Applied Soft Computing最新文献

{"title":"Multimodal transformers for image and audio polyphonic music transcription","authors":"María Alfaro-Contreras,&nbsp;Noelia Luna-Barahona,&nbsp;Carlos Pérez-Sancho,&nbsp;Jorge Calvo-Zaragoza,&nbsp;Jose J. Valero-Mas","doi":"10.1016/j.asoc.2026.114643","DOIUrl":"10.1016/j.asoc.2026.114643","url":null,"abstract":"<div><div>The attainment of structured representations of music sources—commonly referred to as music transcription—has long been a central challenge in the field of Music Information Retrieval. Traditionally, research has addressed this task through Optical Music Recognition for sheet music and through Automatic Music Transcription for audio recordings. More recently, multimodal approaches that integrate both image and audio modalities have emerged, aiming to exploit their complementary strengths. However, these existing methods have so far been evaluated only in controlled settings with monophonic music. In this work, we present the first multimodal image-and-audio framework for polyphonic music transcription, built upon the Transformer architecture. Specifically, we design and evaluate six distinct modality-fusion strategies, differing in the stage at which the modalities are integrated (early, intermediate, or late fusion). Our results demonstrate that multimodality can be beneficial for polyphonic transcription—producing comparable or superior performance across all datasets tested and, in the best-performing experiments, improving the performance of the best unimodal transcription scenario by 9% on average—but its impact depends on the strategy: some fusion schemes yield consistent gains, whereas others fail to improve upon the unimodal baseline.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114643"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039722","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":"AssemPrompt: A lightweight multi-module collaborative framework for efficient and accurate medical image segmentation","authors":"Hongyan Zhao,&nbsp;Yunteng Hu,&nbsp;Shijie Xu","doi":"10.1016/j.asoc.2026.114801","DOIUrl":"10.1016/j.asoc.2026.114801","url":null,"abstract":"<div><div>Medical image segmentation tasks urgently require efficient modeling of multi-level structures and multi-scale boundary features of organs/tissues under limited computational resources. Existing methods struggle to balance high-precision segmentation requirements with real-time constraints for clinical deployment. This paper proposes a multi-module collaborative architecture for medical image segmentation, with its core comprising: (1) The AssemFormer module employs a dual-path parallel mechanism, integrating depthwise separable convolution and linear-complexity self-attention to unify local anatomical details and long-range contextual dependencies through a unified tensor assembly strategy; (2) The PromptGenBlock module introduces a dynamic prompt mechanism based on input distribution, adaptively enhancing target region features using a learnable parameter library while providing category guidance; (3) The GABD module effectively establishes cross-scale spatial associations and strengthens discriminative expression of lesions or boundaries through high-low frequency feature decoupling aggregation, variable dilation rate convolution, and mask-aware attention; (4) The FusionModule combines LHMC multi-scale context modeling with CAM channel attention recalibration, enhancing feature fusion stability and target selectivity through normalization and nonlinear activation. This lightweight architecture achieves a favorable balance between accuracy and efficiency, reaching 81.26% mIoU and 89.66% DSC on ISIC2018 with only 0.0914 M parameters and 0.0897G FLOPs, and shows strong performance across GlaS, ISIC2016, ISIC2017 and CVC-ClinicDB.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114801"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191851","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":"Exploring barriers to metaverse-enabled healthcare 4.0 patient monitoring using interval-valued pythagorean fuzzy DEMATEL","authors":"Sachin B. Khot ,&nbsp;Shweta S. Patil ,&nbsp;Yu-Chen Hu ,&nbsp;Juber M. Mulla","doi":"10.1016/j.asoc.2026.114737","DOIUrl":"10.1016/j.asoc.2026.114737","url":null,"abstract":"<div><div>The merging of immersive technologies with real-time clinical data in healthcare 4.0 has significantly transformed patient monitoring and management in the metaverse. This study systematically reviewed the applications of metaverse technologies in healthcare. This study further advances the examination of the challenges of adopting metaverse-enabled healthcare systems using an Interval-Valued Pythagorean Fuzzy Decision-Making Trial and Evaluation Laboratory (IVPF-DEMATEL) framework. To evaluate the challenges, this study considered inputs based on evidence received from experts at a prominent healthcare institution in Western Maharashtra, India. A systematic literature review identified 12 critical challenges in the technical, regulatory, social, and economic domains. Expert-based IVPF-DEMATEL evaluations revealed that data security and privacy, infrastructure and interoperability, and regulatory compliance are the most prominent causative barriers. User experience, cultural acceptance, and sustainability concerns have emerged as downstream effects. The cause–and–effect analysis highlights the hierarchical structure of these barriers, demonstrating that addressing core technical and regulatory issues will generate positive ripple effects across