Information Fusion最新文献

{"title":"STATIC-LIO: A sliding window and terrain-assisted dynamic points removal LiDAR Inertial Odometry","authors":"Xuzhe Duan ,&nbsp;Qingwu Hu ,&nbsp;Mingyao Ai ,&nbsp;Pengcheng Zhao ,&nbsp;Meng Wu ,&nbsp;Jiayuan Li ,&nbsp;Chao Xiong","doi":"10.1016/j.inffus.2025.103132","DOIUrl":"10.1016/j.inffus.2025.103132","url":null,"abstract":"<div><div>With the development of diverse Light Detection and Ranging (LiDAR) sensors, LiDAR-based localization and mapping has become an essential issue in the fields of robotics and autonomous driving. However, the moving object in dynamic environments often introduces errors in LiDAR localization and leaves undesirable traces in the point cloud map. In this work, we propose a novel LiDAR inertial odometry (LIO) framework named STATIC-LIO, Sliding window and Terrain AssisTed dynamIC points removal LiDAR Inertial Odometry, which fuses the geometric, terrestrial, and motion information to enhance the localization and mapping performance. The terrestrial information is extracted through a fast progressive ground segmentation module designed to be compatible with various LiDARs. With the assistance of the terrestrial information, an online dynamic point voting mechanism is proposed to determine the motion information and remove the dynamic points in a point-wise manner. The ground segmentation and dynamic points removal modules are coupled within the sliding window-based STATIC-LIO framework to estimate odometry by leveraging geometric correspondences from ground and static points. We extensively evaluate the proposed framework on both public and real-world datasets encompassing a variety of LiDAR types. The experimental results demonstrate the effectiveness of STATIC-LIO across various datasets and applications, showcasing its superior accuracy by reducing localization errors by up to 92.4% compared to the state-of-the-art LIO framework.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103132"},"PeriodicalIF":14.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715390","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":"AMF-VSN: Adaptive multi-process fusion video steganography based on invertible neural networks","authors":"Yangwen Zhang ,&nbsp;Yuling Chen ,&nbsp;Hui Dou ,&nbsp;Yan Meng ,&nbsp;Haojin Zhu","doi":"10.1016/j.inffus.2025.103130","DOIUrl":"10.1016/j.inffus.2025.103130","url":null,"abstract":"<div><div>The security challenges in information transmission have attracted considerable research focus, particularly in the field of video steganography. Although deep learning advancements have created new research opportunities for video steganography, current models encounter deployment difficulties on mobile devices due to their substantial parameter requirements, which restrict their adaptability to mobile platform constraints. To address these challenges, we propose an adaptive multi-process fusion video steganography based on invertible neural networks. This model incorporates flexible balance adjustment factor and Multi Cross Stage Partial Dense (MCSPDense) Block, which adjusts the parameter count of the MCSPDense Block and the quality of the stego video through the flexible balance adjustment factor. Additionally, the Simple Redundancy Prediction Module (SRPM) has been designed to further minimize model parameters while enhancing the quality of video steganography and restoration. Furthermore, we develop two operational modes to accommodate varying mobile device requirements: Secure Communication (SC) mode for enhanced transmission security and High Quality Recovery (HQR) mode for superior video restoration. Experimental results confirm that compared to existing solutions, our AMF-VSN framework has improved steganography and recovery performance by 3.474 dB and 2.521 dB respectively, reduced parameters by 66.08%, and maintained strong security in mobile deployment scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103130"},"PeriodicalIF":14.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739986","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":"Reflections and attentiveness on eXplainable Artificial Intelligence (XAI). The journey ahead from criticisms to human–AI collaboration","authors":"Francisco Herrera","doi":"10.1016/j.inffus.2025.103133","DOIUrl":"10.1016/j.inffus.2025.103133","url":null,"abstract":"<div><div>The emergence of deep learning over the past decade has driven the development of increasingly complex AI models, amplifying the need for Explainable Artificial Intelligence (XAI). As AI systems grow in size and complexity, ensuring interpretability and transparency becomes essential, especially in high-stakes applications. With the rapid expansion of XAI research, addressing emerging debates and criticisms requires a comprehensive examination. This paper explores the complexities of XAI from multiple perspectives, proposing six key axes that shed light on its role in human–AI interaction and collaboration. First, it examines the imperative of XAI under the dominance of black-box AI models. Given the lack of definitional cohesion, the paper argues that XAI must be framed through the lens of audience and understanding, highlighting its different uses in AI–human interaction. The recent BLUE vs. RED XAI distinction is analyzed through this perspective. The study then addresses the criticisms of XAI, evaluating its maturity, current trajectory, and limitations in handling complex problems. The discussion then shifts to explanations as a bridge between AI models and human understanding, emphasizing the importance of usability of explanations in human–AI decision making. Key aspects such as AI reliance, human intuition, and emerging collaboration theories — including the human-algorithm centaur and co-intelligence paradigms — are explored in connection with XAI. The medical field is considered as a case study, given its extensive research on collaboration between doctors and AI through explainability. The paper proposes a framework to evaluate the maturity of XAI using three dimensions: practicality, auditability, and AI governance. Provide the final lessons learned focused on trends and questions to tackle in the near future. This is an in-depth exploration of the impact and urgency of XAI in the era of pervasive expansion of AI. Three Key reflections from this study include: (a) XAI must enhance cognitive engagement with explanations, (b) it must evolve to fully address why, what, and for what purpose explanations are needed, and (c) it plays a crucial role in building societal trust in AI. By advancing XAI in these directions, we can ensure that AI remains transparent, auditable, and accountable, and aligned with human needs.