Information Fusion最新文献

{"title":"A systematic review for artificial intelligence-driven assistive technologies to support children with neurodevelopmental disorders","authors":"Alen Shahini ,&nbsp;Aditya Prabhakara Kamath ,&nbsp;Ekta Sharma ,&nbsp;Massimo Salvi ,&nbsp;Ru-San Tan ,&nbsp;Siuly Siuly ,&nbsp;Silvia Seoni ,&nbsp;Rahul Ganguly ,&nbsp;Aruna Devi ,&nbsp;Ravinesh Deo ,&nbsp;Prabal Datta Barua ,&nbsp;U. Rajendra Acharya","doi":"10.1016/j.inffus.2025.103441","DOIUrl":"10.1016/j.inffus.2025.103441","url":null,"abstract":"<div><div>This systematic review examines AI-powered assistive technologies for children with neurodevelopmental disorders, with a focus on dyslexia (DYS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Our analysis of 84 studies from 2018 to 2024 provides the first thorough cross-disorder comparison of AI implementation patterns. According to our data, each condition has different success rates and technological preferences. AI applications are expanding quickly, especially in research on ASD (56 % of studies), followed by ADHD (36 %), and DYS (8 %). In almost half of the reviewed studies, computer-assisted technologies, which have demonstrated encouraging results in terms of treatment support and diagnostic accuracy, became the main mode of intervention. Despite high accuracy in controlled settings, the implementation of these technologies in clinical practice faces significant challenges. While human oversight remains essential in clinical applications, future advancements should prioritize privacy protection and the ability to assess tools longitudinally. Notably, multimodal approaches that integrate various data types have improved diagnostic accuracy; recent research has shown that they can detect ASD with up to 99.8 % accuracy and ADHD with up to 97.4 % accuracy. A promising trend is the combination of mobile applications and wearable technology, especially for real-time monitoring and intervention. This review highlights the potential and current limitations of AI-driven tools in supporting children with neurodevelopmental disorders. Future development should focus not on replacing clinical expertise, but on augmenting it. Research efforts should aim at creating tools that enhance professional judgment while preserving the essential human components of assessment and intervention.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103441"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322194","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":"Dynamic decision-making paradigm for multi-modal information in a human–computer interaction perspective: Fusing composite rough set and incremental learning","authors":"Bingzhen Sun ,&nbsp;Xixuan Zhao ,&nbsp;Yuhua Qian ,&nbsp;Xiaoli Chu","doi":"10.1016/j.inffus.2025.103411","DOIUrl":"10.1016/j.inffus.2025.103411","url":null,"abstract":"<div><div>The knowledge contained in different modal information and their perspectives on the description of specific objects are different. With the development of data science and computer technology, uncertain decision-making with multi-modal, nonlinear, unbalanced, and incomplete characteristics has become a trend, posing a great challenge to traditional uncertain decision-making methods. In view of this, this paper proposes a dynamic decision-making paradigm for multi-modal information by fusing composite rough set and machine learning under the perspective of human–computer interaction. First, a multi-modal hybrid attribute information system (MHAIS) is constructed for incomplete multi-modal hybrid attribute information in numerical, textual and image modals, and several degradation scenarios of MHAIS are discussed. Second, to achieve multi-modal information fusion and reduce redundant attributes, multi-modal hybrid binary relationships are constructed for numerical, textual and image modals in MHAIS, and multi-modal composite rough set and its attribute reduction method is given. Then, uncertainty decision-making models conforming to different realistic decision-making situations are established from the perspectives of three multi-modal information fusion strategies, namely feature-level fusion, model-level fusion and decision-level fusion, respectively. Finally, the above process is extended to the dynamic decision-making process, and the corresponding incremental learning paradigm is given. An example study using real datasets of rheumatoid arthritis patients from Guangdong Provincial Hospital of Traditional Chinese Medicine and 10 public datasets is carried out in this paper to verify the scientific and superiority of the proposed method. On the one hand, this paper introduces the idea of human–computer interaction and different strategies of multi-modal information fusion into realistic uncertain decision-making problems, and on the other hand, it makes a new contribution of the integration of rough set theory and machine learning in the development of management science, decision science and computer science.