不断丰富与深化,促进了薯蓣丸的临床运用.薯蓣丸的临床应用多集中在神经系统、血液系统、呼吸系统,尤其在肿瘤、阿尔茨海默病、慢性阻塞性肺疾病、疲劳综合征等疾病中具有较佳的疗效.

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author: 王秀红
date: 2003年1期
keywords: 微泵;无阀型微泵;有阀型微泵;研究进展
quote: 31
source: 《排灌机械》
site: 万方
title: 微泵研究进展
created_at: 2025-09-11 03:07:22.153470
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXBnangyMDAzMDEwMDIaCHp6d3A1bWk0

summary:
  介绍了近十几年特别是近几年来微泵研究的最新成果,根据有无可动阀及不同的驱动方式,比较系统地介绍了压电驱动微泵、静电驱动微型泵、双金属驱动微泵、形状记忆合金薄膜驱动微泵、热驱动薄膜微泵、收缩-扩张型微泵、电液动力微泵(E H D泵)、热驱动型微泵、热气致动微泵、基于粘性的微泵、双膜微泵、磁动力微泵等各种微泵的结构、原理、应用情况,展望了微泵的发展前景.

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author: 阮红柱;张星达;冯志超
date: 2025年8期
keywords: 空化;水煤浆;研究进展
quote: 0
source: 《黑龙江科学》
site: 万方
title: 空化水煤浆的研究进展
created_at: 2025-09-11 03:07:22.167225
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmhsamt4MjAyNTA4MDEzGgh6endwNW1pNA%3D%3D

summary:
  水煤浆作为一种高效的清洁能源,长期以来受到广泛关注.综述了国内外在空化水煤浆方面的研究进展,指出空化后水煤浆具有浆体颗粒粒度级配变细、分散性好、浆体静态稳定性及流变性能提升的特点.探讨了空化技术在水煤浆制备中的应用,分析不同方法的优劣和处理参数的影响,评估空化水煤浆的实际应用效果,为研究人员提供全面信息,促进空化水煤浆技术的发展和应用.

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author: 陆伦维;张健;尹艳艳
date: 2025年1期
keywords: 露酒;风味物质;研究进展;产品开发
quote: 3
source: 《酿酒科技》
site: 万方
title: 中国露酒研究进展
created_at: 2025-09-11 03:07:22.179901
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEW5pYW5namtqMjAyNTAxMDI2Ggh6endwNW1pNA%3D%3D

summary:
  露酒是我国历史悠久的民族特色美酒,具有深厚的文化底蕴.团体标准T/CBJ 9101-2021的发布,调整了露酒的概念,提升了露酒的地位.随着大健康理念逐步深入人心,露酒成为了备受消费者关注的酒种,具有广阔的发展前景.本文简述了露酒的概念与分类,综述了露酒功效、功能成分提取、杀菌和除浊、风味物质等关键工艺控制技术的研究进展,阐述了我国露酒市场代表产品和行业共性问题,指出了露酒的发展方向,以期为露酒新产品开发、品质提升和风味个性化研究提供参考和借鉴.

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author: 孙连忠;张佳敏;张保平
date: 2025年18期
keywords: 氢损伤;氢脆机理;金属材料;物理与数值模拟;研究进展
quote: 0
source: 《科学技术与工程》
site: 万方
title: 金属材料氢脆研究进展
created_at: 2025-09-11 03:07:22.194053
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEGt4anN5Z2MyMDI1MTgwMDIaCHp6d3A1bWk0

summary:
  氢能作为21世纪极具前景的一种低碳、高效的二次能源,是全球能源转型发展的重要载体之一.然而,金属材料的氢脆现象即:氢以不同形态扩散到金属中以固溶态存在或生成氢化物,致金属晶格高度变形,降低韧性及延性,引起脆化至断裂,因其具有隐蔽性、突发性及失效形式多样性等特点,成为制约氢能储运系统服役寿命和安全性的关键因素.随着实验手段和数值模拟方法的不断完善,中外学者近年来针对氢脆问题进行了大量研究工作.综述了氢脆研究的最新进展:首先阐述了氢脆失效概念及氢脆发生机理,并分析了储氢压力容器的氢脆特征及不同影响因素,其次总结了现有评价材料氢脆敏感性的宏观至微观、静态和动态实验方法的特点及主要应用范围,着重讨论了与数值模拟分析方法结合的研究进展,包括其在实际工程应用中的适用性与局限性,以期为氢环境中金属材料抗氢脆性能评价方法的进一步发展提供借鉴和参考.

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author: 吴国栋;吴贞畅;王雪妮
date: 2025年1期
keywords: 图对比学习;研究进展;数据增广;样本对;对比粒度
quote: 0
source: 《小型微型计算机系统》
site: 万方
title: 图对比学习研究进展
created_at: 2025-09-11 03:07:22.220786
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEnh4d3hqc2p4dDIwMjUwMTAwNhoIenp3cDVtaTQ%3D

summary:
  图对比学习可以提取无标注数据自身信息作为自监督信号指导模型训练,并帮助缓解图神经网络对标签数据的依赖及结构不公平等问题,已成为图神经网络领域的研究热点.本文从数据增广方式、样本对构造、对比学习粒度3个方面对现有图对比学习研究进行了深入探讨,分析了已有不同图对比学习研究方法各自的优点与不足.在此基础上,指出了现有图对比学习研究存在的问题,并提出了自适应性图对比学习、上下文图对比学习、动态图对比学习、超图对比学习、因果推断图对比学习、无负样本图对比学习及基于大语言模型的图对比学习等未来图对比学习的研究方向.

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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: 2025-09-11 03:07:22.235328
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: 2025-09-11 03:07:22.249903
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: 2025-09-11 03:07:22.264535
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: 2025-09-11 03:07:22.278375
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: 2025-09-11 03:07:22.292955
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: 2025-09-11 03:07:22.310015
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: 2025-09-11 03:07:22.323966
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: 2025-09-11 03:07:22.337052
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: 2025-09-11 03:07:22.351835
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: 2025-09-11 03:07:22.364639
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


author: Asif Newaz;Asif Ur Rahman Adib;Rajit Sahil;Mashfique Mehzad
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08780
pdf_url: https://arxiv.org/abs/2509.08780
site: arxiv
title: An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images
created_at: 2025-09-11 03:07:22.379555
summary:
  Abstract:Background: Arsenicosis is a serious public health concern in South and Southeast Asia, primarily caused by long-term consumption of arsenic-contaminated water. Its early cutaneous manifestations are clinically significant but often underdiagnosed, particularly in rural areas with limited access to dermatologists. Automated, image-based diagnostic solutions can support early detection and timely interventions.

  Methods: In this study, we propose an end-to-end framework for arsenicosis diagnosis using mobile phone-captured skin images. A dataset comprising 20 classes and over 11000 images of arsenic-induced and other dermatological conditions was curated. Multiple deep learning architectures, including convolutional neural networks (CNNs) and Transformer-based models, were benchmarked for arsenicosis detection. Model interpretability was integrated via LIME and Grad-CAM, while deployment feasibility was demonstrated through a web-based diagnostic tool.

  Results: Transformer-based models significantly outperformed CNNs, with the Swin Transformer achieving the best results (86\\% accuracy). LIME and Grad-CAM visualizations confirmed that the models attended to lesion-relevant regions, increasing clinical transparency and aiding in error analysis. The framework also demonstrated strong performance on external validation samples, confirming its ability to generalize beyond the curated dataset.

  Conclusion: The proposed framework demonstrates the potential of deep learning for non-invasive, accessible, and explainable diagnosis of arsenicosis from mobile-acquired images. By enabling reliable image-based screening, it can serve as a practical diagnostic aid in rural and resource-limited communities, where access to dermatologists is scarce, thereby supporting early detection and timely intervention.

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author: Ali Amini;Mohammad Alijanpour;Behnam Latifi;Ali Motie Nasrabadi
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08779
pdf_url: https://arxiv.org/abs/2509.08779
site: arxiv
created_at: 2025-09-11 03:07:22.395586
summary:
  Abstract:Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts on patients and the healthcare system but is often labor-intensive and time-consuming. This paper presents a novel method to improve ADHD diagnosis precision and timeliness by leveraging Deep Learning (DL) approaches and electroencephalogram (EEG) signals. We introduce ADHDeepNet, a DL model that utilizes comprehensive temporal-spatial characterization, attention modules, and explainability techniques optimized for EEG signals. ADHDeepNet integrates feature extraction and refinement processes to enhance ADHD diagnosis. The model was trained and validated on a dataset of 121 participants (61 ADHD, 60 Healthy Controls), employing nested cross-validation for robust performance. The proposed two-stage methodology uses a 10-fold cross-subject validation strategy. Initially, each iteration optimizes the model's hyper-parameters with inner 2-fold cross-validation. Then, Additive Gaussian Noise (AGN) with various standard deviations and magnification levels is applied for data augmentation. ADHDeepNet achieved 100% sensitivity and 99.17% accuracy in classifying ADHD/HC subjects. To clarify model explainability and identify key brain regions and frequency bands for ADHD diagnosis, we analyzed the learned weights and activation patterns of the model's primary layers. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) visualized high-dimensional data, aiding in interpreting the model's decisions. This study highlights the potential of DL and EEG in enhancing ADHD diagnosis accuracy and efficiency.

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title:
  ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals


author: Maryam Ansarifard;Mostafa Rahmani;Mohit K. Sharma;Kishor C. Joshi;George Exarchakos;Alister Burr
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08776
pdf_url: https://arxiv.org/abs/2509.08776
site: arxiv
created_at: 2025-09-11 03:07:22.418968
summary:
  Abstract:Massive MIMO systems rely on accurate Channel State Information (CSI) feedback to enable high-gain beam- forming. However, the feedback overhead scales linearly with the number of antennas, presenting a major bottleneck. While recent deep learning methods have improved CSI compres- sion, most overlook the impact of quantization and entropy coding, limiting their practical deployability. In this work, we propose an end-to-end CSI compression framework that inte- grates a Spatial Correlation-Guided Attention Mechanism with quantization and entropy-aware training. Our model effectively exploits the spatial correlation among the antennas, thereby learning compact, entropy-optimized latent representations for efficient coding. This reduces the required feedback bitrates without sacrificing reconstruction accuracy, thereby yielding a superior rate-distortion trade-off. Experiments show that our method surpasses existing end-to-end CSI compression schemes, exceeding benchmark performance by an average of 21.5% on indoor datasets and 18.9% on outdoor datasets. The proposed framework results in a practical and efficient CSI feedback scheme.

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title:
  CSI Compression Beyond Latents: End-to-End Hybrid Attention-CNN Networks with Entropy Regularization


author: Zhaoxun "Lorenz" Liu;Wagner H. Souza;Jay Han;Amin Madani
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08756
pdf_url: https://arxiv.org/abs/2509.08756
site: arxiv
created_at: 2025-09-11 03:07:22.437513
summary:
  Abstract:Mass casualty incidents (MCIs) overwhelm healthcare systems and demand rapid, accurate patient-hospital allocation decisions under extreme pressure. Here, we developed and validated a deep reinforcement learning-based decision-support AI agent to optimize patient transfer decisions during simulated MCIs by balancing patient acuity levels, specialized care requirements, hospital capacities, and transport logistics. To integrate this AI agent, we developed MasTER, a web-accessible command dashboard for MCI management simulations. Through a controlled user study with 30 participants (6 trauma experts and 24 non-experts), we evaluated three interaction approaches with the AI agent (human-only, human-AI collaboration, and AI-only) across 20- and 60-patient MCI scenarios in the Greater Toronto Area. Results demonstrate that increasing AI involvement significantly improves decision quality and consistency. The AI agent outperforms trauma surgeons (p < 0.001) and enables non-experts to achieve expert-level performance when assisted, contrasting sharply with their significantly inferior unassisted performance (p < 0.001). These findings establish the potential for our AI-driven decision support to enhance both MCI preparedness training and real-world emergency response management.

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title:
  Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform


author: Andreas Burger;Luca Thiede;Alán Aspuru-Guzik;Nandita Vijaykumar
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08734
pdf_url: https://arxiv.org/abs/2509.08734
site: arxiv
title: DEQuify your force field: More efficient simulations using deep equilibrium models
created_at: 2025-09-11 03:07:22.455241
summary:
  Abstract:Machine learning force fields show great promise in enabling more accurate molecular dynamics simulations compared to manually derived ones. Much of the progress in recent years was driven by exploiting prior knowledge about physical systems, in particular symmetries under rotation, translation, and reflections. In this paper, we argue that there is another important piece of prior information that, thus fa,r hasn't been explored: Simulating a molecular system is necessarily continuous, and successive states are therefore extremely similar. Our contribution is to show that we can exploit this information by recasting a state-of-the-art equivariant base model as a deep equilibrium model. This allows us to recycle intermediate neural network features from previous time steps, enabling us to improve both accuracy and speed by 10\%-20\% on the MD17, MD22, and OC20 200k datasets, compared to the non-DEQ base model. The training is also much more memory efficient, allowing us to train more expressive models on larger systems.

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author: Zubair Faruqui;Mackenzie S. McIntire;Rahul Dubey;Jay McEntee
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08717
pdf_url: https://arxiv.org/abs/2509.08717
site: arxiv
title: Explainability of CNN Based Classification Models for Acoustic Signal
created_at: 2025-09-11 03:07:22.469848
summary:
  Abstract:Explainable Artificial Intelligence (XAI) has emerged as a critical tool for interpreting the predictions of complex deep learning models. While XAI has been increasingly applied in various domains within acoustics, its use in bioacoustics, which involves analyzing audio signals from living organisms, remains relatively underexplored. In this paper, we investigate the vocalizations of a bird species with strong geographic variation throughout its range in North America. Audio recordings were converted into spectrogram images and used to train a deep Convolutional Neural Network (CNN) for classification, achieving an accuracy of 94.8\%. To interpret the model's predictions, we applied both model-agnostic (LIME, SHAP) and model-specific (DeepLIFT, Grad-CAM) XAI techniques. These techniques produced different but complementary explanations, and when their explanations were considered together, they provided more complete and interpretable insights into the model's decision-making. This work highlights the importance of using a combination of XAI techniques to improve trust and interoperability, not only in broader acoustics signal analysis but also argues for broader applicability in different domain specific tasks.

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author: Tam Thuc Do;Philip A. Chou;Gene Cheung
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08685
pdf_url: https://arxiv.org/abs/2509.08685
site: arxiv
title: Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
created_at: 2025-09-11 03:07:22.484428
summary:
  Abstract:Given encoded 3D point cloud geometry available at the decoder, we study the problem of lossy attribute compression in a multi-resolution B-spline projection framework. A target continuous 3D attribute function is first projected onto a sequence of nested subspaces \mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_{L}, where \mathcal{F}^{(p)}_{l} is a family of functions spanned by a B-spline basis function of order p at a chosen scale and its integer shifts. The projected low-pass coefficients F_l^* are computed by variable-complexity unrolling of a rate-distortion (RD) optimization algorithm into a feed-forward network, where the rate term is the sparsity-promoting \ell_1-norm. Thus, the projection operation is end-to-end differentiable. For a chosen coarse-to-fine predictor, the coefficients are then adjusted to account for the prediction from a lower-resolution to a higher-resolution, which is also optimized in a data-driven manner.