user-centric and sociocultural dimensions. The findings provide actionable insights for policymakers, healthcare managers, and technology developers, underscoring the need for secure, interoperable, and ethically governed digital ecosystems to realize the metaverse’s transformative potential in healthcare. To enhance methodological robustness, inter-expert consistency analysis and sensitivity analysis using ±10 % variation in fuzzy membership bounds are conducted. The findings demonstrate that both the cause–and–effect classification and prominence rankings remain stable under moderate perturbations, confirming the reliability of the proposed framework. This study contributes to the growing body of Healthcare 4.0 research by offering empirical evidence from the Indian context and proposing strategic directions for the sustainable and equitable adoption of immersive patient care technologies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114737"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191881","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":"Adaptive ant colony optimization with crossover-guided search for dynamic multi-criteria traveling salesman problem","authors":"Sanjai Pathak ,&nbsp;Ashish Mani ,&nbsp;Amlan Chatterjee","doi":"10.1016/j.asoc.2026.114668","DOIUrl":"10.1016/j.asoc.2026.114668","url":null,"abstract":"<div><div>Many real-world instances of the dynamic multi-criteria traveling salesman problem (DMC-TSP) present unpredictable challenges, making them complex dynamic optimization problems. These challenges arise from factors such as the addition of new locations, fluctuating travel times and costs due to changing traffic or weather conditions, or cancellations of pre-scheduled stops, requiring real-time adaptive optimization strategies. While ant colony optimization (ACO) has proven effective for static optimization problems, existing ACO variants have limited adaptability for dynamic multi-criteria environments. To address this significant gap, we introduce a generic test problem generator explicitly for DMC-TSP, capable of creating benchmark problems with known global-optimal solutions. We then propose a novel self-adaptive ant optimization (SAAO) algorithm tailored for DMC-TSP, integrating two new edge assembly crossover operators and a self-adaptive local search operator, explicitly designed to mitigate local-optima stagnation observed in traditional swarm algorithms. Our method demonstrates an effective balance between exploration and exploitation. Comprehensive comparative experiments against four state-of-the-art algorithms, supported by rigorous statistical validation, confirm that the proposed method significantly outperforms existing techniques—achieving a 15–22% improvement in offline performance over baseline algorithms—in terms of adaptability, robustness, and overall efficiency in solving DMC-TSPs.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114668"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080110","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-objective evolutionary algorithms and integer programming for the optimization of underwater acoustic sensor network design","authors":"Laurent Lemarchand ,&nbsp;Ronan Serré ,&nbsp;Bilal Latrach ,&nbsp;Mathis Hamelotte ,&nbsp;Catherine Dezan ,&nbsp;David Dellong ,&nbsp;Myriam Lajaunie","doi":"10.1016/j.asoc.2026.114685","DOIUrl":"10.1016/j.asoc.2026.114685","url":null,"abstract":"<div><div>Acoustic surveillance of oceans is of great interest for monitoring both wildlife and anthropogenic activities. In this paper, we focus on passive, non-communicating sonar systems that are suitable for efficiently monitoring the marine environment. With such a network, no live tracking is possible; instead, we aim to record activities in the most discreet manner for surveillance purposes. General guidelines can be established to assist in designing a network of underwater acoustic sensors. These guidelines serve as both objectives and constraints, such as sensor detection capabilities, deployment area alternatives, acoustic propagation properties, and potential noise source areas.</div><div>The optimization of network design, in terms of the distribution and locations of sensors (or groups of sensors physically stacked on a mooring line, for example, and referred to as <em>anchors</em>), involves trade-offs between network cost, coverage of the targeted marine area, network redundancy, and localization capabilities. In this paper, we propose several approaches to address these various constraints and optimization objectives. These approaches rely on numerical simulations of underwater acoustic propagation, which allow for accounting for environmental characteristics such as bathymetry, sea floor properties, and sound speed profiles.</div><div>A discretized model of the network design problem is presented. It relies on a grid representation of anchor locations, and a complete Integer Programming formulation of the problem is defined. The model is used to identify sets of solutions that represent trade-offs among the different optimization criteria. Both exact and heuristic multi-objective approaches are proposed to exploit the model. The former are applicable to smaller sets of instances and optimization objectives than the latter. However, the exact approaches enable the derivation of precise bounds on the optimized criteria, as well as initial solutions that can enhance heuristic multi-objective methods based on evolutionary multi-objective optimization frameworks. This supports the designer’s choice of the final network configuration.