Information Fusion最新文献

{"title":"Code-driven programming prediction enhanced by LLM with a feature fusion approach","authors":"Shengyingjie Liu ,&nbsp;Jianxin Li ,&nbsp;Qian Wan ,&nbsp;Bo He ,&nbsp;Zhijun Huang ,&nbsp;Qing Li","doi":"10.1016/j.inffus.2026.104165","DOIUrl":"10.1016/j.inffus.2026.104165","url":null,"abstract":"<div><div>Programming education is essential for equipping individuals with digital literacy skills and developing the problem-solving abilities necessary for success in the modern workforce. In online programming tutoring systems, knowledge tracing (KT) techniques are crucial for programming prediction, as they monitor user performance and model user cognition. However, both universal and programming-specific knowledge transfer methods depend on traditional state-driven paradigms that indirectly predict programming outcomes based on users’ knowledge states. It does not align with the core objective of programming prediction, which is to determine whether submitted code can solve the question. To address this, we present the code-driven feature fusion KT (CFKT), which integrates large language models (LLM) and encoders for both individualized and common code features. It consists of two modules: pass prediction and code prediction. The pass prediction module leverages LLM to incorporate semantic information from the question and code through embedding, extracting key features that determine code correctness through proxy tasks and effectively narrowing the solution space with vectorization. The code prediction module integrates user historical data and data from other users through feature fusion blocks, allowing for accurate predictions of submitted code and effectively mitigating the cold start problem. Experiments on multiple real-world public programming datasets demonstrate that CFKT significantly outperforms existing baseline methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104165"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014543","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":"Crowdsourced federated learning with inconsistent label representation","authors":"Yunlong He,&nbsp;Fei Chen,&nbsp;Hanlin Zhang,&nbsp;Jia Yu","doi":"10.1016/j.inffus.2026.104194","DOIUrl":"10.1016/j.inffus.2026.104194","url":null,"abstract":"<div><div>When personalized federated learning meets crowdsourced label annotation, it can potentially form a complete ecosystem from large-scale data labeling, through model training in massive devices, toward flexible service for diverse end users. Actually, most common crowdsourced annotators can hardly follow a uniform annotation regulation and make the annotations in their own way. Even though they can share the cognitive consistency on the perception, the label annotation can still be expressed in various ways. This situation can be specifically serious in the federated learning scenario, in which the diverse label expressions are always kept locally in distributed clients for privacy concerns and can hardly be unified. In this work, we are motivated to propose CrowdFed, a systematic solution for crowdsourced federated learning systems with underlying label representation skew issue. Specifically, the global model is trained through federated learning for global categorical alignment, and the personalized layers are learned through an auxiliary network in each client for local representation alignment. Furthermore, a category-level similarity matching strategy is presented for the alignment of inconsistent label representations between the local category and the global category. Evaluated by four benchmark datasets, our proposed strategy proves its superiority in terms of system efficiency and cost.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104194"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089494","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":"GC-Fed: Gradient centralized federated learning with partial client participation","authors":"Jungwon Seo ,&nbsp;Ferhat Ozgur Catak ,&nbsp;Chunming Rong ,&nbsp;Kibeom Hong ,&nbsp;Minhoe Kim","doi":"10.1016/j.inffus.2026.104148","DOIUrl":"10.1016/j.inffus.2026.104148","url":null,"abstract":"<div><div>Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but suffers from client drift in highly heterogeneous data settings. Many existing approaches mitigate drift by providing clients with common reference points, typically derived from past information, to align objectives or gradient directions. However, under severe partial participation, such history-dependent references may become unreliable, as the set of client data distributions participating in each round can vary drastically. To overcome this limitation, we propose a method that mitigates client drift without relying on past information by constraining the update space through Gradient Centralization (GC). Specifically, we introduce <span>Local GC</span> and <span>Global GC</span>, which apply GC at the local and global update stages, respectively, and further present <span>GC-Fed</span>, a hybrid formulation that generalizes both. Theoretical analysis and extensive experiments on benchmark FL tasks demonstrate that <span>GC-Fed</span> effectively alleviates client drift and achieves up to 20 % accuracy improvement under data heterogeneous and partial participation conditions.