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A maximum satisfaction-based feedback mechanism for non-cooperative behavior management with agreeableness personality traits detection in group decision making Inform. Fusion (IF 14.7) Pub Date : 2025-02-01 Yujia Liu, Yuwei Song, Jian Wu, Changyong Liang, Francisco Javier Cabrerizo
Non-cooperative behaviors will lead to consensus failure in group decision making problems. As a result, managing non-cooperative behavior is a significant challenge in group consensus reaching processes, which involves two main research questions:(1) How to define non-cooperative behavior? (2) How to design an appropriate model to manage non-cooperative behavior? Existing studies often overlook the
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Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining Inform. Fusion (IF 14.7) Pub Date : 2025-01-31 Haoqian Chang, Xiangqian Wang, Alexandra I. Cristea, Xiangrui Meng, Zuxiang Hu, Ziqi Pan
Accurate prediction of gas concentrations at longwall mining faces is critical for safety production, yet current methods still face challenges in interpretability and reliability. This study aims to enhance prediction accuracy and model interpretability by employing advanced feature selection techniques. We integrate Shapley Additive Explanations (SHAP) into feature selection process to identify and
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Multi-agent reinforcement learning with weak ties Inform. Fusion (IF 14.7) Pub Date : 2025-01-29 Huan Wang, Xu Zhou, Yu Kang, Jian Xue, Chenguang Yang, Xiaofeng Liu
Existing multi-agent reinforcement learning (MARL) algorithms focus primarily on maximizing global game gains or encouraging cooperation between agents, often overlooking the weak ties between them. In multi-agent environments, the quality of the information exchanged is crucial for optimal policy learning. To this end, we propose a novel MARL framework that integrates weak-tie theory with graph modeling
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Counterfactual explanations for remaining useful life estimation within a Bayesian framework Inform. Fusion (IF 14.7) Pub Date : 2025-01-28 Jilles Andringa, Marcia L. Baptista, Bruno F. Santos
Machine learning has contributed to the advancement of maintenance in many industries, including aviation. In recent years, many neural network models have been proposed to address the problems of failure identification and estimating the remaining useful life (RUL). Nevertheless, the black-box nature of neural networks often limits their transparency and interpretability. Interpretability (or explainability)
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Multimodal Document Analytics for Banking Process Automation Inform. Fusion (IF 14.7) Pub Date : 2025-01-27 Christopher Gerling, Stefan Lessmann
Traditional banks are increasingly challenged by FinTechs, particularly in leveraging advanced technologies to enhance operational efficiency. Our study addresses this by focusing on improving the efficiency of document-intensive business processes in banking. We review the landscape of business documents in the customer banking segment, which often includes text, layout, and visuals, indicating that
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SenCounter: Towards category-agnostic action counting in open sensor sequences Inform. Fusion (IF 14.7) Pub Date : 2025-01-27 Shuangshuang Cao, Yanwen Wu, Yin Tang, Di Ge, Yanmei Ma, Cong Xiao
Repetition counting of multiple actions in sensor-based data is a critical task for human-centric applications like health monitoring and exercise training. Existing sensor-based repetition counting (SRC) methods are limited to single-action scenarios and predefined categories, which do not scale well in real-world scenarios. To address this, we introduce the Open Sensor Sequences Counting (OSSC) task
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Unfolding coupled convolutional sparse representation for multi-focus image fusion Inform. Fusion (IF 14.7) Pub Date : 2025-01-25 Kecheng Zheng, Juan Cheng, Yu Liu
Multi-focus image fusion (MFIF) aims to generate an all-in-focus image from multiple partially focused images of the same scene captured with different focal settings. In this paper, we present a coupled convolutional sparse representation (CCSR) model for MFIF. Instead of being solved by an iterative thresholding algorithm, the proposed CCSR model is unfolded into a learnable neural network (termed
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Explainable multi-frequency and multi-region fusion model for affective brain-computer interfaces Inform. Fusion (IF 14.7) Pub Date : 2025-01-24 Tao Wang, Rui Mao, Shuang Liu, Erik Cambria, Dong Ming