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author: Tong Wu;Anna Scaglione;Sandy Miguel;Daniel Arnold
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08672
pdf_url: https://arxiv.org/abs/2509.08672
site: arxiv
title: Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations
created_at: 2025-09-11 03:07:22.497821
summary:
  Abstract:This work addresses a fundamental challenge in applying deep learning to power systems: developing neural network models that transfer across significant system changes, including networks with entirely different topologies and dimensionalities, without requiring training data from unseen reconfigurations. Despite extensive research, most ML-based approaches remain system-specific, limiting real-world deployment. This limitation stems from a dual barrier. First, topology changes shift feature distributions and alter input dimensions due to power flow physics. Second, reconfigurations redefine output semantics and dimensionality, requiring models to handle configuration-specific outputs while maintaining transferable feature extraction. To overcome this challenge, we introduce a Universal Graph Convolutional Network (UGCN) that achieves transferability to any reconfiguration or variation of existing power systems without any prior knowledge of new grid topologies or retraining during implementation. Our approach applies to both transmission and distribution networks and demonstrates generalization capability to completely unseen system reconfigurations, such as network restructuring and major grid expansions. Experimental results across power system applications, including false data injection detection and state forecasting, show that UGCN significantly outperforms state-of-the-art methods in cross-system zero-shot transferability of new reconfigurations.

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author: Sara Behnamian;Rasoul Khaksarinezhad;Andreas Langer
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08670
pdf_url: https://arxiv.org/abs/2509.08670
site: arxiv
created_at: 2025-09-11 03:07:22.527939
summary:
  Abstract:We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long-range motion patterns. The training objective is based on a classical variational formulation using total variation (TV) regularization. Specifically, we minimize an energy functional that combines L^1 and L^2 data fidelity terms to enforce brightness constancy, along with a TV term that promotes spatial smoothness and coherent flow fields. Experiments on synthetic and benchmark datasets show that FractalPINN-Flow produces accurate, smooth, and edge-preserving optical flow fields. The model is especially effective for high-resolution data and scenarios with limited annotations.

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title:
  FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization


date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08640
pdf_url: https://arxiv.org/abs/2509.08640
site: arxiv
created_at: 2025-09-11 03:07:22.543213
author:
  Lauren H. Cooke;Matthias Jung;Jan M. Brendel;Nora M. Kerkovits;Borek Foldyna;Michael T. Lu;Vineet K. Raghu

summary:
  Abstract:Chest radiographs (CXRs) are among the most common tests in medicine. Automated image interpretation may reduce radiologists\' workload and expand access to diagnostic expertise. Deep learning multi-task and foundation models have shown strong performance for CXR interpretation but are vulnerable to shortcut learning, where models rely on spurious and off-target correlations rather than clinically relevant features to make decisions. We introduce RoentMod, a counterfactual image editing framework that generates anatomically realistic CXRs with user-specified, synthetic pathology while preserving unrelated anatomical features of the original scan. RoentMod combines an open-source medical image generator (RoentGen) with an image-to-image modification model without requiring retraining. In reader studies with board-certified radiologists and radiology residents, RoentMod-produced images appeared realistic in 93\% of cases, correctly incorporated the specified finding in 89-99\% of cases, and preserved native anatomy comparable to real follow-up CXRs. Using RoentMod, we demonstrate that state-of-the-art multi-task and foundation models frequently exploit off-target pathology as shortcuts, limiting their specificity. Incorporating RoentMod-generated counterfactual images during training mitigated this vulnerability, improving model discrimination across multiple pathologies by 3-19\% AUC in internal validation and by 1-11\% for 5 out of 6 tested pathologies in external testing. These findings establish RoentMod as a broadly applicable tool for probing and correcting shortcut learning in medical AI. By enabling controlled counterfactual interventions, RoentMod enhances the robustness and interpretability of CXR interpretation models and provides a generalizable strategy for improving foundation models in medical imaging.

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title:
  RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcuts


author: van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA.
original_link: https://pubmed.ncbi.nlm.nih.gov/35576821/
site: pubmed
title: Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
created_at: 2025-09-11 03:07:22.557275
summary:
  With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

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author: Mousavi SM, Beroza GC.
original_link: https://pubmed.ncbi.nlm.nih.gov/35951699/
site: pubmed
title: Deep-learning seismology.
created_at: 2025-09-11 03:07:22.571898
summary:
  Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology.

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author: Jiang Y, Yang M, Wang S, Li X, Sun Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/32277744/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7170661/pdf/CAC2-40-154.pdf
site: pubmed
title: Emerging role of deep learning-based artificial intelligence in tumor pathology.
created_at: 2025-09-11 03:07:22.586452
summary:
  The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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author: Craik A, He Y, Contreras-Vidal JL.
original_link: https://pubmed.ncbi.nlm.nih.gov/30808014/
site: pubmed
title: Deep learning for electroencephalogram (EEG) classification tasks: a review.
created_at: 2025-09-11 03:07:22.601152
summary:
  Objective: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks?

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author: Stahlschmidt SR, Ulfenborg B, Synnergren J.
original_link: https://pubmed.ncbi.nlm.nih.gov/35089332/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8921642/pdf/bbab569.pdf
site: pubmed
title: Multimodal deep learning for biomedical data fusion: a review.
created_at: 2025-09-11 03:07:22.615237
summary:
  Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease.

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author: Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J.
original_link: https://pubmed.ncbi.nlm.nih.gov/36616657/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9823653/pdf/sensors-23-00062.pdf
site: pubmed
title: Deep Learning in Diverse Intelligent Sensor Based Systems.
created_at: 2025-09-11 03:07:22.629160
summary:
  Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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author: Alvi M, Batstone D, Mbamba CK, Keymer P, French T, Ward A, Dwyer J, Cardell-Oliver R.
original_link: https://pubmed.ncbi.nlm.nih.gov/37716298/
site: pubmed
title: Deep learning in wastewater treatment: a critical review.
created_at: 2025-09-11 03:07:22.643205
summary:
  Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.

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author: Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J.
original_link: https://pubmed.ncbi.nlm.nih.gov/36560157/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9784550/pdf/sensors-22-09790.pdf
site: pubmed
title: Deep Learning in Medical Hyperspectral Images: A Review.
created_at: 2025-09-11 03:07:22.657913
summary:
  With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.

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author: Li Z, Jiang M, Wang S, Zhang S.
original_link: https://pubmed.ncbi.nlm.nih.gov/36167282/
site: pubmed
title: Deep learning methods for molecular representation and property prediction.
created_at: 2025-09-11 03:07:22.672943
summary:
  With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.

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author: Ding W, Nakai K, Gong H.
original_link: https://pubmed.ncbi.nlm.nih.gov/35348602/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9116377/pdf/bbac102.pdf
site: pubmed
title: Protein design via deep learning.
created_at: 2025-09-11 03:07:22.688998
summary:
  Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

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author: Shuqi Zhu;Qian Lu;Yun Xiang
date: 2021
original_link: https://ieeexplore.ieee.org/document/9680384/
site: ieeeXplore
created_at: 2025-09-11 03:07:22.704663
summary:
  Agricultural innovation is an important force driving the high-quality development of agriculture. Based on 2258 articles in China National Knowledge Infrastructure(CNKI) core journal database, and using Cite-Space software, we investigated the research progress, research hotspots and changes in the field of agricultural innovation in China from 2005 to 2020, trying to clarify the research trends and frontiers in the field of agricultural innovation. The results show that: ① The number of publications in the field of agricultural innovation in China presents an overall trend of “increasing volatility in the early stage, a short-term decline in the medium-term, and a rapid rebound in the near term”, but the cooperative research between authors and institutions is relatively weak; ② The hotspots of agricultural innovation research focus on technology Innovation, institutional innovation, innovation evaluation and modern agriculture, etc., and show phase characteristics; ① The evolution of research hot topics in the field of agricultural innovation in China reflects a strong background of the times. Therefore, in the future, interdisciplinary research will be a new hotspot and direction in the field of agricultural innovation. It is urgent to carry out systematic research on theories, methods, and applications, and strengthen the research on the integration of agricultural innovation and practice.

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title:
  Research Progress, Hot Spots and Prospects in the Field of Agricultural Innovation in China: —CiteSpace analysis based on 2258 articles


author: M. W. Smith
date: 1938
original_link: https://ieeexplore.ieee.org/document/6431032/
site: ieeeXplore
title: The importance of research and development in maintaining technical progress
created_at: 2025-09-11 03:07:22.718769
summary:
  Research and development work usually are regarded as highly technical subjects and generally are so treated, but it is recognized that management has a very definite interest in their influence on technical progress, particularly in the industrial fields. This article is presented with this point in mind, and various suggestions and illustrations are offered as to how this kind of activity may be stimulated and more effectively controlled.

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author: Jia Zhiqin;Zhang Jian
date: 2019
original_link: https://ieeexplore.ieee.org/document/9098241/
site: ieeeXplore
title: Research Progress and Development Trend Evolution of Recommendation System
created_at: 2025-09-11 03:07:22.733804
summary:
  Based on CNKI and Web of Science, this paper uses the methods of bibliometric, social network analysis, Co-word Analysis and data visualization to analyze the trends in the volume of publications, the organization, high-frequency keywords and their relevance, and explore the research hotspots and development trends in this field. Through the analysis of the development process of foreign research, we can find the change in international influence of domestic research.

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author: Yu Zhang;Jiti Zhou;Chengyu Li;Guodong Wang;Shiyuan Guo
date: 2011
original_link: https://ieeexplore.ieee.org/document/5965790/
site: ieeeXplore
title: Research progress on liquid-phase catalysis of SO2 oxidation by transition metal ions
created_at: 2025-09-11 03:07:22.747403
summary:
  The strong electron-accepting ability of transition metal ions enables them to catalyze the oxidation of S(IV) to S(VI) in solution. This reaction has been applied in many fields, such as flue gas desulfurization, hydrometallurgy, atmospheric chemistry, and chemical engineering. To understand the characteristics of the catalysis, this paper reviews the status of research on this reaction in the different fields. This work aims to promote the mutual appreciation of research results among different fields, and reports on the progress in studies on the reaction.

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author: Chang Ya-wen
date: 2020
original_link: https://ieeexplore.ieee.org/document/9361952/
site: ieeeXplore
title: Research Progress of Chinese Public Health System Reform under COVID-19
created_at: 2025-09-11 03:07:22.762015
summary:
  Objective To review the research progress of Chinese public health system reform under COVID-19 based on the framework WHO's building blocks of a health system, key points of this paper is the impact of the epidemic shock on research progress, analyze the main problems existing in present research, and put forward the future research direction and policy suggestions. Methods Researches on Chinese public health system published in Chinese and English language in CNKI, WanFang, PubMed and Web of Science were included using search terms “Public Health”, “Disease Prevention and Control”, “COVID-19”, “China” and combinations of these terms. Results In terms of the provision of public health services, relevant researches mainly focus on hospital and grass-roots level; In regard to the public health workforce, researches mainly focus on the reform of medical education and the mental health of health workforce; in terms of public health information, researches mainly focus on the application of new technology in disease prevention and control; in public health products, vaccines and technologies, researches study the storage and transportation of emergency drugs, the emergency authorization of medical devices and the procedures for drug approval; About health financing, relevant researches focus on the challenge of the epidemic to medical insurance, and even the legalization of hospitals to receive social donations; Finally, in terms of leadership and governance, researches focus on governance system, global health governance and community governance. Conclusion Although the number of relevant researches have been significantly increased, there are still some deficiencies, which will be the focus of future studies: less researches on the accessibility, quality, efficiency and equity of public health system in China; no substantial progress in reform path; ignoring the importance of demand side.

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author: Jung Mo Yang;Dong Uk Cho
date: 2024
original_link: https://ieeexplore.ieee.org/document/10827757/
site: ieeeXplore
title: The Analysis of Effect of the Research Management Professionals on R&D Achievements of SMEs
created_at: 2025-09-11 03:07:22.776073
summary:
  The Ministry of SMEs and Startups has established a system to allocate labor costs for research and management experts within the overhead of national research and development projects in order to distribute research management professionals of SMEs. In order to analyze the impact of the spread of these research management experts on the research and development achievements of SMEs, a survey was conducted on 970 people who completed the training by deriving quantitative and qualitative achievements measurement indicators. As a result, it was found that companies that have completed research management professionals training have a strong will to carry out research and development and a high probability of selecting national research and development projects. In addition, it was analyzed that it had a positive effect on research fund execution and research progress management in carrying out national R&D projects.

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author: Qiandong Zhu;Li Long;Huimin Xiang;Liping Xiao
date: 2025
original_link: https://ieeexplore.ieee.org/document/11034453/
site: ieeeXplore
created_at: 2025-09-11 03:07:22.796287
summary:
  Soil is the second largest reservoir of carbon on Earth and soil carbon stocks is vital for carbon sequestration to mitigate the global climate change. This research aimed to systematically explore the publishing, research progress and hotspots of soil carbon stock during the past ten years. The Web of Science dataset with 23,933 records and VOSviewer software were employed to analyze the research status and focus. The results included yearly output, geographical distribution. Then they were conducted by cited sources co-citation, cited references co-citation, author keywords co-occurrence analysis to reveal the interrelationship among these articles. A total of six clusters were identified, which included: (1) carbon sequestration; (2) climate change; (3) carbon storage; (4) land use; (5) soil minerals and organic matter; (6) greenhouse gas and permafrost. The three future research direction were proposed, which comprised: (1) microbial community and carbon cycling; (2) climate change mitigation; (3) the digital technology and machine learning.

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title:
  Exploring the Worldwide Publishing, Research Progress and Hotspots on Soil Carbon Stocks from 2015 to 2024


author: Yanru Hu;Changfeng Jing
date: 2021
original_link: https://ieeexplore.ieee.org/document/9780995/
site: ieeeXplore
title: The Latest Progress of Research on the Urban Functional Areas Based on CiteSpace Methods
created_at: 2025-09-11 03:07:22.809548
summary:
  With the rapid development of Information Communication Technology (ICT), research on urban functional areas has been one of the hot research issue in urban study domain. CiteSpace as the bibliometric tools was widely used to detect the studies tendency toward several dimensions like country/institution, authors, cited references and keywords. Therefore, The aim at this work is set to understand the research progress of urban functional areas in recent 30 years. This article explored the research hot-spots and development trended, and visualized the literature measurements of domestic and foreign literature. Finally, we tried to propose the challenge and research direction for future work based on the literature quantity and quality analysis.

