Knowledge-Based Systems最新文献

{"title":"CoGMoE: Sparse and specialized framework for multi-agent collaborative perception via graph mixture-of-experts","authors":"Xingpeng Li ,&nbsp;Enwen Hu ,&nbsp;Siyuan Jin ,&nbsp;Baoding Zhou ,&nbsp;Jingrong Liu","doi":"10.1016/j.knosys.2026.115329","DOIUrl":"10.1016/j.knosys.2026.115329","url":null,"abstract":"<div><div>Multi-agent collaborative perception significantly improves autonomous driving safety by sharing complementary information to overcome individual limitations owing to occlusions. A primary goal is to navigate the critical trade-off between perception performance and communication bandwidth. However, existing methods struggle to achieve this balance, treating all information equally without considering each agent’s specific situation. To address this issue, this study proposes CoGMoE, a novel collaborative perception method that models the V2V communication as a structured, hierarchical reasoning process. Specifically, CoGMoE provides three distinct advantages: i) it selects a sparse set of semantically salient keypoints from each vehicle, significantly reducing communication overhead while preserving important information; ii) it constructs a hierarchical communication graph that establishes direct alignment links between the corresponding position areas of different vehicles, explicitly separating them from the internal links used for context reasoning; and iii) it uses a graph mixture-of-experts (GraphMoE) architecture governed by multi-round expert deliberation to dynamically assign experts for each link type, achieving superior robustness using iterative feature refinement. Extensive experiments on both simulated and real-world datasets demonstrate that our proposed CoGMoE outperforms state-of-the-art collaborative perception methods in achieving detection accuracy and communication bandwidth trade-off.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115329"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081157","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":"Multimodal summarization via coarse-and-fine granularity synergy and region counterfactual reasoning filter","authors":"Rulong Liu ,&nbsp;Qing He ,&nbsp;Yuji Wang ,&nbsp;Nisuo Du ,&nbsp;Zhihao Yang","doi":"10.1016/j.knosys.2026.115356","DOIUrl":"10.1016/j.knosys.2026.115356","url":null,"abstract":"<div><div>Multimodal Summarization (MS) generates high-quality summaries by integrating textual and visual information. However, existing MS research faces several challenges, including (1) ignoring fine-grained key information between visual and textual modalities and interaction with coarse-grained information, (2) cross-modal semantic inconsistency, which hinders alignment and fusion of visual and textual feature spaces, and (3) ignoring inherent heterogeneity of an image when filtering visual information, which causes excessive filtering or excessive retention. To address these issues, we propose Coarse-and-Fine Granularity Synergy and Region Counterfactual Reasoning Filter (CFCR) for MS. Specifically, we design Coarse-and-Fine Granularity Synergy (CFS) to capture both global (coarse-grained) and important detailed (fine-grained) information in text and image modalities. Based on this, we design Dual-granularity Contrastive Learning (DCL) for mapping coarse-grained and fine-grained visual features into the text semantic space, thereby reducing semantic inconsistency caused by modality differences at dual granularity levels, and facilitating cross-modal alignment. To address the issue of excessive filtering or excessive retention in visual information filtering, we design a Region Counterfactual Reasoning Filter (RCF) that employs Counterfactual Reasoning to determine the validity of image regions and generate category labels. These labels are then used to train Image Region Selector to select regions beneficial for summarization. Extensive experiments on the representative MMSS and MSMO dataset show that CFCR outperforms multiple strong baselines, particularly in terms of selecting and focusing on critical details, demonstrating its effectiveness in MS.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115356"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081244","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":"Dual prompts guided cross-domain transformer for unified day-night image dehazing","authors":"Jianlei Liu ,&nbsp;Jiaming Niu ,&nbsp;Xiang Chen ,&nbsp;Yuting Pang ,&nbsp;Shilong Wang","doi":"10.1016/j.knosys.2026.115362","DOIUrl":"10.1016/j.knosys.2026.115362","url":null,"abstract":"<div><div>Although considerable progress has been made in image dehazing, most existing methods are constrained to a single degradation type or specific haze pattern. However, in real-world environments, haze manifests in diverse forms owing to variations in illumination, day-night transitions, and other coupled degradation factors. A new task has been assigned to address the following challenges: unified day-night image dehazing (UDND), with the aim to restore haze-degraded images across daytime and nighttime conditions within a single unified framework. For this task, we propose UDNDformer, a cross-domain Transformer guided by dual-prompt learning, which integrates both hard prompt learning (HPL) and soft prompt learning (SPL). The HPL module reconstructs scene before encoding transferable haze representations in a frozen form, ensuring consistent degradation modeling across domains. By contrast, the SPL module employs learnable tensors that interact with encoded features to adaptively capture temporal haze variations and dynamically modulate restoration during decoding for condition-aware guidance. This dual-prompt design enables UDNDformer to achieve adaptive haze perception and flexible degradation modeling under diverse illumination conditions, thereby markedly enhancing the restoration quality in unified day-night scenarios. Extensive experimentation demonstrates that UDNDformer consistently outperforms state-of-the-art methods across multiple day-night benchmarks and demonstrates notable improvements in downstream vision tasks, validating its effectiveness and strong generalizability to real-world applications.