Knowledge-Based Systems最新文献

{"title":"Boosting semi-supervised regressor via confidence-weighted consistency regularization","authors":"Liyan Liu ,&nbsp;Luxuan Feng ,&nbsp;Fan Min","doi":"10.1016/j.knosys.2025.113319","DOIUrl":"10.1016/j.knosys.2025.113319","url":null,"abstract":"<div><div>Semi-supervised regression aims to train a learner by utilizing both labeled and unlabeled data. Boosting is a popular approach in enhancing the performance of the base learner, which is often quite simple. One question is: can we boost different sophisticated semi-supervised regressors (SSRs) to provide better performance? In this paper, we propose a confidence-weighted consistency regularization (BS2C) algorithm to answer this question. First, we construct a neural network that works in parallel with an off-the-shelf SSR to provide pseudo-labels. In this way, the prediction ability of the regressor gradually shifts to the network. Second, we integrate supervised and consistency losses using a dynamic weighting strategy. Consequently, the impact of unlabeled data increases during training iterations. Third, we compute confidence and weights for pseudo-labels to guide the training. Therefore, the negative effect from network errors is reduced. Experiments were performed on fifteen real-world datasets using five popular SSRs, also in comparison with an existing boosting method. The results indicate that BS2C can boost SSRs in most cases, and superior than the counterpart. The source code is available at <span><span>https://github.com/F1uency/BS2C</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113319"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatial–spectral and temporal dual prototype network for motor imagery brain–computer interface","authors":"Can Han ,&nbsp;Chen Liu ,&nbsp;Jun Wang ,&nbsp;Yaqi Wang ,&nbsp;Crystal Cai ,&nbsp;Dahong Qian","doi":"10.1016/j.knosys.2025.113315","DOIUrl":"10.1016/j.knosys.2025.113315","url":null,"abstract":"<div><div>Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain–computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent complexity of EEG signals relative to the small-sample size. To address this issue, we propose a spatial–spectral and temporal dual prototype network (SST-DPN). First, we design a lightweight attention mechanism to uniformly model the spatial–spectral relationships across multiple EEG electrodes, enabling the extraction of powerful spatial–spectral features. Then, we develop a multi-scale variance pooling module tailored for EEG signals to capture long-term temporal features. This module is parameter-free and computationally efficient, offering clear advantages over the widely used transformer models. Furthermore, we introduce dual prototype learning to optimize the feature space distribution and training process, thereby improving the model’s generalization ability on small-sample MI datasets. Our experimental results show that the SST-DPN outperforms state-of-the-art models with superior classification accuracy (84.11% for dataset BCI4-2A, 86.65% for dataset BCI4-2B). Additionally, we use the BCI3-4A dataset with fewer training data to further validate the generalization ability of the proposed SST-DPN, achieving superior performance with 82.03% classification accuracy. Benefiting from the lightweight parameters and superior decoding performance, our SST-DPN shows great potential for practical MI-BCI applications. The code is publicly available at <span><span>https://github.com/hancan16/SST-DPN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113315"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681298","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":"OptNet: Optimization-inspired network beyond deep unfolding for structural artifact reduction","authors":"Ke Jiang,&nbsp;Yingshuai Zhao,&nbsp;Baoshun Shi","doi":"10.1016/j.knosys.2025.113235","DOIUrl":"10.1016/j.knosys.2025.113235","url":null,"abstract":"<div><div>Structural artifact reduction (SAR), such as metal artifact reduction (MAR) in computed tomography (CT) images and single image deraining (SID), aims to remove the artifacts with repeated structural patterns from the corrupted images. Recently, deep unfolding networks, also known as model-driven networks, have achieved remarkable performance, but they typically require multiple proximity sub-networks to replace the corresponding proximal operators for multivariable updates, increasing the number of learnable parameters. Moreover, existing SAR methods ignore advanced priors, such as textual priors, leaving room for further recovery performance improvement. To address these limitations, we rethink the deep unfolding framework and propose a universal optimization-inspired network architecture, termed OptNet, which introduces a novel multi-channel network design to reduce learnable parameter count while enhancing performance via incorporating textual priors. Concretely, we design the so-called OptNet with contrastive loss to perform multivariable updates, replacing multiple proximity sub-networks typically in iterative optimization algorithms with a multi-channel sub-network, thus reducing the learnable parameter count. OptNet is flexible and can incorporate any advanced priors. Specially, we integrate a pre-trained contrastive language-image pretraining (CLIP) model into an elaborated information fusion module (IFM) to incorporate textual priors, enabling multimodal information interaction that guides more accurate structural artifact reduction, enhancing generalizability across various degradation levels. Extensive experiments demonstrate that OptNet outperforms existing methods, achieving improvements of up to 0.25 dB on MAR and 0.6 dB on SID tasks, while surpassing its deep unfolding variant with a 1.33 dB gain on MAR and reducing parameters by approximately 50%.