Information Fusion最新文献

{"title":"A pan-sharpening model using dual-branch attention-guided diffusion networks","authors":"Huangqimei Zheng ,&nbsp;Chengyi Pan ,&nbsp;Xin Jin ,&nbsp;Michal Wozniak ,&nbsp;Puming Wang ,&nbsp;Shin-Jye Lee ,&nbsp;Qian Jiang","doi":"10.1016/j.inffus.2025.103076","DOIUrl":"10.1016/j.inffus.2025.103076","url":null,"abstract":"<div><div>Pan-sharpening has significant applications in remote sensing image processing. By fusing high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images, it generates high-spatial-resolution images with multispectral information (HRMS). Most deep learning methods, while excelling at extracting single-modality image features, face limitations in capturing the global joint distribution of cross-modality images. To address these issues, this paper introduces a novel pan-sharpening model, the Dual-Branch Attention-Guided Diffusion Network (DADiff), which incorporates diffusion models capable of effectively reconstructing the latent distribution of images. DADiff consists of a Diffusion Branch and an Attention-Guided Branch. The diffusion branch captures the global joint features of PAN and MS images during the denoising process, constructing a cross-modal distribution for HRMS images. Meanwhile, the attention-guided branch enhances the high-frequency details and local features of PAN and MS images through a multi-scale convolutional dense connection module and an improved attention mechanism. Leveraging the diffusion model’s global modeling capability and the multi-scale attention mechanism’s detail-capturing advantage, this network significantly enhances the spatial and spectral fidelity of generated HRMS images. Extensive experimental results validate the effectiveness of the proposed modules. We conducted experiments on the WorldViewII, QuickBird, and Maryland datasets, and the results confirm the superiority of DADiff over state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103076"},"PeriodicalIF":14.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679506","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":"Cross-lingual aspect-based sentiment analysis: A survey on tasks, approaches, and challenges","authors":"Jakub Šmíd,&nbsp;Pavel Král","doi":"10.1016/j.inffus.2025.103073","DOIUrl":"10.1016/j.inffus.2025.103073","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that focuses on understanding opinions at the aspect level, including sentiment towards specific aspect terms, categories, and opinions. While ABSA research has seen significant progress, much of the focus has been on monolingual settings. Cross-lingual ABSA, which aims to transfer knowledge from resource-rich languages (such as English) to low-resource languages, remains an under-explored area, with no systematic review of the field. This paper aims to fill that gap by providing a comprehensive survey of cross-lingual ABSA. We summarize key ABSA tasks, including aspect term extraction, aspect sentiment classification, and compound tasks involving multiple sentiment elements. Additionally, we review the datasets, modelling paradigms, and cross-lingual transfer methods used to solve these tasks. We also examine how existing work in monolingual and multilingual ABSA, as well as ABSA with LLMs, contributes to the development of cross-lingual ABSA. Finally, we highlight the main challenges and suggest directions for future research to advance cross-lingual ABSA systems.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103073"},"PeriodicalIF":14.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619907","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":"DyLas: A dynamic label alignment strategy for large-scale multi-label text classification","authors":"Lin Ren,&nbsp;Yongbin Liu,&nbsp;Chunping Ouyang,&nbsp;Ying Yu,&nbsp;Shuda Zhou,&nbsp;Yidong He,&nbsp;Yaping Wan","doi":"10.1016/j.inffus.2025.103081","DOIUrl":"10.1016/j.inffus.2025.103081","url":null,"abstract":"<div><div>Large-scale multi-label Text Classification (LMTC) is an advanced facet of NLP that entails assigning multiple labels to text documents from an extensive label space, often comprising thousands to millions of possible categories. This classification task is pivotal across various domains, including e-commerce product tagging, news categorization, medical code assignment, and legal document analysis, where accurate multi-label predictions drive search efficiency, recommendation systems, and regulatory compliance. However, LMTC poses significant challenges, the dynamic nature of label sets, which traditional supervised learning approaches find difficult to address due to their reliance on annotated data. In light of this challenge, this work introduces a novel approach leveraging Large Language Models (LLMs) for dynamic label alignment in LMTC tasks, based on counterfactual analysis, called DyLas (<u>Dy</u>namic <u>L</u>abel <u>A</u>lignment <u>S</u>trategy). Through a multi-step strategy, we aim to mitigate the issues arising from dynamic label sets. We evaluate the performance of LMTC on the 8 LLMs by 4 datasets and apply DyLas to 3 closed-source and 3 open-weight LLMs. Compared to the single-step approach, our method, DyLas, achieves improvements in almost all metrics across the datasets. Our method can also work well in dynamic label set environments. This work not only demonstrates the potential of LLMs to address complex classification challenges, but is also, to the best of our knowledge, the first to address dynamic label set challenges in LMTC tasks with LLMs without requiring additional model training.