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104148"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962592","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":"Unleashing Mamba’s expressive power: A non-tradeoff approach to spatio-temporal forecasting","authors":"Zhiqi Shao ,&nbsp;Ze Wang ,&nbsp;Haoning Xi ,&nbsp;Michael G.H. Bell ,&nbsp;Xusheng Yao ,&nbsp;D. Glenn Geers ,&nbsp;Junbin Gao","doi":"10.1016/j.inffus.2026.104172","DOIUrl":"10.1016/j.inffus.2026.104172","url":null,"abstract":"<div><div>Real-time spatiotemporal forecasting, particularly in traffic systems, requires balancing computational cost and predictive accuracy-a challenge that conventional methods struggle to address effectively. In this work, we propose a non-trade-off framework called Spatial-Temporal Selective State Space (ST-Mamba), which leverages two key components to achieve both efficiency and accuracy concurrently. The Spatial-Temporal Mixer (ST-Mixer) dynamically fuses spatial and temporal features to capture complex dependencies, and the STF-Mamba layer incorporates Mamba’s selective state-space formulation to capture long-range dynamics efficiently. Beyond empirical improvements, we address a critical gap in the literature by presenting a theoretical analysis of ST-Mamba’s expressive power. Specifically, we establish its ability to approximate a broad class of Transformer and formally demonstrate its equivalence to at least two consecutive attention layers within the same framework. This result highlights ST-Mamba’s capacity to capture long-range dependencies while reducing computational overhead efficiently, reinforcing its theoretical and practical advantages over conventional transformer-based models. Through extensive evaluations of real-world traffic datasets, <span>ST-Mamba</span> demonstrates a 61.11% reduction in runtime alongside a 0.67% improvement in predictive performance compared to leading approaches, underscoring its potential to set a new benchmark for real-time spatiotemporal forecasting.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104172"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033290","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":"PromptMix: LLM-aided prompt learning for generalizing vision-language models","authors":"Yongcai Chen ,&nbsp;Qinghua Zhang ,&nbsp;Xinfa Shi ,&nbsp;Lei Zhang","doi":"10.1016/j.inffus.2026.104186","DOIUrl":"10.1016/j.inffus.2026.104186","url":null,"abstract":"<div><div>Intelligent engineering tasks step into real application with the development of deep learning techniques. However, performance in real conditions often falls into decline caused by scarce data, or subtle, easily confused patterns. Although vision-language models with prompt learning provide a new way for learning without retraining the backbone, these approaches still suffer from problems of overfitting under low-data regimes or poor expressive ability of prompts. To address these challenges, we propose a novel framework <em>PromptMix</em> that jointly considers semantic prompt learning, multimodal information fusion, and the alignment between pre-trained and domain-specific data. Specifically, PromptMix integrates three key components: (1) a <em>Modality-Agnostic Shared Representation</em> module to construct a shared latent space that mitigates the distribution discrepancies between pre-trained and target data, (2) a <em>LLM-Aided Prompt Evolution</em> mechanism to semantically enrich and iteratively refine learnable context prompts, and (3) a <em>Cross-Attentive Adapter</em> to enhance multimodal information fusion and robustness under low-sample conditions. Experiments on seven datasets, including six public benchmarks and one custom industrial dataset, demonstrate that PromptMix effectively enhances vision-language model adaptability, improves semantic representations, and achieves robust generalization under both base-to-novel and few-shot learning scenarios, delivering superior performance in engineering applications with limited labeled data.