IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第4页

Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach 高效解决百万维多目标问题：方向采样和微调方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-18 DOI: 10.1109/TETCI.2024.3386866

Haokai Hong;Min Jiang;Qiuzhen Lin;Kay Chen Tan

{"title":"Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach","authors":"Haokai Hong;Min Jiang;Qiuzhen Lin;Kay Chen Tan","doi":"10.1109/TETCI.2024.3386866","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386866","url":null,"abstract":"We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in VLSMOPs intensifies the curse of dimensionality and poses significant challenges for existing large-scale evolutionary multiobjective algorithms, rendering them more difficult to solve within the constraints of practical computing resources. To overcome this issue, we propose a novel approach called the very large-scale multiobjective optimization framework (VMOF). The method efficiently samples general yet suitable evolutionary directions in the very large-scale space and subsequently fine-tunes these directions to locate the Pareto-optimal solutions. To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations. Furthermore, a technique is designed for fine-tuning directions specific to tracking Pareto-optimal solutions. To understand the designed framework, we present our analysis of the framework and then evaluate VMOF using widely recognized benchmarks and real-world problems spanning dimensions from 100 to 1,000,000. Experimental results demonstrate that our method exhibits superior performance not only on LSMOPs but also on VLSMOPs when compared to existing algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4197-4209"},"PeriodicalIF":5.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving Consensus of Double-Integrator Multi-Agent Systems With Input Constraints 有输入约束的双积分多代理系统的隐私保护共识

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386692

Qingyun Deng;Kexin Liu;Yinyan Zhang

引用次数: 0

Hierarchical Multimodal Graph Learning for Outfit Compatibility Modelling 用于服装搭配建模的分层多模态图学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386774

Rucong Xu;Jianfeng Wang;Ming Li;Junyan Xu;Yun Li

{"title":"Hierarchical Multimodal Graph Learning for Outfit Compatibility Modelling","authors":"Rucong Xu;Jianfeng Wang;Ming Li;Junyan Xu;Yun Li","doi":"10.1109/TETCI.2024.3386774","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386774","url":null,"abstract":"Outfit compatibility modelling plays a significant role in e-commerce decision-making, but the existing methods are restricted to modelling the visual and textual information and have neglected the direct contribution of category labels and the differences in semantic richness among different modalities. This paper addresses these issues by developing a hierarchical multimodal graph learning framework for outfit compatibility modelling called HMGL-OCM, which consists of an item-level graph network and a modality-level graph network. The former augments local information of the modal features within an item, and the latter performs global learning of interactions between different items within the modality. Further, a novel cross-modality propagation method at the modality-level graph network stage is developed, which leverages features from other modality networks for complementary modelling while retaining the current modality information density of the item. Extensive experimentation on three real-world fashion datasets corroborates that the HMGL-OCM model surpasses state-of-the-art methodologies.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4130-4142"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SMOD: An Accurate and Efficient Segmentation-Based Medical Object Detector SMOD：基于分割的精确高效医疗物体检测器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386620

