CAAI Transactions on Intelligence Technology最新文献

{"title":"Guest Editorial: Special Issue on Al Technologies and Applications in Medical Robots","authors":"Xiaozhi Qi, Zhongliang Jiang, Ying Hu, Jianwei Zhang","doi":"10.1049/cit2.70019","DOIUrl":"https://doi.org/10.1049/cit2.70019","url":null,"abstract":"<p>The integration of artificial intelligence (AI) into medical robotics has emerged as a cornerstone of modern healthcare, driving transformative advancements in precision, adaptability and patient outcomes. Although computational tools have long supported diagnostic processes, their role is evolving beyond passive assistance to become active collaborators in therapeutic decision-making. In this paradigm, knowledge-driven deep learning systems are redefining possibilities—enabling robots to interpret complex data, adapt to dynamic clinical environments and execute tasks with human-like contextual awareness.</p><p>The purpose of this special issue is to showcase the latest developments in the application of AI technology in medical robots. The main content includes but is not limited to passive data adaptation, force feedback tracking, image processing and diagnosis, surgical navigation, exoskeleton systems etc. These studies cover various application scenarios of medical robots, with the ultimate goal of maximising AI autonomy.</p><p>We have received 31 paper submissions from around the world, and after a rigorous peer review process, we have finally selected 9 papers for publication. The selected collection of papers covers various fascinating research topics, all of which have achieved key breakthroughs in their respective fields. We believe that these accepted papers have guiding significance for their research fields and can help researchers enhance their understanding of current trends. Sincere thanks to the authors who chose our platform and all the staff who provided assistance for the publication of these papers.</p><p>In the article ‘Model adaptation via credible local context representation’, Tang et al. pointed out that conventional model transfer techniques require labelled source data, which makes them inapplicable in privacy-sensitive medical domains. To address these critical problems of source-free domain adaptation (SFDA), they proposed a credible local context representation (CLCR) method that significantly enhances model generalisation through geometric structure mining in feature space. This method innovatively constructs a two-stage learning framework: introducing a data-enhanced mutual information regularisation term in the pretraining stage of the source model to enhance the model's learning of sample discriminative features; design a deep space fixed step walking strategy during the target domain adaptation phase, dynamically capture the local credible contextual features of each target sample and use them as pseudo-labels for semantic fusion. Experiments on the three benchmark datasets of Office-31, Office Home and VisDA show that CLCR achieves an average accuracy of 89.2% in 12 cross-domain tasks, which is 3.1% higher than the existing optimal SFDA method and even surpasses some domain adaptation methods that require the participation of source data. This work provides a new approach to address the privacy performance c","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"635-637"},"PeriodicalIF":8.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extrapolation Reasoning on Temporal Knowledge Graphs via Temporal Dependencies Learning","authors":"Ye Wang,&nbsp;Binxing Fang,&nbsp;Shuxian Huang,&nbsp;Kai Chen,&nbsp;Yan Jia,&nbsp;Aiping Li","doi":"10.1049/cit2.70013","DOIUrl":"https://doi.org/10.1049/cit2.70013","url":null,"abstract":"<p>Extrapolation on Temporal Knowledge Graphs (TKGs) aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order. The temporally adjacent facts in TKGs naturally form event sequences, called event evolution patterns, implying informative temporal dependencies between events. Recently, many extrapolation works on TKGs have been devoted to modelling these evolutional patterns, but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns. However, the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent. To this end, a <b><i>T</i></b><i>emporal</i> <b><i>Re</i></b><i>lational Co</i><b><i>n</i></b><i>text-based Temporal</i> <b><i>D</i></b><i>ependencies Learning Network</i> (TRenD) is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns, especially those temporal dependencies caused by interactive patterns of relations. Trend incorporates a semantic context unit to capture semantic correlations between relations, and a structural context unit to learn the interaction pattern of relations. By learning the temporal contexts of relations semantically and structurally, the authors gain insights into the underlying event evolution patterns, enabling to extract comprehensive historical information for future prediction better. Experimental results on benchmark datasets demonstrate the superiority of the model.