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104186"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033288","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":"Adversarial perturbation for RGB-T tracking via intra-modal excavation and cross-modal collusion","authors":"Xinyu Xiang ,&nbsp;Xuying Wu ,&nbsp;Shengxiang Li ,&nbsp;Qinglong Yan ,&nbsp;Tong Zou ,&nbsp;Hao Zhang ,&nbsp;Jiayi Ma","doi":"10.1016/j.inffus.2026.104183","DOIUrl":"10.1016/j.inffus.2026.104183","url":null,"abstract":"<div><div>Existing adversarial perturbation attack for visual object trackers mainly focuses on RGB modality, yet research on RGB-T trackers’ adversarial perturbation remains unexplored. To address this gap, we propose an <strong>I</strong>ntra-modal excavation and <strong>C</strong>ross-modal collusion adversarial perturbation attack algorithm (ICAttack) for RGB-T Tracking. Firstly, we establish a novel intra-modal adversarial clues excavation (ImAE) paradigm. By leveraging the unique distribution properties of each modality as a prior, we independently extract the attack cues of different modalities from the public noise space. Building upon this, we develop a cross-modal adversarial collusion (CmAC) strategy, which enables implicit and dynamic interaction between the adversarial tokens of two modalities. This interaction facilitates negotiation and collaboration, achieving a synergistic attack gain for RGB-T trackers that surpasses the effect of a single-modality attack. The above process, from intra-modal excavation to cross-modal collusion, creates a progressive and systematic attack framework for RGB-T trackers. Besides, by introducing the spatial adversarial intensity control module and precise response disruption loss, we further enhance both the attack stealthiness and precision of our adversarial perturbations. The control module reduces attack strength in less critical areas to improve stealth. The disruption loss uses a small mask on the tracker’s brightest semantic response region, concentrating the perturbation to interfere with the tracker’s target awareness precisely. Extensive evaluations of attack performances in different SOTA victimized RGB-T trackers demonstrate the advantages of ICAttack in terms of specificity and effectiveness of cross-modal attacks. Moreover, we offer a user-friendly interface to promote the practical deployment of adversarial perturbations. Our code is publicly available at <span><span>https://github.com/Xinyu-Xiang/ICAttack</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104183"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033285","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 adaptive regularized topological segmentation network integrating inter-class relations and occlusion information for vehicle component recognition","authors":"Xunqi Zhou ,&nbsp;Zhenqi Zhang ,&nbsp;Zifeng Wu ,&nbsp;Qianming Wang ,&nbsp;Jing Teng ,&nbsp;Jinlong Liu ,&nbsp;Yongjie Zhai","doi":"10.1016/j.inffus.2026.104157","DOIUrl":"10.1016/j.inffus.2026.104157","url":null,"abstract":"<div><div>In intelligent vehicle damage assessment, component recognition faces challenges such as significant intra-class variability and minimal inter-class differences, which hinder detection, as well as occlusions and ambiguous boundaries, which complicate segmentation. We generalize these problems into three core aspects: inter-object relational modeling, semantic-detail information balancing, and occlusion-aware decoupling. To this end, we propose the Adaptive Regularized Topological Segmentation (ARTSeg) network, comprising three complementary modules: Inter-Class Graph Constraint (ICGC), Constrained Detail Feature Backtracking (CDFB), and Topological Decoupling Segmentation (TDS). Each module is purposefully designed, integrated in a progressive structure, and synergistically reinforces the others to enhance overall performance. Specifically, ICGC clusters intra-class features and establishes implicit topological constraints among categories during feature extraction, enabling the model to better capture inter-class relationships and improve detection representation. Subsequently, CDFB evaluates the impact of channel-wise feature information within each candidate region on segmentation accuracy and computational cost, dynamically selecting appropriate feature resolutions for individual instances while balancing the demands of detection and segmentation tasks. Finally, TDS introduces topological associations between occluded and occluding regions at the feature level and decouples them at the task level, explicitly modeling generalized occlusion regions and enhancing segmentation performance. We quantitatively and qualitatively evaluate ARTSeg on a 59-category vehicle component dataset constructed for insurance damage assessment, achieving notable improvements in addressing the aforementioned problems. Experiments on two public datasets, DSMLR and Carparts, further validate the generalization capability of the proposed method. Results indicate that ARTSeg provides practical guidance for component recognition in intelligent vehicle damage assessment.