Pub Date : 2026-02-09DOI: 10.1016/j.inffus.2026.104210
Lu Yuan, Zihan Wang, Zhengxuan Zhang, Lei Shi
In the digital era, social media accelerates the spread of misinformation. Existing detection methods often rely on shallow linguistic or propagation features and lack principled multimodal fusion, failing to capture creators’ emotional manipulation and readers’ psychological responses, which limits prediction accuracy. We propose the Dual-Aspect Empathy Framework (DAE), which derives creator and reader perspectives by fusing separately modeled cognitive and emotional empathy. Creators’ cognitive strategies and affective appeals are analyzed, while Large Language Models (LLMs) simulate readers’ judgments and emotional reactions, providing richer and more human-like signals than conventional classifiers, and partially alleviating the analytical challenge posed by insufficient human feedback. An empathy-aware filtering mechanism is further designed to refine outputs, enhancing authenticity and diversity. The pipeline integrates multimodal feature extraction, empathy-oriented representation learning, LLM-based reader simulation, and empathy-aware filtering. Experiments on benchmark datasets such as PolitiFact, GossipCop and Pheme show that the fusion-based DAE consistently outperforms state-of-the-art baselines, offering a novel and human-centric paradigm for misinformation detection.
{"title":"Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection","authors":"Lu Yuan, Zihan Wang, Zhengxuan Zhang, Lei Shi","doi":"10.1016/j.inffus.2026.104210","DOIUrl":"https://doi.org/10.1016/j.inffus.2026.104210","url":null,"abstract":"In the digital era, social media accelerates the spread of misinformation. Existing detection methods often rely on shallow linguistic or propagation features and lack principled multimodal fusion, failing to capture creators’ emotional manipulation and readers’ psychological responses, which limits prediction accuracy. We propose the Dual-Aspect Empathy Framework (DAE), which derives creator and reader perspectives by fusing separately modeled cognitive and emotional empathy. Creators’ cognitive strategies and affective appeals are analyzed, while Large Language Models (LLMs) simulate readers’ judgments and emotional reactions, providing richer and more human-like signals than conventional classifiers, and partially alleviating the analytical challenge posed by insufficient human feedback. An empathy-aware filtering mechanism is further designed to refine outputs, enhancing authenticity and diversity. The pipeline integrates multimodal feature extraction, empathy-oriented representation learning, LLM-based reader simulation, and empathy-aware filtering. Experiments on benchmark datasets such as PolitiFact, GossipCop and Pheme show that the fusion-based DAE consistently outperforms state-of-the-art baselines, offering a novel and human-centric paradigm for misinformation detection.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"30 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.rcim.2026.103252
Moien Reyhani, Christian Hartl-Nesic, Andreas Kugi
Hand-eye calibration is a fundamental prerequisite for vision-based applications in industrial robotics. While this issue is largely addressed for 3D cameras, it remains a challenge for Laser Profile Scanners (LPSs) due to their inherent 2D measurement limitations. Performing hand-eye calibration with an uncalibrated robot often results in inaccurate calibration outcomes. Traditional methods for calibrating the robot prior to hand-eye calibration using highly accurate optical systems are often prohibitively expensive and time-consuming, making them impractical for the high demands of modern production environments. This study proposes a novel methodology for co-optimized elasto-geometrical calibration of the robot, while modeling joint compliance, and the hand-eye calibration of a dual LPS mounted on the robot’s end-effector. The two LPSs are configured such that their laser lines intersect on the object, thereby forming an intersecting Laser Line System (LLS). This necessitates the calibration of the LPSs relative to each other, which constitutes eye-to-eye calibration in the context of multiple LPSs. Therefore, this work also introduces an approach for the eye-to-eye calibration of the LPSs. Both proposed approaches leverage the iterative closest point (ICP) algorithm within a bilevel optimization framework and utilize an artifact specifically designed for the fast and stable calibration of LPSs. Notably, these methods are extensible to multiple laser lines with diverse configurations and are applicable to analogous calibration artifacts. To the best of the authors’ knowledge, this is the first study to address the eye-to-eye calibration of LPSs and to explicitly incorporate joint stiffness effects into the hand-eye calibration of LPSs. The experimental results demonstrate that the proposed calibration strategy achieves a registration error of 0.154mm when using a 3D-printed artifact, while employing a high-precision artifact results in an improved accuracy with a registration error as low as 0.068mm.