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"815-826"},"PeriodicalIF":8.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Processing Water-Medium Spinal Endoscopic Images Based on Dual Transmittance","authors":"Ning Hu,&nbsp;Qing Zhang","doi":"10.1049/cit2.70016","DOIUrl":"https://doi.org/10.1049/cit2.70016","url":null,"abstract":"<p>Real-time water-medium endoscopic images can assist doctors in performing operations such as tissue cleaning and nucleus pulpous removal. During medical operating procedures, it is inevitable that tissue particles, debris and other contaminants will be suspended within the viewing area, resulting in blurred images and the loss of surface details in biological tissues. Currently, few studies have focused on enhancing such endoscopic images. This paper proposes a water-medium endoscopic image processing method based on dual transmittance in accordance with the imaging characteristics of spinal endoscopy. By establishing an underwater imaging model for spinal endoscopy, we estimate the transmittance of the endoscopic images based on the boundary constraints and local image contrast. The two transmittances are then fused and combined with transmittance maps and ambient light estimations to restore the images before attenuation, ultimately enhancing the details and texture of the images. Experiments comparing classical image enhancement algorithms demonstrate that the proposed algorithm could effectively improve the quality of spinal endoscopic images.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"678-688"},"PeriodicalIF":8.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Syn-Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection","authors":"Huaijin Liu,&nbsp;Jixiang Du,&nbsp;Yong Zhang,&nbsp;Hongbo Zhang,&nbsp;Jiandian Zeng","doi":"10.1049/cit2.70001","DOIUrl":"https://doi.org/10.1049/cit2.70001","url":null,"abstract":"<p>Data augmentation plays an important role in boosting the performance of 3D models, while very few studies handle the 3D point cloud data with this technique. Global augmentation and cut-paste are commonly used augmentation techniques for point clouds, where global augmentation is applied to the entire point cloud of the scene, and cut-paste samples objects from other frames into the current frame. Both types of data augmentation can improve performance, but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling, which may be counterproductive and may hurt the overall performance. In addition, LiDAR is susceptible to signal loss, external occlusion, extreme weather and other factors, which can easily cause object shape changes, while global augmentation and cut-paste cannot effectively enhance the robustness of the model. To this end, we propose Syn-Aug, a synchronous data augmentation framework for LiDAR-based 3D object detection. Specifically, we first propose a novel rendering-based object augmentation technique (Ren-Aug) to enrich training data while enhancing scene realism. Second, we propose a local augmentation technique (Local-Aug) to generate local noise by rotating and scaling objects in the scene while avoiding collisions, which can improve generalisation performance. Finally, we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames. We verify the proposed framework with four different types of 3D object detectors. Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets, proving the effectiveness and generality of Syn-Aug. On KITTI, four different types of baseline models using Syn-Aug improved mAP by 0.89%, 1.35%, 1.61% and 1.14% respectively. On nuScenes, four different types of baseline models using Syn-Aug improved mAP by 14.93%, 10.42%, 8.47% and 6.81% respectively. The code is available at https://github.com/liuhuaijjin/Syn-Aug.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"912-928"},"PeriodicalIF":8.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry-Enhanced Implicit Function for Detailed Clothed Human Reconstruction With RGB-D Input","authors":"Pengpeng Liu,&nbsp;Zhi Zeng,&nbsp;Qisheng Wang,&nbsp;Min Chen,&nbsp;Guixuan Zhang","doi":"10.1049/cit2.70009","DOIUrl":"https://doi.org/10.1049/cit2.70009","url":null,"abstract":"<p>Realistic human reconstruction embraces an extensive range of applications as depth sensors advance. However, current state-of-the-art methods with RGB-D input still suffer from artefacts, such as noisy surfaces, non-human shapes, and depth ambiguity, especially for the invisible parts. The authors observe the main issue is the lack of geometric semantics without using depth input priors fully. This paper focuses on improving the representation ability of implicit function, exploring an effective method to utilise depth-related semantics effectively and efficiently. The proposed geometry-enhanced implicit function enhances the