An affective brain-computer interface (aBCI) has demonstrated great potential in the field of emotion recognition. However, existing aBCI models encounter significant challenges in explainability and the effective fusion of multi-frequency and multi-region features, which greatly limits their practical applicability. To address these issues, this paper proposes an explainable multi-frequency and multi-region
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Hallucinations of large multimodal models: Problem and countermeasures Inform. Fusion (IF 14.7) Pub Date : 2025-01-23 Shiliang Sun, Zhilin Lin, Xuhan Wu
The integration of multimodal capabilities into large models has unlocked unprecedented potential for tasks that involve understanding and generating diverse data modalities, including text, images, and audio. However, despite these advancements, such systems often suffer from hallucinations, that is, inaccurate, irrelevant, or entirely fabricated contents, which raise significant concerns about their
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Personalized trust incentive mechanisms with personality characteristics for minimum cost consensus in group decision making Inform. Fusion (IF 14.7) Pub Date : 2025-01-23 Yumei Xing, Jian Wu, Francisco Chiclana, Sha Wang, Zhaoguang Zhu
Traditional group decision making is usually to force inconsistent decision-makers (Namely, decision makers whose consensus degree does not reach a predefined level/consensus threshold.) to revise their opinions in order to improve the group consensus level. But decision-makers with conservative, neutral and radical behaviors differ in the extent to which they adjust their opinions. Hence, this paper
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MAFCD: Multi-level and adaptive conditional diffusion model for anomaly detection Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Zhichao Wu, Li Zhu, Zitao Yin, Xirong Xu, Jianmin Zhu, Xiaopeng Wei, Xin Yang
In the real-world Internet of Things (IoT) systems, a variety of Internet-connected sensory devices, spanning from chemical processing equipment to material handling machinery and server machines are typically monitored with multivariate time series. Anomaly detection in these systems is pivotal for identifying potentially dangerous or unsafe conditions and implementing timely preventive measures.
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STV[formula omitted]: Skip connection driven Two-stream property fusion Variational AutoEncoder for cross-region wastewater treatment plant semantic segmentation Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Yuze Li, Yan Zhang, Sukanya Randhawa, Chunling Yang, Alexander Zipf
Wastewater treatment plant (WWTP) plays a crucial role in achieving social sustainable development goals. Precise information on WWTPs obtained through advanced semantic segmentation technologies benefits multiple applications, including urban planning, environmental protection and public health. However, the diverse architectural styles, scales and surroundings of WWTPs across regions, influenced
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Semi-Supervised Social Bot Detection with Relational Graph Attention Transformers and Characteristics of the social environment Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Di Huang, Jinbao Song, Xingyu Zhang
Social bot detection is an important and challenging task in social network analysis and maintaining social network security. In recent years, graph neural networks (GNNs) have been widely used and applied to social bot detection research, effectively improving the performance of detection methods. However, current graph-based methods rarely extract and fuse the rich relationship information contained
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Federated graph transformer with mixture attentions for secure graph knowledge fusions Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Zhi Li, Chaozhuo Li, Ming Li, Liqun Yang, Jian Weng
Federated learning offers a framework for collaborative machine learning without compromising data privacy, an especially critical feature when dealing with sensitive graph-structured data in fields like social networks and healthcare. Despite recent advancements, traditional Federated Graph Neural Networks (FedGNNs) struggle to handle dynamic, heterogeneous graph structures and fail to capture long-range
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ViTs as backbones: Leveraging vision transformers for feature extraction Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Omar Elharrouss, Yassine Himeur, Yasir Mahmood, Saed Alrabaee, Abdelmalik Ouamane, Faycal Bensaali, Yassine Bechqito, Ammar Chouchane
The emergence of Vision Transformers (ViTs) has marked a significant shift in the field of computer vision, presenting new methodologies that challenge traditional convolutional neural networks (CNNs). This review offers a thorough exploration of ViTs, unpacking their foundational principles, including the self-attention mechanism and multi-head attention, while examining their diverse applications