</div><div>Numerical experiments conducted on a set of sixteen semi-synthetic test cases, as well as on an actual network deployed at sea, demonstrate that an exact <span><math><mi>ϵ</mi></math></span>-constraint method is applicable to industrial-scale instances involving up to 2000 monitoring points. It provides the complete Pareto set for the coverage <em>vs.</em> cost problem in a few minutes. One of the proposed heuristic methods yields, in less than a second, sets of solutions with a loss of quality below 1% for the same design problem. When the triangulation capabilities of the network are optimized as a complementary objective for noise source localization, hybrid heuristic approaches are able to improve the results of the basic algorithm by up to 156% on average.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114685"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080201","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":"Reinforcement learning-driven nonsingular fast terminal sliding mode control for pipe crack sealing manipulator","authors":"Santosh Kumar,&nbsp;S.K. Dwivedy","doi":"10.1016/j.asoc.2026.114708","DOIUrl":"10.1016/j.asoc.2026.114708","url":null,"abstract":"<div><div>This study explores control challenges associated with the Pipe Crack Sealing Manipulator (PCSM), a tree-shaped robot designed to seal cracks within concrete pipes. Equipped with five specialized branches, the PCSM adeptly navigates both vertical and horizontal pipe environments. A detailed CAD model was developed in SolidWorks, and data were meticulously exported to Simulink for refined control operations. SolidWorks simulations revealed that the PCSM’s fifth branch excelled in extensive crack repairs. The control of pipe crack sealing manipulators presents significant challenges due to uncertain dynamics, random disturbances, signal delay, and sensor noise inherent in confined environments. To overcome these issues, this study proposes a reinforcement learning–driven nonsingular fast terminal sliding mode control (RLNFTSMC) framework that enhances robustness, precision, and adaptability for crack sealing operations. The implemented RLNFTSMC strategy, includes an equivalent part utilizing actor-critic neural networks for dynamic estimation and a switching part that treats the mass matrix as a lumped value. Comparative analysis with nonsingular fast terminal sliding mode control (NFTSMC), adaptive NFTSMC, radial basis function neural network control (RBFNNC), and neural network-enhanced nonsingular fast terminal sliding mode control (NENFTSMC) shows that RLNFTSMC significantly improves the PCSM’s reliability and effectiveness in environments marked by random disturbances and unknown dynamics.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114708"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080293","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":"Hybrid-conditional generative adversarial network framework for climate fault detection in vertical farming environments","authors":"P.K.S. Tejes ,&nbsp;Abhishek Dasore ,&nbsp;Norhashila Hashim ,&nbsp;Bukke Kiran Naik ,&nbsp;Challa Monisha Reddy","doi":"10.1016/j.asoc.2026.114727","DOIUrl":"10.1016/j.asoc.2026.114727","url":null,"abstract":"<div><div>Fault diagnosis in vertical farming systems (VFS) is constrained by the lack of labeled fault data, especially under rare or complex airflow disturbance conditions. This limits the training and evaluation of machine learning (ML) models for real-time microclimate monitoring and control. This study aims to develop and assess a Hybrid Conditional Generative Adversarial Network (Hybrid-CGAN) to generate synthetic microclimate sequences for specific airflow fault scenarios using computational fluid dynamics (CFD)-based simulation data. Four airflow fault scenarios, such as, reduced airflow from the upper inlet plenum, rack short-circuit, non-functional rear exhaust fan, and high static pressure at the return-duct junction, were simulated using a steady-state CFD model of a multi-tier plant factory. Diurnal perturbations generated 24-hour temporal sequences. Synthetic datasets trained Hybrid-CGAN and were compared with baselines such as Synthetic Minority Over-sampling Technique (SMOTE), Random Oversampling (ROS), and unconditional GANs. Performance was evaluated using Dynamic Time Warping (DTW), Uniform Manifold Approximation and Projection (UMAP) visualizations, and accuracy of Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LGBM), and k-Nearest Neighbors (k-NN) models. Hybrid-CGAN achieved the lowest DTW score of 0.10 at 100 % CFD data, representing a 61.03 % improvement over standard GAN (DTW = 10.45) and 59.76 % over simulation-only baseline (DTW = 10.12). Classifiers trained on Hybrid-CGAN-augmented datasets achieved accuracies of 0.95 (GRU), 0.96 (LGBM), and 0.94 (k-NN). UMAP showed high clustering similarity between real and synthetic sequences. This study establishes a practical paradigm by integrating CFD-based airflow simulations with Hybrid-CGAN framework for fault-specific synthetic data generation in VFS.