Concurrency and Computation-Practice & Experience最新文献_第2页

MultiMark: Multi-Descriptor Fuzzy Assisted Secure NIfTI Image Transfer Framework With Features Control Authentication MultiMark：多描述符模糊辅助安全NIfTI图像传输框架与特征控制认证

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-10-01 DOI: 10.1002/cpe.70311

Priyank Khare, Divyanshu Awasthi, Vinay Kumar Srivastava

{"title":"MultiMark: Multi-Descriptor Fuzzy Assisted Secure NIfTI Image Transfer Framework With Features Control Authentication","authors":"Priyank Khare, Divyanshu Awasthi, Vinay Kumar Srivastava","doi":"10.1002/cpe.70311","DOIUrl":"https://doi.org/10.1002/cpe.70311","url":null,"abstract":"<div>\u0000 \u0000 <p>The communication technology has recently advanced rapidly. This advancement aims to provide secure transmission with better reliability, especially in telemedicine applications, where secure and reliable transmission is necessary. This article presents a novel dual image watermarking technique for the integrity protection of medical records. This technique employs a redundant multiresolution domain for the watermarking process. Different sub-bands are chosen after the entropy computation of the host image. Efficient and more stable lower–upper (LU) decomposition is applied successively over the previously obtained sub-bands to choose a more stable matrix. Two hybrid watermarks are generated using the patient identity and the medical logo for embedding. A multi-descriptor fuzzy inference system (FIS) is used to compute the optimal scaling factor. Texture, entropy, and change in image per pixel (CIPP) are selected as the membership functions for FIS. The performance of the proposed method is verified against a different set of attacks. Robustness is also verified with a denoising convolutional neural network (DnCNN) for the presented method. The average improvement in robustness is 25.50%, while 35.75% in imperceptibility. In the proposed method, KAZE features are also successfully matched for effective and efficient authentication.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Three-Stage Prediction Model Based on Transformer for Chronic Kidney Disease 基于变压器的慢性肾脏病三期预测模型

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70322

Yifeng Lu, Wenxiu Chang, Deyao Yang, Yuxuan Jiang

{"title":"A Three-Stage Prediction Model Based on Transformer for Chronic Kidney Disease","authors":"Yifeng Lu, Wenxiu Chang, Deyao Yang, Yuxuan Jiang","doi":"10.1002/cpe.70322","DOIUrl":"https://doi.org/10.1002/cpe.70322","url":null,"abstract":"<div>\u0000 \u0000 <p>Chronic kidney disease (CKD) is a serious global health threat. At the terminal stage, kidney function is nearly completely lost. Therefore, predicting the development of CKD based on a patient's visits can enable doctors to intervene early and delay the disease's progression. In this paper, we propose a three-stage prediction model named Imputation-Capture-Prediction (ICP) and based on the Transformer architecture, for chronic kidney disease (CKD) using electronic health records (EHRs). The first stage is to address the missing data problem in EHR, and ICP employs a two-stage imputation method, using the deep learning method SAITS module after recent padding. The second stage is designed to better capture this temporal dependency and the relationships between features, where ICP incorporates a two-branch architecture and introduces two modules: Time-Aware Convolution (TC) and Dynamic-Static-Medical Graph Attention Network (DSMGAT), to extract diverse feature information. The TC module is designed to capture the relationships within visit records, accounting for the unequal lengths of visit intervals while emphasizing the importance of recent records. The DSMGAT module, on the other hand, considers various categories of record features, using a Graph Attention Network (GAT) with learnable weights to model the relationships among them. Then we use a Feed-Forward Network to predict the estimated glomerular filtration rate (eGFR). To evaluate the effectiveness of our method, we compared it with several advanced approaches using a real EHR dataset, TFHCKD. The Mean Absolute Error (MAE) and Mean Squared Error (MSE) were 0.0344 and 0.0028, respectively, demonstrating a significant improvement over existing methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

APVFGL: A Robust Vertical Federated Graph Learning Framework Against Poisoning Attacks APVFGL：抗中毒攻击的健壮垂直联邦图学习框架

