Information Fusion最新文献

{"title":"Team collaboration-oriented multi-agent pathfinding and probabilistic verification","authors":"Xia Wang ,&nbsp;Jun Liu ,&nbsp;Chris D. Nugent ,&nbsp;Shaobing Xu ,&nbsp;Guanfeng Wu","doi":"10.1016/j.inffus.2026.104125","DOIUrl":"10.1016/j.inffus.2026.104125","url":null,"abstract":"<div><div>Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104125"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957309","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":"Subgraph-focused biomedical knowledge embedding with bi-semantic encoder for multi-type drug-drug interaction prediction","authors":"Xiangpeng Bi ,&nbsp;Wenjian Ma ,&nbsp;Huasen Jiang ,&nbsp;Qing Cai ,&nbsp;Jie Nie ,&nbsp;Zhiqiang Wei ,&nbsp;Shugang Zhang","doi":"10.1016/j.inffus.2025.104109","DOIUrl":"10.1016/j.inffus.2025.104109","url":null,"abstract":"<div><div>Identifying multi-type drug-drug interactions (DDIs) enables more precise assessment of drug safety risks and provides targeted guidance for combination therapy, making it a critical task in pharmacology. Given it can directly integrate diverse biomedical information and effectively model the intricate mechanisms underlying drug interaction, knowledge graph (KG)-based approaches have emerged for predicting DDIs. Recent advances have shown great promise in this regard; however, existing solutions still overlook three critical issues: 1) neglect of information sparsity, 2) neglect of polyadic interactions, and 3) lack of fusion paradigm, which severely hinder the comprehensive identification and understanding of drug interaction patterns. To address these issues, we introduce a <strong>Bi-Sem</strong>antic enco<strong>D</strong>er-d<strong>R</strong>iven knowledge s<strong>U</strong>b<strong>G</strong>raph representation learning framework (<strong>Bi-SemDRUG</strong>) for multi-type DDI prediction. Bi-SemDRUG proposes a multi-view knowledge subgraph partitioning strategy to extract drug-related refined topological structures from large-scale knowledge graphs, thereby reducing the interference of irrelevant information. Furthermore, Bi-SemDRUG incorporates a bi-semantic subgraph encoder that effectively uncovers multi-order semantic relationships embedded within the knowledge subgraphs. Finally, we propose a general paradigm for information fusion to facilitate the integration of multi-level drug-related information. Exhaustive experiments on three benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance compared to other baseline methods and exhibits good generalization in large-scale DDI prediction. Additionally, case studies emphasize its capacity to offer a more comprehensive insight into the underlying mechanisms of DDIs.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104109"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885636","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":"FedEGL: Edge-assisted federated graph learning","authors":"Haitao Wang ,&nbsp;Aojie Luo ,&nbsp;Wenchao Xu ,&nbsp;Haozhao Wang ,&nbsp;Yichen Li ,&nbsp;Yining Qi ,&nbsp;Rui Zhang ,&nbsp;Ruixuan Li","doi":"10.1016/j.inffus.2025.104118","DOIUrl":"10.1016/j.inffus.2025.104118","url":null,"abstract":"<div><div>Federated graph learning excels in learning graph-structured data that are distributed across multiple clients. However, the partition of graph data results in each client only possessing a subgraph, lacking its neighbor nodes, which significantly degrades accuracy. Although exchanging original nodes can address this issue, it requires interaction with a remote server, not only causing significant communication delays but also leaking data privacy. To tackle this, this paper proposes an edge-server-assisted federated graph learning approach, namely FedEGL, which aggregates and exchanges intermediate features of <em>approximated</em> nodes through a third-party edge server, performing cross-client feature alignment and dynamic weighted aggregation while dynamically allocating privacy budgets with adaptive differential privacy to preserve node privacy. Additionally, differential privacy is introduced to protect the privacy of approximated node features by dynamically allocating privacy budgets. Experimental results show that our method achieves accuracy close to that in centralized settings, with the classification accuracy improved by up to 8% compared to the latest baseline. This method can improve model accuracy while protecting privacy, providing an effective solution to the subgraph partitioning problem in federated graph learning.