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114727"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080194","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":"Adaptive multi-region multi-directional competitive swarm optimizer algorithm for large-scale multi-objective problems","authors":"Debao Chen ,&nbsp;Yuanyuan Ge ,&nbsp;Feng Zou ,&nbsp;Fangzhen Ge ,&nbsp;Lei Zhang","doi":"10.1016/j.asoc.2026.114741","DOIUrl":"10.1016/j.asoc.2026.114741","url":null,"abstract":"<div><div>Competitive swarm optimization has shown impressive performance in addressing large-scale multi-objective optimization problems. However, its competitive mechanism can lead to contention among local optimal solutions within the current population. As the decision space grows more complex, the diversity of the population in competitive swarm optimization often decreases, resulting in “premature convergence”. To address this issue, we propose an adaptive multi-region multi-directional competitive swarm optimization algorithm, named AMRCSO. First, this algorithm introduces an adaptive reference-guided subregion division strategy that segments the objective space based on the population’s distribution, enhancing search diversity and coverage. Additionally, a multi-directional exploration strategy is employed to explore each subregion of the decision space, enabling parallel and multi-directional exploration that accelerates convergence to the global optimal solution. Furthermore, an effective population initialization and tri-stage updating strategy are implemented to improve population diversity and enhance convergence accuracy, respectively. Experimental results on large-scale multi-objective benchmark functions, ranging from 500 to 5000 decision variables, demonstrate that the AMRCSO algorithm achieves superior convergence accuracy and distribution compared to existing algorithms for large-scale multi-objective optimization problems.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114741"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190706","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":"ProtoMark: A transfer prototype network with Markov-enhanced features for few-shot intrusion detection","authors":"Yixin Xu ,&nbsp;Jingwen Wang ,&nbsp;Yue Chen ,&nbsp;Junfeng Wang ,&nbsp;He Sun ,&nbsp;Zhiyang Fang","doi":"10.1016/j.asoc.2026.114806","DOIUrl":"10.1016/j.asoc.2026.114806","url":null,"abstract":"<div><div>With the increasing prevalence of targeted attacks against Local Area Networks (LANs), the demand for adaptive intrusion detection tailored to heterogeneous networks has become increasingly prominent. However, as penetration data from LANs is typically scarce due to its sensitivity, conventional data-driven approaches face significant limitations. To fill this gap, we propose ProtoMark, a highly transferable prototype network that incorporates Markovian traffic characteristics into feature representation, establishing an enhanced Transfer Prototype Network framework for effective intrusion detection. Specifically, the model fuses Markov features of traffic byte sequences with statistical features to generate new feature images, thereby enriching the learnable content of the model. ProtoMark combines fully connected neural networks and prototype networks to further enhance the model’s generalization ability to build a transferable prototype network model. The model achieves intrusion detection by constructing prototypes of different types of traffic and optimizing its transfer performance by fine-tuning the fully connected layer (FC) parameters. Experimental results show that ProtoMark performs superior network intrusion detection on the CICIDS2017 and CICDDoS2019 datasets, outperforming existing state-of-the-art methods. Under the 2-way 5-shot setting, the average accuracy of ProtoMark is 94.76% and 95.37% on the two datasets. Additionally, ablation studies further validate the effectiveness of Markov features and the significant contribution of the transfer layer to the model’s generalization ability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114806"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191884","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":"Topic-aware influence maximization based on deep reinforcement learning and group relative optimization","authors":"Yingqi Zou ,&nbsp;Guanyu Li ,&nbsp;Yanhao Wang ,&nbsp;Bo Ning ,&nbsp;Shaohan Wang","doi":"10.1016/j.asoc.2026.114786","DOIUrl":"10.1016/j.asoc.2026.114786","url":null,"abstract":"<div><div>Information diffusion on social networks is increasingly complex and diverse. Identifying suitable users to recommend and spread specific information is critical for social network analysis. The Topic-aware Influence Maximization (TIM) problem aims to identify a seed set that maximizes the influence spread under a given topic distribution. However, existing TIM methods suffer from severe computational inefficiencies. Meanwhile, current Deep Reinforcement Learning (DRL)-based methods mostly ignore the interplay between network structure and topic heterogeneity. To address these challenges, this paper proposes GR-TIM, an end-to-end DRL-based framework for TIM. GR-TIM first estimates the pre-global influence using Graph Neural Networks (GNNs). Then, a group relative optimization strategy partitions users based on topic and community structures. We further leverage intra-group collaboration to apply the global-local optimization paradigm to agent training and inter-group competition to achieve adaptive seed selection for the target topic. Experiments on six real-world datasets demonstrate that GR-TIM outperforms state-of-the-art DRL-based methods in terms of multi-topic influence spread and reduces runtime by two to three orders of magnitude compared to existing simulation-based methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114786"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191834","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}