Information Fusion最新文献

{"title":"The bi-level consensus model with dual social networks for group decision making","authors":"Xiujuan Ma ,&nbsp;Xinwang Liu ,&nbsp;Zaiwu Gong ,&nbsp;Fang Liu","doi":"10.1016/j.inffus.2024.102714","DOIUrl":"10.1016/j.inffus.2024.102714","url":null,"abstract":"<div><div>The pursuit of consensus within social networks is a burgeoning area of research, pivotal for harmonizing decision-making amidst diverse opinions. However, existing studies often neglect the crucial balance between costs and benefits in optimizing consensus outcomes. Addressing this gap, this paper introduces a novel bi-level consensus optimization model within the framework of the dual social network. This model aims to achieve an equilibrium between minimizing costs and maximizing benefits, crucial for sustainable decision-making processes. The dual social network framework incorporates positive and negative interactions stemming from trust and opinion similarities, delineating nodes into close, distant, and mixed types based on their relational dynamics. Central to the model is a heterogeneous cost function that integrates individual influence and opinion adjustment, accounting comprehensively for moderator tolerance and incentivization mechanisms. To solve this multi-faceted optimization challenge, the paper proposes a solution leveraging a multi-objective particle swarm algorithm. Through simulation experiments conducted across four distinct social network decision-making scenarios, including a case study on capital investment in an epidemic response center, the paper validates the efficacy and practical applicability of the algorithm. The results underscore the model’s capability to achieve balanced consensus outcomes, offering insights into optimizing decision processes within complex social environments.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102714"},"PeriodicalIF":14.7,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359390","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 survey on pragmatic processing techniques","authors":"Rui Mao ,&nbsp;Mengshi Ge ,&nbsp;Sooji Han ,&nbsp;Wei Li ,&nbsp;Kai He ,&nbsp;Luyao Zhu ,&nbsp;Erik Cambria","doi":"10.1016/j.inffus.2024.102712","DOIUrl":"10.1016/j.inffus.2024.102712","url":null,"abstract":"<div><div>Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102712"},"PeriodicalIF":14.7,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329498","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-attention guided loss-based deep dual-branch fusion network for liver tumor classification","authors":"Rui Wang ,&nbsp;Xiaoshuang Shi ,&nbsp;Shuting Pang ,&nbsp;Yidi Chen ,&nbsp;Xiaofeng Zhu ,&nbsp;Wentao Wang ,&nbsp;Jiabin Cai ,&nbsp;Danjun Song ,&nbsp;Kang Li","doi":"10.1016/j.inffus.2024.102713","DOIUrl":"10.1016/j.inffus.2024.102713","url":null,"abstract":"<div><div>Recently, convolutional neural networks (CNNs) and multiple instance learning (MIL) methods have been successfully applied to MRI images. However, CNNs directly utilize the whole image as the model input and the downsampling strategy (like max or mean pooling) to reduce the size of the feature map, thereby possibly neglecting some local details. And MIL methods learn instance-level or local features without considering spatial information. To overcome these issues, in this paper, we propose a novel cross-attention guided loss-based dual-branch framework (LCA-DB) to leverage spatial and local image information simultaneously, which is composed of an image-based attention network (IA-Net), a patch-based attention network (PA-Net) and a cross-attention module (CA). Specifically, IA-Net directly learns image features with loss-based attention to mine significant regions, meanwhile, PA-Net captures patch-specific representations to extract crucial patches related to the tumor. Additionally, the cross-attention module is designed to integrate patch-level features by using attention weights generated from each other, thereby assisting them in mining supplement region information and enhancing the interactive collaboration of the two branches. Moreover, we employ an attention similarity loss to further reduce the semantic inconsistency of attention weights obtained from the two branches. Finally, extensive experiments on three liver tumor classification tasks demonstrate the effectiveness of the proposed framework, e.g., on the LLD-MMRI–7, our method achieves 69.2%, 65.9% and 88.5% on the seven-class liver tumor classification tasks in terms of accuracy, F<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> score and AUC, with the superior classification and interpretation performance over recent state-of-the-art methods. The source code of LCA-DB is available at <span><span>https://github.com/Wangrui-berry/Cross-attention</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102713"},"PeriodicalIF":14.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322701","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-aware multimodal pre-training for fake news detection","authors":"Litian Zhang ,&nbsp;Xiaoming Zhang ,&nbsp;Ziyi Zhou ,&nbsp;Xi Zhang ,&nbsp;Philip S. Yu ,&nbsp;Chaozhuo Li","doi":"10.1016/j.inffus.2024.102715","DOIUrl":"10.1016/j.inffus.2024.102715","url":null,"abstract":"<div><div>Amidst the rapid propagation of multimodal fake news across various social media platforms, the identification and filtering of disinformation have emerged as critical areas of academic research. A salient characteristic of fake news lies in its diversity, encompassing text–image inconsistency, content–knowledge inconsistency, and content fabrication. However, existing endeavors are generally tailored to a specific subset of fake news, leading to limited universality. Moreover, these models primarily rely on scarce and exorbitant manually labeled annotations, which is incapable of providing sufficient learning signals to detect a variety of fake news. To address these challenges, we propose a novel knowledge-aware multimodal pre-training paradigm for fake news detection, dubbed KAMP. Our motivation lies in incorporating unsupervised correlations through pre-training tasks as complementary to alleviate the dependency on annotations. KAMP consists of a novel multimodal learning model and various delicate pre-training tasks to simultaneously capture valuable knowledge from single modality, multiple modalities, and background knowledge graphs. Our proposal undergoes comprehensive evaluation across two widely utilized datasets, and experimental results demonstrate the superiority of our proposal.