{"title":"Co-optimized elasto-geometrical and hand-eye calibration of industrial robots with integrated dual laser profile scanners","authors":"Moien Reyhani, Christian Hartl-Nesic, Andreas Kugi","doi":"10.1016/j.rcim.2026.103252","DOIUrl":"https://doi.org/10.1016/j.rcim.2026.103252","url":null,"abstract":"<mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mtext>Hand-eye</mml:mtext></mml:math> calibration is a fundamental prerequisite for vision-based applications in industrial robotics. While this issue is largely addressed for 3D cameras, it remains a challenge for Laser Profile Scanners (LPSs) due to their inherent 2D measurement limitations. Performing <mml:math altimg=\"si334.svg\" display=\"inline\"><mml:mtext>hand-eye</mml:mtext></mml:math> calibration with an uncalibrated robot often results in inaccurate calibration outcomes. Traditional methods for calibrating the robot prior to <mml:math altimg=\"si334.svg\" display=\"inline\"><mml:mtext>hand-eye</mml:mtext></mml:math> calibration using highly accurate optical systems are often prohibitively expensive and time-consuming, making them impractical for the high demands of modern production environments. This study proposes a novel methodology for co-optimized elasto-geometrical calibration of the robot, while modeling joint compliance, and the <mml:math altimg=\"si334.svg\" display=\"inline\"><mml:mtext>hand-eye</mml:mtext></mml:math> calibration of a dual LPS mounted on the robot’s end-effector. The two LPSs are configured such that their laser lines intersect on the object, thereby forming an intersecting Laser Line System (LLS). This necessitates the calibration of the LPSs relative to each other, which constitutes <mml:math altimg=\"si241.svg\" display=\"inline\"><mml:mtext>eye-to-eye</mml:mtext></mml:math> calibration in the context of multiple LPSs. Therefore, this work also introduces an approach for the <mml:math altimg=\"si241.svg\" display=\"inline\"><mml:mtext>eye-to-eye</mml:mtext></mml:math> calibration of the LPSs. Both proposed approaches leverage the iterative closest point (ICP) algorithm within a bilevel optimization framework and utilize an artifact specifically designed for the fast and stable calibration of LPSs. Notably, these methods are extensible to multiple laser lines with diverse configurations and are applicable to analogous calibration artifacts. To the best of the authors’ knowledge, this is the first study to address the <mml:math altimg=\"si241.svg\" display=\"inline\"><mml:mtext>eye-to-eye</mml:mtext></mml:math> calibration of LPSs and to explicitly incorporate joint stiffness effects into the <mml:math altimg=\"si334.svg\" display=\"inline\"><mml:mtext>hand-eye</mml:mtext></mml:math> calibration of LPSs. The experimental results demonstrate that the proposed calibration strategy achieves a registration error of 0.154<ce:hsp sp=\"0.16667\"></ce:hsp>mm when using a 3D-printed artifact, while employing a high-precision artifact results in an improved accuracy with a registration error as low as 0.068<ce:hsp sp=\"0.16667\"></ce:hsp>mm.","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"89 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1177/2167647X261423109
Xianfeng Gong, Mingyang Mao
This study intends to identify the critical factors that shape college students' adoption of AI-generated news, with a specific focus on integrating Big Data methodologies into the Technology Acceptance Model (TAM) framework. Building on TAM, the research incorporates "trust" as a core variable to develop a dual-path theoretical model that combines technological cognition (e.g., perceived usefulness, perceived ease of use) and psychological emotions. Unlike traditional TAM-based studies relying solely on questionnaire data, this research enriches its data sources by leveraging Big Data techniques-including the collection and analysis of college students' real-time behavioral data (e.g., AI news reading duration, sharing frequency, source verification clicks) and unstructured text data (e.g., sentiment orientation in comment sections)-to complement the survey data from 300 college students. Through a questionnaire survey of 300 college students and data analysis using the structural equation model, the study found that trust has the strongest direct positive impact on the willingness to use (β = 0.49, p < 0.001), and its influence is significantly greater than perceived usefulness (β = 0.35, p < 0.001). Meanwhile, although perceived ease of use does not directly affect the willingness to use, it has significant indirect effects by enhancing trust and perceived usefulness. The results show that in the AI news context with high-risk perception, trust is a more crucial psychological mechanism than traditional technological cognitive factors. These findings have expanded the explanatory boundaries of the TAM model in new technology fields and provided empirical evidence and practical inspiration for AI developers to optimize system credibility and for educators to conduct algorithmic literacy training.
