Information Fusion最新文献

{"title":"A Review of BioTree Construction in the Context of Information Fusion: Priors, Methods, Applications and Trends","authors":"Zelin Zang ,&nbsp;Yongjie Xu ,&nbsp;Chenrui Duan ,&nbsp;Yue Yuan ,&nbsp;Yue Shen ,&nbsp;Jinlin Wu ,&nbsp;Zhen Lei ,&nbsp;Stan Z. Li","doi":"10.1016/j.inffus.2025.103108","DOIUrl":"10.1016/j.inffus.2025.103108","url":null,"abstract":"<div><div>Biological tree (BioTree) analysis is a foundational tool in biology, enabling the exploration of evolutionary and differentiation relationships among organisms, genes, and cells. Traditional tree construction methods, while instrumental in early research, face significant challenges in handling the growing complexity and scale of modern biological data, particularly in integrating multimodal datasets. Advances in deep learning (DL) offer transformative opportunities by enabling the fusion of biological prior knowledge with data-driven models. These approaches address key limitations of traditional methods, facilitating the construction of more accurate and interpretable BioTrees. This review highlights critical biological priors essential for phylogenetic and differentiation tree analyses and explores strategies for integrating these priors into DL models to enhance accuracy and interpretability. Additionally, the review systematically examines commonly used data modalities and databases, offering a valuable resource for developing and evaluating multimodal fusion models. Traditional tree construction methods are critically assessed, focusing on their biological assumptions, technical limitations, and scalability issues. Recent advancements in DL-based tree generation methods are reviewed, emphasizing their innovative approaches to multimodal integration and prior knowledge incorporation. Finally, the review discusses diverse applications of BioTrees in various biological disciplines, from phylogenetics to developmental biology, and outlines future trends in leveraging DL to advance BioTree research. By addressing the challenges of data complexity and prior knowledge integration, this review aims to inspire interdisciplinary innovation at the intersection of biology and DL.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103108"},"PeriodicalIF":14.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on data fusion approaches in IoT-based smart cities: Smart applications, taxonomies, challenges, and future research directions","authors":"Berna Cengiz ,&nbsp;Iliyasu Yahya Adam ,&nbsp;Mehmet Ozdem ,&nbsp;Resul Das","doi":"10.1016/j.inffus.2025.103102","DOIUrl":"10.1016/j.inffus.2025.103102","url":null,"abstract":"<div><div>Rapidly increasing urbanization leads to the need for more comfortable and reliable living spaces. The smart city paradigm needs to be renewed daily to provide smarter solutions to citizens’ needs and problems and ensure sustainable living. The Internet of Things is one of the most widely used smart city methodologies. The Internet of Things (IoT) aims to enable objects in the physical world and cyberspace to communicate. IoT technology produces a very large amount of raw and generally heterogeneous data. Data fusion techniques are gaining popularity to manage this large amount of data. With data fusion methods, smarter structures can be built from this raw data to reduce data size, optimize traffic, and extract useful information. This study provides a comprehensive perspective and opportunities for different areas of IoT applications of smart city systems. In addition to providing a detailed taxonomy framework for data fusion levels in various criteria, the seven-layer IoT architecture implemented in smart cities is discussed. Finally, the paper concludes by mentioning the difficulties encountered in applying data fusion methods, proposing solutions to these difficulties, and presenting future work trends based on the studies conducted.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103102"},"PeriodicalIF":14.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715391","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}

{"title":"Pseudo 5D hyperspectral light field for image semantic segmentation","authors":"Ruixuan Cong ,&nbsp;Hao Sheng ,&nbsp;Da Yang ,&nbsp;Rongshan Chen ,&nbsp;Zhenglong Cui","doi":"10.1016/j.inffus.2025.103042","DOIUrl":"10.1016/j.inffus.2025.103042","url":null,"abstract":"<div><div>Light field (LF) encodes both intensity information and directional information of all light rays into high-dimensional signal, which facilitates various advanced applications due to its rich description. However, current mainstream research adopts two-plane parametrization to describe 4D LF, losing the information stored in the spectral dimension that can delineate more scene details. On this account, we introduce 5D hyperspectral light field (H-LF) to achieve robust semantic segmentation for the first time. To alleviate data redundancy while preserving useful information to a large extent, we use pseudo H-LF with sparsely non-repetitive angular-spectral distribution as an alternative and propose a network called PHLFNet. Specifically, our network successively performs feature-level angular-spectral joint blending and semantic-level angular-spectral joint enhancement to fully exploit the complementary information embedded in pseudo H-LF, in which the former executes preliminary information fusion and calibration across all modalities, and the latter distills unique semantic cues of each auxiliary modality to boost feature of segmented central view image. To guarantee the accuracy of semantic cues distillation, we design boundary consistency semantic label propagation to handle cross-spectral color inconsistency and cross-angular pixel misalignment in pseudo H-LF, thereby generating semantic labels of each auxiliary modality to provide supervision. Extensive experimental results illustrate that PHLFNet achieves outstanding performance compared with relevant state-of-the-art methods, demonstrating the significance of introducing H-LF for semantic segmentation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103042"},"PeriodicalIF":14.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746893","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}