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103081"},"PeriodicalIF":14.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629350","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":"An ensemble clustering method via learning the CA matrix with fuzzy neighbors","authors":"Zekang Bian ,&nbsp;Linbiao Yu ,&nbsp;Jia Qu ,&nbsp;Zhaohong Deng ,&nbsp;Shitong Wang","doi":"10.1016/j.inffus.2025.103105","DOIUrl":"10.1016/j.inffus.2025.103105","url":null,"abstract":"<div><div>Although existing studies have confirmed that ensemble clustering methods based on co-association (CA) have been widely employed successfully, they still have the following drawback: the clustering performance and stability of ensemble clustering results heavily depend on the CA matrix. To enhance clustering performance while maintaining the stability of ensemble clustering results, an ensemble clustering method via learning the CA matrix with fuzzy neighbors (EC–CA–FN) is proposed in this study. First, EC–CA–FN constructs an accurate CA matrix by using both intra-cluster and inter-cluster relationships of pairwise samples from all base clustering results. Second, to improve the stability of ensemble clustering results, EC–CA–FN introduces a fuzzy index and the rank constraints on the constructed accurate CA matrix. This method invents a new ensemble clustering framework that learns the optimal fuzzy CA (FCA) matrix by adaptively assigning fuzzy neighbors of samples, thus obtaining the optimal clustering structure. Third, an alternative optimization method and weighting mechanism are adopted to achieve the optimal FCA matrix and adaptively assign all base clustering results. The experimental results on all adopted datasets indicate the effectiveness of EC–CA–FN in terms of both clustering performance and the stability of ensemble clustering results.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103105"},"PeriodicalIF":14.7,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601460","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":"A reliability-enhanced Brain–Computer Interface via Mixture-of-Graphs-driven Information Fusion","authors":"Bo Dai ,&nbsp;Yijun Wang ,&nbsp;Xinyu Mou ,&nbsp;Xiaorong Gao","doi":"10.1016/j.inffus.2025.103069","DOIUrl":"10.1016/j.inffus.2025.103069","url":null,"abstract":"<div><div>Reliable Brain-Computer Interface (BCI) systems are essential for practical applications. Current BCIs often suffer from performance degradation due to environmental noise and external interference. These environmental factors significantly compromise the quality of EEG data acquisition. This study presents a novel Mixture-of-Graphs-driven Information Fusion (MGIF) framework to enhance BCI system robustness through the integration of multi-graph knowledge for stable EEG representations. Initially, the framework constructs complementary graph architectures: electrode-based structures for capturing spatial relationships and signal-based structures for modeling inter-channel dependencies. Subsequently, the framework employs filter bank-driven multi-graph constructions to encode spectral information and incorporates a self-play-driven fusion strategy to optimize graph embedding combinations. Finally, an adaptive gating mechanism is implemented to monitor electrode states and enable selective information fusion, thereby minimizing the impact of unreliable electrodes and environmental disturbances. Extensive evaluations through offline datasets and online experiments validate the framework’s effectiveness. Results demonstrate that MGIF achieves significant improvements in BCI reliability across challenging real-world environments.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103069"},"PeriodicalIF":14.7,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635810","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":"Heterogeneous graph neural networks with post-hoc explanations for multi-modal and explainable land use inference","authors":"Xuehao Zhai ,&nbsp;Junqi Jiang ,&nbsp;Adam Dejl ,&nbsp;Antonio Rago ,&nbsp;Fangce Guo ,&nbsp;Francesca Toni ,&nbsp;Aruna Sivakumar","doi":"10.1016/j.inffus.2025.103057","DOIUrl":"10.1016/j.inffus.2025.103057","url":null,"abstract":"<div><div>Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use inference. However, existing studies often process samples independently, ignoring the spatial correlations among neighbouring objects and heterogeneity among different services. Furthermore, the inherently low interpretability of complex deep learning methods poses a significant barrier in urban planning, where transparency and extrapolability are crucial for making long-term policy