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113235"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642318","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":"Multi-modal supervised domain adaptation with a multi-level alignment strategy and consistent decision boundaries for cross-subject emotion recognition from EEG and eye movement signals","authors":"Magdiel Jiménez-Guarneros,&nbsp;Gibran Fuentes-Pineda","doi":"10.1016/j.knosys.2025.113238","DOIUrl":"10.1016/j.knosys.2025.113238","url":null,"abstract":"<div><div>Multi-modal emotion recognition systems from Electroencephalogram (EEG) and eye tracking signals have overcome the limitation of incomplete information expressed by a single modality, leveraging the complementarity of multiple modal information. However, the applicability of these systems is still restricted to new users since signal patterns vary across subjects, decreasing the recognition performance. In this sense, supervised domain adaptation has emerged as an effective method to solve such problem by reducing distribution differences between multi-modal signals from known subjects and a new one. Nevertheless, existing works exhibit a sub-optimal feature distribution alignment, avoiding a correct knowledge transfer. Likewise, although multi-modal approaches present robustness by learning a shared latent space, EEG data are still exposed to noise and perturbations, producing misclassifications in sensitive decision boundaries. To solve these issues, we introduced a multi-modal supervised domain adaptation method, named Multi-level Alignment and Consistent Decision Boundaries (MACDB), which introduces a three-fold strategy for multi-level feature alignment comprising modality-specific normalization, angular cosine distance, and Joint Maximum Mean Discrepancy to achieve (1) an alignment per modality, (2) an alignment between modalities, and (3) an alignment across domains. Also, robust decision boundaries are encouraged over the EEG feature space by ensuring consistent predictions with respect to adversarial perturbations on EEG data. We evaluated our proposal on three public datasets, SEED, SEED-IV and SEED-V, employing leave-one-subject-out cross-validation. Experiments showed that the effectiveness of our proposal achieves an average accuracy of 86.68%, 85.03%, and 86.48% on SEED, SEED-IV, and SEED-V across the three available sessions, outperforming the state-of-the-art results.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113238"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642317","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":"Advanced deep learning framework for ECG arrhythmia classification using 1D-CNN with attention mechanism","authors":"Mohammed Guhdar ,&nbsp;Abdulhakeem O. Mohammed ,&nbsp;Ramadhan J. Mstafa","doi":"10.1016/j.knosys.2025.113301","DOIUrl":"10.1016/j.knosys.2025.113301","url":null,"abstract":"<div><div>Cardiovascular diseases, particularly cardiac arrhythmias, remain a leading cause of global mortality, necessitating efficient and accurate diagnostic tools. Despite advances in deep learning for ECG analysis, current models face challenges in cross-population performance, signal noise robustness, limited training data efficiency, and clinical result interpretability. Additionally, most current approaches struggle to generalize across different ECG databases and require extensive computational resources for real-time analysis. This paper presents a novel hybrid deep learning framework for automated ECG analysis, combining one-dimensional convolutional neural networks (1D-CNN) with a specialized attention mechanism. The proposed architecture implements a four-stage CNN backbone enhanced with a squeeze-and-excitation attention block, enabling adaptive feature selection across multiple scales. The model incorporates advanced regularization techniques, including focal loss, L2 regularization, and an ensemble approach with mixed precision training. We conducted extensive experiments across multiple datasets to evaluate generalization capabilities. This study utilizes two standard databases: the MIT-BIH Arrhythmia Database (48 half-hour recordings sampled at 360 Hz) and the PTB Diagnostic ECG Database (549 records from 290 subjects sampled at 1000 Hz). Through rigorous validation including five-fold cross-validation and statistical significance testing, our model attained remarkable performance, achieving 99.48% accuracy on MIT-BIH, 99.83% accuracy on PTB, and 99.64% accuracy on the combined dataset, with corresponding F1-scores of 0.99, 1.00, and 1.00 respectively. The findings demonstrate robust generalization across varied ECG morphologies and recording conditions, with particular effectiveness in handling class imbalance without data augmentation. The model’s reliable performance across multiple datasets indicates significant potential for clinical applications in automated cardiac diagnostics.