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102715"},"PeriodicalIF":14.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329497","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":"Fault stands out in contrast: Zero-shot diagnosis method based on dual-level contrastive fusion network for control moment gyroscopes predictive maintenance","authors":"Hebin Liu ,&nbsp;Qizhi Xu ,&nbsp;Hongyan He","doi":"10.1016/j.inffus.2024.102710","DOIUrl":"10.1016/j.inffus.2024.102710","url":null,"abstract":"<div><div>Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive maintenance with CMGs poses significant challenges. Therefore, a zero-shot fault diagnosis method based on a dual-level contrastive learning fusion network was proposed. First, to address the difficulty in training CMG fault diagnosis models without fault data, a contrastive learning method based on CMG clusters was proposed to extract invariant features from healthy CMGs and achieve zero-shot diagnosis for predictive maintenance. Second, considering the limitations of information from a single sensor, a cross-sensor contrastive learning method was proposed to fuse features from different sensors. Third, to tackle the challenges of extracting weak potential fault features, a dual-level joint training method was introduced to enhance the model’s feature extraction capability. Finally, the proposed method was validated using real dataset collected from CMGs serviced on an in-orbit spacecraft. The results demonstrate that the method can achieve zero-shot fault diagnosis for control moment gyroscopes predictive maintenance. The code is available at <span><span>https://github.com/IceLRiver/DCF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102710"},"PeriodicalIF":14.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320190","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":"Graph refinement and consistency self-supervision for tensorized incomplete multi-view clustering","authors":"Wei Liu ,&nbsp;Xiaoyuan Jing ,&nbsp;Deyu Zeng ,&nbsp;Tengyu Zhang","doi":"10.1016/j.inffus.2024.102709","DOIUrl":"10.1016/j.inffus.2024.102709","url":null,"abstract":"<div><p>In practical multi-view applications, some data in each view are missing. Although recent incomplete multi-view clustering (IMC) approaches have achieved encouraging performance, two challenges remain. They utilize the tensor nuclear norm to explore the high-order correlations among view-specific similarity graphs. Moreover, they only infer the missing views but do not recover the consensus cluster structure across complete views. To address these issues, we propose a new method called graph Refinement and consistency Self-Supervision for Tensorized Incomplete Multi-view Clustering (RS-TIMC). Specifically, RS-TIMC introduces graph decomposition to remove the diverse similarities from the view-specific graphs and utilizes the tensor Schatten-p norm to model the consistent parts. Additionally, by extracting features from the original observable data and inferring the missing instances, RS-TIMC enables the cluster structure of each complete view to be adjusted. Finally, RS-TIMC utilizes consistent similarity graphs to recover the shared local geometric structure across all complete views. Experimental evaluations on several datasets indicate that our method outperforms the start-of-the-art approaches.</p></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102709"},"PeriodicalIF":14.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272565","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 dual branch graph neural network based spatial interpolation method for traffic data inference in unobserved locations","authors":"Wujiang Zhu ,&nbsp;Xinyuan Zhou ,&nbsp;Shiyong Lan ,&nbsp;Wenwu Wang ,&nbsp;Zhiang Hou ,&nbsp;Yao Ren ,&nbsp;Tianyi Pan","doi":"10.1016/j.inffus.2024.102703","DOIUrl":"10.1016/j.inffus.2024.102703","url":null,"abstract":"<div><div>Complete traffic data collection is crucial for intelligent transportation system, but due to various factors such as cost, it is not possible to deploy sensors at every location. Using spatial interpolation, the traffic data for unobserved locations can be inferred from the data of observed locations, providing fine-grained measurements for improved traffic monitoring and control. However, existing methods are limited in modeling the dynamic spatio-temporal dependencies between traffic locations, resulting in unsatisfactory performance of spatial interpolation for unobserved locations in traffic scene. To address this issue, we propose a novel dual branch graph neural network (DBGNN) for spatial interpolation by exploiting dynamic spatio-temporal correlation among traffic nodes. The proposed DBGNN is composed of two branches: the main branch and the auxiliary branch. They are designed to capture the wide-range dynamic spatial correlation and the local detailed spatial diffusion between nodes, respectively. Finally, they are fused via a self-attention mechanism. Extensive experiments on six public datasets demonstrate the advantages of our DBGNN over the state-of-the-art baselines. The codes will be available at <span><span>https://github.com/SYLan2019/DBGNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102703"},"PeriodicalIF":14.