Expert Systems with Applications最新文献

{"title":"DORF-EASNet: physics-driven real-time seafloor classification via entropy‑regularized acoustic features and adaptive model activation","authors":"Xi Zhao,&nbsp;Qiangqiang Yuan,&nbsp;Quanyin Zhang,&nbsp;Jiadan Xu","doi":"10.1016/j.eswa.2026.131461","DOIUrl":"10.1016/j.eswa.2026.131461","url":null,"abstract":"<div><div>Real-time seabed sediment classification (SSC) is crucial for underwater navigation, operations, and habitat assessment. Conventional methods relying on post-mission multibeam-echosounder (MBES) data processing impede in situ decision-making. We propose a novel, real-time SSC method deployable on both shipborne and Autonomous Underwater Vehicle (AUV) platforms, integrating three core components. Primarily, an efficient preprocessing pipeline comprising georeferencing, radiometric normalization, noise suppression, and incidence-angle correction enables rapid conversion of raw MBES backscatter into geometry-consistent tiles, supporting real-time operation with sub-second responsiveness. Afterwards, the system extracts multi-modal descriptors by combining entropy-regularised angular-response fitting for acoustic backscatter, object-level texture analysis using adaptive graph segmentation, and curvature-aware terrain metrics derived from quadratic surface fitting under entropy constraints by considering the physical responses and spatial distribution of MBES images and point clouds. Finally, a Dynamic Optimal Random Forest with Entropy-Adaptive Subnetwork Selection (DORF-EASNet) dynamically selects between a global classifier and lightweight domain-specific sub-models to match local acoustic complexity, achieving a balance between inference efficiency and physical interpretability. Field experiments conducted in Jiaozhou Bay and the South China Sea demonstrate the proposed framework’s robustness across platforms and sensing configurations, achieving macro-F1 scores of 0.881 and 0.913, respectively, while maintaining real-time processing capability exceeding that of conventional offline methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131461"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122660","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":"Preference learning based on maximizing membership degree with heterogeneous information for landslide early warning","authors":"Jiajia Jiang ,&nbsp;Min Zhan ,&nbsp;Gaocan Gong ,&nbsp;Lin Wang ,&nbsp;Quanbo Zha","doi":"10.1016/j.eswa.2026.131381","DOIUrl":"10.1016/j.eswa.2026.131381","url":null,"abstract":"<div><div>Preference learning involves developing a model that reflects a decision-maker's preference based on the provided information. Existing preference learning models for multi-criteria classification overlook exploring the membership degree of an alternative to a predefined class, especially in the face of heterogeneous types of criteria information. To address this issue, this paper proposes a preference learning model based on membership degree maximization with heterogeneous information. First, based on the additive utility function, we construct a preference model that integrates numerical and linguistic criteria information to express the utilities of alternatives. Within this model, four types of utility functions are considered to describe the variation characteristics of criteria. Next, triangular fuzzy numbers are employed to capture the membership degree of each alternative within the predefined classes, and a learning model is developed by maximizing the membership degrees of alternatives to their corresponding predefined classes. Finally, the proposed model is applied to landslide early warning to verify its feasibility.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131381"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122666","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 depthwise convolutional variational autoencoder for anomaly detection in complex traffic scenarios from UAV views","authors":"Arslan Saleem,&nbsp;Cem Direkoglu","doi":"10.1016/j.eswa.2026.131425","DOIUrl":"10.1016/j.eswa.2026.131425","url":null,"abstract":"<div><div>Traffic anomaly detection (AD) is essential for improving public safety, reducing risks, and enabling quick responses in intelligent surveillance systems. Aerial traffic monitoring, particularly using Unmanned Aerial Vehicles (UAV), has gained attention due to its potential to address challenges like dynamic urban environments, yet it remains underexplored. Detecting anomalies in drone-captured video involves unique obstacles: rare events, small and overlapping objects, multi-scale targets, and complex backgrounds. To address these challenges, we propose the Depthwise Convolutional Variational Autoencoder (DwCVAE), a novel model designed to enhance AD in drone-based traffic surveillance. DwCVAE leverages depthwise convolutions, which allow efficient and detailed feature extraction, improving model sensitivity to subtle and multi-scale anomalies. The proposed DwCVAE adopts an encoder-latent-decoder VAE architecture, in which stacked depthwise convolutional layers in the encoder emphasize spatially localized feature learning while maintaining channel-wise efficiency, and a compact variational latent space captures the distribution of normal traffic dynamics. Built on variational autoencoder (VAE) architecture, DwCVAE creates compact latent representations that capture normal traffic patterns, enabling reliable detection of deviations. Anomalies are