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113301"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642316","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":"Towards regression testing and regression-free update for deep learning systems","authors":"Shuyue Li,&nbsp;Ming Fan,&nbsp;Ting Liu","doi":"10.1016/j.knosys.2025.113292","DOIUrl":"10.1016/j.knosys.2025.113292","url":null,"abstract":"<div><div>Recently, deep neural networks have become prevalent in various systems, such as autonomous driving, privacy leakage detection, and sensitive data protection, and naturally raise wide concerns about their reliability. Current evaluation of the behaviors of DNN models is focused on their overall performance in a statistical way, e.g., measured by accuracy. However, the regression problem on model performance is also an important issue, especially in real-world applications. If a “new and improved” model exhibits errors absent in the old one, frustrated users may abandon the product. Moreover, the reliability of the model could be hard to maintain in the long term. Given its severity and importance, we aimed to detect and fix the regressions on DNN models without affecting the overall performance, and we made a preliminary study on two common situations where regressions occur, i.e., randomness and data evolution. Specifically, we formulated it into a constraint optimization problem by taking the regression-free conditions as constraints and approximating with the combination of two proxies. First, we suppressed the regressions by making the new model mimic the original model and design a quadratic penalty during model training. Second, given the trade-off between similarity to the old model and eligible performance of the new model, we designed a novel biased neuron response variability technique to suppress regression without performance degradation. To evaluate the effectiveness of our technique, we experimented on MINIST, CIFAR-10,and Fashion-MNIST datasets. The results show that our method can reduce the regression rate from 1.53% to 0.69% on average for the random seed change situation, and it was also effective for the data evolution situation.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113292"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681296","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":"Empowering early predictions: A paradigm shift in diabetes risk assessment with Deep Active Learning","authors":"Ifra Shaheen ,&nbsp;Nadeem Javaid ,&nbsp;Azizur Rahim ,&nbsp;Nabil Alrajeh ,&nbsp;Neeraj Kumar","doi":"10.1016/j.knosys.2025.113284","DOIUrl":"10.1016/j.knosys.2025.113284","url":null,"abstract":"<div><div>Diabetes is one of the most widespread chronic diseases worldwide, affecting millions and posing significant health risks. Effective management depends on early detection and risk assessment, enabling medical professionals to take timely action and mitigate long-term healthcare consequences. However, there is a critical need for a reliable and accurate detection system to support medical professionals in clinical and computational assessments. Existing detection systems, particularly those based on traditional deep learning models, often fail to address challenges such as class imbalance, the inability to model non-linear patterns, high annotation costs, and the lack of explainability inherent in black-box models. To address these challenges in diabetes prediction, this study applies the proximity weighted synthetic oversampling technique to resolve class imbalance issues in the Behavioral Risk Factor Surveillance System (BRFSS) dataset, ensuring a balanced representation of healthy and diabetic individuals. Subsequently, we propose a novel Diabetic Class-based Sampling Pointer Network (DCSPNetwork) for early diabetes prediction by assessing high-risk factors. The DCSPNetwork effectively captures non-linear patterns in the BRFSS dataset, reduces overfitting risks, and minimizes labeling costs. Experimental results demonstrate the superior performance of DCSPNetwork, achieving an improvement score of 5.88% in accuracy, 8.14% in precision, 9.76% in recall, 8.33% in F1-score, and 4.3% in area under the receiver operating characteristics curve score, and a remarkable decrease of 30.3% in log loss, 72.90% in training time, and 30.30% in inference time compared to its benchmark pointer network model. Using a 10-fold cross-validation approach, we verified the performance and generalizability of our DCSPNetwork, ensuring consistent results across various data splits and demonstrating its robustness. To further support the DCSPNetwork’s efficacy and dependability, statistical validation is carried out utilizing a t-test and a 95% confidence interval. We incorporated explainable artificial intelligence techniques, local interpretable model-agnostic explanations, and Shapley additive explanations to enhance interpretability and transparency in predictions. These techniques improved the reliability of our model and offered insightful information about feature contributions in DCSPNetwork’s predictions. The results indicate that DCSPNetwork is a reliable and effective model for early diabetes risk assessment, combining high performance with interpretability.