Applied Soft Computing最新文献_第2页

Forecasting realized volatility using deep learning quantile function

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113016

Jungyoon Song , Hyunju Lee , Jongu Lee , Woojin Chang

{"title":"Forecasting realized volatility using deep learning quantile function","authors":"Jungyoon Song , Hyunju Lee , Jongu Lee , Woojin Chang","doi":"10.1016/j.asoc.2025.113016","DOIUrl":"10.1016/j.asoc.2025.113016","url":null,"abstract":"<div><div>The accurate prediction of realized volatility is an essential component of effective investment strategies. Existing studies have often focused on modeling selective features of intraday return series, overlooking the comprehensive information embedded within them due to challenges such as microstructure noise and the complexity of handling numerous data points. To address these limitations, this paper proposes a novel deep learning quantile function (DLQF) framework that directly leverages intraday return series to forecast realized volatility. The proposed model integrates a Bi-LSTM network to capture the long memory of realized volatility and a quantile function implemented as a deep neural network to extract rich information from intraday returns. A loss function based on <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> distance measures is defined to estimate the probabilistic distribution of intraday returns, enabling both intraday return prediction and realized volatility estimation. Empirical results demonstrate that DLQF outperforms traditional benchmarks across major ETFs, including SPY, DIA, and QQQ, which represent the S&P 500, Dow Jones Industrial Average, and Nasdaq 100, respectively. This model offers significant potential for applications in portfolio optimization, option pricing, and risk management.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113016"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759721","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}

引用次数: 0

Strategic integration of adaptive sampling and ensemble techniques in federated learning for aircraft engine remaining useful life prediction

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113067

Ancha Xu , Renbing Wang , Xinming Weng , Qi Wu , Liangliang Zhuang

{"title":"Strategic integration of adaptive sampling and ensemble techniques in federated learning for aircraft engine remaining useful life prediction","authors":"Ancha Xu , Renbing Wang , Xinming Weng , Qi Wu , Liangliang Zhuang","doi":"10.1016/j.asoc.2025.113067","DOIUrl":"10.1016/j.asoc.2025.113067","url":null,"abstract":"<div><div>In industrial manufacturing, predicting the remaining useful life of machines is crucial for improving operational efficiency and reducing maintenance costs. However, data privacy concerns and commercial competition make traditional centralized data collection methods insufficient to meet these needs. Federated learning offers a decentralized training approach that protects data privacy, but existing research faces challenges such as inadequate performance of single models, data quality disparities, and improper client selection strategies. To address these issues, this study proposes an adaptive sampling-based ensemble federated learning framework. By integrating the predictions of multiple models, the framework reduces model errors and enhances prediction accuracy and generalization capability. Additionally, we design an adaptive sampling method that dynamically adjusts the client selection strategy based on data quality, focusing on clients with low-quality data to ensure that their contributions are effectively utilized. Experimental results show that the proposed framework significantly outperforms existing benchmark methods on the turbofan engine dataset, with a 12% reduction in RMSE and a 35% decrease in Score. Ablation experiments and sensitivity analysis confirm that the framework maintains reliable predictive performance and efficiency in dealing with issues such as data imbalance, missing data, and scale changes. Supplementary materials for this article are available online.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113067"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738481","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}

引用次数: 0

Attention-aware ensemble learning for face-periocular cross-modality matching

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113044

Tiong-Sik Ng, Andrew Beng Jin Teoh

{"title":"Attention-aware ensemble learning for face-periocular cross-modality matching","authors":"Tiong-Sik Ng, Andrew Beng Jin Teoh","doi":"10.1016/j.asoc.2025.113044","DOIUrl":"10.1016/j.asoc.2025.113044","url":null,"abstract":"<div><div>Face and periocular regions serve as complementary biometric modalities in identity recognition. The face-periocular cross-modality matching (FPCM) provides a versatile solution, especially when traditional face recognition systems encounter challenges due to occlusions or the presence of sunglasses, which can obscure the periocular region, rendering it less effective in periocular recognition systems. This paper introduces a novel approach based on attention-aware ensemble learning (AEL) to address these challenges. This notion is embodied in AELNet, which features an attention-aware shared-parameter encoder and multiple classifier heads. AELNet is designed to harness the complementary features of the face and periocular regions, enhancing the quality of joint embeddings. A key aspect of AELNet is its ability to foster diversity among the classifier heads through unique embedding techniques and batch sampling strategies, ultimately boosting FPCM performance. We demonstrate the effectiveness of the AELNet by conducting extensive experiments on five unconstrained periocular-face datasets as a benchmark. Codes are publicly available at <span><span>https://github.com/tiongsikng/ael_net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113044"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738477","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}