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103411"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322196","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":"Intermediate features matter in prototype-guided personalized federated learning","authors":"Peifeng Zhang ,&nbsp;Jiahui Chen ,&nbsp;Chunxiang Xiang ,&nbsp;Huiwu Huang ,&nbsp;Huaien Jiang ,&nbsp;Kaixiang Yang","doi":"10.1016/j.inffus.2025.103381","DOIUrl":"10.1016/j.inffus.2025.103381","url":null,"abstract":"<div><div>The recent rise of Federated Learning (FL) as a privacy-preserving distributed learning paradigm has attracted significant attention in both research and application. Among the emerging topics of FL, personalized FL (pFL) has emerged as a focal point, with the primary challenge being the development of efficient, customized solutions for heterogeneous data environments. Recent efforts integrating prototype learning into FL have shown promise, yet they often neglect the utilization of intermediate features. We are thus motivated to address this gap by proposing a novel approach named FedPSC. This method first employs an embedding scheme to learn global category prototypes that are used to align local training processes across different clients. Most importantly, it explores the potential of multi-level category prototypes by leveraging intermediate features, thereby further aligning local feature learning at different hierarchical levels. Additionally, FedPSC incorporates supervised contrastive learning with a simple yet effective modification, extending it to the intermediate level as well, which complements the category prototypes and enhances model learning. Our comprehensive experiments on public benchmark datasets indicate that FedPSC outperforms recent FL methods in multiple aspects, particularly in terms of accuracy.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103381"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338565","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":"Explaining spatio-temporal graph convolutional networks with spatio-temporal constraints perturbation for action recognition","authors":"Rui Yu,&nbsp;Yanshan Li,&nbsp;Ting Shi,&nbsp;Weixin Xie","doi":"10.1016/j.inffus.2025.103387","DOIUrl":"10.1016/j.inffus.2025.103387","url":null,"abstract":"<div><div>Understanding and explaining spatio-temporal graph convolutional networks (STGCNs) for human skeleton action recognition is crucial to improving the security and trustworthiness of action recognition algorithms. However, the complexity of geometric spatio-temporal features in skeleton-based spatio-temporal graphs, the high dependence of geometric features on temporal information, and the dynamics of STGCNs challenge existing graph neural networks (GNNs) explanation methods. It is thorny for these methods to explain the geometric spatio-temporal features intertwined in STGCNs. To this end, we take the human skeleton action recognition based on STGCNs as the research object, and propose a spatio-temporal constraints explanation method for STGCNs (STGExplainer). Firstly, we construct a geometric transition model of human motion in spatio-temporal graphs, which utilizes the Rodrigues’ rotation equation to describe the relationship among nodes in geometric space over time. This model is adopted to geometrically perturb STGCNs. Then, in order to evaluate the importance of geometric spatio-temporal features in STGCNs, we design a geometric perturbation module based on spatio-temporal constraints. The module includes a spatio-temporal constraints-based objective function and an optimization algorithm based on the gradient alternating direction method of multipliers (ADMM), which identifies the geometric spatio-temporal features that are more critical to the model after geometric perturbation of spatio-temporal constraints on STGCNs. The proposed objective function makes the explanation results highly sparse and consistent with the input in terms of geometric consistency of the spatio-temporal structure. Finally, to solve the spatio-temporal importance optimization problem with spatio-temporal constraints, an optimization algorithm based on the gradient ADMM is designed. The algorithm decomposes the optimized spatio-temporal and geometric spatial importance distributions, and then gradually generates more accurate geometric spatio-temporal feature explanations. Experimental results on real datasets show that STGExplainer achieves excellent performance.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103387"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338567","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":"Semi-supervised medical image classification via Cross-Training and Dual-Teacher fusion model","authors":"Yanyuan Chen ,&nbsp;Dexuan Xu ,&nbsp;Yiwei Lou ,&nbsp;Hang Li ,&nbsp;Weiping Ding ,&nbsp;Yu Huang","doi":"10.1016/j.inffus.2025.103389","DOIUrl":"10.1016/j.inffus.2025.103389","url":null,"abstract":"<div><div>Pseudo-labeling approaches, a powerful paradigm for semi-supervised learning in medical image analysis, involve a teacher network to generate pseudo-labels and a student network to utilize the generated pseudo-labels. However, the generation and utilization of pseudo-labels are tightly coupled as both the teacher and student model share the same network. The inability of a single model to self-correct effectively can cause confirmation biases and potential error accumulation as the training proceeds. To address the problems, a novel semi-supervised framework fusing <strong>C</strong>ross-<strong>T</strong>raining and <strong>D</strong>ual-<strong>T</strong>eacher (CTDT) is proposed in this paper. Firstly, a novel cross-training strategy is introduced, which adopts distinct architectural inductive biases within semi-supervised learning framework, enabling different models to mutually correct each other due to their varying learning capabilities and effectively preventing the direct accumulation of errors. Further, a dual-teacher fusion module is proposed to alleviate confirmation biases, which fuses complementary knowledge from diverged teachers to capture distinctive feature representations from unlabeled data and co-guide the student model. Extensive experiments on two public medical image classification benchmarks, i.e. skin lesion diagnosis with ISIC2018 challenge and colorectal cancer histology slides classification with NCT-CRC-HE, justify that our method (CTDT) achieves an average improvement of 2.48% on the NCT-CRC-HE and 3.13% on the ISIC2018.