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LUTBIO: A Comprehensive multimodal biometric database targeting middle-aged and elderly populations for enhanced identity authentication Inform. Fusion (IF 14.7) Pub Date : 2025-01-22 Rui Yang, Qiuyu Zhang, Lingtao Meng, Chunxia Wang, Yingjie Hu
Multimodal biometric databases are critical for enhancing the security and accuracy of identity authentication systems and advancing research in multimodal fusion. However, developing these databases is challenged by high acquisition costs, privacy concerns, and ownership issues. This paper introduces a multimodal biometric database named LUTBIO, which includes nine types of biometric data: voice,
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Adversarial evasion attacks detection for tree-based ensembles: A representation learning approach Inform. Fusion (IF 14.7) Pub Date : 2025-01-21 Gal Braun, Seffi Cohen, Lior Rokach
Research on adversarial evasion attacks primarily focuses on neural network models due to their popularity in fields such as computer vision and natural language processing, as well as their properties that facilitate the search for adversarial examples with minimal input changes. However, decision trees and tree ensembles, widely used for their high performance and interpretability in domains dominated
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Multi-modal disease segmentation with continual learning and adaptive decision fusion Inform. Fusion (IF 14.7) Pub Date : 2025-01-21 Xu Xu, Junxin Chen, Dipanwita Thakur, Duo Hong
Multi-modal disease segmentation is essential for the diagnosis and treatment of patients. Advanced algorithms have been proposed, however, two challenging issues remain unsolved, i.e., lacked knowledge share and limited modal relation. To this end, we develop a novel framework for multi-modal disease segmentation. It is based on improved continual learning and adaptive decision fusion. Specifically
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CD-UDepth: Complementary dual-source information fusion for underwater monocular depth estimation Inform. Fusion (IF 14.7) Pub Date : 2025-01-21 Jiawei Guo, Jieming Ma, Feiyang Sun, Zhiqiang Gao, Ángel F. García-Fernández, Hai-Ning Liang, Xiaohui Zhu, Weiping Ding
Underwater depth estimation is crucial for marine applications such as autonomous navigation and robotics. However, monocular depth estimation in underwater environments remains challenging due to the rapid attenuation of the red light spectrum in deep waters, causing bluish-green color distortion, while suspended particles and limited illumination lead to blurry effects. These underwater degradations
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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics Inform. Fusion (IF 14.7) Pub Date : 2025-01-20 Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria
The utilization of large language models (LLMs) for Healthcare has generated both excitement and concern due to their ability to effectively respond to free-text queries with certain professional knowledge. This survey outlines the capabilities of the currently developed Healthcare LLMs and explicates their development process, to provide an overview of the development road map from traditional Pretrained
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Conv-SdMLPMixer: A hybrid medical image classification network based on multi-branch CNN and multi-scale multi-dimensional MLP Inform. Fusion (IF 14.7) Pub Date : 2025-01-20 Zitong Ren, Shiwei Liu, Liejun Wang, Zhiqing Guo
Addressing the common issues of high noise and relatively small lesion areas in medical image datasets, this paper proposes a novel hybrid network model named Conv-SdMLPMixer. This model combines the strengths of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Specifically, the paper first introduces a multi-path inverted residual bottleneck CNN (MIRB-CNN) structure designed
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A static event-triggered background-impulse Kalman filter for wireless sensor networks with non-Gaussian measurement noise Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Xinkai You, Kangqi Xiao, Gang Wang
Event-triggered mechanisms (ETMs) have received increasing attention since they provide a way to reduce the communication burden by preventing sensors from transmitting unnecessary measurement values. This article focuses on the problem of a static ETM-based Kalman filter (static ET-KF) failing to work in the case of non-Gaussian measurement noise. To tackle this problem, we combine the static ETM
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ATD Learning: A secure, smart, and decentralised learning method for big data environments Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Laith Alzubaidi, Sabah Abdulazeez Jebur, Tanya Abdulsattar Jaber, Mohanad A. Mohammed, Haider A. Alwzwazy, Ahmed Saihood, Harshala Gammulle, Jose Santamaria, Ye Duan, Clinton Fookes, Raja Jurdak, Yuantong Gu