identified through reconstruction-based scoring, where events that deviate from the learned normal representations yield higher reconstruction errors. This depthwise approach marks a key innovation, optimizing both computational efficiency and detection accuracy. We design four additional models: Convolutional Variational Autoencoder (CVAE), Dilated Convolutional VAE (DCVAE), Separable Convolutional VAE (SCVAE), and Convolutional LSTM VAE (CLSTMVAE) to systematically assess the effectiveness of DwCVAE. Additionally, we evaluate DwCVAE against state-of-the-art weakly supervised and unsupervised models on two benchmark datasets, Drone-Anomaly and UIT-Adrone. DwCVAE achieves an AUC of 74.95 with an EER of 0.30 on Drone-Anomaly, and an AUC of 79.77 with an EER of 0.27 on UIT-ADrone, demonstrating its superior performance in complex aerial surveillance tasks.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131425"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122667","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":"MAGF: Multi-scale attention and gated fusion for multi-modal glaucoma grading","authors":"Haixi Cheng ,&nbsp;Chaoqun Hong ,&nbsp;Bo Zhang ,&nbsp;Huihui Fang ,&nbsp;Yanwu Xu ,&nbsp;Si Yong Yeo","doi":"10.1016/j.eswa.2026.131388","DOIUrl":"10.1016/j.eswa.2026.131388","url":null,"abstract":"<div><div>Glaucoma is one of the leading causes of irreversible blindness worldwide. Color fundus photography (CFP) and optical coherence tomography (OCT) are two primary imaging modalities for glaucoma diagnosis. Recently, multi-modal approaches that combine CFP and OCT have demonstrated higher diagnostic accuracy compared to single-modal methods. However, the high similarity among medical image poses presents a challenge for extracting effective features. Additionally, low-quality features can degrade fusion performance, potentially leading to inaccurate grading results. To address these challenges, we propose a Multi-scale Attention and Gated Fusion (MAGF) framework, which incorporates a dual-branch feature extraction architecture with targeted attention, a Multi-scale Attention Fusion Module (MAFM) for enhancing OCT features, and a Gated Fusion Module (GFM) for adaptive integration of CFP and OCT modalities. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) performance in glaucoma grading.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131388"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122714","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 heterogeneous Hopfield neural network with discrete memristor: modeling, dynamics, and application in medical image encryption","authors":"Huiqun Zou ,&nbsp;Yang Lu ,&nbsp;Wenjiao Li ,&nbsp;Wenhui Li ,&nbsp;Xiuli Chai","doi":"10.1016/j.eswa.2026.131457","DOIUrl":"10.1016/j.eswa.2026.131457","url":null,"abstract":"<div><div>Memristors and activation functions critically shape the nonlinear dynamics of Hopfield neural networks. While previous studies explored memristor modeling and heterogeneous activation separately, their combination to form heterogeneous memristive networks remains insufficiently explored. This paper bridges this gap by proposing a novel Heterogeneous Hopfield Neural Network with Discrete Memristor (HHNN-DM), coupling a discrete memristor with heterogeneous activations to mimic neural diversity. By analyzing dissipation, equilibrium stability, bifurcation diagrams, and Lyapunov exponents, we demonstrate that heterogeneous activation mechanisms significantly enhance network complexity and unpredictability under memristive interactions. This synergy gives rise to rich chaotic behaviors, such as periodic orbits, bifurcations, transient chaos, and chaotic bursting. As these biologically inspired chaotic dynamics, the resulting high-quality chaotic sequences are well suited for cryptographic applications. Furthermore, a Heterogeneous Hopfield Neural Network–based Medical Image Encryption Algorithm (HHNN-MIEA) is developed to enhance security in remote medical image transmission, integrating an X-fractal curve sorting matrix for permutation with multi-logical diffusion driven by chaotic sequences. Experimental results verify that the HHNN-MIEA achieves high security in aspects such as key sensitivity, information entropy without compromising efficiency, highlighting its effectiveness, robustness and reliable solution for secure medical image transmission.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131457"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122715","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":"Local attention alignment fusion network for domain adaptive water body segmentation","authors":"Hao Liu ,&nbsp;Xiaobin Zhu ,&nbsp;Xu Qizhi ,&nbsp;Chun Yang ,&nbsp;Hongyang Zhou ,&nbsp;Yongjie Xia ,&nbsp;Xucheng Yin","doi":"10.1016/j.eswa.2026.131382","DOIUrl":"10.1016/j.eswa.2026.131382","url":null,"abstract":"<div><div>Water body segmentation is crucial for various tasks, e.g., disaster early warning and ecological management. Existing deep learning-based methods mainly focus on specific scenarios, often encountering significant performance drops on multi-source satellite data with large domain differences. In this paper, we propose a novel Local Attention Alignment Fusion Network for domain adaptive water body segmentation (dubbed LAAFNet). Our LAAFNet explores spatial relationships between pseudo-RGB and pseudo-NIR images, and then extracts invariant features via local attention to improve the representative capability of