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104118"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894683","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":"Integrating visual and audio cues for emotion and gender recognition: A multi modal and multi task approach","authors":"Giuseppe De Simone ,&nbsp;Luca Greco ,&nbsp;Alessia Saggese ,&nbsp;Mario Vento","doi":"10.1016/j.inffus.2025.104071","DOIUrl":"10.1016/j.inffus.2025.104071","url":null,"abstract":"<div><div>Gender and emotion recognition are traditionally analyzed independently using audio and video modalities, which introduces challenges when fusing their outputs and often results in increased computational overhead and latency. To address these limitations, in this work we introduces MAGNET (Multimodal Architecture for GeNder and Emotion Tasks), a novel multimodal multitask learning framework that jointly performs gender and emotion recognition by simultaneously analyzing audio and visual inputs. MAGNET employs soft parameter sharing, guided by GradNorm to balance task-specific learning dynamics. This design not only enhances recognition accuracy through effective modality fusion but also reduces model complexity by leveraging multitask learning. As a result, our approach is particularly well-suited for deployment on embedded devices, where computational efficiency and responsiveness are critical. Evaluated on the CREMA-D dataset, MAGNET consistently outperforms unimodal baselines and current state-of-the-art methods, demonstrating its effectiveness for efficient and accurate soft biometric analysis.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104071"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785020","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":"Span-aware temporal aggregation network for video moment retrieval","authors":"Xingyu Shen ,&nbsp;Jinshi Xiao ,&nbsp;Xiang Zhang ,&nbsp;Long Lan ,&nbsp;Xinwang Liu","doi":"10.1016/j.inffus.2025.104075","DOIUrl":"10.1016/j.inffus.2025.104075","url":null,"abstract":"<div><div>Video Moment Retrieval (VMR) aims to identify the temporal span in an untrimmed video that semantically corresponds to a natural language query. Existing methods often overlook temporal invariance, making them sensitive to variations in query span and limiting their performance, especially for retrieving short-span moments. To address this limitation, we propose a Span-aware Temporal Aggregation (STA) network that introduces span-aware features to capture temporal invariant patterns, thereby enhancing robustness to varying query spans. STA consists of two key components: (i) A span-aware feature aggregation (SFA) module constructs span-specific visual representations that are aligned with the query to generate span-aware features, which are then integrated into local candidate moments; (ii) a Query-guided Moment Reasoning (QMR) module, which dynamically adapts the receptive fields of temporal convolutions based on query span semantics to achieve fine-grained reasoning. Extensive experiments on three challenging benchmark datasets demonstrate that STA consistently outperforms state-of-the-art methods, with particularly notable gains for short-span moments.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104075"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785025","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":"GCEPANet: A lightweight and efficient remote sensing image cloud removal network model for optical-SAR image fusion","authors":"Qinglong Zhou ,&nbsp;Xing Wang ,&nbsp;Jiahao Fang ,&nbsp;Wenbo Wu ,&nbsp;Bingxian Zhang","doi":"10.1016/j.inffus.2025.104090","DOIUrl":"10.1016/j.inffus.2025.104090","url":null,"abstract":"<div><div>To mitigate severe cloud interference in optical remote sensing imagery and address the challenges of deploying complex cloud removal models on satellite platforms, this study proposes a lightweight gated parallel attention network, GCEPANet. By integrating optical and SAR data, the network fully exploits the penetration capability of SAR imagery and combines a Gated Convolution Module (GCONV) with an Enhanced Parallel Attention Module (EPA) to establish a “cloud perception–cloud refinement” cooperative mechanism. This mechanism enables the model to identify and filter features according to cloud intensity, effectively separating the feature flows of clear and cloudy regions, and adaptively compensating for cloud-induced degradation to reconstruct the true structural and radiative characteristics of surface objects. Furthermore, a joint spectral–structural loss is introduced to simultaneously constrain spectral consistency and structural fidelity. Extensive experiments on the SEN12MS-CR dataset demonstrate that the proposed GCEPANet consistently outperforms existing methods across multiple metrics, including PSNR, SSIM, MAE, RMSE, SAM, and ERGAS. Compared with the SCTCR model, GCEPANet achieves a 0.9306 dB improvement in PSNR, reduces the number of parameters by 85.5% (to 12.77M), and decreases FLOPs by 76.0% (to 9.71G). These results demonstrate that the proposed method achieves superior cloud removal performance while significantly reducing model complexity, providing an efficient and practical solution for real-time on-orbit cloud removal in optical–SAR fused remote sensing imagery.