Big data and its distributed approach to data management have evolved significantly in recent years, giving rise to a huge volume of data generated from new services, devices (e.g. IoT), and applications. Recently, federated learning (FL) has been proposed for training deep learning models on distributed data in order to address significant challenges previously described in the literature, e.g., those
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Short-term OD flow prediction for urban rail transit control: A multi-graph spatiotemporal fusion approach Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Xue Xing, Bing Wang, Xin Ning, Gang Wang, Prayag Tiwari
There is growing pressure to manage and run urban rail transit networks as more people select this mode of transportation for their travel. It is, therefore, essential to create a precise system to predict passenger movements from origins to destinations (OD). The effectiveness of the present methods in simulating links between stations using real-time passenger flow data is limited. This paper suggests
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Retrieval-Augmented Dialogue Knowledge Aggregation for expressive conversational speech synthesis Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Rui Liu, Zhenqi Jia, Feilong Bao, Haizhou Li
Conversational speech synthesis (CSS) aims to take the current dialogue (CD) history as a reference to synthesize expressive speech that aligns with the conversational style. Unlike CD, stored dialogue (SD) contains preserved dialogue fragments from earlier stages of user–agent interaction, which include style expression knowledge relevant to scenarios similar to those in CD. Note that this knowledge
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Fractional light gradient boosting machine ensemble learning model: A non-causal fractional difference descent approach Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Haixin Wu, Yaqian Mao, Jiacheng Weng, Yue Yu, Jianhong Wang
This paper is the first to propose the fractional light gradient boosting machine ensemble learning model, and verifies its effectiveness through regression and classification tasks. Firstly, causal fractional difference and anti-causal fractional difference are presented based on the definitions of causal and anti-causal first differences, thus proposing a non-causal fractional difference by Z-transform
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Seeing helps hearing: A multi-modal dataset and a mamba-based dual branch parallel network for auditory attention decoding Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Cunhang Fan, Hongyu Zhang, Qinke Ni, Jingjing Zhang, Jianhua Tao, Jian Zhou, Jiangyan Yi, Zhao Lv, Xiaopei Wu
EEG-based auditory attention decoding (AAD) aims to identify the attended speaker from the listener’s EEG signals. Existing datasets mainly focus on auditory stimuli, ignoring real-world multi-modal inputs. To address this, a new multi-modal AAD dataset (MM-AAD) is constructed, representing the first dataset to include audio–visual stimuli. Additionally, prior studies mostly extract single-domain features
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Optimizing the environmental design and management of public green spaces: Analyzing urban infrastructure and long-term user experience with a focus on streetlight density in the city of Las Vegas, NV Inform. Fusion (IF 14.7) Pub Date : 2025-01-18 Xiwei Shen, Jie Kong, Yang Song, Xinyi Wang, Grant Mosey
In Las Vegas and many other desert cities, the unique climatic conditions, marked by high daytime temperatures, naturally encourage residents to seek outdoor recreational activities during the cooler evening hours. However, the approach to streetlight management has been less than optimal, leading to inadequate illumination in public parks after dark. This lack of proper lighting compromises not only
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Degradation-Decoupled and semantic-aggregated cross-space fusion for underwater image enhancement Inform. Fusion (IF 14.7) Pub Date : 2025-01-16 Xinwei Xue, Jincheng Yuan, Tianjiao Ma, Long Ma, Qi Jia, Jinjia Zhou, Yi Wang
The enhancement of underwater imaging has recently garnered significant attention due to the development of marine resources. Complex underwater environments cause images to suffer from various degradations, such as color casts and haze effects. These degradation factors are tangled in the original color space, making them challenging to eliminate using existing methods. Moreover, current underwater
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DF-BSFNet: A bilateral synergistic fusion network with novel dynamic flow convolution for robust road extraction Inform. Fusion (IF 14.7) Pub Date : 2025-01-15 Chong Zhang, Huazu Zhang, Xiaogang Guo, Heng Qi, Zilong Zhao, Luliang Tang