{"title":"METC: A Hybrid Deep Learning Framework for Cross-Network Encrypted DNS over HTTPS Traffic Detection and Tunnel Identification","authors":"Ming Zuo ,&nbsp;Changyong Guo ,&nbsp;Haiyan Xu ,&nbsp;Zhaoxin Zhang ,&nbsp;Yanan Cheng","doi":"10.1016/j.inffus.2025.103125","DOIUrl":"10.1016/j.inffus.2025.103125","url":null,"abstract":"<div><div>With the widespread adoption of DNS over HTTPS (DoH), network privacy and security have significantly improved, but detecting encrypted DoH traffic remains challenging, especially in heterogeneous environments. Existing research primarily focuses on desktops, neglecting mobile-specific detection.</div><div>To address this gap, we propose METC, a multi-stage hybrid learning framework for encrypted DoH traffic detection. We develop a mobile traffic collection tool supporting IPv6 and real-time inference and release the first mobile DoH dataset, comprising 38.21 GB of data.</div><div>METC integrates Convolutional Neural Networks (CNNs), Bidirectional Gated Recurrent Units (BiGRUs), and multi-head attention mechanisms, effectively capturing local traffic patterns, temporal dependencies, and key features to enhance cross-network generalization. Our CNN-BiGRU-Attention model achieves an F1-score of 97.34% in mobile DoH detection and 99.96%, 95.99%, and 94.65% in DoH-based tunnel traffic identification across three datasets. Additionally, it accurately identifies 10 tunneling tools, outperforming XGBoost in cross-network scenarios.</div><div>In summary, METC offers an innovative and efficient solution for encrypted DoH traffic detection and tunnel identification, advancing deep learning applications in network security.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103125"},"PeriodicalIF":14.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715393","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}

{"title":"Knowledge Graphs for Multi-modal Learning: Survey and Perspective","authors":"Zhuo Chen ,&nbsp;Yichi Zhang ,&nbsp;Yin Fang ,&nbsp;Yuxia Geng ,&nbsp;Lingbing Guo ,&nbsp;Jiaoyan Chen ,&nbsp;Xiaoze Liu ,&nbsp;Jeff Z. Pan ,&nbsp;Ningyu Zhang ,&nbsp;Huajun Chen ,&nbsp;Wen Zhang","doi":"10.1016/j.inffus.2025.103124","DOIUrl":"10.1016/j.inffus.2025.103124","url":null,"abstract":"<div><div>Integrated with multi-modal learning, knowledge graphs (KGs) as structured knowledge repositories, can enhance AI for processing and understanding complex, real-world data. This paper provides a comprehensive survey of cutting-edge research on KG-aware multi-modal learning. For these core areas, we provide task definitions, evaluation benchmarks, and comprehensive insights into key breakthroughs, offering detailed explanations critical for conducting related research. Furthermore, we also discuss current challenges, highlighting emerging trends and future research directions. The repository for this paper can be found at <span><span>https://github.com/zjukg/KG-MM-Survey</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103124"},"PeriodicalIF":14.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740002","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}