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103389"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471834","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":"iAMeta: Advancing multimodal metaphor detection using MLLMs and information debiasing","authors":"Xiaoyu He ,&nbsp;Long Yu ,&nbsp;Shengwei Tian","doi":"10.1016/j.inffus.2025.103348","DOIUrl":"10.1016/j.inffus.2025.103348","url":null,"abstract":"<div><div>With the widespread use of multimodal data, metaphors are increasingly expressed through the combination of images and text, leading to the emergence of multimodal metaphor detection research. However, existing methods face challenges such as missing contextual information and information bias, hindering accurate metaphor interpretation. To address these issues, we propose iAMeta, a multimodal metaphor detection framework based on multimodal large language models (MLLMs). This framework introduces a knowledge generator inspired by contrastive thinking, enabling gradual inference of metaphor, non-metaphor, and overall metaphor prior knowledge. A multitask learning-based sentiment feature control mechanism is employed to calibrate sentimental bias caused by prior knowledge interference and ensure that the extracted sentiment features are consistent with the original emotional tone. Additionally, a causal reasoning framework is introduced to reduce false associations between images and labels, further enhancing the model’s generalization ability. Experimental results demonstrate that iAMeta excels in both multimodal metaphor detection and sentiment analysis tasks and performs well in handling complex scenarios like sarcasm.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103348"},"PeriodicalIF":14.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322192","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":"Research on multi-channel adaptive coupling prediction system of fusing physical information for spatio-temporal correlation heterogeneity of methane concentration","authors":"Ningke Xu ,&nbsp;Shuang Li ,&nbsp;Cheng Lu ,&nbsp;Yi Zhang","doi":"10.1016/j.inffus.2025.103418","DOIUrl":"10.1016/j.inffus.2025.103418","url":null,"abstract":"<div><div>The dynamic evolution of methane concentration in underground coal mine is the core risk source that induces methane accidents. In order to solve the problem of limited prediction accuracy caused by ignoring the spatio-temporal correlation heterogeneity of methane concentration in existing prediction models and the limitation of interpretability in existing data-driven models, this study proposes a multi-channel adaptive coupling prediction method that fuses physical information. By modeling adaptive fine-grained dependencies across multiple channels, we achieved targeted extraction of dynamic response characteristics of methane concentration from multi-source data in underground coal mines during spatio-temporal evolution processes. For the first time, an error loss term that integrates physical information has been developed for gas concentration prediction, with the final model output obtained by aggregating relevant information through an adaptive graph learning module. The results of the application in different regions of the coal mine show that the proposed method has better versatility and prediction accuracy in the methane concentration prediction task. Through the explainable modeling of dynamic dependencies and the explicit integration of physical constraints, the transparency and credibility of prediction results are significantly improved, which can effectively prevent the occurrence of methane accidents in coal mines and promote the development of the coal mine industry in a sustainable direction.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103418"},"PeriodicalIF":14.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290699","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":"FER-VMamba: A robust facial expression recognition framework with global compact attention and hierarchical feature interaction","authors":"Hui Ma ,&nbsp;Sen Lei ,&nbsp;Heng-Chao Li ,&nbsp;Turgay Celik","doi":"10.1016/j.inffus.2025.103371","DOIUrl":"10.1016/j.inffus.2025.103371","url":null,"abstract":"<div><div>Facial Expression Recognition (FER) has broad applications in driver safety, human–computer interaction, and cognitive psychology research, where it helps analyze emotional states and enhance social interactions. However, FER in static images faces challenges due to occlusions and pose variations, which hinder the model’s effectiveness in real-world scenarios. To address these issues, we propose FER-VMamba, a robust and efficient architecture designed to improve FER performance in complex scenarios. FER-VMamba comprises two core modules: the Global Compact Attention Module (GCAM) and the Hierarchical Feature Interaction Module (HFIM). GCAM extracts compact global semantic features through Multi-Scale Hybrid Convolutions (MixConv), refining them with a Spatial Channel Attention Mechanism (SCAM) to improve robustness against occlusions and pose variations. HFIM captures local and global dependencies by segmenting feature maps into non-overlapping partitions, which the FER-VSS module processes with Conv-SCAM-Conv for local features and Visual State-Space (VSS) for global dependencies. Additionally, self-attention and relation-attention mechanisms in HFIM refine features by modeling inter-partition relationships, further improving the accuracy of expression recognition. Extensive experiments on the RAF and AffectNet datasets demonstrate that FER-VMamba achieves state-of-the-art performance. Furthermore, we introduce FSL-FER-VMamba, an extension of FER-VSS optimized for cross-domain few-shot FER, providing strong adaptability to domain shifts. <span><span>https://github.com/SwjtuMa/FER-VMamba.