decisions. To overcome these challenges, we introduce an explainable framework for inferring land use that synergises heterogeneous graph neural networks (HGNs) with Explainable AI techniques, enhancing both accuracy and explainability. We evaluate the proposed approach on three cities with different urban layout and mobility combinations: London (with tube, bus and bike sharing data sources), San Francisco (parking and bike sharing), and New York City (metro and bike sharing). The empirical experiments demonstrate that the proposed HGNs significantly outperform baseline graph neural networks for all six land use indicators, especially in terms of ‘office’ and ‘sustenance’. We then deploy feature attribution (at both temporal and spacial levels) and counterfactual explanations, which shed light on several important findings. The node-based feature attribution explanations show that the symmetrical nature of the ‘residence’ and ‘work’ categories predicted by the framework aligns well with the commuters’ ‘work’ and ‘recreation’ activities. Meanwhile, the spatial feature attribution explanations indicates that the heightened central importance of commercial categories and the dominance of residential influences in outer zones align closely with typical urban structures. Finally, the counterfactual explanations reveal that variations in node features and types are primarily responsible for the differences observed between the predicted land use distribution and the ideal mixed state. These analyses demonstrate that the proposed HGNs can suitably support urban stakeholders in their urban planning and policy-making. The source code is available at <span><span>https://github.com/xuehao0806/GNN-land-use</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103057"},"PeriodicalIF":14.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679500","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":"Horizontal multi-party data publishing via discriminator regularization and adaptive noise under differential privacy","authors":"Pengfei Zhang ,&nbsp;Xiang Fang ,&nbsp;Zhikun Zhang ,&nbsp;Xianjin Fang ,&nbsp;Yining Liu ,&nbsp;Ji Zhang","doi":"10.1016/j.inffus.2025.103046","DOIUrl":"10.1016/j.inffus.2025.103046","url":null,"abstract":"<div><div>With the rapid proliferation of data collection and storage technologies, the growing demand for horizontal multi-party data publishing has created an urgent need for robust privacy-preserving mechanisms that can effectively handle sensitive distributed data across multiple organizations. While existing approaches attempt to address this challenge, they often fail to balance privacy protection with data utility, struggle to achieve effective information fusion across heterogeneous data distributions, and incur significant computational overhead. In this paper, we introduce the <em>NATION</em> approach, an innovative GAN-based framework that advances multi-party data publishing through sophisticated information fusion techniques while maintaining stringent differential privacy guarantees and computational efficiency. In <em>NATION</em>, we modify the traditional GAN architecture through a distributed design where multiple discriminators are strategically allocated across parties while centralizing the generator at a semi-trusted server, enabling seamless fusion of distributed knowledge with minimal computational cost. Building on this foundation, we introduce two key technical innovations: an iterative-aware adaptive noise <em>IAN</em> method that dynamically optimizes noise injection based on training convergence, and a global-aware discriminator regularization <em>GDR</em> method that leverages Bregman Divergence to enhance inter-discriminator information exchange while ensuring model stability. Through comprehensive theoretical analysis and extensive experimental evaluation on real-world datasets, we demonstrate that <em>NATION</em> consistently outperforms state-of-the-art approaches by up to 7% in accuracy while providing provable privacy guarantees, which makes a significant advancement in secure GAN-based information fusion for privacy-sensitive applications.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103046"},"PeriodicalIF":14.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592991","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":"Hyperbolic graph attention network fusing long-context for technical keyphrase extraction","authors":"Yushan Zhao ,&nbsp;Kuan-Ching Li ,&nbsp;Shunxiang Zhang ,&nbsp;Tongzhou Ye","doi":"10.1016/j.inffus.2025.103061","DOIUrl":"10.1016/j.inffus.2025.103061","url":null,"abstract":"<div><div>Technical Keyphrase Extraction (TKE) is crucial for summarizing the core content of scientific and technical texts. Existing keyphrase extraction models typically focus on calculating phrase and sentence correlations that can limit their ability to understand long contexts and uncover hierarchical semantic information, leading to biased results. To address these limitations, a hyperbolic graph technical attention network is designed and applied to a novel unsupervised Technical KeyPhrase Extraction (TKPE) model, achieving the fusion of complex hierarchical semantic representations and long-context information by constructing global embeddings of the technical text in hyperbolic space for high-fidelity representation with minimal dimensions. A technical attention score is calculated based on technical terminology degree and hierarchical relevance to guide the extraction process. Additionally, the network utilizes geodesic variations between embedded nodes to reveal meaningful hierarchical clustering relationships, thus enabling semantic structural understanding of technical text data and efficient extraction of the most relevant technical keyphrases. This work exploits the long-context understanding capability of large language models to generate candidate phrases guided by an effective prompt template that reduces information loss when importing candidate phrases in a hyperbolic graph attention network. Experiments performed on benchmark technical datasets demonstrate that the proposed model outperforms recent state-of-the-art baseline keyphrase extraction models.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103061"},"PeriodicalIF":14.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583088","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":"Unveiling node relationships for traffic forecasting: A self-supervised approach with MixGT","authors":"Qiang Lai,&nbsp;Peng Chen","doi":"10.1016/j.inffus.2025.103070","DOIUrl":"10.1016/j.inffus.2025.103070","url":null,"abstract":"<div><div>In traffic forecasting, a key challenge lies in capturing both long-term temporal dependencies and inter-node relationships. While recent work has addressed long-term dependencies using Transformer-based models, the handling of inter-node relationships remains limited. Most studies rely on predefined or adaptive adjacency matrices, which fail to capture rich, dynamic relationships such as traffic similarity and strength, features embedded in time-varying data and challenging to model effectively. To comprehensively understand and leverage these inter-node relationships, we propose a unified framework: Pretrained Graph Transformer (PreGT) and Mix Graph Transformer (MixGT). PreGT, through self-supervised masking and reconstruction of nodes, learns latent representations of inter-node relationships from time-varying node features. MixGT integrates relationship matrix construction and utilization modules, effectively leveraging the latent representations from PreGT through graph convolution and attention mechanisms to enhance the model’s ability to capture dynamic inter-node relationship features. Experimental validation on real traffic flow datasets demonstrates the effectiveness of our framework in predicting traffic flow by accurately capturing inter-node relationships.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103070"},"PeriodicalIF":14.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579946","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":"SSLMM: Semi-Supervised Learning with Missing Modalities for Multimodal Sentiment Analysis","authors":"Yiyu Wang ,&nbsp;Haifang Jian ,&nbsp;Jian Zhuang ,&nbsp;Huimin Guo ,&nbsp;Yan Leng","doi":"10.1016/j.inffus.2025.103058","DOIUrl":"10.1016/j.inffus.2025.103058","url":null,"abstract":"<div><div>Multimodal Sentiment Analysis (MSA) integrates information from text, audio, and visuals to understand human emotions, but real-world applications face two challenges: (1) expensive annotation costs reduce the effectiveness of fully supervised methods, and (2) missing modality severely impact model robustness. While there are studies addressing these issues separately, few focus on solving both within a single framework. In real-world scenarios, these challenges often occur together, necessitating an algorithm that can handle both. To address this, we propose a Semi-Supervised Learning with Missing Modalities (SSLMM) framework. SSLMM combines self-supervised learning, alternating interaction information, semi-supervised learning, and modality reconstruction to tackle label scarcity and modality missing simultaneously. Firstly, SSLMM captures latent structural information through self-supervised pre-training. It then fine-tunes the model using semi-supervised learning and modality reconstruction to reduce dependence on labeled data and improve robustness to modality missing. The framework uses a graph-based architecture with an iterative message propagation mechanism to alternately propagate intra-modal and inter-modal messages, capturing emotional associations within and across modalities. Experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS demonstrate that under the condition where the proportion of labeled samples and the missing modality rate are both 0.5, SSLMM achieves binary classification (negative vs. positive) accuracies of 80.2%, 81.7%, and 77.1%, respectively, surpassing existing methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"120 ","pages":"Article 103058"},"PeriodicalIF":14.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619909","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}