Jie Du;Chuyang Chen;Yuanman Li;Yaolin Zhu;Peng Liu;Tianfu Wang

{"title":"SMOD: An Accurate and Efficient Segmentation-Based Medical Object Detector","authors":"Jie Du;Chuyang Chen;Yuanman Li;Yaolin Zhu;Peng Liu;Tianfu Wang","doi":"10.1109/TETCI.2024.3386620","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386620","url":null,"abstract":"Medical object detection simultaneously locates and recognizes the objects/lesions in medical images, and thus it is of high clinical relevance. There have been many kinds of detectors used in medical object detection tasks, such as transformer-based detectors. However, to the best of our knowledge, segmentation-based detectors for medical images have not been exploited yet. In this paper, a novel Segmentation-based Medical Object Detector (SMOD) with our newly designed \u0000<italic>Seg2Det loss function\u0000 and \u0000<italic>Non-connected Region Merging\u0000 (NRM) method is proposed, which provides: i) fast inference speed of detection due to the efficient segmentation-based detection framework without processing on anchors; and ii) high detection accuracy as our novel Seg2Det loss can effectively replace detection by segmentation of bounding boxes and our NRM method can enclose segmentation predictions with proper boxes. Our SMOD has been evaluated over two common medical object detection tasks and compared with both anchor-based and anchor-free state-of-the-art (SOTA) detectors. In experiments, our SMOD outperforms all compared detectors and achieves the fastest inference speed. Specifically, our SMOD increases the overall mean Average Precision (mAP) score by at least 2.38% and 3.55% for two pneumonia detection datasets, respectively; for the detection of gastrointestinal polyps, our SMOD also achieves at least a 2.59% increase on the mAP score; in terms of inference speed, our SMOD can process more than 80 images per second on all compared datasets; and in visualization results, our SMOD achieves satisfactory performance in detecting lesions from images with low-contrast lesions, blurred object boundaries, small lesions, and multiple lesions.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4106-4118"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Contrastive Learning for Tracking Dynamic Communities in Temporal Networks 跟踪时态网络中动态群落的图对比学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386844

Yun Ai;Xianghua Xie;Xiaoke Ma

{"title":"Graph Contrastive Learning for Tracking Dynamic Communities in Temporal Networks","authors":"Yun Ai;Xianghua Xie;Xiaoke Ma","doi":"10.1109/TETCI.2024.3386844","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386844","url":null,"abstract":"Temporal networks are ubiquitous because complex systems in nature and society are evolving, and tracking dynamic communities is critical for revealing the mechanism of systems. Moreover, current algorithms utilize temporal smoothness framework to balance clustering accuracy at current time and clustering drift at historical time, which are criticized for failing to characterize the temporality of networks and determine its importance. To overcome these problems, we propose a novel algorithm by \u0000<underline>j\u0000oining \u0000<underline>N\u0000on-negative matrix factorization and \u0000<underline>C\u0000ontrastive learning for \u0000<underline>D\u0000ynamic \u0000<underline>C\u0000ommunity detection (jNCDC). Specifically, jNCDC learns the features of vertices by projecting successive snapshots into a shared subspace to learn the low-dimensional representation of vertices with matrix factorization. Subsequently, it constructs an evolution graph to explicitly measure relations of vertices by representing vertices at current time with features at historical time, paving a way to characterize the dynamics of networks at the vertex-level. Finally, graph contrastive learning utilizes the roles of vertices to select positive and negative samples to further improve the quality of features. These procedures are seamlessly integrated into an overall objective function, and optimization rules are deduced. To the best of our knowledge, jNCDC is the first graph contrastive learning for dynamic community detection, that provides an alternative for the current temporal smoothness framework. Experimental results demonstrate that jNCDC is superior to the state-of-the-art approaches in terms of accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3422-3435"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model 基于狼群分工模型的无人机蜂群动态任务分配方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386614

Qiang Peng;Husheng Wu;Na Li;Feng Wang

{"title":"A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model","authors":"Qiang Peng;Husheng Wu;Na Li;Feng Wang","doi":"10.1109/TETCI.2024.3386614","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386614","url":null,"abstract":"The dynamic task allocation problem for Unmanned Aerial Vehicle (UAV) swarms is characterized by dynamic uncertainty, high nonlinearity, and multimodality, which increasingly becomes a focal point and challenge within the realm of task allocation. This study investigates the mixed interaction modes of “individual-individual,” “individual-environment,” and “swarm-environment,” drawing inspiration from the labor division observed in wolf packs. We design a mechanism for alliance formation that is predicated on both individual democratic choice and centralized swarm decision-making, integrating the concept of alliance formation with a labor division model. Moreover, we propose a democratic-centralization model (DCM) that incorporates a bottom-up labor division approach aligned with the wolf pack alliance framework. This model is adeptly adapted to the dynamic task allocation for UAV swarms, employing a strategy of “individual cyclic competition plus overall coordinated decision-making.” We compare simulation experiments using the DCM with three contemporary, leading methods addressing the dynamic allocation problem for UAV swarms tasked with dynamic operations. The results demonstrate that our model can efficiently orchestrate the dynamic allocation of UAV swarms, exhibiting considerable dynamic adaptability, collaborative efficiency, and robustness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4075-4089"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-Temporal Hybrid Attentive Graph Network for Diagnosis of Mental Disorders on fMRI Time-Series Data 基于 fMRI 时间序列数据的时空混合注意力图网络用于精神障碍诊断