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115362"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015908","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":"PoseDefCycleGAN: Identity-preserving face frontalization with deformable convolutions and pose-aware supervision","authors":"Shakeel Muhammad Ibrahim ,&nbsp;Shujaat Khan ,&nbsp;Young-Woong Ko ,&nbsp;Jeong-Gun Lee","doi":"10.1016/j.knosys.2026.115358","DOIUrl":"10.1016/j.knosys.2026.115358","url":null,"abstract":"<div><div>Face recognition systems have achieved impressive accuracy in controlled environments but continue to face challenges under extreme pose variations. To address this limitation, we propose a novel face frontalization framework, PoseDefCycleGAN, that combines the strengths of CycleGAN, deformable convolution, and pose-guided supervision. Our method leverages deformable convolution in the final layer of the generator to dynamically adapt the receptive field, enabling better reconstruction of complex facial geometries. Additionally, we incorporate a lightweight pose classification network to enforce pose-aware regularization, encouraging the generation of semantically consistent frontal images. The proposed model is trained using unpaired data and optimized with a combination of adversarial, cycle consistency, identity-preserving, and pose regularization losses. Extensive experiments on MultiPIE, AFW, and LFW datasets demonstrate that the method improves both visual fidelity and face recognition, particularly at extreme yaw angles: on MultiPIE we reduce FID to 15.90 (from 18.32 with CycleGAN) and achieve 98.9% rank-1 accuracy at  ± 90^∘; on LFW we obtain 90.20% accuracy with LPIPS=0.3052. Quantitative evaluations further validate the contribution of deformable convolutions and pose supervision. Our work presents a robust solution for pose-invariant face recognition and establishes a strong benchmark for identity-preserving face frontalization. Model implementation is available on the author’s GitHub page <span><span>https://github.com/Shak97/PoseDefCycleGAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115358"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081103","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":"Affective computing in the era of large language models: A survey from the NLP perspective","authors":"Yiqun Zhang ,&nbsp;Xiaocui Yang ,&nbsp;Xingle Xu ,&nbsp;Zeran Gao ,&nbsp;Yijie Huang ,&nbsp;Shiyi Mu ,&nbsp;Shi Feng ,&nbsp;Daling Wang ,&nbsp;Yifei Zhang ,&nbsp;Kaisong Song ,&nbsp;Ge Yu","doi":"10.1016/j.knosys.2026.115411","DOIUrl":"10.1016/j.knosys.2026.115411","url":null,"abstract":"<div><div>Affective Computing (AC) integrates computer science, psychology, and cognitive science to enable machines to recognize, interpret, and simulate human emotions across domains such as social media, finance, healthcare, and education. AC commonly centers on two task families: Affective Understanding (AU) and Affective Generation (AG). While fine-tuned pre-trained language models (PLMs) have achieved solid AU performance, they often generalize poorly across tasks and remain limited for AG, especially in producing diverse, emotionally appropriate responses. The advent of Large Language Models (LLMs) (e.g., ChatGPT and LLaMA) has catalyzed a paradigm shift by offering in-context learning, broader world knowledge, and stronger sequence generation. This survey presents an Natural Language Processing (NLP)-oriented overview of AC in the LLM era. We (i) consolidate traditional AC tasks and preliminary LLM-based studies; (ii) review adaptation techniques that improve AU/AG, including Instruction Tuning (full and parameter-efficient methods), Prompt Engineering (zero/few-shot, chain-of-thought, agent-based prompting), and Reinforcement Learning (RL). For the latter, we summarize RL from human preferences, verifiable/programmatic rewards, and model(s) feedback, which provide preference- or rule-grounded optimization signals that can help steer AU/AG toward empathy, safety, and planning, achieving finer-grained or multi-objective control. To assess progress, we compile benchmarks and evaluation practices for both AU and AG. We also discuss open challenges–from ethics, data quality, and safety to robust evaluation and resource efficiency–and outline research directions. We hope this survey clarifies the landscape and offers practical guidance for building affect-aware, reliable, and responsible LLM systems.