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104090"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845111","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":"VLDBench Evaluating multimodal disinformation with regulatory alignment","authors":"Shaina Raza ,&nbsp;Ashmal Vayani ,&nbsp;Aditya Jain ,&nbsp;Aravind Narayanan ,&nbsp;Vahid Reza Khazaie ,&nbsp;Syed Raza Bashir ,&nbsp;Elham Dolatabadi ,&nbsp;Gias Uddin ,&nbsp;Christos Emmanouilidis ,&nbsp;Rizwan Qureshi ,&nbsp;Mubarak Shah","doi":"10.1016/j.inffus.2025.104092","DOIUrl":"10.1016/j.inffus.2025.104092","url":null,"abstract":"<div><div>Detecting disinformation that blends manipulated text and images has become increasingly challenging, as AI tools make synthetic content easy to generate and disseminate. While most existing AI-safety benchmarks focus on single-modality misinformation (i.e., false content shared without intent to deceive), intentional multimodal disinformation, such as propaganda or conspiracy theories that imitate credible news; remains largely unaddressed. In this work, we introduce the <strong>V</strong>ision-<strong>L</strong>anguage <strong>D</strong>isinformation Detection <strong>Bench</strong>mark (<span><strong>VLDBench</strong></span>), the first large-scale resource supporting both unimodal (text-only) and multimodal (text + image) disinformation detection. <span><strong>VLDBench</strong></span> comprises approximately 62,000 labeled text-image pairs across 13 categories, curated from 58 news outlets. Using a semi-automated pipeline followed by expert review, 22 domain experts invested over 500 hours to produce high-quality annotations with substantial inter-annotator agreement. Evaluation of state-of-the-art LLMs and VLMs on <span><strong>VLDBench</strong></span> shows that adding visual cues improves detection accuracy, with gains ranging from 5 points for strong baselines (e.g., LLaMA-3.2-11B-Vision 74.82% vs. LLaMA-3.2-1B-Instruct 70.29%) to 25-30 points for smaller families (e.g., LLaVA-v1.5-Vicuna7B 72.32% vs. Vicuna-7B-v1.5 55.21%), reflecting complementary evidence from images (e.g., meme-like visuals, image-text consistency) that text alone cannot capture. We provide data and code for evaluation, fine-tuning and robustness tests to support disinformation analysis. Developed in alignment with the AI Goverance frameworks (MIT AI Risk Repository), <span><strong>VLDBench</strong></span> offers a principled foundation for advancing trustworthy disinformation detection in multimodal media.<span><div><div><table><tbody><tr><th><figure><img></figure></th><td><strong>Project:</strong></td><td><span><span>https://vectorinstitute.github.io/VLDBench/</span><svg><path></path></svg></span></td></tr><tr><th><figure><img></figure></th><td><strong>Data:</strong></td><td><span><span>https://huggingface.co/datasets/vector-institute/VLDBench</span><svg><path></path></svg></span></td></tr><tr><th><figure><img></figure></th><td><strong>Code:</strong></td><td><span><span>https://github.com/VectorInstitute/VLDBench</span><svg><path></path></svg></span></td></tr></tbody></table></div></div></span></div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104092"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822968","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 hierarchical information policy fusion framework with multimodal large language models for autonomous guidewire navigation in endovascular procedures","authors":"Haoyu Wang ,&nbsp;Taylor Yiu ,&nbsp;Serena Lee ,&nbsp;Ka Gao ,&nbsp;Hangling Sun ,&nbsp;Chenyu Zhou ,&nbsp;Anji Li ,&nbsp;Qiangqiang Fu ,&nbsp;Yu Wang ,&nbsp;Bin Chen","doi":"10.1016/j.inffus.2025.104115","DOIUrl":"10.1016/j.inffus.2025.104115","url":null,"abstract":"<div><div>Robotic-assisted endovascular interventions promise to transform cardiovascular therapy by improving procedural precision and minimizing cardiologists’ exposure to occupational risks. However, current systems are limited by their reliance on manual control and lack of adaptability to complex vascular anatomies. To address these challenges, we propose a novel <em><strong>H</strong></em>ierarchical <em><strong>A</strong></em>utonomous <em><strong>G</strong></em>uidewire <em><strong>N</strong></em>avigation and <em><strong>D</strong></em>elivery (<em><strong>HAG-ND</strong></em>) framework that leverages the strengths of multimodal large language models (MLLMs) and a novel reinforcement learning module inspired by Deep Q-Networks (DQNs). The high-level MLLM is trained on diverse blood vessel and guidewire scenarios from various angles and positions, enabling it to assess the suitability and timing of substance release at the target location. Within the MLLM, a parliamentary mechanism is introduced, where multiple specialized models, each focusing on a specific aspect of the vascular environment, vote on the optimal course of action. The low-level reinforcement learning module focuses on optimizing autonomous guidewire navigation to the designated target site by learning from the rich semantic understanding provided by the MLLM. Experimental evaluations demonstrate that the HAG-ND framework significantly improves the accuracy and reliability of guidewire positioning and targeted delivery compared to existing methods. By harnessing the complementary capabilities of MLLMs and novel reinforcement learning techniques in a hierarchical architecture, HAG-ND represents a significant step towards fully autonomous and adaptive robotic-assisted endovascular interventions.