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103133"},"PeriodicalIF":14.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739985","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":"PPSTSL: A Privacy-preserving Dynamic Spatio-temporal Graph Data Federated Split Learning for traffic forecasting","authors":"Yan Feng ,&nbsp;Quan Qian","doi":"10.1016/j.inffus.2025.103129","DOIUrl":"10.1016/j.inffus.2025.103129","url":null,"abstract":"<div><div>In intelligent transportation, federated learning has garnered significant attention for its privacy protection and model optimization capabilities. However, existing approaches still struggle with high computational and communication costs. Moreover, challenges such as the uneven distribution of traffic nodes and time sequences, global dynamic spatio-temporal correlation, and security concerns in the spatio-temporal modeling process remain insufficiently addressed in distributed transportation research. To tackle these challenges, this study proposed a Privacy-Preserving Dynamic Spatio-temporal Graph Data Federated Split Learning (PPSTSL) method. By incorporating a split learning framework for spatio-temporal modeling under privacy protection, PPSTSL enables the integration of multi-client data to enhance global model performance while mitigating computational and communication overhead. Specifically, we present Federated Distribution-Aligned Temporal Dependency Modeling (FedDATDep) to optimize the model parameters and enrich temporal features. Additionally, we design Federated Spatio-Temporal Fusion (FedTSFus) to achieve global dynamic spatio-temporal dependencies while preserving privacy. Furthermore, we propose a Positive-Negative Coupled Coding (PNCC) mechanism to enhance the computational and communication efficiency of the introduced security techniques. Experimental results show that PPSTSL achieves superior model performance in both independent and non-independent identical distribution scenarios (IID and Non-IID) on the SZ-Taxi and Los-Loop traffic datasets, while demonstrating strong scalability. Furthermore, the optimized security techniques not only enhance privacy protection but also reduce communication and computational overhead. The proposed PPSTSL approach addresses crucial limitations in existing distributed research methods in intelligent transportation, presenting a promising direction for future research and practical implementations.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103129"},"PeriodicalIF":14.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725257","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":"Triplets-based large-scale multi-view spectral clustering","authors":"Tianchuan Yang ,&nbsp;Chang-Dong Wang ,&nbsp;Jipeng Guo ,&nbsp;Xiangcheng Li ,&nbsp;Man-Sheng Chen ,&nbsp;Shuping Dang ,&nbsp;Haiqiang Chen","doi":"10.1016/j.inffus.2025.103134","DOIUrl":"10.1016/j.inffus.2025.103134","url":null,"abstract":"<div><div>By integrating complementary information from multiple views to reach consensus, graph-based multi-view clustering describes data structure competently, thereby attracting considerable attention. It is time-bottlenecked by graph construction and eigen-decomposition in the big data era. Existing methods usually utilize anchor graph learning to address this issue. However, problems of unsupervised representative anchor selection, consensus or view-specific anchors, anchor alignment, etc., remain challenging. Moreover, excessive information is discarded for the sake of efficiency. Motivated by the essence of the anchor-based methods that utilizing representative point-to-point relations to reduce graph complexity, we generate triplets for each view based on neighborhood similarity to preserve point-to-point relations and local structure, and propose triplets-based large-scale multi-view spectral clustering (TLMSC). Subsequently, the triplet enhancement strategy is designed to select representative triplet relations to improve efficiency and clustering performance. Specifically, positive examples of triplets are filtered according to the view consensus to significantly increase the probability of positive examples belonging to the same cluster. The most indistinguishable hard negative examples are generated based on probabilities to improve discrimination performance. Guided by the enhanced triplets and its weights, an improved low-dimensional embedding is constructed through optimization, which further serves as an input to the proposed fast sparse spectral clustering (FSSC) to obtain clustering results. Numerous experiments validate the efficiency and superior performance of the proposed TLMSC. An average improvement of 12.25% at least in ACC compared to 10 state-of-the-art methods on 18 datasets. The code is available at <span><span>github.com/ytccyw/TLMSC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"121 ","pages":"Article 103134"},"PeriodicalIF":14.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715392","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 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}