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author: Ming Q. Ding;Lili Li;Yinghua Du;Yinfu Hu;Jun Cai;Jinjun Feng
date: 2015
original_link: https://ieeexplore.ieee.org/document/7223882/
site: ieeeXplore
title: Progress in research and development of TWT diamond windows at BVERI
created_at: 2025-09-11 03:07:22.822766
summary:
  In the past years, we have fabricated a number of diamond windows for TWTs ranging from W-band to 340 GHz. This paper describes recent progress in research and development of the TWT diamond window at Beijing Vacuum Electronics Research Institute (BVERI).

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author: Didier M. Valdes;Erika C. Jaramillo;Jorge J. Ferrer
date: 2008
original_link: https://ieeexplore.ieee.org/document/4720611/
site: ieeeXplore
created_at: 2025-09-11 03:07:22.836305
summary:
  This paper describes a work in progress focused on graduate education activities that will immerse graduate students in Research Ethics. The project aims to develop ethical awareness and basic skills in moral deliberation. Because these workshops will verify common concerns in Research Ethics, it is expected that they will also produce results valuable to other institutions. The ethical deliberation workshop is the second in a series of four workshops introducing the students to the basic issues in Research Ethics. The moral deliberation workshop will be followed by a Case Analysis Workshop which includes the analysis of famous cases of Research Ethics misconduct as well as hypothetical cases that are likely to be encountered in the studentpsilas career. The hypothetical cases include situations related to fabrication, falsification and plagiarism.

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title:
  Work in progress — introducing graduate students to ethical deliberation and Case Analysis in Research Ethics


date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08827
pdf_url: https://arxiv.org/abs/2509.08827
site: arxiv
title: A Survey of Reinforcement Learning for Large Reasoning Models
created_at: 2025-09-11 03:07:22.850565
author:
  Kaiyan Zhang;Yuxin Zuo;Bingxiang He;Youbang Sun;Runze Liu;Che Jiang;Yuchen Fan;Kai Tian;Guoli Jia;Pengfei Li;Yu Fu;Xingtai Lv;Yuchen Zhang;Sihang Zeng;Shang Qu;Haozhan Li;Shijie Wang;Yuru Wang;Xinwei Long;Fangfu Liu;Xiang Xu;Jiaze Ma;Xuekai Zhu;Ermo Hua;Yihao Liu

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date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08816
pdf_url: https://arxiv.org/abs/2509.08816
site: arxiv
title: A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
created_at: 2025-09-11 03:07:22.863748
author:
  Simon Pfreundschuh;Malarvizhi Arulraj;Ali Behrangi;Linda Bogerd;Alan James Peixoto Calheiros;Daniele Casella;Neda Dolatabadi;Clement Guilloteau;Jie Gong;Christian D. Kummerow;Pierre Kirstetter;Gyuwon Lee;Maximilian Maahn;Lisa Milani;Giulia Panegrossi;Rayana Palharini;Veljko Petković;Soorok Ryu;Paolo Sanò;Jackson Tan

summary:
  Abstract:Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development.

  To address this gap, the International Precipitation Working Group has developed SatRain, the first AI-ready benchmark dataset for satellite-based detection and estimation of rain, snow, graupel, and hail. SatRain includes multi-sensor satellite observations representative of the major platforms currently used in precipitation remote sensing, paired with high-quality reference estimates from ground-based radars corrected using rain gauge measurements. It offers a standardized evaluation protocol to enable robust and reproducible comparisons across machine learning approaches.

  In addition to supporting algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate, detailed, and globally consistent precipitation estimates.

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author: Aadit Sengupta;Pratinav Seth;Vinay Kumar Sankarapu
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08592
pdf_url: https://arxiv.org/abs/2509.08592
site: arxiv
title: Interpretability as Alignment: Making Internal Understanding a Design Principle
created_at: 2025-09-11 03:07:22.877815
summary:
  Abstract:Large neural models are increasingly deployed in high-stakes settings, raising concerns about whether their behavior reliably aligns with human values. Interpretability provides a route to internal transparency by revealing the computations that drive outputs. We argue that interpretability especially mechanistic approaches should be treated as a design principle for alignment, not an auxiliary diagnostic tool. Post-hoc methods such as LIME or SHAP offer intuitive but correlational explanations, while mechanistic techniques like circuit tracing or activation patching yield causal insight into internal failures, including deceptive or misaligned reasoning that behavioral methods like RLHF, red teaming, or Constitutional AI may overlook. Despite these advantages, interpretability faces challenges of scalability, epistemic uncertainty, and mismatches between learned representations and human concepts. Our position is that progress on safe and trustworthy AI will depend on making interpretability a first-class objective of AI research and development, ensuring that systems are not only effective but also auditable, transparent, and aligned with human intent.

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author: Sascha Diefenbacher;Anna Hallin;Gregor Kasieczka;Michael Krämer;Anne Lauscher;Tim Lukas
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08535
pdf_url: https://arxiv.org/abs/2509.08535
site: arxiv
title: Agents of Discovery
created_at: 2025-09-11 03:07:22.891486
summary:
  Abstract:The substantial data volumes encountered in modern particle physics and other domains of fundamental physics research allow (and require) the use of increasingly complex data analysis tools and workflows. While the use of machine learning (ML) tools for data analysis has recently proliferated, these tools are typically special-purpose algorithms that rely, for example, on encoded physics knowledge to reach optimal performance. In this work, we investigate a new and orthogonal direction: Using recent progress in large language models (LLMs) to create a team of agents -- instances of LLMs with specific subtasks -- that jointly solve data analysis-based research problems in a way similar to how a human researcher might: by creating code to operate standard tools and libraries (including ML systems) and by building on results of previous iterations. If successful, such agent-based systems could be deployed to automate routine analysis components to counteract the increasing complexity of modern tool chains. To investigate the capabilities of current-generation commercial LLMs, we consider the task of anomaly detection via the publicly available and highly-studied LHC Olympics dataset. Several current models by OpenAI (GPT-4o, o4-mini, GPT-4.1, and GPT-5) are investigated and their stability tested. Overall, we observe the capacity of the agent-based system to solve this data analysis problem. The best agent-created solutions mirror the performance of human state-of-the-art results.

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author: Edwine Nabahirwa;Wei Song;Minghua Zhang;Yi Fang;Zhou Ni
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08490
pdf_url: https://arxiv.org/abs/2509.08490
site: arxiv
created_at: 2025-09-11 03:07:22.906656
summary:
  Abstract:Underwater object detection (UOD) is vital to diverse marine applications, including oceanographic research, underwater robotics, and marine conservation. However, UOD faces numerous challenges that compromise its performance. Over the years, various methods have been proposed to address these issues, but they often fail to fully capture the complexities of underwater environments. This review systematically categorizes UOD challenges into five key areas: Image quality degradation, target-related issues, data-related challenges, computational and processing constraints, and limitations in detection methodologies. To address these challenges, we analyze the progression from traditional image processing and object detection techniques to modern approaches. Additionally, we explore the potential of large vision-language models (LVLMs) in UOD, leveraging their multi-modal capabilities demonstrated in other domains. We also present case studies, including synthetic dataset generation using DALL-E 3 and fine-tuning Florence-2 LVLM for UOD. This review identifies three key insights: (i) Current UOD methods are insufficient to fully address challenges like image degradation and small object detection in dynamic underwater environments. (ii) Synthetic data generation using LVLMs shows potential for augmenting datasets but requires further refinement to ensure realism and applicability. (iii) LVLMs hold significant promise for UOD, but their real-time application remains under-explored, requiring further research on optimization techniques.

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title:
  A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models


author: Abdul Rehman;Ilona Heldal;Cristina Costescu;Carmen David;Jerry Chun-Wei Lin
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08353
pdf_url: https://arxiv.org/abs/2509.08353
site: arxiv
title: An Adaptive Scoring Framework for Attention Assessment in NDD Children via Serious Games
created_at: 2025-09-11 03:07:22.921396
summary:
  Abstract:This paper introduces an innovative adaptive scoring framework for children with Neurodevelopmental Disorders (NDD) that is attributed to the integration of multiple metrics, such as spatial attention patterns, temporal engagement, and game performance data, to create a comprehensive assessment of learning that goes beyond traditional game scoring. The framework employs a progressive difficulty adaptation method, which focuses on specific stimuli for each level and adjusts weights dynamically to accommodate increasing cognitive load and learning complexity. Additionally, it includes capabilities for temporal analysis, such as detecting engagement periods, providing rewards for sustained attention, and implementing an adaptive multiplier framework based on performance levels. To avoid over-rewarding high performers while maximizing improvement potential for students who are struggling, the designed framework features an adaptive temporal impact framework that adjusts performance scales accordingly. We also established a multi-metric validation framework using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation, and Spearman correlation, along with defined quality thresholds for assessing deployment readiness in educational settings. This research bridges the gap between technical eye-tracking metrics and educational insights by explicitly mapping attention patterns to learning behaviors, enabling actionable pedagogical interventions.

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author: Hazem Ibrahim;Fengyuan Liu;Khalid Mengal;Aaron R. Kaufman;Yasir Zaki;Talal Rahwan
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08299
pdf_url: https://arxiv.org/abs/2509.08299
site: arxiv
created_at: 2025-09-11 03:07:22.937232
summary:
  Abstract:Scientific progress fundamentally depends on researchers' ability to access and build upon the work of others. Yet, a majority of published work remains behind expensive paywalls, limiting access to universities that can afford subscriptions. Furthermore, even when articles are accessible, the underlying datasets could be restricted, available only through a "reasonable request" to the authors. One way researchers could overcome these barriers is by relying on informal channels, such as emailing authors directly, to obtain paywalled articles or restricted datasets. However, whether these informal channels are hindered by racial and/or institutional biases remains unknown. Here, we combine qualitative semi-structured interviews, large-scale observational analysis, and two randomized audit experiments to examine racial and institutional disparities in access to scientific knowledge. Our analysis of 250 million articles reveals that researchers in the Global South cite paywalled papers and upon-request datasets at significantly lower rates than their Global North counterparts, and that these access gaps are associated with reduced knowledge breadth and scholarly impact. To interrogate the mechanisms underlying this phenomenon, we conduct two randomized email audit studies in which fictional PhD students differing in racial background and institutional affiliation request access to paywalled articles (N = 18,000) and datasets (N = 11,840). We find that racial identity more strongly predicts response rate to paywalled article requests compared to institutional affiliation, whereas institutional affiliation played a larger role in shaping access to datasets. These findings reveal how informal gatekeeping can perpetuate structural inequities in science, highlighting the need for stronger data-sharing mandates and more equitable open access policies.

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title:
  Causal evidence of racial and institutional biases in accessing paywalled articles and scientific data


author: Yisong Zhang;Ran Cheng;Guoxing Yi;Kay Chen Tan
date: 10 September, 2025
original_link: https://arxiv.org/abs/2509.08269
pdf_url: https://arxiv.org/abs/2509.08269
site: arxiv
created_at: 2025-09-11 03:07:22.951931
summary:
  Abstract:Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey addresses this gap by providing a comprehensive review of recent developments and organizing them within a structured framework. We classify existing research into two main stages: LLMs for optimization modeling and LLMs for optimization solving. The latter is further divided into three paradigms according to the role of LLMs in the optimization workflow: LLMs as stand-alone optimizers, low-level LLMs embedded within optimization algorithms, and high-level LLMs for algorithm selection and generation. For each category, we analyze representative methods, distill technical challenges, and examine their interplay with traditional approaches. We also review interdisciplinary applications spanning the natural sciences, engineering, and machine learning. By contrasting LLM-driven and conventional methods, we highlight key limitations and research gaps, and point toward future directions for developing self-evolving agentic ecosystems for optimization. An up-to-date collection of related literature is maintained at this https URL.

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title:
  A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving


author: Francisco M. Castro-Macías;Francisco J. Sáez-Maldonado;Pablo Morales-Álvarez;Rafael Molina
date: 9 September, 2025
original_link: https://arxiv.org/abs/2509.08129
pdf_url: https://arxiv.org/abs/2509.08129
site: arxiv
title: torchmil: A PyTorch-based library for deep Multiple Instance Learning
created_at: 2025-09-11 03:07:22.966108
summary:
  Abstract:Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility and accessibility. To address this, we present torchmil, an open-source Python library built on PyTorch. torchmil offers a unified, modular, and extensible framework, featuring basic building blocks for MIL models, a standardized data format, and a curated collection of benchmark datasets and models. The library includes comprehensive documentation and tutorials to support both practitioners and researchers. torchmil aims to accelerate progress in MIL and lower the entry barrier for new users. Available at this https URL.

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author: David Robinson;Animesh Gupta;Rizwan Quershi;Qiushi Fu;Mubarak Shah
date: 2 September, 2025
original_link: https://arxiv.org/abs/2509.07994
pdf_url: https://arxiv.org/abs/2509.07994
site: arxiv
title: STROKEVISION-BENCH: A Multimodal Video And 2D Pose Benchmark For Tracking Stroke Recovery
created_at: 2025-09-11 03:07:22.980565
summary:
  Abstract:Despite advancements in rehabilitation protocols, clinical assessment of upper extremity (UE) function after stroke largely remains subjective, relying heavily on therapist observation and coarse scoring systems. This subjectivity limits the sensitivity of assessments to detect subtle motor improvements, which are critical for personalized rehabilitation planning. Recent progress in computer vision offers promising avenues for enabling objective, quantitative, and scalable assessment of UE motor function. Among standardized tests, the Box and Block Test (BBT) is widely utilized for measuring gross manual dexterity and tracking stroke recovery, providing a structured setting that lends itself well to computational analysis. However, existing datasets targeting stroke rehabilitation primarily focus on daily living activities and often fail to capture clinically structured assessments such as block transfer tasks. Furthermore, many available datasets include a mixture of healthy and stroke-affected individuals, limiting their specificity and clinical utility. To address these critical gaps, we introduce StrokeVision-Bench, the first-ever dedicated dataset of stroke patients performing clinically structured block transfer tasks. StrokeVision-Bench comprises 1,000 annotated videos categorized into four clinically meaningful action classes, with each sample represented in two modalities: raw video frames and 2D skeletal keypoints. We benchmark several state-of-the-art video action recognition and skeleton-based action classification methods to establish performance baselines for this domain and facilitate future research in automated stroke rehabilitation assessment.

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original_link: https://pubmed.ncbi.nlm.nih.gov/28697708/
site: pubmed
created_at: 2025-09-11 03:07:22.995351
author:
  Bernhardt J, Hayward KS, Kwakkel G, Ward NS, Wolf SL, Borschmann K, Krakauer JW, Boyd LA, Carmichael ST, Corbett D, Cramer SC.

summary:
  The first Stroke Recovery and Rehabilitation Roundtable established a game changing set of new standards for stroke recovery research. Common language and definitions were required to develop an agreed framework spanning the four working groups: translation of basic science, biomarkers of stroke recovery, measurement in clinical trials and intervention development and reporting. This paper outlines the working definitions established by our group and an agreed vision for accelerating progress in stroke recovery research.