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115411"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081158","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":"Fact splices and entity aggregation networks for sparse temporal knowledge graph completion","authors":"Lin Zhu ,&nbsp;Jiahui Hu ,&nbsp;Luyi Bai","doi":"10.1016/j.knosys.2026.115387","DOIUrl":"10.1016/j.knosys.2026.115387","url":null,"abstract":"<div><div>Advancements in artificial intelligence has markedly highlighted the importance of temporal knowledge graphs. However, since factors such as limitations in data collection and the immaturity of knowledge extraction techniques, temporal knowledge graphs often remain incomplete. Moreover, the rapid growth of real-world information has significantly exacerbated the sparsity of these graphs, severely affecting their practical application effects. Precisely for these reasons, the enhancement of sparse temporal knowledge graphs has become a significant focus of investigation in current academic research. In the context of completing sparse temporal knowledge graphs, previous methods have enriched entity representations through neighbor information aggregation, alleviated the sparsity of the graphs, and improved the completion effect. However, these methods have limitations. On the one hand, they focus on the information of neighboring entities and neglect the role of the relationship vectors between the current entity and its neighbor entities. On the other hand, they fail to distinguish the aggregation weights according to the roles of adjacent entities, thus limiting the further improvement of the completion effect. Accordingly, this paper investigates an information aggregation method based on relevant facts and a role-oriented attention network to enrich entity representations. Given that the importance of relationship vectors is often overlooked, we propose a fact vector generation strategy through a chained relation extractor and a fact vector splicer to excavate the information of relationship vectors. Aiming at the problem that previous methods failed to distinguish the role weights of adjacent entities, we propose a role-oriented attention network. This network aggregates context information and assigns weights according to the roles of the aggregated information, thereby generating more accurate entity representations. According to the experimental results, our model outperforms state-of-the-art baseline models in the selected metrics.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115387"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015907","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":"SSTrack: Joint scale-aware temporal prompts and spatio-temporal prior transformer for visual object tracking","authors":"Sugang Ma ,&nbsp;Zhen Wan ,&nbsp;Bin Hu ,&nbsp;Jinyu Zhang ,&nbsp;Zhiqiang Hou ,&nbsp;Xiangmo Zhao","doi":"10.1016/j.knosys.2026.115370","DOIUrl":"10.1016/j.knosys.2026.115370","url":null,"abstract":"<div><div>Existing visual tracking algorithms have made impressive progress by leveraging the powerful global modeling capabilities of Transformers. However, these approaches typically focus on designing complex network models while neglecting temporal information and scale variations. These limitation makes them susceptible to tracking failures caused by target occlusion and deformation. Additionally, most trackers adopt ViT-based attention mechanisms. These trackers rely entirely on input images and lack task-relevant prior knowledge about the target. To address these issues, this paper proposes SSTrack, a novel visual tracking algorithm that integrates scale-aware temporal prompts and a spatio-temporal prior Transformer. Specifically, a scale-aware temporal information propagation mechanism is first designed, which allows the tracker to enable the model to learn the scale changes of the target between the preceding and following frames by propagating multi-scale temporal prompts across consecutive frames. Furthermore, we introduce a spatio-temporal prior module to provide the tracker with spatio-temporal prior knowledge of the target locations and appearances, combing spatio-temporal prior module with the self-attention module. Extensive experiments on seven benchmark datasets, including LaSOT, TrackingNet, and GOT-10k, demonstrate the superior tracking performance of SSTrack. The code and pre-trained models will be available at <span><span>here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115370"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015909","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":"Enhancing Heterogeneous Graph Learning with Semantic-Aware Meta-Path Diffusion and Dual Optimization","authors":"Guanghua Ding ,&nbsp;Rui Tang ,&nbsp;Xian Mo","doi":"10.1016/j.knosys.2026.115385","DOIUrl":"10.1016/j.knosys.2026.115385","url":null,"abstract":"<div><div>Heterogeneous graph learning aims to extract semantic and structural information from multiple node types, edges, and meta-paths, learning low-dimensional embeddings that preserve core characteristics to support downstream tasks. To address the core challenges of insufficient semantic mining and weak learning synergy in heterogeneous graph learning, this paper proposes a heterogeneous graph learning method integrating <u>S</u>emantic-aware <u>M</u>eta-path perturbation and <u>C</u>ollaborative <u>D</u>ual-learning optimization(SMCD). First, the method constructs auxiliary meta-paths based on the original meta-paths, and then designs two augmentation schemes to generate augmented views: For semantic-level augmentation, it performs edge perturbation based on semantic similarity, and enhances the semantics of core meta-paths with the semantics of auxiliary meta-paths via a diffusion model; For task-level augmentation, it utilizes a diffusion model and semantic weights to select the top-k semantically relevant nodes for each node in the core meta-path graph, reconstructing the meta-paths graph structure. Then, a two-stage attention aggregation graph encoder is adopted to output the final node embeddings. Finally, a self-supervised and supervised (i.e., Dual-learning) collaborative optimization strategy that flexibly adapts to label distribution is used to optimize the objective-this not only balances the discriminability and generality of representations but also adapts to scenarios with different degrees of label scarcity. Experimental results on three public datasets illustrate that our proposed method achieves remarkable advantages in both node classification and node clustering tasks. Our datasets and source code are available.