引用次数: 0

Federated Bayesian optimization on random Fourier additive margin features and random kernel mapping

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.112925

Fazhen Jiang , Xiaoyuan Yang

引用次数: 0

Covariance matrix adaptation driven dynamic multi-population colony predation optimizer: Insights, qualitative analysis, and constrained engineering optimization

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113041

Xinsen Zhou , Jie Xing , Wenyong Gui , Ali Asghar Heidari , Zhennao Cai , Huiling Chen , Guoxi Liang

{"title":"Covariance matrix adaptation driven dynamic multi-population colony predation optimizer: Insights, qualitative analysis, and constrained engineering optimization","authors":"Xinsen Zhou , Jie Xing , Wenyong Gui , Ali Asghar Heidari , Zhennao Cai , Huiling Chen , Guoxi Liang","doi":"10.1016/j.asoc.2025.113041","DOIUrl":"10.1016/j.asoc.2025.113041","url":null,"abstract":"<div><div>The Colony Predation Algorithm (CPA) is a straightforward population-based algorithm with few control parameters. Nevertheless, its initial design has limitations, including a tendency for local optimization and limited search ability, leading to subpar solutions. In response to these concerns, a novel approach named the Covariance Matrix Adaptive-Driven Dynamic Multi-Population Colony Predation Algorithm (ICPA) is introduced. Qualitative analysis experiments are carried out to determine ICPA's feasibility, including historical search trajectory analyses and balanced diversity assessments. Additionally, a comparative study involving 11 well-known algorithms, 11 state-of-the-art algorithms, and four champion algorithms (EBOwithCMAR, SPS_L_SHADE_EIG, LSHADE_cnEpSi, and LSHADE) using the IEEE CEC 2014 test suite confirmed its superior optimization capabilities. Statistical tests consistently rank ICPA first on the Friedman test (with scores of 2.17, 3.37, and 2.27) and demonstrate its outperformance of state-of-the-art algorithms in at least 40 % of tested functions in the Wilcoxon sign-rank test. The Bonferroni Dunn post-hoc statistical test reveals that ICPA significantly outperforms 61.53 % of the compared algorithms. Additionally, the efficacy of ICPA was evaluated on seven constrained real-world engineering problems, encompassing gear train design, speed reducer design, multi-disc clutch and brake design, cantilever beam design, three-bar truss design, I-beam design, and combined economic emission dispatch. The experimental outcomes underscore the potential of ICPA in addressing practical engineering challenges, thereby validating its optimization effectiveness. Utilizing extensive experimentation and comparative analyses, the feasibility, superiority, and effectiveness of ICPA have been substantiated, encompassing both qualitative and quantitative perspectives.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113041"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759720","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}

引用次数: 0

Dynamic q-rung orthopair hesitant fuzzy decision-making model based on Banzhaf value of fuzzy measure

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113036

Yibo Wang , Xiuqin Ma , Hongwu Qin , Yuanyuan Chen , Jemal H. Abawajy

{"title":"Dynamic q-rung orthopair hesitant fuzzy decision-making model based on Banzhaf value of fuzzy measure","authors":"Yibo Wang , Xiuqin Ma , Hongwu Qin , Yuanyuan Chen , Jemal H. Abawajy","doi":"10.1016/j.asoc.2025.113036","DOIUrl":"10.1016/j.asoc.2025.113036","url":null,"abstract":"<div><div>Multi-attribute decision-making in dynamic fuzzy environments holds significant practical importance in fields. The changing fuzzy environmental factors and the hesitant psychology during the dynamic period commonly involve potential historical impacts of decisions and dynamic fuzzy intercorrelation. Quantitative indicators of attribute importance derived from fuzzy measures can account for intercorrelation but cannot ensure the dynamic feedback of historical decisions. For these reasons, the existing fuzzy decision-making methods have failure and poor stability when dealing with complex, hesitant dynamic fuzzy decision-making problems. Therefore, this paper aims to construct a dynamic decision-making model within the q-rung orthopair hesitant fuzzy(q-ROHF) environment. First, this paper derives the q-ROHF cross-entropy and four combinations of entropy formulas to derive the calculation method for the fuzzy measure of dynamic attributes. The Banzhaf value is used as a weighting metric to quantify the degree of impact of dynamic attributes to the decision result. Second, based on the Banzhaf value, a dynamic weighting algorithm incorporating a historical feedback mechanism and dynamic intercorrelation is proposed. Further, this paper derives a fuzzy preference relation(FPR) using the q-ROHF generalized fuzzy distance and presents an algorithm for updating the dynamic FPR matrix. Finally, a dynamic FPR-based AQM method is used to construct the dynamic decision-making model. The feasibility and effectiveness of the decision-making model in response to changing complex factors are demonstrated through two distinct real-world cases. Through robustness analysis and advantage analysis, it has been verified that the model exhibits superior dynamic decision-making stability and hesitant decision-making stability compared to existing models. The model can be a powerful tool for dealing with dynamic decision-making problems that involve hesitation and uncertainty.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113036"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746206","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}