Accurate and robust road extraction with good continuity and completeness is crucial for the development of smart city and intelligent transportation. Remote sensing images and vehicle trajectories are attractive data sources with rich and complementary multimodal road information, and the fusion of them promises to significantly promote the performance of road extraction. However, existing studies
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KDFuse: A high-level vision task-driven infrared and visible image fusion method based on cross-domain knowledge distillation Inform. Fusion (IF 14.7) Pub Date : 2025-01-14 Chenjia Yang, Xiaoqing Luo, Zhancheng Zhang, Zhiguo Chen, Xiao-jun Wu
To enhance the comprehensiveness of fusion features and meet the requirements of high-level vision tasks, some fusion methods attempt to coordinate the fusion process by directly interacting with the high-level semantic feature. However, due to the significant disparity between high-level semantic domain and fusion representation domain, there is potential for enhancing the effectiveness of the collaborative
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A data-fusion spatiotemporal matrix factorization approach for citywide traffic flow estimation and prediction under insufficient detection Inform. Fusion (IF 14.7) Pub Date : 2025-01-13 Zhengchao Zhang, Meng Li
Citywide traffic flow is essential for the urban traffic planning, traffic signal control, and automotive emission management. However, it is impractical to directly detect the traffic flow of each road segment due to the unaffordable costs of detector installment and maintenance. Under the insufficient detection, the traffic flows of road segments without detectors are totally unknown. Thus, it is
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An adaptive consensus model with hybrid feedback mechanism: Exploring interference effects under evidence theory Inform. Fusion (IF 14.7) Pub Date : 2025-01-13 Jingmei Xiao, Mei Cai, Guo Wei, Suqiong Hu
The consensus reaching process (CRP) is crucial for achieving broad agreement in group decision-making (GDM). In the CRP, factors such as epistemic uncertainty and opinion interference of experts may cause cognitive biases and irrational behaviors. Therefore, this paper proposes a new adaptive consensus model based on quantum probability theory (QPT) in the context of evidence theory and develops a
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Bidomain uncertainty gated recursive network for pan-sharpening Inform. Fusion (IF 14.7) Pub Date : 2025-01-13 Junming Hou, Xinyang Liu, Chenxu Wu, Xiaofeng Cong, Chentong Huang, Liang-Jian Deng, Jian Wei You
Pan-sharpening aims to integrate the complementary information of different modalities of satellite images, e.g., texture-rich PAN images and multi-spectral (MS) images, to produce more informative fusion images for various practical tasks. Currently, most deep learning based pan-sharpening techniques primarily concentrate on developing various elaborate architectures to enhance their representation
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Fractal dimension and clinical neurophysiology fusion to gain a deeper brain signal understanding: A systematic review Inform. Fusion (IF 14.7) Pub Date : 2025-01-13 Sadaf Moaveninejad, Simone Cauzzo, Camillo Porcaro
Fractal dimension (FD) analysis, a powerful tool that has significantly advanced our understanding of brain complexity, evolving from basic geometrical characterization to the nuanced analysis of neurophysiological signals. This review integrates the theoretical foundations of FD calculation with its practical applications in clinical neurophysiology, focusing on the Higuchi method. This method, widely
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Multi-modal adapter for RGB-T tracking Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 He Wang, Tianyang Xu, Zhangyong Tang, Xiao-Jun Wu, Josef Kittler
The Transformer architectures have recently been attracting increasing attention, owing to their superiority in performance. However, it is difficult to take advantage of them for downstream tasks with limited data, such as visual object tracking with visible and thermal infrared modalities (RGB-T Tracking), where it is impossible to ensure that the training procedure will reach satisfactory completion
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Prototype-based cross-modal object tracking Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 Lei Liu, Chenglong Li, Futian Wang, Longfeng Shen, Jin Tang
Cross-modal object tracking is an important research topic in the field of information fusion, and it aims to address imaging limitations in challenging scenarios by integrating switchable visible and near-infrared modalities. However, existing tracking methods face some difficulties in adapting to significant target appearance variations in the presence of modality switch. For instance, model update