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104115"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894682","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":"DAK-Pose: Dual-augmentor knowledge fusion for generalizable video-based 3D human pose estimation","authors":"Yachuan Wang,&nbsp;Bin Zhang,&nbsp;Hao Yuan","doi":"10.1016/j.inffus.2025.104100","DOIUrl":"10.1016/j.inffus.2025.104100","url":null,"abstract":"<div><div>Real-world deployment of video-based 3D human pose estimation remains challenging, as limited annotated data collected in constrained lab settings cannot fully capture the complexity of human motion. While motion synthesis for data augmentation has emerged as a mainstream solution to enhance generalization, existing synthesis methods suffer from inherent trade-offs: kinematics-based motion synthesis approaches preserve anatomical plausibility but sacrifice temporal coherence, while coordinate-based methods ensure motion smoothness but violate biomechanical constraints. This results in persistent domain gaps when synthetic data is directly used in the observation space to train pose estimation models. To overcome this, we propose DAK-Pose, which shifts augmentation to the feature space. We disentangle motion into structural and dynamic features, and design two complementary augmentors: (1) A structure-prioritized module enforces kinematic constraints for anatomical validity, and (2) a dynamic-prioritized module generates diverse temporal patterns. Auxiliary encoders trained on synthetic motions generated by these augmentors transfer domain-invariant knowledge to the pose estimator through adversarial alignment. Experiments on Human3.6M, MPI-INF-3DHP, and 3DPW datasets show that DAK-Pose achieves state-of-the-art cross-dataset performance.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104100"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894681","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 comprehensive survey and taxonomy of mamba: Applications, Challenges, and Future Directions","authors":"Qiguang Miao ,&nbsp;Linxing Jia ,&nbsp;Kun Xie ,&nbsp;Kaiyuan Fu ,&nbsp;Zongkai Yang","doi":"10.1016/j.inffus.2025.104094","DOIUrl":"10.1016/j.inffus.2025.104094","url":null,"abstract":"<div><div>Transformer-based architectures have achieved notable success across natural language processing, computer vision, and multimodal learning, yet they face persistent challenges such as high computational complexity and limited adaptability to dynamic environments. State Space Models (SSMs) have emerged as a competitive alternative, offering linear-time complexity and the ability to implicitly capture long-range dependencies. Building on this foundation, the Mamba model introduces time-varying parameterization to dynamically adjust state transitions based on input context, combined with selective state updates, content-aware scanning strategies, and hardware-efficient design. These innovations enable Mamba to maintain linear complexity while delivering higher throughput and significantly reduced memory consumption compared to both Transformer-based and conventional SSM architectures. This survey systematically reviews the theoretical foundations, architectural innovations, and application progress of the Mamba model. First, we trace the evolution of SSMs, highlighting the key design principles that underpin Mamba’s dynamic state transition and selective computation mechanisms. Second, we summarize Mamba’s structural innovations in modeling dynamics and multimodal fusion, categorizing its applications across multiple modalities, including vision, speech, point clouds, and multimodal data. Finally, we evaluate representative applications in medical image analysis, recommendation systems, reinforcement learning, and generative modeling, identifying advantages, limitations, and open challenges. The review concludes by outlining future research directions focused on improving generalization, causal reasoning, interpretability, and computational efficiency. This work aims to provide a concise yet comprehensive reference for researchers and practitioners, promoting further development and deployment of Mamba-based architectures across diverse real-world scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104094"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813863","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}