引用次数: 0

Transcending modularity: A memetic algorithm combining triangle motif and edge information for community detection

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113082

Xiangyi Teng , Xinyue Luo , Jing Liu

{"title":"Transcending modularity: A memetic algorithm combining triangle motif and edge information for community detection","authors":"Xiangyi Teng , Xinyue Luo , Jing Liu","doi":"10.1016/j.asoc.2025.113082","DOIUrl":"10.1016/j.asoc.2025.113082","url":null,"abstract":"<div><div>The research towards community detection plays a crucial role in revealing the topological structure and functional characteristics of complex networks. Nowadays, modularity and its variants are the most popular community quality evaluation metric applied in the detection of community structures. However, these modularity-based methods rarely consider higher-order structural information such as motifs in a network, which may lead to an incomplete and inaccurate understanding of the network. While some motif-based methods have been proposed, they suffer from resolution limitations and completely ignore lower-order structures. To bridge this gap and address community detection problem from a more hybrid view focusing on both lower-order and higher-order structures, this paper first proposes an adaptive hybrid-order modularity optimization function termed as TE-Modularity, which harmonizes triangle motif and edge information. It transcends traditional modularity by considering both lower-order and higher-order structures and can be applicable to all types of networks. In addition, we design a memetic algorithm called TE-MA, that uses TE-Modularity as the objective function to solve the community detection problem. A novel mutation operator based on triangle motifs is proposed, which can effectively accelerate the convergence of the proposed algorithm. Furthermore, we develop a new multipoint local search strategy, striking a good balance between the efficiency and quality of the algorithm. Through experiments conducted on different optimizers and comparisons with several state-of-the-art community detection methods, our approach's effectiveness and superiority are demonstrated on both real and synthetic networks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113082"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759718","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}

引用次数: 0

Efficient fine-tuning of small-parameter large language models for biomedical bilingual multi-task applications

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113084

Yinghong Li , Yudong Yan , Zhuohao Tong , Yu Wang , Yinqi Yang , Mingze Bai , Dan Pu , Jiazheng Xie , Chuan Liu , Bo Li , Mingwei Liu , Kunxian Shu

{"title":"Efficient fine-tuning of small-parameter large language models for biomedical bilingual multi-task applications","authors":"Yinghong Li , Yudong Yan , Zhuohao Tong , Yu Wang , Yinqi Yang , Mingze Bai , Dan Pu , Jiazheng Xie , Chuan Liu , Bo Li , Mingwei Liu , Kunxian Shu","doi":"10.1016/j.asoc.2025.113084","DOIUrl":"10.1016/j.asoc.2025.113084","url":null,"abstract":"<div><div>The escalating computational costs of large language models (LLMs) have catalyzed the pursuit of more efficient alternatives, particularly in specialized domains like biomedicine. In this study, we propose BioQwen, a series of small-parameter biomedical bilingual (Chinese–English) multi-task models designed to mitigate the resource demands of LLMs while achieving high performance.</div><div>BioQwen is trained on carefully curated open-source biomedical datasets, employing a stringent preprocessing pipeline with thorough quality filtering and standardized formatting. Through an efficient two-stage fine-tuning strategy, BioQwen models with 0.5B, 1.5B, and 1.8B parameters attain competitive performance across a variety of comprehension and generative tasks. For comprehension tasks, BioQwen-1.8B achieves a Macro F1 score of 0.730 and a balanced accuracy of 0.802 on the BC5CDR dataset, surpassing the 7B-parameter Taiyi model’s scores of 0.685 and 0.757. In generative tasks, BioQwen delivers superior zero-shot results on the iCliniq dataset, outperforming all baselines across multiple metrics. Comparisons with established small-parameter LLMs (e.g., Llama3.2 1B) further substantiate the effectiveness of domain-specific fine-tuning.</div><div>Significantly, BioQwen’s reduced iteration time highlights its computational efficiency, and its quantized version demonstrates successful deployment on mobile devices, confirming its viability in resource-constrained settings. This study demonstrates the potential of strategically fine-tuned small-parameter LLMs to deliver resource-efficient, high-performing solutions for biomedical bilingual applications, expanding accessibility and usability in the field.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113084"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738480","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}