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Efficient Multispectral Object Detection with attentive feature aggregation leveraging zero-shot implicit illumination guidance Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 Zhongxia Xiong, Ziying Yao, Xuan Liu, Wenyao Zhao, Jie Cao, Xinkai Wu
With visible imagery and thermal sensing data, multispectral object detection facilitates around-the-clock perception for applications such as autonomous driving. Infrared input serves as auxiliary data for cross-modality feature aggregation, a common approach demonstrated to be successful by numerous previous studies. Nevertheless, despite the inclusion of complex and time-consuming modules in many
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Rethinking neural architecture representation for predictors: Topological encoding in pixel space Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 Caiyang Yu, Jian Wang, Yifan Wang, Wei Ju, Chenwei Tang, Jiancheng Lv
Neural predictors (NPs) aim to swiftly evaluate architectures during the neural architecture search (NAS) process. Precise evaluations with NPs heavily depend on the representation of training samples (i.e., the architectures), as the representation determines how well the NP captures the intrinsic properties and intricate dependencies of the architecture. Existing methods, which represent neural architectures
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A novel bi-objective R-mathematical programming method for risk group decision making Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 Guolin Tang, Runqing Fu, Hamidreza Seiti, Francisco Chiclana, Peide Liu
Most risk-based multi-attribute group decision-making (R-MAGDM) frameworks often assume that attributes are independent and rarely consider the decision-maker’s (DM) psychological behaviours. However, in many cases, attributes tend to interact with each other, and DMs often display bounded rationality during the decision-making process. A new R-mathematical programming method is developed to address
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SelfFed: Self-adaptive Federated Learning with Non-IID data on Heterogeneous Edge Devices for Bias Mitigation and Enhance Training Efficiency Inform. Fusion (IF 14.7) Pub Date : 2025-01-10 Neha Singh, Mainak Adhikari
Federated learning (FL) offers a decentralized and collaborative training solution on resource-constraint Edge Devices (EDs) to improve a global model without sharing raw data. Standard Synchronous FL (SFL) approaches provide significant advantages in terms of data privacy and reduced communication overhead, however, face several challenges including Non-independent and identically distributed (Non-IID)
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Few-shot multi-label intent recognition augmented by label knowledge generalization method Inform. Fusion (IF 14.7) Pub Date : 2025-01-09 ZhaoYan Li, YaJun Du, Shun Yang, XiaoLiang Chen, XianYong Li
Recognizing user intents is one of the most critical aspects of human–computer interaction. However, most research typically assumes a single intent, whereas real-world user utterances often involve multiple intents. Few-shot multi-label intent recognition is a more challenging task as the model learns from very limited data and multiple labels can interfere with each other. However, existing methods
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From attributes to natural language: A survey and foresight on text-based person re-identification Inform. Fusion (IF 14.7) Pub Date : 2025-01-09 Fanzhi Jiang, Su Yang, Mark W. Jones, Liumei Zhang
Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodal analysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptions. Despite the wide range of applicable areas such as security surveillance, video retrieval, person tracking, and social media analytics, there is a notable absence of comprehensive
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DEMO: A Dynamics-Enhanced Learning Model for multi-horizon trajectory prediction in autonomous vehicles Inform. Fusion (IF 14.7) Pub Date : 2025-01-09 Chengyue Wang, Haicheng Liao, Kaiqun Zhu, Guohui Zhang, Zhenning Li
Autonomous vehicles (AVs) rely on accurate trajectory prediction of surrounding vehicles to ensure the safety of both passengers and other road users. Trajectory prediction spans both short-term and long-term horizons, each requiring distinct considerations: short-term predictions rely on accurately capturing the vehicle’s dynamics, while long-term predictions rely on accurately modeling the interaction
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Towards a robust multi-view information bottleneck using Cauchy–Schwarz divergence Inform. Fusion (IF 14.7) Pub Date : 2025-01-08 Qi Zhang, Mingfei Lu, Jingmin Xin, Badong Chen