{"title":"Do multimodal large language models understand welding?","authors":"Grigorii Khvatskii ,&nbsp;Yong Suk Lee ,&nbsp;Corey Angst ,&nbsp;Maria Gibbs ,&nbsp;Robert Landers ,&nbsp;Nitesh V. Chawla","doi":"10.1016/j.inffus.2025.103121","DOIUrl":"10.1016/j.inffus.2025.103121","url":null,"abstract":"<div><div>This paper examines the performance of Multimodal LLMs (MLLMs) in skilled production work, with a focus on welding. Using a novel data set of real-world and online weld images, annotated by a domain expert, we evaluate the performance of two state-of-the-art MLLMs in assessing weld acceptability across three contexts: RV &amp; Marine, Aeronautical, and Farming. While both models perform better on online images, likely due to prior exposure or memorization, they also perform relatively well on unseen, real-world weld images. Additionally, we introduce WeldPrompt, a prompting strategy that combines Chain-of-Thought generation with in-context learning to mitigate hallucinations and improve reasoning. WeldPrompt improves model recall in certain contexts but exhibits inconsistent performance across others. These results underscore the limitations and potentials of MLLMs in high-stakes technical domains and highlight the importance of fine-tuning, domain-specific data, and more sophisticated prompting strategies to improve model reliability. The study opens avenues for further research into multimodal learning in industry applications.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103121"},"PeriodicalIF":14.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenRAN: GenFusion-guided Reversible Anonymization Network for face privacy preserving","authors":"Ruilin Wang ,&nbsp;Lingchen Gu ,&nbsp;Jing Li ,&nbsp;Jun Wang ,&nbsp;Jiande Sun ,&nbsp;Wenbo Wan","doi":"10.1016/j.inffus.2025.103120","DOIUrl":"10.1016/j.inffus.2025.103120","url":null,"abstract":"<div><div>The rapid advancement of social networks has made it possible to obtain personal face images without permission. While recent advances in face privacy preservation focus on anonymizing facial features, their effectiveness is limited by challenges in achieving high fidelity for both anonymized and recovered faces in practical scenarios. To address these challenges, we introduce GenFusion, which incorporates Virtual Face Generation (VFG) into the Bi-branch Fusion process with coupling reversibility. Accordingly, we propose a GenFusion-based Reversible Anonymization Network (GenRAN) for enhanced face privacy preservation. Our approach integrates a Multi-Fusion (MF) module, enabling an anonymization encoder to create natural and realistic anonymized faces by fusing original images with virtual faces generated through the VFG module. Furthermore, high-fidelity recovery of the original face from the anonymized version is achieved via an anonymization decoder, which employs a Multi-Recovery module that shares unified parameters with the MF module. Additionally, we introduce a Feature Mixing guided Dense Block to adaptively blend original facial details into the anonymized images, maintain high realism across varying image types. Extensive experiments show that proposed GenRAN can achieve better performance on actual privacy preserving scenarios, while obtaining high perceptual fidelity of anonymized and recovered faces than SOTA methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103120"},"PeriodicalIF":14.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715394","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}

{"title":"RGB-D Domain adaptive semantic segmentation with cross-modality feature recalibration","authors":"Qizhe Fan ,&nbsp;Xiaoqin Shen ,&nbsp;Shihui Ying ,&nbsp;Juan Wang ,&nbsp;Shaoyi Du","doi":"10.1016/j.inffus.2025.103117","DOIUrl":"10.1016/j.inffus.2025.103117","url":null,"abstract":"<div><div>Unsupervised domain adaptive (UDA) semantic segmentation aims to train models that effectively transfer knowledge from synthetic to real-world images, thereby reducing the reliance on manual annotation. Currently, most existing UDA methods primarily focus on RGB image processing, largely overlooking depth information as a valuable geometric cue that complements RGB representations. Additionally, while some approaches attempt to incorporate depth information by inferring it from RGB images as an auxiliary task, inaccuracies in depth estimation can still result in localized blurring or distortion in segmentation outcomes. To comprehensively address these limitations, we propose an innovative RGB-D UDA framework CMFRDA, which seamlessly integrates both RGB and depth images as inputs, fully leveraging their distinct yet complementary properties to improve segmentation performance. Specifically, to mitigate the prevalent object boundary noise in depth information, we propose a Depth Feature Rectification Module (DFRM), which effectively suppresses noise while enhancing the representation of fine structural details. Nevertheless, despite the effectiveness of DFRM, challenges remain due to the presence of noisy signals arising from incomplete surface data beyond the operational range of depth sensors, as well as potential mismatches between modalities. In order to overcome these challenges, we further introduce a Cross-Modality Feature Recalibration (CMFR) block. CMFR comprises two key components: Channel-wise Consistency Recalibration (CCR) and Spatial-wise Consistency Recalibration (SCR). CCR suppresses noise from incomplete surfaces in depth by leveraging the complementary information provided by RGB features, while SCR exploits the distinctive advantages of both modalities to mutually recalibrate each other, thereby ensuring consistency between RGB and depth modalities. By seamlessly integrating DFRM and CMFR, our CMFRDA framework effectively improves the performance of UDA semantic segmentation. Multitudinous experiments demonstrate that our CMFRDA achieves competitive performance on two widely-used UDA benchmarks GTA <span><math><mo>→</mo></math></span> Cityscapes and Synthia <span><math><mo>→</mo></math></span> Cityscapes.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103117"},"PeriodicalIF":14.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714553","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}