引用次数: 0

A semi-supervised multi-source remote sensing image classification network based on adaptive pseudo-label generation

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113055

Yining Feng , Lu Wang , Jiarui Jin , Xianghai Wang

{"title":"A semi-supervised multi-source remote sensing image classification network based on adaptive pseudo-label generation","authors":"Yining Feng , Lu Wang , Jiarui Jin , Xianghai Wang","doi":"10.1016/j.asoc.2025.113055","DOIUrl":"10.1016/j.asoc.2025.113055","url":null,"abstract":"<div><div>Earth observation technology leveraging remote sensing (RS) imagery serves as a valuable non-contact detection method with broad applications in classification research. Hyperspectral (HS) image classification, while effective in various domains, faces challenges due to the unique characteristics of HS data. Fusion diverse RS data sources can mitigate redundancy and enhance classification efficiency. However, many deep learning approaches for multi-source RS classification rely heavily on abundant labeled data, which can be time-consuming and often impractical. To address the limitations in feature extraction and classification accuracy stemming from the scarcity of labeled multi-source RS image samples in complex scenes, we propose a novel semi-supervised multi-source RS image classification network based on adaptive pseudo-label generation (S2CNet-APG). This framework incorporates attention modules that effectively embed active RS features into HS features, enhancing performance through squeezing and excitation (SE) driven attention mechanisms. Our semi-supervised learning approach employs adaptive thresholds to manage the quantity of pseudo-labels derived from unlabeled samples, while maintaining the spatial consistency of the information to ensure quality. This dual strategy effectively balances the quantity and quality of pseudo-labels, enabling accurate classification with limited labeled samples and transitioning multi-source RS image classification from a supervised to a semi-supervised paradigm. We conducted extensive experiments on three real-world multi-source RS datasets, achieving superior results that validate the efficacy of the proposed method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113055"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738484","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}

引用次数: 0

Rethinking the multi-scale feature hierarchy in object detection transformer (DETR)

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-28 DOI: 10.1016/j.asoc.2025.113081

Fanglin Liu , Qinghe Zheng , Xinyu Tian , Feng Shu , Weiwei Jiang , Miaohui Wang , Abdussalam Elhanashi , Sergio Saponara

{"title":"Rethinking the multi-scale feature hierarchy in object detection transformer (DETR)","authors":"Fanglin Liu , Qinghe Zheng , Xinyu Tian , Feng Shu , Weiwei Jiang , Miaohui Wang , Abdussalam Elhanashi , Sergio Saponara","doi":"10.1016/j.asoc.2025.113081","DOIUrl":"10.1016/j.asoc.2025.113081","url":null,"abstract":"<div><div>The Detection Transformer (DETR) has emerged as the dominant paradigm in the field of object detection due to its end-to-end architectural design. Researchers have explored various aspects of DETR, including its structure, pre-training strategies, attention mechanisms, and query embeddings, achiving significant progress. However, high computational costs limit the efficient use of multi-scale feature maps and hinder the full exploitation of complex multi-branch structures. We examine the negative impact of multi-scale features on the computational cost of DETRs and find that introducing long sequence data to the encoder is suboptimal. In this work, we aim to further push the boundaries of DETR’s performance and efficiency from the model structure perspective, thus developing the fusion detection Transformer (F-DETR) with heterogeneous scale multi-branch structure. To the best of our knowledge, this is the first explicit attempt to integrate multi-scale features into the end-to-end DETR structure. Specifically, we propose a multi-branch structure to simultaneously utilize feature maps at different levels, facilitating the interaction of local and global features. Additionally, we select certain joint latent variables from the interactive information flow to initialize the object container, a technique commonly used in query-based detectors. Experimental results show that F-DETR achieves a 43.9 % AP using 36 training epochs on the popular public COCO dataset. Furthermore, our approach demonstrates a better trade-off between accuracy and complexity compared to the original DETR.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113081"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738105","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}

引用次数: 0