Efficiently preserving task-relevant information while removing noise and redundancy in multi-view data remains a core challenge. The information bottleneck principle offers an information-theoretic framework to compress data while retaining essential information for the task. However, estimating mutual information in high-dimensional spaces is computationally intractable. Commonly used variational
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TMVF: Trusted Multi-View Fish Behavior Recognition with correlative feature and adaptive evidence fusion Inform. Fusion (IF 14.7) Pub Date : 2025-01-08 Zhenxi Zhao, Xinting Yang, Chunjiang Zhao, Chao Zhou
Utilizing multi-view learning to analyze fish behavior is crucial for fish disease early warning and developing intelligent feeding strategies. Trusted multi-view classification based on Dempster–Shafer Theory (DST) can effectively resolve view conflicts and significantly improve accuracy. However, these DST-based methods often assume that view source domain data are “independent”, and ignore the associations
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CoreNet: Conflict Resolution Network for point-pixel misalignment and sub-task suppression of 3D LiDAR-camera object detection Inform. Fusion (IF 14.7) Pub Date : 2025-01-07 Yiheng Li, Yang Yang, Zhen Lei
Fusing multi-modality inputs from different sensors is an effective way to improve the performance of 3D object detection. However, current methods overlook two important conflicts: point-pixel misalignment and sub-task suppression. The former means a pixel feature from the opaque object is projected to multiple point features of the same ray in the world space, and the latter means the classification
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Oral multi-pathology segmentation with Lead-Assisting Backbone Attention Network and synthetic data generation Inform. Fusion (IF 14.7) Pub Date : 2025-01-07 Qiankun Li, Huabao Chen, Xiaolong Huang, Mengting He, Xin Ning, Gang Wang, Feng He
Oral diseases are prevalent among the public, and artificial intelligence-based automatic oral multi-pathology diagnosis can help patient treatment and reduce healthcare pressure. However, previous works generally focused on segmenting single oral pathology rather than the challenging problem of oral multi-pathology segmentation. Furthermore, clinical practices for oral multi-pathology often face significant
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A degradation-aware guided fusion network for infrared and visible image Inform. Fusion (IF 14.7) Pub Date : 2025-01-07 Xue Wang, Zheng Guan, Wenhua Qian, Jinde Cao, Runzhuo Ma, Cong Bi
Most IVIF methods focus solely on visual feature fusion, neglecting degraded scene information, which results in suboptimal solutions that do not fully reflect implicit scene information. To tackle the challenge, we develop a degradation-aware fusion network for infrared and visible images. By learning implicit degradation estimation, our model not only effectively integrates complementary information
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Constructive preference elicitation for multi-criteria decision analysis using an estimate-then-select strategy Inform. Fusion (IF 14.7) Pub Date : 2025-01-07 Qian Liang, Zhen Zhang, Yingsheng Su
In multi-criteria decision analysis (MCDA), generating decision recommendations for alternatives using the preference disaggregation paradigm has emerged as a significant approach to alleviate the cognitive burden on decision makers (DMs). In this context, the quality of decision examples provided by the DM is essential for constructing a reliable and robust preference model. However, existing studies
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Decoupled cross-attribute correlation network for multimodal sentiment analysis Inform. Fusion (IF 14.7) Pub Date : 2025-01-07 Xianbing Zhao, Xuejiao Li, Ronghuan Jiang, Buzhou Tang
Multimodal sentiment analysis is a burgeoning and crucial branch in the affective computing domain with the rise of user-generated video. Most existing multimodal sentiment analysis methods focus on exploring the reinforcement of modalities through cross-modal interaction paradigms. The existing literature does not adequately explore fine-grained feature-level reinforcement, while efficient features
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TVT-Transformer: A Tactile-visual-textual fusion network for object recognition Inform. Fusion (IF 14.7) Pub Date : 2025-01-06 Baojiang Li, Liang Li, Haiyan Wang, Guochu Chen, Bin Wang, Shengjie Qiu
In the pursuit of higher levels of intelligence, embodied intelligences need to integrate information from multiple perceptual channels through a multimodal information fusion mechanism to comprehensively understand the surrounding scene and the manipulated objects. Most of the current multimodal perception research focuses on the fusion of vision-based, and most of them involve only the fusion of