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326323","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":"Fortifying NLP models against poisoning attacks: The power of personalized prediction architectures","authors":"Teddy Ferdinan,&nbsp;Jan Kocoń","doi":"10.1016/j.inffus.2024.102692","DOIUrl":"10.1016/j.inffus.2024.102692","url":null,"abstract":"<div><p>In Natural Language Processing (NLP), state-of-the-art machine learning models heavily depend on vast amounts of training data. Often, this data is sourced from third parties, such as crowdsourcing platforms, to enable swift and efficient annotation collection for supervised learning. Yet, such an approach is susceptible to poisoning attacks where malicious agents deliberately insert harmful data to skew the resulting model behavior. Current countermeasures to these attacks either come at a significant cost, lack full efficacy, or are simply non-applicable. This study introduces and evaluates the potential of personalized model architectures as a defense against these threats. By comparing two top-performing personalized model architectures, User-ID and HuBi-Medium, against a standard non-personalized baseline across two NLP tasks and various simulated attack scenarios, we found that the personalized model architectures significantly outperformed the baseline. The robustness advantage increased with the rise in malicious annotations. Notably, the User-ID model excelled in safeguarding predictions for legitimate users from the influence of malicious annotations. Our findings emphasize the benefit of adopting personalized model architectures to bolster NLP system defenses against poisoning attacks.</p></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102692"},"PeriodicalIF":14.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1566253524004706/pdfft?md5=3a6019ed5699d3ea16b3237461a74599&pid=1-s2.0-S1566253524004706-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272904","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":"Recent advances in complementary label learning","authors":"Yingjie Tian ,&nbsp;Haoran Jiang","doi":"10.1016/j.inffus.2024.102702","DOIUrl":"10.1016/j.inffus.2024.102702","url":null,"abstract":"<div><div>Complementary Label Learning (CLL), a crucial aspect of weakly supervised learning, has seen significant theoretical and practical advancements. However, a comprehensive review of the field has been lacking. This survey provides the first exhaustive compilation and synthesis of state-of-the-art CLL approaches, filling a critical gap in the literature and serving as a foundational resource for the community. Key contributions of this survey include an extensive categorization of CLL methodologies, clarifying diverse algorithms based on their principles and applications. This classification scheme enhances understanding of the CLL landscape, highlighting its versatility across varied settings. Additionally, the survey examines the evolution of CLL, showcasing its adaptability and potential in addressing complex applications. It also explores experimental frameworks, including processes for generating complementary labels and datasets and numerical evaluation of existing state-of-the-art. Moreover, the survey delves into how CLL integrates with and enhances other weakly supervised and semi-supervised learning approaches, deepening understanding of its role in the broader machine learning ecosystem. Overall, this survey not only compiles CLL research but also guides future explorations, steering the field towards new horizons in weakly supervised learning.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102702"},"PeriodicalIF":14.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322702","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":"Multiplex graph aggregation and feature refinement for unsupervised incomplete multimodal emotion recognition","authors":"Yuanyue Deng ,&nbsp;Jintang Bian ,&nbsp;Shisong Wu ,&nbsp;Jianhuang Lai ,&nbsp;Xiaohua Xie","doi":"10.1016/j.inffus.2024.102711","DOIUrl":"10.1016/j.inffus.2024.102711","url":null,"abstract":"<div><p>Multimodal Emotion Recognition (MER) involves integrating information of various modalities, including audio, visual, text and physiological signals, to comprehensively grasp human sentiments, which has emerged as a vibrant area within human–computer interaction. Researchers have developed many methods for this task, but many of these methods rely on labeled supervised learning and struggle to address the issue of missing some modalities of data. To address these issues, we propose a Multiplex Graph Aggregation and Feature Refinement framework for unsupervised incomplete MER, comprising four modules: Completion, Aggregation, Refinement, and Embedding. Specifically, we first capture the correlation information between samples using the graph structures, which aids in the completion of missing data and the multiplex aggregation of multimodal data. Then, we perform refinement operations on the aggregated features as well as alignment and enhancement operations on the embedding features to obtain the fused feature representations, which are consistent, highly separable and conducive to emotion recognition. Experimental results on multimodal emotion recognition datasets demonstrate that our method achieves state-of-the-art performance among unsupervised methods, validating its effectiveness.</p></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"114 ","pages":"Article 102711"},"PeriodicalIF":14.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272905","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}