geometric semantics with the extra voxel-aligned features from point clouds, promoting the completion of missing parts for unseen regions while preserving the local details on the input. For incorporating multi-scale pixel-aligned and voxel-aligned features, the authors use the Squeeze-and-Excitation attention to capture and fully use channel interdependencies. For the multi-view reconstruction, the proposed depth-enhanced attention explicitly excites the network to “sense” the geometric structure for a more reasonable feature aggregation. Experiments and results show that our method outperforms current RGB and depth-based SOTA methods on the challenging data from Twindom and Thuman3.0, and achieves a detailed and completed human reconstruction, balancing performance and efficiency well.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"858-870"},"PeriodicalIF":8.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layer-Level Adaptive Gradient Perturbation Protecting Deep Learning Based on Differential Privacy","authors":"Zhang Xiangfei,&nbsp;Zhang Qingchen,&nbsp;Jiang Liming","doi":"10.1049/cit2.70008","DOIUrl":"https://doi.org/10.1049/cit2.70008","url":null,"abstract":"<p>Deep learning’s widespread dependence on large datasets raises privacy concerns due to the potential presence of sensitive information. Differential privacy stands out as a crucial method for preserving privacy, garnering significant interest for its ability to offer robust and verifiable privacy safeguards during data training. However, classic differentially private learning introduces the same level of noise into the gradients across training iterations, which affects the trade-off between model utility and privacy guarantees. To address this issue, an adaptive differential privacy mechanism was proposed in this paper, which dynamically adjusts the privacy budget at the layer-level as training progresses to resist member inference attacks. Specifically, an equal privacy budget is initially allocated to each layer. Subsequently, as training advances, the privacy budget for layers closer to the output is reduced (adding more noise), while the budget for layers closer to the input is increased. The adjustment magnitude depends on the training iterations and is automatically determined based on the iteration count. This dynamic allocation provides a simple process for adjusting privacy budgets, alleviating the burden on users to tweak parameters and ensuring that privacy preservation strategies align with training progress. Extensive experiments on five well-known datasets indicate that the proposed method outperforms competing methods in terms of accuracy and resilience against membership inference attacks.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"929-944"},"PeriodicalIF":8.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Retrosynthesis Top-K Prediction Based on the Latent Generation Process","authors":"Yupeng Liu,&nbsp;Han Zhang,&nbsp;Rui Hu","doi":"10.1049/cit2.70005","DOIUrl":"https://doi.org/10.1049/cit2.70005","url":null,"abstract":"<p>In the field of organic synthesis, the core objective of retrosynthetic methods is to deduce possible synthetic routes and precursor molecules for complex target molecules. Traditional retrosynthetic methods, such as template-based retrosynthesis, have high accuracy and interpretability in specific types of reactions but are limited by the scope of the template library, making it difficult to adapt to new or uncommon reaction types. Moreover, sequence-to-sequence retrosynthetic prediction methods, although they enhance the flexibility of prediction, often overlook the complexity of molecular graph structures and the actual interactions between atoms, which limits the accuracy and reliability of the predictions. To address these limitations, this paper proposes a Molecular Retrosynthesis Top-k Prediction based on the Latent Generation Process (MRLGP) that uses latent variables from graph neural networks to model the generation process and produce diverse set of reactants. Utilising an encoding method based on Graphormer, the authors have also introduced topology-aware positional encoding to better capture the interactions between atomic nodes in the molecular graph structure, thereby more accurately simulating the retrosynthetic process. The MRLGP model significantly enhances the accuracy and diversity of predictions by correlating discrete latent variables with the reactant generation process and progressively constructing molecular graphs using a variational autoregressive decoder. Experimental results on benchmark datasets such as USPTO-50k, USPTO-Full, and USPTO-DIVERSE demonstrate that MRLGP outperforms baseline models on multiple Top-k evaluation metrics. Additionally, ablation experiments conducted on the USPTO-50K dataset further validate the effectiveness of the methods used in the encoder and decoder parts of the model.