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386612

Rui Liu;Zhi-An Huang;Yao Hu;Lei Huang;Ka-Chun Wong;Kay Chen Tan

{"title":"Spatio-Temporal Hybrid Attentive Graph Network for Diagnosis of Mental Disorders on fMRI Time-Series Data","authors":"Rui Liu;Zhi-An Huang;Yao Hu;Lei Huang;Ka-Chun Wong;Kay Chen Tan","doi":"10.1109/TETCI.2024.3386612","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386612","url":null,"abstract":"Facing the prevalence of mental disorders around the world, the burden of healthcare services becomes increasingly imminent. To lessen patients' suffering, the timely diagnosis and therapy of mental disorders are particularly essential. Functional magnetic resonance imaging (fMRI), as the de facto non-invasive neuroimaging technique, can effectively examine the spatial and temporal patterns of brain activity. Recently, computer-aided diagnosis (CAD) approaches have emerged to assist doctors in interpreting fMRI images. However, existing CAD methods cannot fully exploit the spatio-temporal dependence in fMRI signals, possibly leading to inaccurate diagnosis. In this study, we propose a spatio-temporal hybrid attentive graph network (ST-HAG) for diagnosing autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) from fMRI data. Specifically, a hybrid graph convolution network is developed to effectively capture complex spatio-temporal dynamics. Meanwhile, a Transformer-based self-attention module helps ST-HAG to extract the full-scale temporal correlation. Finally, we use a gated fusion unit to learn discriminative spatio-temporal graph representations for classification. Cross-validation experiments demonstrate that the proposed ST-HAG achieves state-of-the-art performance with a mean accuracy of 71.9% and 74.8% for ASD and ADHD on ABIDE (1035 subjects) and ADHD-200 (939 subjects) datasets, respectively. Moreover, thanks to the adopted dynamic graph attentive representation, the potent interpretability enables ST-HAG to detect the remarkable temporal association patterns among different brain regions based on dynamic functional connectivity networks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4046-4058"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Discrete Representation to Continuous Modeling: A Novel Audio-Visual Saliency Prediction Model With Implicit Neural Representations 从离散表征到连续建模：具有内隐神经表征的新型视听显著性预测模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386619

Dandan Zhu;Kaiwei Zhang;Kun Zhu;Nana Zhang;Weiping Ding;Guangtao Zhai;Xiaokang Yang

{"title":"From Discrete Representation to Continuous Modeling: A Novel Audio-Visual Saliency Prediction Model With Implicit Neural Representations","authors":"Dandan Zhu;Kaiwei Zhang;Kun Zhu;Nana Zhang;Weiping Ding;Guangtao Zhai;Xiaokang Yang","doi":"10.1109/TETCI.2024.3386619","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386619","url":null,"abstract":"In the era of deep learning, audio-visual saliency prediction is still in its infancy due to the complexity of video signals and the continuous correlation in the temporal dimension. Most existing approaches treat videos as 3D grids of RGB values and model them using discrete neural networks, leading to issues such as video content-agnostic and sub-optimal feature representation ability. To address these challenges, we propose a novel dynamic-aware audio-visual saliency (DAVS) model based on implicit neural representations (INRs). The core of our proposed DAVS model is to build an effective mapping by exploiting a parametric neural network that maps space-time coordinates to the corresponding saliency values. Specifically, our model incorporates an INR-based video generator that decomposes videos into image, motion, and audio feature vectors, learning video content-adaptive features via a parametric neural network. This generator efficiently encodes videos, naturally models continuous temporal dynamics, and enhances feature representation capability. Furthermore, we introduce a parametric audio-visual feature fusion strategy in the saliency prediction procedure, enabling intrinsic interactions between modalities and adaptively integrating visual and audio cues. Through extensive experiments on benchmark datasets, our proposed DAVS model demonstrates promising performance and intriguing properties in audio-visual saliency prediction.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4059-4074"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Structure Learning With Automatic Search of Hyperparameters Based on Genetic Programming 基于遗传编程自动搜索超参数的图结构学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-17 DOI: 10.1109/TETCI.2024.3386833