cross-domain features. Moreover, we design a novel Difficult Sample Point Loss (DSPLoss) to address the presence of potential positive samples within negative regions across domains through a pixel-level contrastive learning strategy. DSPLoss leverages a Cauchy-Schwarz-based constraint to regulate the upper bound of feature similarity in the pixel-level inner product space. This constraint enhances the separation between water bodies and background in hard samples, allowing the model to learn a clearer decision boundary and thereby improving its generalization capability. Notably, we construct a large-scale Water Generation Testing Dataset (WGTDataset) to evaluate water body segmentation in real-world applications. Experimental results demonstrate that the LAAFNet outperforms the state-of-the-art (SOTA) methods. The codes and dataset are available on: <span><span>https://github.com/LH325/LAAFNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131382"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122716","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":"AI-driven zero trust and blockchain framework for secure electric vehicle infrastructure","authors":"Clement Daah ,&nbsp;Ysabel Fallot ,&nbsp;Amna Qureshi ,&nbsp;Irfan Awan ,&nbsp;Savas Konur","doi":"10.1016/j.eswa.2026.131577","DOIUrl":"10.1016/j.eswa.2026.131577","url":null,"abstract":"<div><div>Electric vehicle (EV) charging infrastructures are increasingly exposed to sophisticated cyber threats, including replay, spoofing, privilege escalation, and geolocation-based attacks. While standards such as ISO 15118 and OCPP 2.0.1 provide interoperability and cryptographic guarantees, they rely on static policies or isolated detection mechanisms, leaving gaps against adaptive adversaries. This paper presents an AI-driven Zero Trust Blockchain (AI-ZTB) framework whose novelty lies in the system-level integration of identity and access management, AI-based risk assessment, and blockchain-backed decentralized auditability with IPFS-based evidence storage, while operational governance remains centrally managed by the service provider. Unlike prior AI-only or blockchain-only frameworks, AI-ZTB introduces a fully integrated and enforceable Zero Trust control loop in which AI-generated risk scores are operationally bound to access enforcement decisions through smart contracts, enabling adaptive, auditable, and context-aware security governance in real time. The framework was implemented in Python with Solidity smart contracts and evaluated through a large-scale network simulation involving batches of 10,000 EV-charging sessions, trained on a dataset of 50,000 legitimate and adversarial behaviours using Random Forest, Autoencoder, and Isolation Forest models. Results demonstrate that AI-ZTB achieves access-decision accuracy above 95%, reducing false acceptance and rejection rates to approximately 3%. A comparative analysis evaluates AI-ZTB against industry standards (ISO 15,118 and OCPP 2.0.1) as secure communication baselines, and against prior integrated frameworks from the literature, highlighting differences in architectural scope, policy enforceability, and auditability rather than protocol-level performance. Despite modest inference and logging overheads, performance remained within real-time operational tolerances. The framework establishes a robust foundation for securing EV infrastructures, with extensibility to smart grids and other cyber-physical environments.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131577"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192588","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":"Federated learning with dynamics-aware loss for label noise","authors":"Chengtian Ouyang ,&nbsp;Jihong Mao ,&nbsp;Zhiquan Liu ,&nbsp;Donglin Zhu ,&nbsp;Changjun Zhou ,&nbsp;Gangqiang Hu ,&nbsp;Taiyong Li","doi":"10.1016/j.eswa.2026.131523","DOIUrl":"10.1016/j.eswa.2026.131523","url":null,"abstract":"<div><div>In the domain of Internet of Things, federated learning is gradually becoming a key technology for achieving safe and efficient implementation of artificial intelligence. Through distributed collaboration mechanisms, it enables edge intelligence while protecting data privacy and reducing communication costs. In the real federated learning system, clients usually exhibit variable levels of label noise, and local training tends to overfit the label noise resulting in decreased generalization performance of the model. Despite the existence of many research findings on the problem of data heterogeneity, these methods are not effective in dealing with label noise. Thus, tackling label noise problem is one of the keys to facilitating the development of federal learning. In the research, an adaptive framework FedDAL is proposed to combat federated learning with label noise. In the pre-training stage, the server identifies noisy clients by the unreliability score. The module named distance-sensitive truncation is designed to improve identification accuracy. In the federated learning stage, noisy clients train local models by dynamics-aware loss to mitigate the adverse effects of label noise. Finally, the server carries out loss normalization and weight adjustment aggregation taking into account the data volume and the aggregate class mean loss. Experimental results on multiple datasets demonstrate that FedDAL effectively addresses label noise overfitting, improves model generalization performance and outperforms state-of-the-art methods across multiple