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"902-911"},"PeriodicalIF":8.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SG-TE: Spatial Guidance and Temporal Enhancement Network for Facial-Bodily Emotion Recognition","authors":"Zhong Huang,&nbsp;Danni Zhang,&nbsp;Fuji Ren,&nbsp;Min Hu,&nbsp;Juan Liu,&nbsp;Haitao Yu","doi":"10.1049/cit2.70006","DOIUrl":"https://doi.org/10.1049/cit2.70006","url":null,"abstract":"<p>To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes, a spatial guidance and temporal enhancement (SG-TE) network is proposed for facial-bodily emotion recognition. First, ResNet50, DNN and spatial ransformer models are used to capture facial texture vectors, bodily skeleton vectors and whole-body geometric vectors, and an intraframe correlation attention guidance (S-CAG) mechanism, which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector, is designed to exploit the spatial potential emotional correlation between face and posture. Second, an interframe significant segment enhancement (T-SSE) structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony. Finally, an adaptive weight assignment (M-AWA) strategy is constructed to realise facial-bodily fusion. The experimental results on the BabyRobot Emotion Dataset (BRED) and Context-Aware Emotion Recognition (CAER) dataset indicate that the proposed network reaches accuracies of 81.61% and 89.39%, which are 9.61% and 9.46% higher than those of the baseline network, respectively. Compared with the state-of-the-art methods, the proposed method achieves 7.73% and 20.57% higher accuracy than single-modal methods based on facial expression or bodily posture, respectively, and 2.16% higher accuracy than the dual-modal methods based on facial-bodily fusion. Therefore, the proposed method, which adaptively fuses the complementary information of face and posture, improves the quality of emotion recognition in real-world scenarios.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"871-890"},"PeriodicalIF":8.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Automatic Feature Points Extraction of Pelvic Surface Based on PointMLP_RegNet","authors":"Wei Kou,&nbsp;Rui Zhou,&nbsp;Hongmiao Zhang,&nbsp;Jianwen Cheng,&nbsp;Chi Zhu,&nbsp;Shaolong Kuang,&nbsp;Lihai Zhang,&nbsp;Lining Sun","doi":"10.1049/cit2.70003","DOIUrl":"https://doi.org/10.1049/cit2.70003","url":null,"abstract":"<p>The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration. This process involves extracting anatomical feature points from pre-operative 3D images which can be challenging because of the complex and variable structure of the pelvis. PointMLP_RegNet, a modified PointMLP, was introduced to address this issue. It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification. A flowchart for an automatic feature points extraction method was presented, and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method. PointMLP_RegNet extracted feature points more accurately, with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm. Compared to PointNet++ and PointNet, it exhibited higher accuracy, robustness and space efficiency. The proposed method will improve the accuracy of anatomical feature points extraction, enhance intra-operative registration precision and facilitate the widespread clinical application of robot-assisted pelvic fracture reduction.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"716-727"},"PeriodicalIF":8.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive learning for nested Chinese Named Entity Recognition via template words","authors":"Yuke Wang,&nbsp;Qiao Liu,&nbsp;Tingting Dai,&nbsp;Junjie Lang,&nbsp;Ling Lu,&nbsp;Yinong Chen","doi":"10.1049/cit2.12403","DOIUrl":"https://doi.org/10.1049/cit2.12403","url":null,"abstract":"<p>Existing Chinese named entity recognition (NER) research utilises 1D lexicon-based sequence labelling frameworks, which can only recognise flat entities. While lexicons serve as prior knowledge and enhance semantic information, they also pose completeness and resource requirements limitations. This paper proposes a template-based classification (TC) model to avoid lexicon issues and to identify nested entities. Template-based classification provides a template word for each entity type, which utilises contrastive learning to integrate the common characteristics among entities with the same category. Contrastive learning makes template words the centre points of their category in the vector space, thus improving generalisation ability. Additionally, TC presents a 2D table-filling label scheme that classifies entities based on the attention distribution of template words. The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously. Experimental results show that TC achieves the state-of-the-art performance on five Chinese datasets.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"450-459"},"PeriodicalIF":8.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}