Pengda Wang;Mingjie Lu;Weiqing Yan;Dong Yang;Zhaowei Liu

{"title":"Graph Structure Learning With Automatic Search of Hyperparameters Based on Genetic Programming","authors":"Pengda Wang;Mingjie Lu;Weiqing Yan;Dong Yang;Zhaowei Liu","doi":"10.1109/TETCI.2024.3386833","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3386833","url":null,"abstract":"Graph neural networks (GNNs) rely heavily on graph structures and artificial hyperparameters, which may increase computation and affect performance. Most GNNs use original graphs, but the original graph data has problems with noise and incomplete information, which easily leads to poor GNN performance. For this kind of problem, recent graph structure learning methods consider how to generate graph structures containing label information. The settings of some hyperparameters will also affect the expression of the GNN model. This paper proposes a genetic graph structure learning method (Genetic-GSL). Different from the existing graph structure learning methods, this paper not only optimizes the graph structure but also the hyperparameters. Specifically, different graph structures and different hyperparameters are used as parents; the offspring are cross-mutated through the parents; and then excellent offspring are selected through evaluation to achieve dynamic fitting of the graph structure and hyperparameters. Experiments show that, compared with other methods, Genetic-GSL basically improves the performance of node classification tasks by 1.2%. With the increase in evolution algebra, Genetic-GSL has good performance on node classification tasks and resistance to adversarial attacks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4155-4164"},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Node Classification in Weighted Complex Networks Using Neighborhood Feature Similarity 利用邻域特征相似性在加权复杂网络中进行节点分类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-12 DOI: 10.1109/TETCI.2024.3380481

Ramya D Shetty;Shrutilipi Bhattacharjee;Kogatam Thanmai

{"title":"Node Classification in Weighted Complex Networks Using Neighborhood Feature Similarity","authors":"Ramya D Shetty;Shrutilipi Bhattacharjee;Kogatam Thanmai","doi":"10.1109/TETCI.2024.3380481","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3380481","url":null,"abstract":"The potential of graph representation learning schemes has attained great acceptance in diverse, complex network applications. Most of the existing Graph Neural Network (GNN) architectures explore the node features aggregation and feature transformation within the neighborhoods, mainly performed on the unweighted graphs. Also, the existing GNN architectures consider all sets of neighborhood features, which are computationally expensive tasks. Practically, most of the real-world graphs are weighted graphs, and it is important to learn the representation of weighted graphs. In this work, we generate and leverage information of the best possible feature combination from the multiple levels of the networks. Edge weights and the connection structure are considered for generating node embedding, and classifying the node more accurately. The proposed framework, Similarity Feature Embedding GNN (SFEGNN), can be efficiently used for node classification in the weighted networks by leveraging feature overlap similarity from the network structure. This novel approach is helpful in modeling weighted networks for node classification and determining how strongly the neighborhood features are correlated. We validate the efficacy of SFEGNN on six benchmark datasets with varying degrees of homophily ratio and found that it is effective even for highly heterophily networks. Our model has empirically outperformed the state-of-the-art GNN framework with the highest accuracy improvement of 28.88%.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3982-3994"},"PeriodicalIF":5.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0