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title:
  Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce.


author: Johnson JL, Adkins D, Chauvin S.
original_link: https://pubmed.ncbi.nlm.nih.gov/32292186/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7055404/pdf/ajpe7120.pdf
site: pubmed
title: A Review of the Quality Indicators of Rigor in Qualitative Research.
created_at: 2025-09-11 03:07:23.009544
summary:
  Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework, both of which contribute to the selection of appropriate research methods that enhance trustworthiness and minimize researcher bias inherent in qualitative methodologies. Qualitative data collection and analyses are often modified through an iterative approach to answering the research question. Researcher reflexivity, essentially a researcher's insight into their own biases and rationale for decision-making as the study progresses, is critical to rigor. This article reviews common standards of rigor, quality scholarship criteria, and best practices for qualitative research from design through dissemination.

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author: Guo JS.
original_link: https://pubmed.ncbi.nlm.nih.gov/31357751/
site: pubmed
title: [Research hotspots and progress of hepatic fibrosis - 2019 viewpoints].
created_at: 2025-09-11 03:07:23.024058
summary:
  The application of modern bioinformatics, 'omics' and molecular biology to research of fibrotic liver diseases holds promise to accelerate the development of new therapeutic targets and therapies for hepatic fibrosis. Specifically, progress is anticipated in delineating pathways of fibrosis reversal and functional compensation, and defining key determinants and presenting factors associated with fibrosis progression and reversion. These efforts will also lead to develop accurate biomarkers and methods for early noninvasive diagnosis, and to accelerate the testing of anti-fibrotic drugs.

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author: Stocchi F, Bravi D, Emmi A, Antonini A.
original_link: https://pubmed.ncbi.nlm.nih.gov/39496848/
site: pubmed
title: Parkinson disease therapy: current strategies and future research priorities.
created_at: 2025-09-11 03:07:23.038046
summary:
  Parkinson disease (PD) is the fastest growing neurological disorder globally and poses substantial management challenges owing to progressive disability, emergence of levodopa-resistant symptoms, and treatment-related complications. In this Review, we examine the current state of research into PD therapies and outline future priorities for advancing our understanding and treatment of the disease. We identify two main research priorities for the coming years: first, slowing the progression of the disease through the integration of sensitive biomarkers and targeted biological therapies, and second, enhancing existing symptomatic treatments, encompassing surgical and infusion therapies, with the goal of postponing complications and improving long-term patient management. The path towards disease modification is impeded by the multifaceted pathophysiology and diverse mechanisms underlying PD. Ongoing studies are directed at α-synuclein aggregation, complemented by efforts to address specific pathways associated with the less common genetic forms of the disease. The success of these efforts relies on establishing robust end points, incorporating technology, and identifying reliable biomarkers for early diagnosis and continuous monitoring of disease progression. In the context of symptomatic treatment, the focus should shift towards refining existing approaches and fostering the development of novel therapeutic strategies that target levodopa-resistant symptoms and clinical manifestations that substantially impair quality of life.

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author: Tang H, Bai Y, Shen W, Zhao J.
original_link: https://pubmed.ncbi.nlm.nih.gov/30900846/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10393661/pdf/1008-9292-2018-47-6-659.pdf
site: pubmed
title: [Research progress on interleukin-6 in lung cancer].
created_at: 2025-09-11 03:07:23.054133
summary:
  As the core of cellular immunotherapy, T cells are important aspects of research and treatment of lung cancer. IL-6 is a costimulatory signal factor of T cells that is directly targeted by lung cancer stem cells. As a highly expressed cytokine in lung cancer cells, IL-6 plays an important role in variety of biological activities such as tumor occurrence, development, invasion and metastasis. This article reviews the research progress on IL-6 in lung cancer, including cancer development and progression, and the therapeutic sensitivity of lung cancer.

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author: Ramirez AH, Gebo KA, Harris PA.
original_link: https://pubmed.ncbi.nlm.nih.gov/34115137/
site: pubmed
title: Progress With the All of Us Research Program: Opening Access for Researchers.
created_at: 2025-09-11 03:07:23.068507
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author: Bast RC Jr, Srivastava S.
original_link: https://pubmed.ncbi.nlm.nih.gov/33262198/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7717495/pdf/nihms-1626983.pdf
site: pubmed
title: The National Cancer Institute Early Detection Research Network: Two Decades of Progress.
created_at: 2025-09-11 03:07:23.084158
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author: Loi S, Settleman J, Joyce JA, Pramesh CS, Bernards R, Fan J, Merchant JL, Moslehi J, Sellers WR.
original_link: https://pubmed.ncbi.nlm.nih.gov/37059060/
site: pubmed
title: The next big questions in cancer research.
created_at: 2025-09-11 03:07:23.097217
summary:
  Our understanding of tumorigenesis and cancer progression as well as clinical therapies for different cancer types have evolved dramatically in recent years. However, even with this progress, there are big challenges for scientists and oncologists to tackle, ranging from unpacking the molecular and cellular mechanisms involved to therapeutics and biomarker development to quality of life in the aftermath of therapy. In this article, we asked researchers to comment on the questions that they think are important to address in the coming years.

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author: Hauser SL.
original_link: https://pubmed.ncbi.nlm.nih.gov/32379266/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7990471/pdf/nihms-1680002.pdf
site: pubmed
title: Progress in Multiple Sclerosis Research: An Example of Bedside to Bench.
created_at: 2025-09-11 03:07:23.113472
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original_link: https://pubmed.ncbi.nlm.nih.gov/27692480/
site: pubmed
title: Research Priorities from Animal Behaviour for Maximising Conservation Progress.
created_at: 2025-09-11 03:07:23.127406
author:
  Greggor AL, Berger-Tal O, Blumstein DT, Angeloni L, Bessa-Gomes C, Blackwell BF, St Clair CC, Crooks K, de Silva S, Fernández-Juricic E, Goldenberg SZ, Mesnick SL, Owen M, Price CJ, Saltz D, Schell CJ, Suarez AV, Swaisgood RR, Winchell CS, Sutherland WJ.

summary:
  Poor communication between academic researchers and wildlife managers limits conservation progress and innovation. As a result, input from overlapping fields, such as animal behaviour, is underused in conservation management despite its demonstrated utility as a conservation tool and countless papers advocating its use. Communication and collaboration across these two disciplines are unlikely to improve without clearly identified management needs and demonstrable impacts of behavioural-based conservation management. To facilitate this process, a team of wildlife managers and animal behaviour researchers conducted a research prioritisation exercise, identifying 50 key questions that have great potential to resolve critical conservation and management problems. The resulting agenda highlights the diversity and extent of advances that both fields could achieve through collaboration.

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author: 宋杰
date: 2025-09-02
keywords: 无监督机器学习;;自动标注;;互信息法;;智能分类;;情感分类;;文本数据;
source: 物联网技术
site: 知网
title: 基于无监督机器学习的文本情感智能分类方法
created_at: 2025-09-11 06:25:42.847406
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-c1CuZ0t8-xd5tQlvmXocpm_Iihw0xgDaCZXjRLQ4d7lQY6LHO-EXnO6qvf-ykxENH83ODTkqfK0BiGeE1s2HOh62s6j_t7EW0GJm5g0So-B8CRD4st7cm3RvEYw6xouKOA_016dA42SlvARiI6IUfEZeBqSkD9swF8_nVQwXnyg==&uniplatform=NZKPT&language=CHS

summary:
  在文本数据的智能语义分类中，情感数据分类一直是一个难点。为此，提出一种基于无监督机器学习技术的文本情感智能分类方法。为了准确捕捉文本的情感倾向，需要对文本情感数据进行预处理。在此基础上，设计一种无监督机器学习方法，利用其中的互信息法提取情感数据特征，从而更客观地分析和理解文本情感数据。然后引入基于规则的分类方法，实现文本情感的智能分类与类别的自动标注。在测试过程中，选择某单位提供的文本情感数据集作为测试集设计对比实验。实验结果表明，所设计的方法应用效果良好，按照规范使用该方法进行文本情感的智能分类，能够确保分类结果的自动化与高精度。

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author: 苟和平;景永霞;韩强
date: 2025-08-25
keywords: 在线课程;;评论文本;;情感分类;;语义分析;
source: 商丘师范学院学报
site: 知网
title: 基于BERT语义分析的课程评论情感分类研究
created_at: 2025-09-11 06:25:42.850408
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-trro2GxCcWkVryFIRLG_mWOYjXOe0UMcWulAdkka-v6FodGAacWSEcCuimSw2LEGG3swMPgiKDUHNtzbC03T24AyvB6RZUu6IUquFUC3Dt4TWMEPOJGS8z2x8ISTnuUM_vWjRRGxPKXdFvoru3NfJFihiYHBFM17DGr9auagv3Q==&uniplatform=NZKPT&language=CHS

summary:
  针对在线课程评论文本结构短小、数量相对有限导致其分类特征获取困难问题，提出一种基于BERT语义分析的SVM情感分类方法，采用BERT预训练模型获取文本词的语义信息，然后在课程评论数据集上微调，获得适应本领域的文本分类表征，解决文本特征一词多义等语义问题，同时采用SVM分类模型实现文本情感分类，克服由于深度学习分类模型在训练数据集不足的情况下特征获取困难问题.实验表明，与单纯采用BERT模型相比，提出的算法能够较好地提高分类准确率.

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author: 王家兴;张万琪;闫航;宋振峰
date: 2025-08-20 15:23
keywords: 多模态方面级情感分析;;语义分割;;方面补充短语;;图像质量增强;;图像情感标签;;联合优化;;对比学习;
source: 计算机科学与探索
site: 知网
title: 基于图像感知增强的多模态方面级情感分析方法
created_at: 2025-09-11 06:25:42.852407
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-lYMJYzktv53o3_Oczm5NoKlaJAj34iBedRo_6NISWhgfLAzQUjmV2vwp6X3HsHOxtJDPFNGo-fgX87WdOi6E8a_XQuHvrGWKwtSuVhndyv2F9t5HknrISvRvn1BJX6Dhjn9TjC8eZM9wJbFu68s9RNlVXIiiWNwoddNfKFxWhlQ==&uniplatform=NZKPT&language=CHS

summary:
  多模态方面级情感分析（Multimodal Aspect-Based Sentiment Analysis，MABSA）旨在融合多模态信息，准确识别特定方面的情感倾向。针对现有方法普遍未关注图像与文本中各个方面词在情感表达上的差异，忽视了图像情感表达对各方面词情感判断的促进或干扰作用。图像未能与文本中具体方面词进行细粒度语义对齐，以及当图像与文本表达情感不一致时导致的语义冲突等问题。本文提出了基于图像感知增强的多模态方面级情感分析方法（Multimodal Aspect-Based Sentiment Analysis Method Based on Image Perception Enhancement，BIPE）。基于方面情感标签，采用标签分布投票机制自动标注了图像情感标签。引入了弱监督的语义分割方法捕获原始图像中目标前景信息。根据图像中目标前景信息，引导方面词构建方面补充短语。同时，为提升图像描述的准确性，采用大语言模型为原始图像生成文本描述。提出联合优化学习模块，模块包括跨模态方面情感分类任务、视觉情感分类任务和对比学习策略。通过联合优化训练，实现文本与图像特征的融合建模，为...

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author: 昝嵘
date: 2025-08-18
keywords: 循环神经网络;;卷积神经网络;;句子级文本;;情感分类;;语义表示;
source: 计算机与网络
site: 知网
title: 基于循环和卷积神经网络的句子级文本情感分类研究
created_at: 2025-09-11 06:25:42.852407
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K-jBNaAcLmOzclKevPgnXVq2zFaULgOxUCVjeKCT83pfhLWBfHntCYfbq3kbBNXug7uTARVZYevn16tGb_2eL3JAdG738Ya3FExbVU5HaW9lJNRPALV29pZ_GrA_KuJj1KG5ljdC6A33-wPAmUnntC2OuZBNoksbIZQgTfb5SgHsQ==&uniplatform=NZKPT&language=CHS

summary:
  现有的句子级文本情感分类中，采用简单的词袋模型在获取句子级全局语义表示时，只能捕捉文本中的部分上下文关系和语义依赖，导致情感分类的准确性降低。为此，提出基于循环和卷积神经网络(Convolutional Neural Network, CNN)的句子级文本情感分类研究。对句子级文本进行预处理，去除高频但无实际情感贡献的停用词，利用Word2Vec词嵌入技术和Skip-gram模型训练词向量。通过长短时记忆(Long Short Term Memory, LSTM)网络对预处理后的文本进行建模，得到句子级的全局语义表示。利用CNN对句子级文本语义特征进行提取，通过设定卷积核参数并进行卷积运算，结合分段池化技术，形成丰富的特征向量。采用softmax函数作为分类机制，将特征向量转化为情感分类的条件概率分布，判断句子级文本的情感类别。实验结果表明，所提方法在混淆矩阵和曲线下面积(Area Under Curve, AUC)值上均优于对比方法，可实现更加精准的句子级文本情感分类。

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author: 李锦辉;刘继;闵兰
date: 2025-08-15
keywords: 知识蒸馏;;BERT-WWM-EXT模型;;双向长短期记忆网络;;领域知识;
source: 重庆邮电大学学报(自然科学版)
site: 知网
title: 基于领域知识蒸馏的旅游文本情感分类轻量模型
created_at: 2025-09-11 06:25:42.854437
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_GwtzLfewHx2cxqG8QvPXlI73EskVGWNqwx_ms0jO129IPt3F66boi-Fnvuc2WFsn07I9_q6m6z6K9E7W9N4jKATHg9D9fG0MulfwfY0lnJGHsNVUu-5BL4nc6xADtIZGk2GoHTaM3JbjaPf2vTXZzKrGOqGPAG7RxJvGuZLmcBw==&uniplatform=NZKPT&language=CHS

summary:
  针对预训练模型参数多且推理时间长导致在真实旅游场景应用受限的问题，提出一种知识蒸馏和领域知识融合的文本情感分类轻量模型(lightweight model for knowledge distillation and domain knowledge fusion, KD-DKF)。构建了旅游领域词典；在BERT-WWM-EXT模型的基础上加入改进的词性因子向量和位置信息相似度矩阵得到融入领域信息的BERT-WWM-EXT模型(domain information BERT-WWM-EXT,DI-BERT-WWM-EXT);考虑旅游场景对高效且轻量模型的需求，结合知识蒸馏理论，选择DI-BERT-WWM-EXT作为教师模型指导双向长短期记忆网络进行蒸馏，完成KD-DKF的构建。实验结果表明，KD-DKF准确率可达85.79%,高于其他8个同类别轻量模型；总训练时间为152.43 s,参数量为9.62×10～6,在保持较高准确率的同时提高了分类效率。