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113284"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642901","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":"Multi-granularity confidence learning for unsupervised text-to-image person re-identification with incomplete modality","authors":"Yongxiang Li ,&nbsp;Dezhong Peng ,&nbsp;Haixiao Huang ,&nbsp;Yizhi Liu ,&nbsp;Huiming Zheng ,&nbsp;Zheng Liu","doi":"10.1016/j.knosys.2025.113304","DOIUrl":"10.1016/j.knosys.2025.113304","url":null,"abstract":"<div><div>Most existing text-to-image person re-identification (TIReID) methods assume fully labeled and complete data for both person images and text descriptions. However, this assumption is unrealistic in real-world scenarios due to high labeling costs and privacy concerns. This work addresses a rarely explored task, i.e., unsupervised TIReID with incomplete modality learning. Prior approaches primarily focus on completing missing modalities and learning cross-modal consistency using global features. However, relying solely on these features limits the ability of model to comprehensively represent identities. Local features, which capture fine-grained details of data patches, are essential for effective cross-modal learning but are often overlooked. To address these limitations, we introduce WIN, a framework comprising three key components: (1) The Reliable Feature Completion (RFC) mechanism uses reciprocal neighbor mining to reconstruct both global and local features, ensuring the extraction of the most valuable information from multiple perspectives. (2) The Dual-Modal Clustering (DMC) mechanism addresses intra-class variability caused by missing labels by jointly clustering pedestrian identities from both modalities and generating pseudo-identity labels for supervised training. (3) To mitigate the inevitable noise introduced by imperfect feature completion and pseudo-label generation, we propose a Multi-Granularity Confidence Learning (MCL) mechanism. MCL leverages both global and local feature representations to estimate confidence scores for each sample and adaptively adjusts the optimization strength based on these scores, robustly bringing semantically similar visual and textual features closer in the common embedding space. Extensive experiments demonstrate the superiority of our method across diverse settings with missing modalities and in open-world environments.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113304"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A classification method based on dominance neighborhood granularity","authors":"Bin Yu ,&nbsp;Xu He ,&nbsp;Weiping Ding","doi":"10.1016/j.knosys.2025.113276","DOIUrl":"10.1016/j.knosys.2025.113276","url":null,"abstract":"<div><div>Classification algorithms, widely utilized in various practical settings, are increasingly demanded in critical domains to not only display high accuracy but also provide interpretable results. Granular Computing (GrC) theory, recognized for its capability to clarify complex data and issues through its concept of granularity, has become an intuitive and straightforward approach in problem-solving. Nevertheless, contemporary granular computing-based classification techniques are largely confined to the processing of Euclidean features of data, thereby neglecting relationships and structures between data within the data set. In response, we introduce a classification method based on dominance neighborhood granularity (CDNG). CDNG, while accommodating Euclidean features, also incorporates non-Euclidean features to obtain diverse information, thereby augmenting predictive accuracy. Specifically, CDNG establishes a dominance neighborhood granularity drawn from neighborhood granularity and dominance granularity. Subsequently, we appraise CDNG using real datasets and performance metrics, including accuracy (ACC), Recall Score (RS), and F1 score (F1), contrasting our findings with other classification methodologies. We conclude with an analysis of parameter experiments and multi-angle noise experiments specific to CDNG. The experimental results affirm that CDNG outperforms other methods in terms of accuracy while maintaining robustness concomitant with high accuracy.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"316 ","pages":"Article 113276"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682621","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":"Robust multi-view fuzzy clustering with exponential transformation and automatic view weighting","authors":"Zhe Liu ,&nbsp;Haoye Qiu ,&nbsp;Muhammet Deveci ,&nbsp;Sukumar Letchmunan ,&nbsp;Luis Martínez","doi":"10.1016/j.knosys.2025.113314","DOIUrl":"10.1016/j.knosys.2025.113314","url":null,"abstract":"<div><div>Multi-view fuzzy clustering has gained widespread attention due to its unique capability to handle uncertainty through flexible membership assignment, allowing samples to belong to multiple clusters with varying supports, thereby providing a comprehensive understanding of multi-view data. This capability is particularly relevant to knowledge-driven systems that require interpretable integration of multi-view data. However, existing multi-view fuzzy clustering algorithms often struggle with handling noise and incorporating flexible weighting strategies for different views effectively. To address these challenges, this paper proposes four robust multi-view fuzzy clustering algorithms (RMFC-ET-VS, RMFC-ET-VP, RMFC-ET-MS, RMFC-ET-MP), which leverage an exponential transformation of Euclidean distance to effectively mitigate the impact of noise and outliers in the data, thereby enhancing clustering stability. Moreover, we introduce vector-based and matrix-based view weighting strategies, employing sum-to-1 and product-to-1 constraints to ensure that the most informative views contribute more effectively during clustering. The proposed algorithms offer a dual emphasis on robust distance metrics and adaptable view weighting, resulting in more accurate and resilient clustering outcomes. Extensive experiments on multiple real-world datasets demonstrate that the proposed algorithms significantly outperform existing multi-view clustering algorithms, both in terms of clustering performance and robustness.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113314"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642905","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}