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Hierarchical global to local calibration for query-focused few-shot node classification Inform. Fusion (IF 14.7) Pub Date : 2025-01-06 Shuzhen Rao, Jun Huang, Zengming Tang
Considering the extreme class imbalance in real-world graphs, increasing attention has been paid to Few-Shot Node Classification (FSNC). However, existing methods in traditional setting face two critical issues. Firstly, random task sampling without considering relationships can lead to a lack of structures within each task, unsuitable for graph data. Secondly, to compensate, they inefficiently aggregate
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Towards symbolic XAI — explanation through human understandable logical relationships between features Inform. Fusion (IF 14.7) Pub Date : 2025-01-06 Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction for explanations, often in the form of heatmaps in post-hoc attribution methods. Alternatively, XAI offers rule-based explanations that are expressive and composed of logical formulas but often fail to faithfully capture
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Ada4DIR: An adaptive model-driven all-in-one image restoration network for remote sensing images Inform. Fusion (IF 14.7) Pub Date : 2025-01-06 Ziyang LiHe, Qiangqiang Yuan, Jiang He, Xianyu Jin, Yi Xiao, Yuzeng Chen, Huanfeng Shen, Liangpei Zhang
Remote sensing images offer the opportunity to observe the Earth’s surface at multiple scales and from various angles. However, during acquisition, factors like blur, noise, haze, and low light can degrade the quality of optical remote sensing images. Deep learning-based image restoration methods are currently the most advanced approach for enhancing the usability of degraded remote sensing data. However
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AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms– A comprehensive review Inform. Fusion (IF 14.7) Pub Date : 2025-01-06 Sijjad Ali, Jia Wang, Victor Chung Ming Leung
The fusion of Artificial Intelligence (AI) into cybersecurity has brought transformative advancements in protecting digital infrastructures from evolving cyber threats. This comprehensive review explores current AI-driven cybersecurity methodologies, emphasizing the capabilities of AI technologies — such as machine learning, deep learning, and natural language processing (NLP) — to enhance threat detection
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Multi-label feature selection with missing labels by weak-label fusion fuzzy discernibility pair Inform. Fusion (IF 14.7) Pub Date : 2025-01-04 Jianhua Dai, Min Li, Chucai Zhang
Multi-label feature selection has attracted increasing attention in recent years. Fuzzy rough set, as an effective granular computing tool, has been widely applied in multi-label feature selection. However, existing methods based on fuzzy rough sets rarely take into account multi-label datasets with missing labels and cannot directly characterize the ability of features to distinguish samples. Hence
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Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier Inform. Fusion (IF 14.7) Pub Date : 2025-01-03 Tripti Goel, Shradha Verma, M. Tanveer, P.N. Suganthan
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that involves gradual memory loss and eventually leads to severe cognitive decline at the final stage. Advanced neuroimaging modalities, including magnetic resonance imaging (MRI), prove advantageous in diagnosing the severity of the progression of AD. T1-W structural MRI and susceptibility-weighted imaging (SWI) are two of the most
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RFFR-Net: Robust feature fusion and reconstruction network for clothing-change person re-identification Inform. Fusion (IF 14.7) Pub Date : 2025-01-03 Mingfu Xiong, Xinxin Yang, Zhihong Sun, Xinrong Hu, Ahmed Ibrahim Alzahrani, Khan Muhammad
In the research field of person re-identification (ReID), especially in clothing-change scenarios (CC-ReID), traditional approaches are hindered by their reliance on clothing features, which are inherently unstable, leading to a significant decline in recognition accuracy when confronted with variations in clothes. To address these problems, this study proposes an innovative framework, the Robust Feature
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A Bayesian network approach for dynamic behavior analysis: Real-time intention recognition Inform. Fusion (IF 14.7) Pub Date : 2025-01-03 Jiaxuan Jiang, Jiapeng Liu, Miłosz Kadziński, Xiuwu Liao
Dynamic intention recognition is widely applied across diverse domains, including autonomous driving, e-commerce, and human–computer interaction, to understand and identify individuals’ evolving behavioral intentions. While observable behaviors often serve as proxies for underlying intentions, accurately establishing the relationships between dynamic behaviors and evolving intentions becomes a challenging