CAAI Transactions on Intelligence Technology最新文献

{"title":"Large Language Models With Contrastive Decoding Algorithm for Hallucination Mitigation in Low-Resource Languages","authors":"Zan Hongying,&nbsp;Arifa Javed,&nbsp;Muhammad Abdullah,&nbsp;Javed Rashid,&nbsp;Muhammad Faheem","doi":"10.1049/cit2.70004","DOIUrl":"10.1049/cit2.70004","url":null,"abstract":"<p>Neural machine translation (NMT) has advanced with deep learning and large-scale multilingual models, yet translating low-resource languages often lacks sufficient training data and leads to hallucinations. This often results in translated content that diverges significantly from the source text. This research proposes a refined Contrastive Decoding (CD) algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in low-resource NMT and improve translation quality. Advanced large language NMT models, including ChatGLM and LLaMA, are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities. The refined CD algorithm evaluates multiple candidate translations using BLEU score, semantic similarity, and Named Entity Recognition accuracy. Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates. Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models. An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations. Notably, the refined methodology increased the BLEU score by approximately 30% compared to baseline models.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1104-1117"},"PeriodicalIF":7.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909954","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":"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":7.3,"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":"Sep-NMS: Unlocking the Aptitude of Two-Stage Referring Expression Comprehension","authors":"Jing Wang,&nbsp;Zhikang Wang,&nbsp;Xiaojie Wang,&nbsp;Fangxiang Feng,&nbsp;Bo Yang","doi":"10.1049/cit2.70007","DOIUrl":"10.1049/cit2.70007","url":null,"abstract":"<p>Referring expression comprehension (REC) aims to locate a specific region in an image described by a natural language. Existing two-stage methods generate multiple candidate proposals in the first stage, followed by selecting one of these proposals as the grounding result in the second stage. Nevertheless, the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate, thereby enormously limiting the overall network performance. To address the above issues, the authors propose an innovative method termed Separate Non-Maximum Suppression (Sep-NMS) for two-stage REC. Particularly, Sep-NMS models information from the two stages independently and collaboratively, ultimately achieving an overall improvement in comprehension and identification of the target objects. Specifically, the authors propose a Ref-Relatedness module for filtering referent proposals rigorously, decreasing the redundancy of referent proposals. A <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>CLIP</mtext>\u0000 <mo>†</mo>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{CLIP}}^{{dagger}}$</annotation>\u0000 </semantics></math> Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects. It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage. Moreover, an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages, ensuring maximum utilisation of the available information. Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods. The datasets used are publicly available: RefCOCO, RefCOCO+: https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg: https://doi.org/10.1109/CVPR.2016.9.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1049-1061"},"PeriodicalIF":7.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910057","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":"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":7.3,"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":"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":7.3,"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":"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":7.3,"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":"Enhancing patient rehabilitation predictions with a hybrid anomaly detection model: Density-based clustering and interquartile range methods","authors":"Murad Ali Khan,&nbsp;Jong-Hyun Jang,&nbsp;Naeem Iqbal,&nbsp;Harun Jamil,&nbsp;Syed Shehryar Ali Naqvi,&nbsp;Salabat Khan,&nbsp;Jae-Chul Kim,&nbsp;Do-Hyeun Kim","doi":"10.1049/cit2.70000","DOIUrl":"10.1049/cit2.70000","url":null,"abstract":"<p>In recent years, there has been a concerted effort to improve anomaly detection techniques, particularly in the context of high-dimensional, distributed clinical data. Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy, personalising treatment plans, and optimising resource allocation to enhance clinical outcomes. Nonetheless, this domain faces unique challenges, such as irregular data collection, inconsistent data quality, and patient-specific structural variations. This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges. The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data, facilitating efficient anomaly identification. Subsequently, a stochastic method based on the Interquartile Range filters unreliable data points, ensuring that medical tools and professionals receive only the most pertinent and accurate information. The primary objective of this study is to equip healthcare professionals and researchers with a robust tool for managing extensive, high-dimensional clinical datasets, enabling effective isolation and removal of aberrant data points. Furthermore, a sophisticated regression model has been developed using Automated Machine Learning (AutoML) to assess the impact of the ensemble abnormal pattern detection approach. Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML. Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhancement in AutoML performance, with an average improvement of 0.041 in the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${R}^{2}$</annotation>\u0000 </semantics></math> score, surpassing the effectiveness of traditional regression models.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"983-1006"},"PeriodicalIF":7.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909905","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":"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":7.3,"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}

{"title":"Tri-M2MT: Multi-modalities based effective acute bilirubin encephalopathy diagnosis through multi-transformer using neonatal Magnetic Resonance Imaging","authors":"Kumar Perumal,&nbsp;Rakesh Kumar Mahendran,&nbsp;Arfat Ahmad Khan,&nbsp;Seifedine Kadry","doi":"10.1049/cit2.12409","DOIUrl":"10.1049/cit2.12409","url":null,"abstract":"<p>Acute Bilirubin Encephalopathy (ABE) is a significant threat to neonates and it leads to disability and high mortality rates. Detecting and treating ABE promptly is important to prevent further complications and long-term issues. Recent studies have explored ABE diagnosis. However, they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging (MRI). To tackle this problem, the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans. The scans include T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and apparent diffusion coefficient maps to get indepth information. Initially, the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and <i>Z</i>-score normalisation for data standardisation. An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy. Furthermore, a multi-transformer approach was used for feature fusion and identify feature correlations effectively. Finally, accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer. The performance of the proposed Tri-M2MT model is evaluated across various metrics, including accuracy, specificity, sensitivity, F1-score, and ROC curve analysis, and the proposed methodology provides better performance compared to existing methodologies.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"434-449"},"PeriodicalIF":7.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856975","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":"Clustering-based recommendation method with enhanced grasshopper optimisation algorithm","authors":"Zihao Zhao,&nbsp;Yingchun Xia,&nbsp;Wenjun Xu,&nbsp;Hui Yu,&nbsp;Shuai Yang,&nbsp;Cheng Chen,&nbsp;Xiaohui Yuan,&nbsp;Xiaobo Zhou,&nbsp;Qingyong Wang,&nbsp;Lichuan Gu","doi":"10.1049/cit2.12408","DOIUrl":"10.1049/cit2.12408","url":null,"abstract":"<p>In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences. These limitations hinder the ability of systems to provide highly accurate and personalised recommendations. To address these challenges, this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm (GOA), termed LCGOA, to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment. By combining the K-means algorithm with the enhanced GOA, which incorporates a Lévy flight mechanism and multi-strategy co-evolution, our method overcomes the centroid sensitivity issue, a key limitation in traditional clustering techniques. Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy, offering more relevant content to users and driving greater customer satisfaction and business growth.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"494-509"},"PeriodicalIF":7.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856976","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}