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103371"},"PeriodicalIF":14.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297056","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-modal guided and refinement-enhanced Retinex network for robust low-light image enhancement","authors":"Tongshun Zhang ,&nbsp;Pingping Liu ,&nbsp;Mengen Cai ,&nbsp;Xiaoyi Wang ,&nbsp;Qiuzhan Zhou","doi":"10.1016/j.inffus.2025.103380","DOIUrl":"10.1016/j.inffus.2025.103380","url":null,"abstract":"<div><div>The Retinex theory has long been a cornerstone in the field of low-light image enhancement, garnering significant attention. However, traditional Retinex-based methods often suffer from insufficient robustness to noise interference, necessitating the introduction of additional regularization terms or handcrafted priors to improve performance. These handcrafted priors and regularization-based approaches, however, lack adaptability and struggle to handle the complexity and variability of low-light environments effectively. To address these limitations, this paper proposes a Cross-Modal Guided and Refinement-Enhanced Retinex Network (CMRetinexNet) that leverages the adaptive guidance potential of auxiliary modalities and incorporates refinement modules to enhance Retinex decomposition and synthesis. Specifically: (a) Considering the characteristics of the reflectance component, we introduce auxiliary modal information to adaptively improve the accuracy of reflectance estimation. (b) For the illumination component, we design a reconstruction module that combines local and frequency-domain information, to iteratively enhance both regional and global illumination levels. (c) To address the inherent uncertainty in the element-wise multiplication of reflectance and illumination components during Retinex synthesis, we propose a synthesis and refinement module that effectively fuses illumination and reflectance components by leveraging cross-channel and spatial contextual information. Extensive experiments on multiple public datasets demonstrate that the proposed model achieves significant improvements in both qualitative and quantitative metrics compared to state-of-the-art methods, validating its effectiveness and superiority in low-light image enhancement.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103380"},"PeriodicalIF":14.7,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322195","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":"CLDTracker: A Comprehensive Language Description for visual Tracking","authors":"Mohamad Alansari,&nbsp;Sajid Javed,&nbsp;Iyyakutti Iyappan Ganapathi,&nbsp;Sara Alansari,&nbsp;Muzammal Naseer","doi":"10.1016/j.inffus.2025.103374","DOIUrl":"10.1016/j.inffus.2025.103374","url":null,"abstract":"<div><div>Visual Object Tracking (VOT) remains a fundamental yet challenging task in computer vision due to dynamic appearance changes, occlusions, and background clutter. Traditional trackers, relying primarily on visual cues, often struggle in such complex scenarios. Recent advancements in Vision–Language Models (VLMs) have shown promise in semantic understanding for tasks like open-vocabulary detection and image captioning, suggesting their potential for VOT. However, the direct application of VLMs to VOT is hindered by critical limitations: the absence of a rich and comprehensive textual representation that semantically captures the target object’s nuances, limiting the effective use of language information; inefficient fusion mechanisms that fail to optimally integrate visual and textual features, preventing a holistic understanding of the target; and a lack of temporal modeling of the target’s evolving appearance in the language domain, leading to a disconnect between the initial description and the object’s subsequent visual changes. To bridge these gaps and unlock the full potential of VLMs for VOT, we propose CLDTracker, a novel <strong>C</strong>omprehensive <strong>L</strong>anguage <strong>D</strong>escription framework for robust visual <strong>Track</strong>ing. Our tracker introduces a dual-branch architecture consisting of a textual and a visual branch. In the textual branch, we construct a rich bag of textual descriptions derived by harnessing the powerful VLMs such as CLIP and GPT-4V, enriched with semantic and contextual cues to address the lack of rich textual representation. We further propose a <strong>T</strong>emporal <strong>T</strong>ext <strong>F</strong>eature <strong>U</strong>pdate <strong>M</strong>echanism (TTFUM) to adapt these descriptions across frames, capturing evolving target appearances and tackling the absence of temporal modeling. In parallel, the visual branch extracts features using a Vision Transformer (ViT), and an attention-based cross-modal correlation head fuses both modalities for accurate target prediction, addressing the inefficient fusion mechanisms. Experiments on six standard VOT benchmarks demonstrate that CLDTracker achieves State-of-The-Art (SOTA) performance, validating the effectiveness of leveraging robust and temporally-adaptive vision–language representations for tracking. Code and models are publicly available at: <span><span>https://github.com/HamadYA/CLDTracker</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103374"},"PeriodicalIF":14.7,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281072","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}