<span><span>¹</span></span></div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115385"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081247","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":"Learning deformable image registration with dilated attention transformer","authors":"Yungeng Zhang ,&nbsp;Yuan Chang ,&nbsp;Xiaohou Shi ,&nbsp;Yaqi Song ,&nbsp;Ke Li ,&nbsp;Feng Wang ,&nbsp;Mingchuan Yang","doi":"10.1016/j.knosys.2026.115372","DOIUrl":"10.1016/j.knosys.2026.115372","url":null,"abstract":"<div><div>Deformable image registration is a fundamental preprocessing step for many applications of medical image analysis. Recently, Transformers have demonstrated potential in deformable image registration. Transformers have the advantage of capturing spatial correlations within or across images by computing pairwise patch relations. However, due to the quadratic computational complexity of Transformers with respect to the sequence length and the fact that volumetric images contain an excessive number of voxels, current Transformer-based registration methods employ two strategies to mitigate the computational cost of Transformers when processing volumetric images. One approach is to integrate Transformers into the bottleneck of a CNN backbone to enhance low-resolution features, neglecting to use Transformers to capture high-resolution anatomical structure correlations. Another approach is to limit self-attention operations to small local windows, thereby restricting the receptive field of Transformers and their ability to deal with large displacements. Moreover, recent methods typically leverage the attention mechanism to enhance feature learning, without matching these features to explicitly calculate the displacement field. In this paper, we separate the processes of feature extraction, feature enhancement, and feature matching in deformable image registration. In order to process high-resolution 3D feature maps, we propose Dilated Attention Transformers. The Dilated Attention Transformers capture intra- and cross-image feature correlations with large receptive fields while ensuring manageable computational costs. Furthermore, in the feature matching process, we introduce a Dilated Matching strategy to accommodate large deformations. Experiments on public brain MRI and liver CT datasets demonstrate that our method performs favorably against the state-of-the-art deformable image registration methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115372"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025751","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":"Temporal householder transformation embedding for temporal knowledge graph completion","authors":"Zhiyu Xu ,&nbsp;Kai Lin ,&nbsp;Pengpeng Qiu ,&nbsp;Tong Shen ,&nbsp;Fu Zhang","doi":"10.1016/j.knosys.2026.115406","DOIUrl":"10.1016/j.knosys.2026.115406","url":null,"abstract":"<div><div>Knowledge Graph Embedding (KGE) has been widely used to address the incompleteness of Knowledge Graph (KG) by predicting missing facts. Temporal Knowledge Graph Embedding (TKGE) extends KGE by incorporating temporal information into fact representations. However, most existing research focuses on static graphs and ignores the temporal dynamics of facts in TKG, which poses significant challenges for link prediction. Furthermore, current TKGE models still struggle with effectively capturing and representing crucial relation patterns, including <em>symmetry, antisymmetry, inversion, composition</em>, and <em>temporal</em>, along with complex relation mapping properties like 1<em>-to-N, N-to-</em>1, and <em>N-to-N</em>. To overcome these challenges, we propose a <strong>Te</strong>mporal <strong>H</strong>ouseholder <strong>T</strong>ransformation <strong>E</strong>mbedding model called TeHTE, which fuses temporal information with Householder transformation to capture both static and temporal features within TKG effectively. In the static module, TeHTE constructs static entity embeddings by reflecting the head entity through a transfer matrix and represents each relation with a pair of vectors to capture relational semantics. In the temporal module, TeHTE integrates temporal information into the entity representation through the time transfer matrix and shared time window, thereby enhancing its ability to capture temporal features. To further enhance modeling capacity, TeHTE learns a set of Householder transformations parameterized by relations to obtain structural embeddings for entities. Moreover, we theoretically demonstrate the ability of TeHTE to model various relation patterns and mapping properties. Experimental results on four benchmark datasets indicate that TeHTE substantially surpasses most existing TKGE approaches on temporal link prediction tasks. Ablation studies further validate the contribution of each component within the TeHTE framework.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"337 ","pages":"Article 115406"},"PeriodicalIF":7.6,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081104","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}