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author: 但志平;鲁雨洁;余肖生;李琳;李碧涛
date: 2025-07-30 13:34
keywords: 文本情感分类;;BERT模型;;注意力机制;;深度学习;;多任务学习;;层次分类;
source: 南京信息工程大学学报
site: 知网
title: 基于分层思想的精细化文本情感分类方法
created_at: 2025-09-11 06:25:42.856076
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K_UfI2iy7uzWooYUFa30wpZbJ1GY93OcOcrRHf-Nz6LoyNNj8dP_iuAq8uea57wpQqPafdlkt-jHgup2l2ZX7aioArwkYB58q030kO9gI16otKLnc4cBT4l7jjI7u32FJfg59agN4YjihHzvqmBy0NrWA7KXg2VLXMQyYhIJ6qRSg==&uniplatform=NZKPT&language=CHS

summary:
  在处理精细化情感分类任务时，现有情感分析方法通常采用扁平化的分类策略，未能充分考虑情感本身的层级关系，导致分类精度受限。为此，本文提出一种基于分层思想的精细化文本情感分类方法。通过将具有相同基础情感含义的细粒度标签归类为同一粗粒度类别，构建层次化的情感标签体系，从而增强模型对情感结构的理解能力。在模型设计上，采用RoBERTa模型的不同Transformer层输出，分别提取低层次和高层次语义特征，以支持粗粒度与细粒度分类器的训练。在基于多任务学习的分层分类策略中，粗分类任务辅助细分类任务，从而提高分类的精度和泛化性。由粗到细的分层分类策略中，粗分类和细分类任务并行进行，每个细分类器独立计算损失，然后结合粗分类的先验信息优化信息选择，实现情感识别的逐步细化。最终，通过设定概率阈值来融合两种策略的优势，进一步提升分类性能。实验结果表明，在GoEmotions和Empathetic Dialogues数据集上，两种策略在F1值和准确率上均超越基线模型。而最终的融合技术又使得性能进一步提升，在两个数据集上的准确率分别达到64.6%和59.4%，优于现有方法。

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author: 杜子君
date: 2025-07-25
source: 数字技术与应用
site: 知网
title: 基于多元特征表示与深度注意力机制的电影评论情感分类多模型性能对比
created_at: 2025-09-11 06:25:42.859089
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8BepcsZ7K9oVPeOC6Vg8ydm6K0o5y2FcQcQexO6fCqumGg2yTwzukIknTcI5D9TpcDipRXTUFogr6JArdL6tToaCr9bo649F4cs5YlOpXcBM9zwuQNf7YXspydvY03NF-CnNP9sldDnc4ygWv2JCtLT4UMwacdnxsDRsVF0NaWOg==&uniplatform=NZKPT&language=CHS

summary:
  <正>本文基于NLTK电影评论数据集，通过使用词袋模型（BoW）、TF-IDF和Word2vec 3种特征表示方法，结合多项式朴素贝叶斯、逻辑回归、支持向量机等传统机器学习模型以及构建基于注意力机制的循环神经网络（RNN）模型，进行情感分类对比实验，探究多元特征表示与深度注意力机制对电影评论情感分类多模型性能的影响。实验结果表明，引入注意力机制的RNN模型显著提升了分类性能。本文为特征表示与模型架构的协同优化提供了参考思路。

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author: 李虹羽;苏锦;何俊
date: 2025-06-25
keywords: 情感分类;;交叉注意力;;自注意力;;多模态方面级情感分析;
source: 信息技术与信息化
site: 知网
title: 联合自注意力和交叉注意力的多模态方面级情感分类
created_at: 2025-09-11 06:25:42.861088
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8L-tK5nBUWCfLxfZa5mkidO58UJdQX2_xnhO-hYuh0CpGGdFmWkciZlsUlXOKpNJiSUGd0cJkoFeq-JtZZk_wzXgq_AbWLKwzl-mtNs43HxV0YeS4XQVdMu0qnnVh8I-UVJUnao7jlc2zIlKoskvb1Di6su1SM4F3BhxCNdHOw5g==&uniplatform=NZKPT&language=CHS

summary:
  多模态方面级情感分析任务旨在结合文本和图像数据特征以提升识别文本中指定方面的情感倾向的准确率。现有研究方法集中于利用注意力机制帮助模型提取方面实体相关的文本和图像特征，但对不同模态之间的信息协同探索较少，对同一模态之中的信息交互关注不足。为此，提出一种联合自注意力和交叉注意力的多模态方面级情感分类模型(multimodal aspect-level sentiment classification with Joint self-attention and cross-attention,MASC-JSCA)。首先利用预训练模型RoBERTa和ViT分别抽取文本、方面实体和图像特征；然后利用交叉注意力机制构造不同模态间交互特征，利用自注意力机制构造模态内自交互特征；最后将不同模态间的相似特征和模态内自交互特征以及模态间交互特征进行动态融合。在公开数据集上的实验结果表明该模型在多模态方面级情感分类任务上展现了更好的效果。

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author: 张黎娜
date: 2025-06-20
keywords: 序贯三支决策;;深度学习;;情感分析;
source: 安徽开放大学学报
site: 知网
title: 基于深度学习和序贯三支决策的在线评论情感分类方法研究
created_at: 2025-09-11 06:25:42.863598
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K8L-tK5nBUWCfLxfZa5mkidO58UJdQX2_x9EUkZDdsioCrurLuCFNnvYrD8pwCQD4PAnXZEGVS_kZVpyMgpdNyJTn5ykDw9a6TyBBeLMtAl1XNO7wpRtiEqTOk-xGFzPMgCkW0xHw0yLDLl0IHYBPgAmeHouherhloHOLkfpojqEA==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网的发展，在线评论已成为消费者决策的重要依据。情感分析作为自然语言处理中的一项关键任务，旨在识别和提取文本中的情感信息，辅助用户决策。文章提出了一种基于深度学习与序贯三支决策的情感分类方法，对评论内容进行分析，提高情感分类的准确性。通过构建双向长短时记忆网络和卷积神经网络相结合的模型，结合序贯三支决策理论，设计出一种情感分类方法。实验结果表明，该方法在多个数据集上均优于传统的情感分类方法，具有更好的准确性和适用性。

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author: 方小丽
date: 2025-06-18
keywords: Transformer模型;;情感分类;;学习率调度;;余弦退火;;梯度范数感知;
source: 智能物联技术
site: 知网
title: 基于Transformer的中文文本情感智能分类与优化
created_at: 2025-09-11 06:25:42.864653
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=bJ89lKU86K82eGrrQSaF2PkehGtV49_1Giqor3bhKbQ-s93hX4WHAAgaTYlwoxyebm7fMCahYljf7yXqbshKVYX13eO_fRFPgiabCdaDd9_1XaI0WuY95ZYLRv64NRpoPNTM4evFlJSCMmin9wDNLhvTbCdBYkOM_g6fgoMBvsBQa-weddHySA==&uniplatform=NZKPT&language=CHS

summary:
  针对中文文本情感分类任务的优化需求，研究Transformer模型基本结构及其在情感分类任务中的应用，提出基于梯度范数感知的分层自适应余弦退火方法来优化传统余弦退火学习率调度策略。文章基于ChnSentiCorp数据集在Hugging Face Transformers框架下进行实验。实验结果表明，文章方法在准确率、精确率、召回率方面均优于传统方法。

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author: 李超男
date: 2016年29期
keywords: 情感分类;情感分析;评价指标;特征选择
quote: 3
source: 《现代计算机》
site: 万方
title: 情感分类综述
created_at: 2025-09-11 06:25:42.865786
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEnhkanNqLXhieTIwMTYyOTAwORoIaDYzaGNkbHo%3D

summary:
  随着电子商务和媒体社交工具的普及，互联网上充斥着极其丰富的信息资源。商业界、政界和学术界敏感的认识到这些数据的宝贵，大批研究者开始分析抽取这些数据中的信息。情感分类受到研究者们的密切关注，因为有效的情感分析可以引导人们消费、帮助商家改进研究新产品、对社会舆情进行监控等。介绍情感分类的机器学习方法和侧重解决的问题，并对目前情感分类的研究进展进行总结归纳。

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author: 陈龙;管子玉;何金红
date: 2017年6期
keywords: 情感分析;情感分类;深度学习;弱监督;主题提取
quote: 212
source: 《计算机研究与发展》
site: 万方
title: 情感分类研究进展
created_at: 2025-09-11 06:25:42.867781
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWpzanlqeWZ6MjAxNzA2MDAzGghoNjNoY2Rseg%3D%3D

summary:
  文本情感分析是多媒体智能理解的重要问题之一.情感分类是情感分析领域的核心问题,旨在解决评论情感极性的自动判断问题.由于互联网评论数据规模与日俱增,传统基于词典的方法和基于机器学习的方法已经不能很好地处理海量评论的情感分类问题.随着近年来深度学习技术的快速发展,其在大规模文本数据的智能理解上表现出了独特的优势,越来越多的研究人员青睐于使用深度学习技术来解决文本分类问题.主要分为2个部分:1)归纳总结传统情感分类技术,包括基于字典的方法、基于机器学习的方法、两者混合方法、基于弱标注信息的方法以及基于深度学习的方法;2)针对前人情感分类方法的不足,详细介绍所提出的面向情感分类问题的弱监督深度学习框架.此外,还介绍了评论主题提取相关的经典工作.最后,总结了情感分类问题的难点和挑战,并对未来的研究工作进行了展望.

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author: 白瑞瑞;王中卿;周国栋
date: 2025年6期
keywords: 图神经网络;情感分类;跨语言
quote: 0
source: 《软件学报》
site: 万方
title: 基于双语依存关联图的跨语言情感分类
created_at: 2025-09-11 06:25:42.868779
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDYwMTgaCGg2M2hjZGx6

summary:
  跨语言情感分类在自然语言处理领域非常重要并且已经得到广泛的研究,因为它可以利用源语言的标签信息构建目标语言的情感分类系统,从而大大减少目标语言中费时而耗力的标注工作.不同语言的表达方式存在明显差异是跨语言情感分类的基本挑战,提出基于双语依存关联图模型的跨语言情感分类方法.虽然不同语言的表达存在差异,但是内部的句法依存关系是相似的.通过在不同语言的词节点之间建边表示双语评论实例的语义相关性,双语依存关联图能够对不同语言词之间依存关系的相似性进行显式建模,从而使图神经网络可以在语言内和语言间整合句法结构信息,进行跨语言情感分类.利用英文和中文两种语言的数据集进行实验,实验结果相较于基线方法提高了 3%.研究表明,利用双语依存关联图能够对不同语言评论实例之间的关联性进行有效建模,从而显著提升跨语言情感分类的准确率.

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author: 公确多杰;索南才让;才藏太
date: 2025年3期
keywords: 情感分类;情感词典;情感词汇本体库;BERT-BiGRU;CINO;藏语句子;深度学习
quote: 0
source: 《计算机工程与设计》
site: 万方
title: 融合词典的BERT-BiGRU的藏语句子情感分类方法
created_at: 2025-09-11 06:25:42.869827
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWpzamdjeXNqMjAyNTAzMDM3GghoNjNoY2Rseg%3D%3D

summary:
  针对藏文情感分析研究中,由于藏文缺乏公开的情感词典和能结合上下文的深度学习模型等导致的分类效果欠佳问题,构建一个规模可观的藏文情感词典,结合基于改进的BERT-BiGRU的藏语句子情感分类方法,在哈工大开源的CI-NO多语言的通用预训练语言模型上进行微调.实验结果表明,本文方法能够有效提高情感分类的准确率,验证了方法的有效性.模型在分类准确率和泛化能力上的表现优于相关工作,为进一步研究藏文文本情感分类问题提供一种思路和实验证据.

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author: 王友卫;刘奥;凤丽洲
date: 2025年5期
keywords: 计算机应用;情感分类;知识蒸馏;数据增强;历史评论
quote: 0
source: 《吉林大学学报（工学版）》
site: 万方
title: 基于知识蒸馏和评论时间的文本情感分类新方法
created_at: 2025-09-11 06:25:42.870828
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIaDYzaGNkbHo%3D

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

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author: 杜广进;拥措
date: 2025年6期
keywords: 情感分类;低资源语言;对比损失;多语言预训练模型
quote: 0
source: 《电脑知识与技术》
site: 万方
title: 基于对比损失的低资源语言情感分类
created_at: 2025-09-11 06:25:42.872388
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISGWRuenN5anMtaXRyenlrc2IyMDI1MDYwMDMaCGg2M2hjZGx6

summary:
  随着自然语言处理技术的快速发展,跨语言预训练模型为低资源语言处理提供了新的研究途径.然而,现有模型在马来语、豪萨语等低资源语言上的性能仍有待提升,主要受限于训练数据的匮乏和质量不均.特别是在情感分类任务中,传统的全参数微调方法难以充分利用有限训练集中标签之间的关系,导致模型在不同类别上的性能差异显著.针对这一挑战,该文提出了一种基于对比损失的低资源语言情感分类方法(CL-LRSC),通过在多语言预训练模型的微调过程中引入对比损失,优化文本在向量空间的表示.该方法在保持传统交叉熵损失的基础上,通过对比学习使相同情感类别的文本表示相互靠近,不同情感类别的文本表示相互远离.在阿姆哈拉语、印尼语和豪萨语三种低资源语言的情感分类实验中,该方法显著提升了分类性能.实验结果表明,在XLM-R模型上,印尼语和豪萨语的F1分数分别从89.19%提升至91.96%和从66.00%提升至71.48%.研究成果为解决低资源语言的情感分类问题提供了新的思路.

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author: 苗鹏宇;刘征宏;代宣军
date: 2025年2期
keywords: 情感分类;集成学习;机器学习;中文评论
quote: 0
source: 《计算机仿真》
site: 万方
title: 基于多分类器投票集成的中文评论情感分类
created_at: 2025-09-11 06:25:42.872388
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamZ6MjAyNTAyMDg5GghoNjNoY2Rseg%3D%3D

summary:
  情感分类是指对网络在线评论进行自动极性分类,如正极、负极、中性.针对单一情感分类模型可能存在不稳定的问题,研究多分类器系统概念在中文评论情感分类的潜在益处,并提出一种基于集成学习的情感分类方法.首先改进朴素贝叶斯(NB)为自适应朴素贝叶斯,对支持向量机(SVM)进行参数优化,同时以SVM为基分类器将元分类器装袋(Bagging)作为集成组件之一,然后使用投票集成组合三个组件并输出分类结果,最后将投票集成与各基分类器、元分类器单独使用在三个数据集上的表现进行对比.实验表明,基于绝对多数投票法的集成分类在股市评论、网购评论、电影评论三个数据集上准确率(Accuracy)、精确率(Precision)、召回率(Recall)、F值(F-measure)均有所提升.由此可见,提出的方法有效地提高了中文评论文本情感分类性能.