distributions of label noise. Our code is available at <span><span>https://github.com/Donglin0730/FedDAL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131523"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192585","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 cumulative, pipeline-oriented framework for extreme imbalanced classification: Large-scale benchmarking of 4,100 rare-event models","authors":"Jaime Villanueva-García ,&nbsp;Ignacio Moral-Arce ,&nbsp;Luis Javier García Villalba","doi":"10.1016/j.eswa.2026.131366","DOIUrl":"10.1016/j.eswa.2026.131366","url":null,"abstract":"<div><div>Extreme class imbalance remains a long-standing challenge in supervised learning, particularly when the minority class corresponds to rare but high-impact events such as fraud, equipment failure, or retirement transitions. Although a wide range of imbalance-handling techniques has been proposed-including penalization, resampling, loss shaping, threshold calibration, optimization, and ensemble strategies-the field largely lacks an operational understanding of how these components should be combined, calibrated, and consolidated within end-to-end pipelines.</div><div>This paper introduces a cumulative, pipeline-oriented framework for imbalanced classification under extreme skew. Rather than categorizing learning algorithms or paradigms, the framework reorganizes the design space of imbalanced learning around <em>where</em> and <em>how</em> imbalance-aware interventions act within the modeling pipeline. By progressively integrating data-level adjustments, objective and decision-rule shaping, and higher-level consolidation mechanisms, the framework exposes the structural logic underlying effective rare-event classifiers.</div><div>We validate this framework through a large-scale benchmark comprising 4109 pipeline configurations applied to administrative microdata from the Spanish Social Security system, where monthly retirement detection exhibits a positive-class prevalence of 2.34%. The results reveal clear and interpretable performance regimes as pipeline complexity increases. Explicit pipeline-level ensemble architectures achieve the strongest recall and balance-sensitive metrics, while well-calibrated and optimized intermediate pipelines frequently attain comparable or superior performance in selected metrics-such as F1-score and Matthews correlation coefficient-relative to ensemble baselines, at substantially lower computational cost and with improved interpretability. Distributional and robustness analyses further show that consolidation mechanisms act primarily by stabilizing the upper tail of the performance distribution rather than by improving isolated best-case outcomes.</div><div>Beyond this application, the proposed framework provides a reproducible benchmarking protocol and a unifying operational lens for understanding when additional methodological sophistication yields robust, stable, and practically meaningful performance gains under extreme class imbalance.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131366"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192782","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-perspective domain-invariant network with energy density-based data augmentation for domain generalization fault diagnosis","authors":"Sukeun Hong,&nbsp;Jaewook Lee,&nbsp;Jongsoo Lee","doi":"10.1016/j.eswa.2026.131583","DOIUrl":"10.1016/j.eswa.2026.131583","url":null,"abstract":"<div><div>Existing domain generalization fault diagnosis methods achieve satisfactory interpolation performance but struggle with extrapolation owing to two fundamental limitations: insufficient source domain coverage and the inability to verify whether learned features represent causal fault characteristics or spurious correlations. To address these challenges, this study proposes a multi-perspective domain-invariant network (MPDIN) with energy–density-based data augmentation. MPDIN employs bootstrap aggregation to train multiple feature extractors on strategically defined domain subsets, establishing hierarchical domain invariance by enforcing subset-level invariance through triplet loss and inter-subset consistency via correlation alignment. This multi-perspective framework effectively suppresses subset-specific spurious correlations while preserving genuine fault characteristics. The energy–density-based augmentation leverages the <span><math><mrow><msup><mrow><mi>ω</mi></mrow><mn>2</mn></msup></mrow></math></span>-proportional relationship between rotational speed and vibration energy to generate realistic extrapolation data beyond source domain boundaries, utilizing raw short-time Fourier transform power spectrograms to preserve absolute energy information essential for physics-based scaling. Experimental validation across four diverse datasets demonstrated substantial improvements in challenging extrapolation scenarios, achieving gains of 19–47%, whereas conventional methods showed significant performance degradation. Manifold analysis confirmed continuity and complete target–source integration, validating the attainment of true domain-invariant learning. Although limitations exist in time-varying scenarios, the proposed methodology provides a principled framework for industrial deployment where targets frequently exceed training envelopes.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131583"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192784","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}