{"title":"Social network group decision making: Characterization, taxonomy, challenges and future directions from an AI and LLMs perspective","authors":"Mingshuo Cao ,&nbsp;Tiantian Gai ,&nbsp;Jian Wu ,&nbsp;Francisco Chiclana ,&nbsp;Zhen Zhang ,&nbsp;Yucheng Dong ,&nbsp;Enrique Herrera-Viedma ,&nbsp;Francisco Herrera","doi":"10.1016/j.inffus.2025.103107","DOIUrl":"10.1016/j.inffus.2025.103107","url":null,"abstract":"<div><div>In the past decade, social network group decision making (SNGDM) has experienced significant advancements. This breakthrough is largely attributed to the rise of social networks, which provides crucial data support for SNGDM. As a result, it has emerged as a rapidly developing research field within decision sciences, attracting extensive attention and research over the past ten years. SNGDM events involve complex decision making processes with multiple interconnected stakeholders, where the evaluation of alternatives is influenced by network relationships. Since this research has evolved from group decision making (GDM) scenarios, there is currently no clear definition for SNGDM problems. This article aims to address this gap by first providing a clear definition of the SNGDM framework. It describes basic procedures, advantages, and challenges, serving as a foundational portrait of the SNGDM framework. Furthermore, this article offers a macro description of the literature on SNGDM over the past decade based on bibliometric analysis. Solving SNGDM problems effectively is challenging and requires careful consideration of the impact of social networks among decision-makers and the facilitation of consensus between different participants. Therefore, we propose a classification and overview of key elements for SNGDM models based on the existing literature: trust models, internal structure, and consensus mechanism for SNGDM. This article identifies the research challenges in SNGDM and points out the future research directions from two dimensions: first, the key SNGDM methodologies and second, the opportunities from artificial intelligence technology, in particular, combining large language models and multimodal fusion technologies. This look will be analyzed from a double perspective, both from the decision problem and from the technology views.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103107"},"PeriodicalIF":14.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678376","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}

{"title":"Wearable gait analysis of Cervical Spondylotic Myelopathy patients by fusing bipedal inertial information","authors":"Xin Shi ,&nbsp;Zhelong Wang ,&nbsp;Sen Qiu ,&nbsp;Fang Lin ,&nbsp;Ruichen Liu ,&nbsp;Kai Tang ,&nbsp;Pengrong Hou ,&nbsp;Qinghao Chu ,&nbsp;Yongtao Chen","doi":"10.1016/j.inffus.2025.103115","DOIUrl":"10.1016/j.inffus.2025.103115","url":null,"abstract":"<div><div>Cervical Spondylotic Myelopathy (CSM) is a degenerative disorder caused by cervical spinal cord compression, resulting in neurological impairment that disrupts motor function and leads to gait disturbances. Refined gait analysis through parameter quantification and multi-level feature fusion is essential for advancing precision medicine and rehabilitation research for CSM. This paper explores portable gait analysis for CSM patients using inertial sensors to enable multi-level analysis via bipedal information fusion. An adaptive threshold-based gait phase segmentation method is proposed, allowing zero-velocity update-aided spatial parameter calculation, with results consistent across optical laboratory and practical test. Using high-precision spatiotemporal parameters and movement intensity features, we further analyzed gait rhythm, stability, and symmetry, introducing an improved EWT-based indicator for rhythm and symmetry. Finally, statistical analysis and machine learning-based feature ranking of CSM gait characteristics were performed, accompanied by a detailed discussion on feature types. The results underscore the critical role of fused features in capturing CSM gait patterns, offering a valuable reference for comprehensive gait analysis for CSM patients.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103115"},"PeriodicalIF":14.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714373","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}