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104157"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995203","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":"Multiple channel access and power control for discount-average weighting criterion over multi-sensor and Markovian fading environments","authors":"Yunbo Song ,&nbsp;Jianrong Zhao ,&nbsp;Kangkai Zheng ,&nbsp;Ticao Jiao","doi":"10.1016/j.inffus.2026.104191","DOIUrl":"10.1016/j.inffus.2026.104191","url":null,"abstract":"<div><div>This paper investigates the joint design of multiple channel access and power control for multi-sensor remote estimation. Smart sensors with energy constraints transmit their local estimates over sharing Markovian fading channels. A novel discount-average weighting criterion (DAWC) is introduced in the infinite horizon, which balances immediate and long-term transmission performance, unlike traditional criteria that focus on one aspect. We formulate the co-design issue including channel selection and power allocation as a Markov decision process (MDP) with DAWC. The existence of ϵ-optimal policy is presented for ergodic MDP via a model checking method, and the switch-like optimal transmission policy is derived from the set of randomized Markov strategies. Further, we prove the existence of ϵ-s-optimal policy that is an ultimately deterministic policy for general MDP. An elaborately devised algorithm is employed to generate optimal transmission decisions utilizing a forward iterative approach. Finally, an example of turbofan engine speed regulation is applied to demonstrate the superiority of previous results.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104191"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071717","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":"Large multimodal models for low-resource languages: A survey","authors":"Marian Lupaşcu,&nbsp;Ana-Cristina Rogoz,&nbsp;Mihai Sorin Stupariu,&nbsp;Radu Tudor Ionescu","doi":"10.1016/j.inffus.2026.104189","DOIUrl":"10.1016/j.inffus.2026.104189","url":null,"abstract":"<div><div>In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehensive analysis of 117 studies across 96 LR languages, we identify key patterns in how researchers tackle the challenges of limited data and computational resources. We categorize works into resource-oriented and method-oriented contributions, further dividing contributions into relevant sub-categories. We compare method-oriented contributions in terms of performance and efficiency, discussing benefits and limitations of representative studies. We find that visual information often serves as a crucial bridge for improving model performance in LR settings, though significant challenges remain in areas such as hallucination mitigation and computational efficiency. In summary, we provide researchers with a clear understanding of current approaches and remaining challenges in making LMMs more accessible to speakers of LR (understudied) languages. We complement our survey with an open-source repository available at: <span><span>https://github.com/marianlupascu/LMM4LRL-Survey</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104189"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071718","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":"Adaptive virtual anchors for efficient and stable clustering over large multi-view attributed graphs","authors":"Mengyao Li ,&nbsp;Zhibang Yang ,&nbsp;Xu Zhou ,&nbsp;Joey Tianyi Zhou ,&nbsp;Quanqing Xu ,&nbsp;Chuanhui Yang ,&nbsp;Kenli Li ,&nbsp;Keqin Li","doi":"10.1016/j.inffus.2026.104190","DOIUrl":"10.1016/j.inffus.2026.104190","url":null,"abstract":"<div><div>Multi-view attributed graphs (MVAG) are well-known for their ability to model complex networks and relationships, which can provide diverse yet complementary information for finding a consensus partition suitable for all views. There have been abundant methods for clustering over multi-view attributed graphs. However, most of them are not suitable for large-scale graphs due to high complexity. Moreover, while existing anchor-based methods can effectively accelerate clustering, they mainly focus on either attribute information or graph structure during anchor selection, and some suffer from stability issues. Inspired by this, in this paper, we propose the adaptive virtual anchor clustering method (AVAC) to boost clustering performance and keep stable results. In particular, we first introduce adaptive virtual anchors for multi-view attributed graphs, which are learned and generated from graphs adaptively. After that, we connect anchor learning and anchor graph construction closely and cyclically to learn virtual anchors dynamically and make them capture real data distribution and topology information more accurately. Last but not least, we design a five-block coordinate descent method with proven convergence to further optimize our virtual anchors more representative of existing nodes. Extensive experiments over both real and synthetic datasets demonstrate the effectiveness, efficiency, and stability of our method. Compared to state-of-the-art approaches, the AVAC algorithm always gains stable results with a significant improvement in accuracy, and achieves a speedup of 1.8 times on public large-scale datasets. The source code is available at <span><span>https://github.com/lmyfree/AVAC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104190"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072489","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}