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author: 陈可嘉;柯永诚
date: 2024年5期
keywords: 深度学习;多特征;情感分类;情感词典;长短时记忆神经网络
quote: 2
source: 《小型微型计算机系统》
site: 万方
title: 融合多特征的在线评论情感分类
created_at: 2025-09-11 06:25:42.874510
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEnh4d3hqc2p4dDIwMjQwNTAwNRoIaDYzaGNkbHo%3D

summary:
  多数基于深度学习的情感分类方法过于追求数据驱动,忽视了文本情感特征对于情感分类的影响;而一些融合情感特征的情感分类方法仅利用了部分相关特征,使得其他情感特征并没有得到充分利用.针对这一现象,提出了一种融合词级特征和句级特征的在线评论情感分类模型.首先利用构建的情感元素词典获取情感词、否定词等多种特征词,然后通过多维特征向量表示将多种文本特征转化为词级特征向量和句级特征向量,最后将这些特征向量融入LSTM网络模型完成情感分类,整个模型简称为 MF-LSTM(sentiment classification model based on multidimensional features and LSTM).MF-LSTM 充分利用 了评论文本的情感先验知识,情感分类能力得到显著提升.在3个中文评论数据集上的实验结果表明MF-LSTM模型相比其他深度学习方法具有更好的分类效果,并且在样本数据不平衡的情况下也能具有较好的鲁棒性.

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author: 杨春霞;韩煜;桂强
date: 2024年3期
keywords: 隐喻识别;情感分类;多任务学习;RoBERTa;图卷积网络;注意力机制
quote: 4
source: 《小型微型计算机系统》
site: 万方
title: 融合RoBERTa-GCN-Attention的隐喻识别与情感分类模型
created_at: 2025-09-11 06:25:42.875517
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEnh4d3hqc2p4dDIwMjQwMzAwORoIaDYzaGNkbHo%3D

summary:
  在隐喻识别与隐喻情感分类任务的联合研究中,现有多任务学习模型存在对隐喻语料中的上下文语义信息和句法结构信息提取不够准确,并且缺乏对粗细两种粒度信息同时捕捉的问题.针对第1个问题,首先改进了传统的RoBERTa模型,在原有的自注意力机制中引入上下文信息,以此提取上下文中重要的隐喻语义特征;其次在句法依存树上使用图卷积网络提取隐喻句中的句法结构信息.针对第2个问题,使用双层注意力机制,分别聚焦于单词和句子层面中对隐喻识别和情感分类有贡献的特征信息.在两类任务6个数据集上的对比实验结果表明,该模型相比基线模型性能均有提升.

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author: 万俊杰;任丽佳;单鸿涛
date: 2023年5期
keywords: 情感分类;BERT;双通道;CNN;BiLSTM-Attention
quote: 4
source: 《小型微型计算机系统》
site: 万方
title: 双通道的BCBLA情感分类模型
created_at: 2025-09-11 06:25:42.876161
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEnh4d3hqc2p4dDIwMjMwNTAwORoIaDYzaGNkbHo%3D

summary:
  针对传统情感分类模型的词向量表示无法解决多义词表征的问题,以及目前基于BERT的多通道情感分类模型研究较少的问题,提出了 一种基于BERT的双通道情感分类模型BCBLA.该模型有BERT+CNN和BERT+BiLSTM-Attention两个通道,首先用预训练模型BERT获取融合文本语境的词向量表征;然后利用通道1的CNN网络增强对文本局部特征提取的能力和通道2的BiLSTM-Attention模型增强对长序列文本处理以及关键情感分类特征提取的能力;最后使用Softmax激励函数对通道1和通道2的融合特征进行分类.为了验证本文提出的模型的有效性,本文在中文谭松波酒店评论和英文Yelp Dataset Challenge两种数据集进行实验,设置了与当前流行的情感分类模型对比、减少通道后的模型对比和更换预训练模型后的对比等3种实验对比方式,最终实验结果表明,本文BCBLA模型在中文和英文两种数据集上测试结果中值分别取得了 92.86％和95.55％的最佳效果.

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author: 白瑞瑞;王中卿;周国栋
source: 软件学报
site: 维普
title: 基于双语依存关联图的跨语言情感分类
created_at: 2025-09-11 06:25:42.877214
original_link:
  https://www.cqvip.com/doc/journal/3501053298?sign=e62c309f361168b1585d95069a53b9cdc4a636b02802a4acce91fd859649a967&expireTime=1789107674660&resourceId=3501053298

summary:
  跨语言情感分类在自然语言处理领域非常重要并且已经得到广泛的研究,因为它可以利用源语言的标签信息构建目标语言的情感分类系统,从而大大减少目标语言中费时而耗力的标注工作.不同语言的表达方式存在明显差异是跨语言情感分类的基本挑战,提出基于双语依存关联图模型的跨语言情感分类方法.虽然不同语言的表达存在差异,但是内部的句法依存关系是相似的.通过在不同语言的词节点之间建边表示双语评论实例的语义相关性,双语依存关联图能够对不同语言词之间依存关系的相似性进行显式建模,从而使图神经网络可以在语言内和语言间整合句法结构信息,进行跨语言情感分类.利用英文和中文两种语言的数据集进行实验,实验结果相较于基线方法提高了3%.研究表明,利用双语依存关联图能够对不同语言评论实例之间的关联性进行有效建模,从而显著提升跨语言情感分类的准确率.

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author: 李浩君;王耀东;汪旭辉
keywords: Transformer;位置编码;BERT;textCNN;文本情感分类
source: 计算机工程与应用
site: 维普
title: 中文短文本情感分类:融入位置感知强化的Transformer-TextCNN模型研究
created_at: 2025-09-11 06:25:42.879170
original_link:
  https://www.cqvip.com/doc/journal/3501068180?sign=b7bdb50653f1de0248fc7f4f75d98a4d7d3481fbb31f00deede60c9d87c3b653&expireTime=1789107674660&resourceId=3501068180

summary:
  针对当前中文短文本情感分类模型文本位置信息与关键特征获取不足的问题,提出了一种融入位置感知强化的Transformer-TextCNN情感分类模型。利用BERT可学习绝对位置编码与正弦位置编码强化模型的位置感知能力,融合Transformer的全局上下文理解能力与TextCNN的局部特征捕捉能力,分别提取中文短文本全局特征与局部特征,构建位置感知强化与特征协同的情感特征输出服务,实现中文短文本情感准确分类。实验结果表明,该模型在视频弹幕数据集上的准确率达到90.23%,在SMP2020数据集上的准确率达到87.38%。相较于最优的基线模型,准确率在视频弹幕数据集和SMP2020数据集上分别提高了1.98和0.44个百分点,在中文短文本情感分类任务中取得更好的分类效果。

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author: 蒋卓林;王宝乐;艾孜尔古丽·玉素甫;韦金矿;王鹏飞
source: 中文信息学报
site: 维普
title: 基于数据增强与主题知识共享的低资源情感分类
created_at: 2025-09-11 06:25:42.880171
original_link:
  https://www.cqvip.com/doc/journal/3485282431?sign=df7ee88b4f59befa70753e4499c5c9412451a24727a02e2a90e7818e8ab4c1b6&expireTime=1789107674660&resourceId=3485282431

summary:
  针对低资源语言社交媒体文本的情感分类是低资源语言舆情分析的基础。低资源语言数据集资源不足,标注困难。数据集的大小以及标签的不平衡会影响情感分类模型的性能。针对上述问题,该文提出了一种适用于低资源语言情感分析的数据增强方法,该方法改进了传统的数据增强方法,提高了数据增强样本生成的质量,用于缓解低资源和标签不平衡情况下的过拟合问题。同时,除了提取文本本身的情感特征,还通过引入主题特征,与语义信息进行融合编码,最终完成情感分类任务。实验结果表明,在构建的维吾尔语数据集上,其准确率与F 1值和多个基线模型相比均有提升,分别达到89.8%和90.3%的分类效果。最后,该模型在公开的英文数据集上也有很好的性能,具有良好的跨语言理解能力。

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author: 张宇哲;郭传杰;靳淑雅;马驰远;苏煜;陶智勇
source: 声学技术
site: 维普
title: 基于多特征提取的语音情感分类
created_at: 2025-09-11 06:25:42.881172
original_link:
  https://www.cqvip.com/doc/journal/3484224792?sign=c1514a940ebc645cae20e26c68e23042a31760068906b8182ea04d0ddbc7edc3&expireTime=1789107674660&resourceId=3484224792

summary:
  情感识别是计算机对人类情感感知过程的模拟,具有重要的研究意义和应用价值。传统的语音识别系统通常使用单一的特征提取方法,但这些方法有时会丢失语音情感信号中的重要信息,导致识别错误。因此,文章基于改进的完全集成噪声自适应经验模式分解,提出了一种组合多特征提取方法来分类无语义情感语音信号。首先,利用基于改进的完全集成噪声自适应经验模式分解将一维情感语音信号分解得到多个内禀模式;然后,提取每个内禀模式的均值、方差、峰度、偏度、能量、中心频率、峰值幅度和排列熵等特征;最后,通过这些特征对愤怒、快乐、悲伤和无情感四种情感进行分类。研究表明,该方法在通过支持向量机8∶2的模型训练后,得到了88.52%的平均识别率,可为情感语音信号的识别工作提供重要参考。

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author: 苏妍嫄;韩翠娟;张亚明
source: 现代情报
site: 维普
title: 融入频域增强自注意力机制的BTBFA混合神经网络情感分类模型
created_at: 2025-09-11 06:25:42.882173
original_link:
  https://www.cqvip.com/doc/journal/3472907072?sign=44030e964c4a2240b662a5c23f7694e8c8fabe8381e456dff4422614f1e04d52&expireTime=1789107674660&resourceId=3472907072

summary:
  [目的/意义]智媒时代基于神经网络模型实现用户情感精准分类,进而深入挖掘海量文本信息潜在价值具有重要意义。[方法/过程]针对现有混合模型层间依赖性强、输出特征重要性差异体现不足等导致的情感分类效果受限问题,基于Stacking集成思想,提出一种融入频域增强自注意力机制的混合神经网络情感分类模型,通过构建由Bert、TextCNN、BiLSTM组成的并行式特征提取基学习器层与融入频域增强自注意力机制的元学习器层,并与词嵌入层和全连接层相融合,系统挖掘文本深层次语义信息以及局部、全局特征,进而通过权重分配以及离散傅里叶变换提升情感分类效果。[结果/结论]酒店评论数据集上的对比实验与消融实验结果均表明,所提模型情感分类性能与其他模型相比具有显著优势,准确率、召回率、F1值分别达到91.7%、95.3%和93.9%,且随Epoch训练轮数增加,模型情感分类准确性不断提升,损失值不断降低,呈现较强的泛化能力。

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author: 苏静;Murtadha Ahmed
keywords: K-近邻;transformer;半监督学习;句子情感分类
source: 重庆大学学报
site: 维普
title: 一种基于半监督的句子情感分类模型
created_at: 2025-09-11 06:25:42.884693
original_link:
  https://www.cqvip.com/doc/journal/3474991241?sign=cc1f7b1ce4058df0a863de9e6d9f2ca75f0389446789b4a512dc89b94b8a496a&expireTime=1789107674660&resourceId=3474991241

summary:
  句子情感分类致力于挖掘文本中的情感语义,以基于BERT(bidirectional encoder representations from transformers)的深度网络模型表现最佳。这类模型的性能极度依赖大量高质量标注数据,而现实中标注样本往往比较稀缺,导致深度神经网络(deep neural network,DNN)容易在小规模样本集上过拟合,难以准确捕捉句子的隐含情感特征。尽管现有的半监督模型有效利用了未标注样本特征,但对引入未标注样本可能导致错误逐渐累积问题没有有效处理。半监督模型在对测试数据集进行预测后不会重新评估和修正上次的标注结果,无法充分挖掘测试数据的特征信息。研究提出一种新型的半监督句子情感分类模型。该模型首先提出基于K-近邻算法的权重机制,为置信度高的样本分配较高权重,尽可能减少错误信息在模型训练中的传播。接着,采用两阶段训练策略,使模型能对测试数据中预测错误的样本进行及时修正,通过多个数据集的测试,证明本模型在小规模样本集上也能获得良好性能。

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author: 韩燚笑;马静
keywords: 多模态;特征融合;情感分类
source: 数据分析与知识发现
site: 维普
title: RCHFN模型:一种多模态特征融合的情感分类方法
created_at: 2025-09-11 06:25:42.885202
original_link:
  https://www.cqvip.com/doc/journal/3477181977?sign=c808a9b40b0ffdae192ecc0d526ad0620db6409420d6b4fce8b4a6a3e7bb4321&expireTime=1789107674660&resourceId=3477181977

summary:
  【目的】针对当前多模态情感模型在特征融合方面存在的困难导致情感分类准确率不佳的问题,提出RCHFN多模态情感分类模型。【方法】采用CLIP和Chinese-BERT-wwm模型分别提取图像和文本特征,同时进行单一模态情感分类。使用由合并残差连接与卷积构成的残差合并模块融合图像和文本特征,得到多模态情感分类结果。最终将单一模态和多模态情感分类结果传递至全连接层,通过调整动态权重获取最终的情感分类结果。【结果】实验结果表明,RCHFN模型在微博数据集、Twitter数据集的情感三分类准确率分别达到81.25%、79.21%,F1值分别为80.43%、78.44%。与其他在同一数据集上基于相似任务的模型相比,模型的准确率分别提高了1.79、1.79个百分点,F1值分别提高了2.39、2.62个百分点。【局限】模型对于不同数据集的泛化性以及在更多模态上的性能需要更多实验证明。【结论】RCHFN型有效地解决了多模态言论特征难以融合以及分类准确率低的问题。

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author: 隋浩然;周晓航;张宁
keywords: 属性情感分类;用户生成内容;机器学习;产品改进;深度学习;属性提取
source: 计算机科学
site: 维普
title: 基于UGC的产品改进:属性提取和属性情感分类的方法与应用综述
created_at: 2025-09-11 06:25:42.886206
original_link:
  https://www.cqvip.com/doc/journal/3472556202?sign=22bc318d8e1cdc2366fcfe2099a080c4b21997cdb36d89d63228d6eda09117e1&expireTime=1789107674660&resourceId=3472556202

summary:
  用户生成内容(User-generated Content,UGC)包含了大量用户对产品及其属性的真实看法。随着数字技术的持续发展,企业日益重视利用UGC来洞察用户需求,从而指导产品改进。在这一过程中,属性提取与属性情感分类被视为两大核心环节。属性提取旨在从UGC中识别出关键的产品属性,其方法可归为有监督学习和无监督学习两类;属性情感分类则用于分析用户对这些属性的情感态度,主要包括基于词典与规则、基于统计机器学习以及基于深度学习的方法。文中首先对属性提取与属性情感分类方法的理论框架及技术要点进行系统梳理,随后结合实际应用进行阐述,以期为利用UGC指导产品改进的企业和研究者提供有价值的参考。最终,探讨了当前属性提取与属性情感分类所面临的挑战及未来的研究方向。