本研究旨在确定影响大学生采用人工智能生成新闻的关键因素，并特别关注将大数据方法整合到技术接受模型（TAM）框架中。本研究以TAM为基础，将“信任”作为核心变量，构建了技术认知（如感知有用性、感知易用性）与心理情绪相结合的双路径理论模型。与传统的基于tam的研究仅仅依赖于问卷数据不同，本研究利用大数据技术——包括收集和分析大学生的实时行为数据（如AI新闻阅读时长、分享频率、来源验证点击）和非结构化文本数据（如评论区情绪倾向）——来丰富其数据源，以补充300名大学生的调查数据。通过对300名大学生的问卷调查，运用结构方程模型进行数据分析，研究发现信任对使用意愿的直接正向影响最强（β = 0.49, p < 0.001），其影响显著大于感知有用性（β = 0.35, p < 0.001）。同时，感知易用性虽然不直接影响使用意愿，但通过增强信任和感知有用性，具有显著的间接影响。结果表明，在具有高风险感知的人工智能新闻情境中，信任是比传统技术认知因素更为关键的心理机制。这些发现拓展了TAM模型在新技术领域的解释边界，为人工智能开发者优化系统可信度和教育工作者开展算法素养培训提供了经验证据和实践启示。
{"title":"Perceived Usefulness, Trust, and Behavioral Intention: A Study on College Student User Adoption Behaviors of Artificial Intelligence Generated News Based on Technology Acceptance Model.","authors":"Xianfeng Gong, Mingyang Mao","doi":"10.1177/2167647X261423109","DOIUrl":"https://doi.org/10.1177/2167647X261423109","url":null,"abstract":"<p><p>This study intends to identify the critical factors that shape college students' adoption of AI-generated news, with a specific focus on integrating Big Data methodologies into the Technology Acceptance Model (TAM) framework. Building on TAM, the research incorporates \"trust\" as a core variable to develop a dual-path theoretical model that combines technological cognition (e.g., perceived usefulness, perceived ease of use) and psychological emotions. Unlike traditional TAM-based studies relying solely on questionnaire data, this research enriches its data sources by leveraging Big Data techniques-including the collection and analysis of college students' real-time behavioral data (e.g., AI news reading duration, sharing frequency, source verification clicks) and unstructured text data (e.g., sentiment orientation in comment sections)-to complement the survey data from 300 college students. Through a questionnaire survey of 300 college students and data analysis using the structural equation model, the study found that trust has the strongest direct positive impact on the willingness to use (β = 0.49, <i>p</i> < 0.001), and its influence is significantly greater than perceived usefulness (β = 0.35, <i>p</i> < 0.001). Meanwhile, although perceived ease of use does not directly affect the willingness to use, it has significant indirect effects by enhancing trust and perceived usefulness. The results show that in the AI news context with high-risk perception, trust is a more crucial psychological mechanism than traditional technological cognitive factors. These findings have expanded the explanatory boundaries of the TAM model in new technology fields and provided empirical evidence and practical inspiration for AI developers to optimize system credibility and for educators to conduct algorithmic literacy training.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X261423109"},"PeriodicalIF":2.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"PGCT-PINN: A Physics-Guided Cooperative Training Framework for Enhanced Resolution and Consistency in Lung EIT Imaging","authors":"Zexin Zhu, Zhixi Zhang, Zuowei Wang, Zitang Yuan, Xiyao Zhao, Anran Ma, Jiangtao Sun, Lijun Xu, Linhong Mo","doi":"10.1109/jiot.2026.3662909","DOIUrl":"https://doi.org/10.1109/jiot.2026.3662909","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"44 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tase.2026.3659154
Xiao Jin, Yongxiong Wang, Shuai Huang, Nan Zhang, Han Chen, Hui Yang, Yiming Li
{"title":"OpenVL: Bridging 2D and 3D Worlds for Open-Vocabulary 3D Scene Understanding","authors":"Xiao Jin, Yongxiong Wang, Shuai Huang, Nan Zhang, Han Chen, Hui Yang, Yiming Li","doi":"10.1109/tase.2026.3659154","DOIUrl":"https://doi.org/10.1109/tase.2026.3659154","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/TIE.2026.3654285
{"title":"IEEE Transactions on Industrial Electronics Information for Authors","authors":"","doi":"10.1109/TIE.2026.3654285","DOIUrl":"https://doi.org/10.1109/TIE.2026.3654285","url":null,"abstract":"","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"73 2","pages":"C4-C4"},"PeriodicalIF":7.2,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11383834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：