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author: 夏家莉;余子恺;邓庆山;刘德喜;彭文忠;罗文军
keywords: 注意力机制;多特征多重融合;在线评论;情感分类;句法依存
source: 计算机应用研究
site: 维普
title: 基于句法依存和注意力机制的多特征多重融合情感分类模型
created_at: 2025-09-11 06:25:42.888208
original_link:
  https://www.cqvip.com/doc/journal/3472884518?sign=7e0ab3c5373584f1d9f41c0389c3d3e2ac1405c4f37078b8a80566cae6813370&expireTime=1789107674660&resourceId=3472884518

summary:
  针对现有的深度学习模型难以提取在线评论的丰富语义信息,从而难以准确提取文本情感的问题,提出了一种基于句法依存关系和注意力机制的多特征多重融合情感分类模型MF-SDAM(multi-feature multiple fusion sentiment classification model based on syntactic dependency and attention mechanism)。该模型首先利用句法依存关系提取在线评论的属性-观点对信息,然后利用动态词嵌入模型BERT获取文本的动态特征向量表示;接着,基于双通道的特征提取策略,分别利用卷积神经网络(TextCNN)和融入注意力机制的双向长短期记忆网络(Att-BiLSTM)提取文本的局部和全局语义特征,为进一步提取特征的全局语义信息,将文本特征与Att-BiLSTM的输出特征进行拼接,并结合注意力机制对情感特征进行加权;最后,基于多融合方式互补的特征融合策略对局部语义特征和全局语义特征进行特征融合,减少关键信息丢失的问题。选取外卖、酒店领域的三个真实公开的在线评论数据集进行效果验证,实验结果表明,MF-SDAM针对在线评论情感分类任务的性能表现优异,其准确率和F_1值在大多数情况下都优于10个基准模型,且对于不平衡数据集具有较好的鲁棒性。

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author: 金旭;崔艳荣;陈杰;陈佳力
keywords: 少样本;细粒度;修正方法;自然语言处理;情感分类;比较任务;文本分类
source: 计算机工程与设计
site: 维普
title: 比较任务修正少样本细粒度情感分类方法
created_at: 2025-09-11 06:25:42.889206
original_link:
  https://www.cqvip.com/doc/journal/3472852169?sign=a2edc62d495a2aa06a45211f8a317ebcecec852b87a9bae261177dffba2d1bc9&expireTime=1789107674660&resourceId=3472852169

summary:
  针对主流少样本学习方法在细粒度情感分类任务中难以区分情感程度相似样本的问题,提出一种基于比较任务的少样本细粒度文本情感分类的修正方法。对于比较任务的训练阶段,构建训练集样本的正负样本并设计一套比较任务模板。在比较任务的预测阶段设计一种将测试集样本与训练集样本情感程度比较结果进行投票分类的方法。利用比较任务的结果对单句分类结果进行修正,取得更稳定的结果。在细粒度情感分类数据集SST5及Amazon Product数据集上进行实验,结果表明,修正方法相较于主流方法获得更优的性能与稳定性。

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author: Yuyang Xu;Bing Li
date: 2017
original_link: https://ieeexplore.ieee.org/document/8110371/
site: ieeeXplore
title: Sentiment Classification Incorporating User Profile
created_at: 2025-09-11 06:25:42.890206
summary:
  With the emergence of the Internet social shopping platform, a large quantity of sentiment corpus is accumulating rapidly. Sentiment classification, which is a specific application of sentiment analysis, has received a lot of attention from researchers in the fields of natural language processing. The traditional method to classify sentiment text is usually limited to the content of text. However, the corpus on the Internet is attached with a large amount of additional information now, which may contribute to sentiment classification. Based on that situation, this paper develops a sentiment classification model which synthesizes text content and user profile to improve the classification accuracy. Concretely, we first calculate the similarity of both reviewers and content between reviews, then use label propagation algorithm to predict sentiment polarity of unlabeled reviews based on labeled reviews. The sentiment classification model incorporating user profile proved to have a higher accuracy compared to models exploiting only text content.

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date: 2010
original_link: https://ieeexplore.ieee.org/document/6077921/
site: ieeeXplore
title: An empirical study of unsupervised sentiment classification of Chinese reviews
created_at: 2025-09-11 06:25:42.891206
summary:
  This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons — individual and combined — are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.

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author: Shangte Tsai;Liuyue Shi
date: 2022
original_link: https://ieeexplore.ieee.org/document/9888185/
site: ieeeXplore
created_at: 2025-09-11 06:25:42.892207
summary:
  In order to improve the efficiency of Chinese text sentiment classification, a text sentiment classification model based on FastText and multi-scale DPCNN are proposed. First, a text vector matrix is constructed using the FastText model. Then, multiple feature maps are extracted from the text vector matrix with a multi-scale filter. Finally, multiple feature maps are fused and input into the DPCNN model for sentiment classification. Experiments are carried out on the Chinese sentiment mining corpus (ChnSentiCorp) dataset, and the comparison results of multiple groups of experiments show that the proposed model can effectively improve the accuracy of text sentiment classification compared with other algorithms.

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title:
  Chinese Text Sentiment Classification Model Based on FastText and Multi-scale Deep Pyramid Convolutional Neural Network


author: Fulian Yin;Beibei Zhang;Pei Su;Juanfang Chai
date: 2016
original_link: https://ieeexplore.ieee.org/document/7867475/
site: ieeeXplore
title: Research on the text sentiment classification about the social hot events on Weibo
created_at: 2025-09-11 06:25:42.893714
summary:
  The public comments on the social hot events on Weibo has attracted lots of attentions in recent years. To remedy the shortage of sentiment analysis about typical events on Weibo, the classification method based on sentiment dictionary is put forward in this paper, and the accuracy rate is close to 50%. This paper also proposed a sentiment classification method based on Naive Bayesian in order to improve the accuracy. We use TF as the weight of feature words and chi-square test to extract features of each category. The accuracy rate is commonly raised to more than 70%, it can reach to 80% when analyzing text of typical events.

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author: Bingyang Chen;Lulu Fan;Xiaobao Fu
date: 2019
original_link: https://ieeexplore.ieee.org/document/8991015/
site: ieeeXplore
title: Sentiment Classification of Tourism Based on Rules and LDA Topic Model
created_at: 2025-09-11 06:25:42.895232
summary:
  Mastering the sentimental state of tourists can provide decision-making reference for scenic spot management and business operation. The core of tourist sentiment analysis is the construction of tourism sentiment classification model. At present, there is less research on emotions in the field of tourism, and the classification accuracy of the existing tourism sentiment model needs to be improved. Therefore, a method of combining sentiment lexicon and machine learning to construct tourism emotion model is presented. The tourist texts were collected from micro-blog and travel website reviews to construct tourism emotional lexicon firstly. By comparing the score of the single sentence text sentiment with threshold, the sentences with the clear emotion polarity were extracted, and we constructed a classifier preliminary with NB algorithm. Then the paper used the LDA topic model to correct the sentiment classification model, and the new model was used to classify of the other sentences. Finally, the two results were integrated as the end classification result. Through experiments on the Lijiang travel text, it is found that the accuracy of the hybrid method increased 13.85% than the sentiment lexicon method, and increased 8.51% than in machine learning.

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author: Wang Tao
date: 2020
original_link: https://ieeexplore.ieee.org/document/9434746/
site: ieeeXplore
title: Emotional classification model of review text based on topic and sentiment characteristics
created_at: 2025-09-11 06:25:42.895840
summary:
  Research on the impact of the high dimension of comment text features and the implicitness of text expression on sentiment classification performance, a deep learning sentiment classification model based on topic and sentiment features is proposed for sentiment classification of comment texts. Firstly, the model uses dependency syntax analysis combined with sentiment dictionary to extract sentiment elements, and uses the implicit LDA topic model to reduce the dimension of the extracted sentiment elements and extract feature items; secondly, the generated feature items are vectorized and combined with the text The vector fusion forms the feature vector of the text; finally, the LSTM model in deep learning is used for text sentiment classification. Experiments show that this model is better than traditional methods for text sentiment classification, and can effectively improve the performance of sentiment classification.

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author: Xin Wang;Guohong Fu
date: 2010
original_link: https://ieeexplore.ieee.org/document/5681624/
site: ieeeXplore
title: Chinese Sentence-Level Sentiment Classification Based on Sentiment Morphemes
created_at: 2025-09-11 06:25:42.897031
summary:
  In this paper, we take morphemes as the basic tokens and present a fine-to-coarse strategy for Chinese sentence-level sentiment classification. This study involves three parts. First, we employ morphological productivity to extract sentiment morphemes from a sentiment dictionary and to calculate their polarity intensity at the same time. Then, we apply the acquired morpheme-level sentiment information to predict the semantic orientation of sentiment words and phrases within an opinionated sentence. Finally, all the sentiment phrases and their polarity scores are combined to determine the semantic orientation of the sentence. The experimental results on NTCIR-6 OAPT test set show our system can achieve state-of-the-art performance.

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date: 2022
original_link: https://ieeexplore.ieee.org/document/10041651/
site: ieeeXplore
created_at: 2025-09-11 06:25:42.898030
author:
  Dinar Ajeng Kristiyanti;Rizki Aulianita;Dwi Andini Putri;Lilyani Asri Utami;Fajar Agustini;Zulia Imami Alfianti

summary:
  The use of public transportation facilities such as MRT, LRT, and Transjakarta by the people of the capital city is an alternative in reducing congestion. However, the services provided by MRT, LRT and Transjakarta transportation service providers vary, such as positive and negative responses. The effectiveness of public transportation facilities can be seen through public opinion. This study aims to classify positive and negative tweet sentiments sourced from Twitter data using the Support Vector Machine (SVM) algorithm. The results of this study indicate that the Support Vector Machine method is able to classify positive and negative sentiment text with an accuracy result of 91.89% with 79.2% positive sentiment and 20.8% negative sentiment.

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title:
  Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector Machine


author: Murahartawaty Arief;Noor Azah Samsudin
date: 2023
original_link: https://ieeexplore.ieee.org/document/10381911/
site: ieeeXplore
created_at: 2025-09-11 06:25:42.900032
summary:
  Customer sentiment analyses improve customer experience and ultimately drive business success. Polarity classification is considered the standard research task in sentiment analysis. This study’s primary focus is conducting a comparative study with 10-fold cross-validation to discover the most effective sentiment classifier to handle neutral sentiment in order to enhance sentiment polarity categorization. This study employs binary classification utilizing supervised machine learning methods such as Naïve Bayes and Support Vector Machine to classify neutral sentiment or a 3-star rating in online customer reviews. Results show that SVM outperforms Naïve Bayes in classifying sentiment with an AUC value of 0,923 for scenarios the neutral class considered removed from the data set. The comparison result based on three scenarios to handle the neutral class indicates that the neutral class may be considered a positive class with an AUC value of 0.904 based on the supervised machine-learning approach. A review with a 3-star rating is seen as positive feedback and slightly better than average or meets expectations. The neutral sentiment was considered an acceptable score of satisfied with some positive aspects. Further investigation is required to multiclass approaches to classify the customer sentiments as positive, negative, or neutral to achieve better accuracy and prevent the risk of misclassification.

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title:
  Neutral Class Handling for Customer Sentiment Analysis In Binary Classification: A Comparative Study of Supervised Machine Learning Classification Algorithm


author: Ghassan Kanaan;Mariam Dabaeen;Raed K. Kanaan;Ghazi Kanaan;Salahiddin Altrahat;Ibrahim Atoum
date: 2025
original_link: https://ieeexplore.ieee.org/document/11049175/
site: ieeeXplore
title: Improving the Classification of Arabic Tweet Sentiment Using Word Embedding Models
created_at: 2025-09-11 06:25:42.901032
summary:
  Enhancing the Classification of Sentiments in Arabic Tweets through Word Embedding Models Sentiment analysis in Arabic tweets is applied in this research using word embedding techniques. The primary objective is to determine how to classify Arabic tweets as positive or negative. The analysis was based on a dataset of 4,546 Arabic tweets obtained from Kaggle. The word embedding model (e.g. Word2Vec and GloVe) was used to facilitate the analysis because its semantic meaning and relations of words were better than what was provided through traditional methods like TF-IDF. To analyze the data, some of the classification techniques that were used include logistic regression, decision trees, and support vector machines. The results of the study showed that SVM has the best accuracy at 87%, followed by decision trees at 80%, and then logistic regression at 85%. This demonstrates that word embedding simulation models are effective for Arabic sentiment analysis on social networking sites. With 80% decision trees, word embedding greatly aided pyramid computing’s divergence modulation, which allows for sentiment and mining analyses of Middle Eastern users’ social activities. SVM performed well at the more sensitive and sophisticated tasks of opinion and market research analysis involving the Arabic language. Even so, a more precise classification can be performed with the application of deep learning algorithms or a hybrid model in future work.

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author: Julius Neumann;Robert Lange;Yuni Susanti;Michael Färber
date: 5 September, 2025
original_link: https://arxiv.org/abs/2509.04982
pdf_url: https://arxiv.org/pdf/2509.04982
site: arxiv
created_at: 2025-09-11 06:25:42.902031
summary:
  Abstract:Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels -- issues that are further intensified by the limited context in short texts. These factors make it difficult to resolve ambiguity and exacerbate data sparsity, hindering effective learning. In this paper, we evaluate the effectiveness of small Transformer-based models (i.e., BERT and RoBERTa, with fewer than 1 billion parameters) for multi-label sentiment classification, with a particular focus on short-text settings. Specifically, we evaluated three key factors influencing model performance: (1) continued domain-specific pre-training, (2) data augmentation using automatically generated examples, specifically generative data augmentation, and (3) architectural variations of the classification head. Our experiment results show that data augmentation improves classification performance, while continued pre-training on augmented datasets can introduce noise rather than boost accuracy. Furthermore, we confirm that modifications to the classification head yield only marginal benefits. These findings provide practical guidance for optimizing BERT-based models in resource-constrained settings and refining strategies for sentiment classification in short-text datasets.

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title:
  Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts


author: Rishit Tyagi;Rahul Bouri;Mohit Gupta
date: 4 September, 2025
original_link: https://arxiv.org/abs/2509.04077
pdf_url: https://arxiv.org/pdf/2509.04077
site: arxiv
title: Improving Narrative Classification and Explanation via Fine Tuned Language Models
created_at: 2025-09-11 06:25:42.903538
summary:
  Abstract:Understanding covert narratives and implicit messaging is essential for analyzing bias and sentiment. Traditional NLP methods struggle with detecting subtle phrasing and hidden agendas. This study tackles two key challenges: (1) multi-label classification of narratives and sub-narratives in news articles, and (2) generating concise, evidence-based explanations for dominant narratives. We fine-tune a BERT model with a recall-oriented approach for comprehensive narrative detection, refining predictions using a GPT-4o pipeline for consistency. For narrative explanation, we propose a ReACT (Reasoning + Acting) framework with semantic retrieval-based few-shot prompting, ensuring grounded and relevant justifications. To enhance factual accuracy and reduce hallucinations, we incorporate a structured taxonomy table as an auxiliary knowledge base. Our results show that integrating auxiliary knowledge in prompts improves classification accuracy and justification reliability, with applications in media analysis, education, and intelligence gathering.

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author: Shiku Kaito;Shinnosuke Matsuo;Daiki Suehiro;Ryoma Bise
date: 4 September, 2025
original_link: https://arxiv.org/abs/2509.04023
pdf_url: https://arxiv.org/pdf/2509.04023
site: arxiv
title: Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
created_at: 2025-09-11 06:25:42.904811
summary:
  Abstract:The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag-level label. The goal of LML is to train a classification model that estimates the class of each instance using the majority label. This problem is valuable in a variety of applications, including pathology image segmentation, political voting prediction, customer sentiment analysis, and environmental monitoring. To solve LML, we propose a Counting Network trained to produce bag-level majority labels, estimated by counting the number of instances in each class. Furthermore, analysis experiments on the characteristics of LML revealed that bags with a high proportion of the majority class facilitate learning. Based on this result, we developed a Majority Proportion Enhancement Module (MPEM) that increases the proportion of the majority class by removing minority class instances within the bags. Experiments demonstrate the superiority of the proposed method on four datasets compared to conventional MIL methods. Moreover, ablation studies confirmed the effectiveness of each module. The code is available at \href{this https URL}{here}.

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author: Iro Lim;Haein Ji;Byungjun Kim
date: 4 September, 2025
original_link: https://arxiv.org/abs/2509.03932
pdf_url: https://arxiv.org/pdf/2509.03932
site: arxiv
created_at: 2025-09-11 06:25:42.905864
summary:
  Abstract:This study introduces KPoEM (Korean Poetry Emotion Mapping) , a novel dataset for computational emotion analysis in modern Korean poetry. Despite remarkable progress in text-based emotion classification using large language models, poetry-particularly Korean poetry-remains underexplored due to its figurative language and cultural specificity. We built a multi-label emotion dataset of 7,662 entries, including 7,007 line-level entries from 483 poems and 615 work-level entries, annotated with 44 fine-grained emotion categories from five influential Korean poets. A state-of-the-art Korean language model fine-tuned on this dataset significantly outperformed previous models, achieving 0.60 F1-micro compared to 0.34 from models trained on general corpora. The KPoEM model, trained through sequential fine-tuning-first on general corpora and then on the KPoEM dataset-demonstrates not only an enhanced ability to identify temporally and culturally specific emotional expressions, but also a strong capacity to preserve the core sentiments of modern Korean poetry. This study bridges computational methods and literary analysis, presenting new possibilities for the quantitative exploration of poetic emotions through structured data that faithfully retains the emotional and cultural nuances of Korean literature.

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title:
  Decoding the Poetic Language of Emotion in Korean Modern Poetry: Insights from a Human-Labeled Dataset and AI Modeling


author: Wenlan Xie
date: 2 September, 2025
original_link: https://arxiv.org/abs/2509.02809
pdf_url: https://arxiv.org/pdf/2509.02809
site: arxiv
created_at: 2025-09-11 06:25:42.906867
summary:
  Abstract:This study presents a hybrid framework for predicting movie success. The framework integrates multi-task learning (MTL), GPT-based sentiment analysis, and Susceptible-Infected-Recovered (SIR) propagation modeling. The study examines limitations in existing approaches. It models static production attributes, information dissemination, and audience sentiment at the same time. The framework uses 5,840 films from 2004 to 2024 and approximate 300,000 user reviews. It shows predictive performance with classification accuracy of 0.964 and regression metrics of MAE 0.388. Ablation analysis indicates component interactions. Selective feature combinations perform better than the comprehensive model. This result questions assumptions about feature integration. The model shows virality patterns between successful and unsuccessful films. Innovations include epidemiological modeling for information diffusion, multidimensional sentiment features from GPT-based analysis, and a shared representation architecture that optimizes multiple success metrics. The framework provides applications in the film production lifecycle. It also contributes to understanding how audience engagement leads to commercial outcomes.

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title:
  Predicting Movie Success with Multi-Task Learning: A Hybrid Framework Combining GPT-Based Sentiment Analysis and SIR Propagation


author: Jun Hou;Le Wang;Xuan Wang
date: 25 August, 2025
original_link: https://arxiv.org/abs/2508.18551
pdf_url: https://arxiv.org/pdf/2508.18551
site: arxiv
title: BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration
created_at: 2025-09-11 06:25:42.907866
summary:
  Abstract:Mixture-of-Experts (MoE) models have become increasingly powerful in multimodal learning by enabling modular specialization across modalities. However, their effectiveness remains unclear when additional modalities introduce more noise than complementary information. Existing approaches, such as the Partial Information Decomposition, struggle to scale beyond two modalities and lack the resolution needed for instance-level control. We propose Beyond Two-modality Weighting (BTW), a bi-level, non-parametric weighting framework that combines instance-level Kullback-Leibler (KL) divergence and modality-level mutual information (MI) to dynamically adjust modality importance during training. Our method does not require additional parameters and can be applied to an arbitrary number of modalities. Specifically, BTW computes per-example KL weights by measuring the divergence between each unimodal and the current multimodal prediction, and modality-wide MI weights by estimating global alignment between unimodal and multimodal outputs. Extensive experiments on sentiment regression and clinical classification demonstrate that our method significantly improves regression performance and multiclass classification accuracy.

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author: Xilai Xu;Zilin Zhao;Chengye Song;Zining Wang;Jinhe Qiang;Jiongrui Yan;Yuhuai Lin
date: 25 August, 2025
original_link: https://arxiv.org/abs/2508.18108
pdf_url: https://arxiv.org/pdf/2508.18108
site: arxiv
title: SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
created_at: 2025-09-11 06:25:42.908865
summary:
  Abstract:With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enriches context via knowledge retrieval, and aggregates results for final sentiment classification. We also introduce SentiMMD, a large-scale multimodal dataset with seven fine-grained sentiment categories. Extensive experiments demonstrate that SentiMM achieves superior performance compared to state-of-the-art baselines, validating the effectiveness of our structured approach.

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author: Daniel Frees;Martin Pollack
date: 25 August, 2025
original_link: https://arxiv.org/abs/2508.17576
pdf_url: https://arxiv.org/pdf/2508.17576
site: arxiv
title: CausalSent: Interpretable Sentiment Classification with RieszNet
created_at: 2025-09-11 06:25:42.909864
summary:
  Abstract:Despite the overwhelming performance improvements offered by recent natural language processing (NLP) models, the decisions made by these models are largely a black box. Towards closing this gap, the field of causal NLP combines causal inference literature with modern NLP models to elucidate causal effects of text features. We replicate and extend Bansal et al's work on regularizing text classifiers to adhere to estimated effects, focusing instead on model interpretability. Specifically, we focus on developing a two-headed RieszNet-based neural network architecture which achieves better treatment effect estimation accuracy. Our framework, CausalSent, accurately predicts treatment effects in semi-synthetic IMDB movie reviews, reducing MAE of effect estimates by 2-3x compared to Bansal et al's MAE on synthetic Civil Comments data. With an ensemble of validated models, we perform an observational case study on the causal effect of the word "love" in IMDB movie reviews, finding that the presence of the word "love" causes a +2.9% increase in the probability of a positive sentiment.

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date: 25 August, 2025
original_link: https://arxiv.org/abs/2508.16876
pdf_url: https://arxiv.org/pdf/2508.16876
site: arxiv
title: Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
created_at: 2025-09-11 06:25:42.910818
author:
  Yue Zhao;Xiaoyu Wang;Dan Wang;Zhonglin Jiang;Qingqing Gu;Teng Chen;Ningyuan Xi;Jinxian Qu;Yong Chen;Luo Ji

summary:
  Abstract:World models have been widely utilized in robotics, gaming, and auto-driving. However, their applications on natural language tasks are relatively limited. In this paper, we construct the dialogue world model, which could predict the user's emotion, sentiment, and intention, and future utterances. By defining a POMDP, we argue emotion, sentiment and intention can be modeled as the user belief and solved by maximizing the information bottleneck. By this user belief modeling, we apply the model-based reinforcement learning framework to the dialogue system, and propose a framework called DreamCUB. Experiments show that the pretrained dialogue world model can achieve state-of-the-art performances on emotion classification and sentiment identification, while dialogue quality is also enhanced by joint training of the policy, critic and dialogue world model. Further analysis shows that this manner holds a reasonable exploration-exploitation balance and also transfers well to out-of-domain scenarios such as empathetic dialogues.

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author: Neemias B. da Silva;John Harrison;Rodrigo Minetto;Myriam R. Delgado;Bogdan T. Nassu;Thiago H. Silva
date: 22 August, 2025
original_link: https://arxiv.org/abs/2508.16873
pdf_url: https://arxiv.org/pdf/2508.16873
site: arxiv
title: Do Multimodal LLMs See Sentiment?
created_at: 2025-09-11 06:25:42.912837
summary:
  Abstract:Understanding how visual content communicates sentiment is critical in an era where online interaction is increasingly dominated by this kind of media on social platforms. However, this remains a challenging problem, as sentiment perception is closely tied to complex, scene-level semantics. In this paper, we propose an original framework, MLLMsent, to investigate the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs) through three perspectives: (1) using those MLLMs for direct sentiment classification from images; (2) associating them with pre-trained LLMs for sentiment analysis on automatically generated image descriptions; and (3) fine-tuning the LLMs on sentiment-labeled image descriptions. Experiments on a recent and established benchmark demonstrate that our proposal, particularly the fine-tuned approach, achieves state-of-the-art results outperforming Lexicon-, CNN-, and Transformer-based baselines by up to 30.9%, 64.8%, and 42.4%, respectively, across different levels of evaluators' agreement and sentiment polarity categories. Remarkably, in a cross-dataset test, without any training on these new data, our model still outperforms, by up to 8.26%, the best runner-up, which has been trained directly on them. These results highlight the potential of the proposed visual reasoning scheme for advancing affective computing, while also establishing new benchmarks for future research.

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author: Huang F, Li X, Yuan C, Zhang S, Zhang J, Qiao S.
original_link: https://pubmed.ncbi.nlm.nih.gov/33600326/
site: pubmed
title: Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis.
created_at: 2025-09-11 06:25:42.913865
summary:
  Long short-term memory (LSTM) neural networks and attention mechanism have been widely used in sentiment representation learning and detection of texts. However, most of the existing deep learning models for text sentiment analysis ignore emotion's modulation effect on sentiment feature extraction, and the attention mechanisms of these deep neural network architectures are based on word- or sentence-level abstractions. Ignoring higher level abstractions may pose a negative effect on learning text sentiment features and further degrade sentiment classification performance. To address this issue, in this article, a novel model named AEC-LSTM is proposed for text sentiment detection, which aims to improve the LSTM network by integrating emotional intelligence (EI) and attention mechanism. Specifically, an emotion-enhanced LSTM, named ELSTM, is first devised by utilizing EI to improve the feature learning ability of LSTM networks, which accomplishes its emotion modulation of learning system via the proposed emotion modulator and emotion estimator. In order to better capture various structure patterns in text sequence, ELSTM is further integrated with other operations, including convolution, pooling, and concatenation. Then, topic-level attention mechanism is proposed to adaptively adjust the weight of text hidden representation. With the introduction of EI and attention mechanism, sentiment representation and classification can be more effectively achieved by utilizing sentiment semantic information hidden in text topic and context. Experiments on real-world data sets show that our approach can improve sentiment classification performance effectively and outperform state-of-the-art deep learning-based methods significantly.

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author: Semary NA, Ahmed W, Amin K, Pławiak P, Hammad M.
original_link: https://pubmed.ncbi.nlm.nih.gov/38130432/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10733963/pdf/fnhum-17-1292010.pdf
site: pubmed
title: Improving sentiment classification using a RoBERTa-based hybrid model.
created_at: 2025-09-11 06:25:42.914415
summary:
  Introduction: Several attempts have been made to enhance text-based sentiment analysis's performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models' shortcomings.

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author: Zhang S.
original_link: https://pubmed.ncbi.nlm.nih.gov/34650498/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8509032/pdf/fpsyg-12-758967.pdf
site: pubmed
title: Sentiment Classification of News Text Data Using Intelligent Model.
created_at: 2025-09-11 06:25:42.916196
summary:
  Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase "traffic jam" expresses negative sentiment in the sentence "I was stuck in a traffic jam on the elevated for 2 h." But in the domain of transportation, the phrase "traffic jam" in the sentence "Bread and water are essential terms in traffic jams" is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

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author: Zhang X, Wu Z, Liu K, Zhao Z, Wang J, Wu C.
original_link: https://pubmed.ncbi.nlm.nih.gov/36772522/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9920561/pdf/sensors-23-01481.pdf
site: pubmed
title: Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU.
created_at: 2025-09-11 06:25:42.917205
summary:
  In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model.

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author: Hariguna T, Ruangkanjanases A.
original_link: https://pubmed.ncbi.nlm.nih.gov/37346589/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10280487/pdf/peerj-cs-09-1378.pdf
site: pubmed
title: Adaptive sentiment analysis using multioutput classification: a performance comparison.
created_at: 2025-09-11 06:25:42.918204
summary:
  The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.

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author: Jian Z, Li J, Wang M, Yao J, Wu Q.
original_link: https://pubmed.ncbi.nlm.nih.gov/40651253/
site: pubmed
title: Aspect sentiment learning for Aspect-Level Sentiment Classification.
created_at: 2025-09-11 06:25:42.920819
summary:
  Aspect-Level Sentiment Classification (ALSC) is a fine-grained Sentiment Analysis (SA) task that aims to determine the sentiments of a sentence toward different aspects. Despite their significant success, most existing methods derive aspect sentiment semantics from individual sentences, overlooking the interrelationships among relevant sentences that could provide a more comprehensive understanding of aspect sentiment semantics. To this end, we propose AspLearn, an aspect-learning method to optimize aspect sentiment semantics and generate more robust aspect-specific sentence features for the ALSC task. In a nutshell, AspLearn employs the Aspect-aware Contrastive Learning (AspCL) to mine valuable aspect-related knowledge from aspect-relevant samples, thereby optimizing aspect sentiment semantics and enhancing the model's performance. AspLearn is a simple yet effective method, with its superior aspect learning capabilities confirmed through extensive experiments on three benchmarks. Notably, AspLearn, using DeBERTa as the backbone, achieves Macro F1 score improvements of 3.13%, 0.76%, and 1.07% over the second-best results on the Laptops, Restaurants, and Twitter datasets, respectively. Furthermore, AspLearn's mechanism can retrieve the most relevant demonstrations for Large Language Models (LLMs), enhancing their sentiment recognition capabilities.

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