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70323

Sanfeng Zhang, Zijian Gong, Zhen Zhang, Wang Yang

{"title":"APVFGL: A Robust Vertical Federated Graph Learning Framework Against Poisoning Attacks","authors":"Sanfeng Zhang, Zijian Gong, Zhen Zhang, Wang Yang","doi":"10.1002/cpe.70323","DOIUrl":"https://doi.org/10.1002/cpe.70323","url":null,"abstract":"<div>\u0000 \u0000 <p>Vertical federated graph learning (VFGL) is a distributed graph learning scheme that addresses data isolation and privacy protection in scenarios where different clients hold the same nodes with distinct feature sets. However, VFGL is also vulnerable to poisoning attacks, while current defense methods based on horizontal federated learning and vertical federated learning are not effective in this context. To address this, this paper proposes APVFGL (Anti-Poison Vertical Federated Graph Learning), a robust VFGL framework resilient to poisoning attacks. APVFGL utilizes dual graph encoders and graph contrastive learning during the local training phase to derive robust node representations. The loss function, based on information bottleneck theory, reduces redundant information in the data to enhance the robustness of the model against poisoning attacks without the complexity of constructing negative samples. Additionally, a Shapley-based aggregation method is introduced on the server side to dynamically assign weights to each client, mitigating the impact of malicious feature manipulation. Experimental results on benchmark datasets demonstrate the superior performance of APVFGL against various poisoning attacks. Even in the case where more than half of the clients are poisoned, APVFGL can still achieve an F1 score of 81.6% and 71.5% on the Cora and Citeseer datasets, with an average reduction of 23.6% in attack success rate, highlighting its robustness and practicality in vertical federated graph learning scenarios.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

HMMSC-YOLO: A Comprehensively Improved Small Target Detection Algorithm 一种综合改进的小目标检测算法HMMSC-YOLO

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70288

Chongyang Fan, Wenfang Li, Chang Lin

{"title":"HMMSC-YOLO: A Comprehensively Improved Small Target Detection Algorithm","authors":"Chongyang Fan, Wenfang Li, Chang Lin","doi":"10.1002/cpe.70288","DOIUrl":"https://doi.org/10.1002/cpe.70288","url":null,"abstract":"<div>\u0000 \u0000 <p>This study addressed the challenges of small target detection in aerial imaging applications, including limited pixel coverage, weak feature representation, and complex background interference, by proposing a collaborative optimisation algorithm named HMMSC-YOLO. Firstly, a CNN-Transformer heterogeneous feature interaction network was constructed to mitigate high-frequency information attenuation during hierarchical transmission of small targets. Secondly, a parameter-shared dilated convolutional chain structure was designed, employing a weight-reuse strategy across multi-branch heterogeneous receptive fields to enhance geometric feature sensitivity towards minuscule targets. A differentiable affine transformation-guided multi-kernel dynamic fusion mechanism was further developed, achieving high-precision geometric alignment of cross-scale features through learnable deformation fields, thereby overcoming the rigid fusion limitations of conventional feature pyramids. A dual-attention-driven feature recalibration architecture was introduced to improve target localisation robustness under complex background interference. Finally, a dual-path collaborative downsampling module was implemented to suppress feature confusion caused by traditional single-path downsampling. Experimental evaluations on the VisDrone2019 dataset demonstrated 1.4% and 1% improvements in mAP50 and mAP50:95 metrics respectively compared to baseline models, alongside 23.3% and 2.5% reductions in parameter quantity and computational costs. The algorithm exhibited superior localisation accuracy and occlusion resistance in dense small target scenarios, establishing an innovative technical framework for practical applications including aerial image analysis and low-light environmental monitoring.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enable Owner Transfer and Data Traceability in Public Auditing Scheme for Cloud Digital Content 实现云数字内容公共审计方案中的所有者转移和数据可追溯性

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70309

Yanting Wang, Yilin Yuan, Shisong Yang, Zichen Li

{"title":"Enable Owner Transfer and Data Traceability in Public Auditing Scheme for Cloud Digital Content","authors":"Yanting Wang, Yilin Yuan, Shisong Yang, Zichen Li","doi":"10.1002/cpe.70309","DOIUrl":"https://doi.org/10.1002/cpe.70309","url":null,"abstract":"<div>\u0000 \u0000 <p>Currently, sharing digital content significantly enhances the value of data, and data purchasing serves as a means of realizing this value after sharing. After a data purchase transaction occurs, although the ownership of the data has been successfully transferred, this process introduces two major challenges: first, maintaining the continuity of cloud data integrity verification after ownership transfers, and second, enabling full-lifecycle traceability of data to clarify copyright attribution. To address these issues, this paper proposes a public auditing scheme that supports both cloud data ownership transfer and data traceability. First, the proposed scheme introduces an update factor as a mathematical structure to enable the update of HVT (homomorphic verifiable tag) on the cloud. Meanwhile, the CS (cloud service) performs tag update computations, thereby ensuring security while reducing the computational and communication overhead for local users. Furthermore, to achieve transparency throughout the data's lifecycle, copyright information of digital content is embedded into blockchain transactions. A data traceability strategy is then designed leveraging a chameleon hash function, and a detailed traceability process is presented. Security analysis demonstrates that our scheme satisfies correctness and reliability, and a series of comparative experiments further validate its feasibility and efficiency in practical applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Multi GPU Sparse Matrix by Sparse Matrix Multiplication 多GPU稀疏矩阵的稀疏矩阵乘法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70313

Artem Mavliutov, Giovanni Isotton, Carlo Janna, Alessandro Celestini, Massimo Bernaschi

引用次数: 0

Improved DDPG-Based Path Planning for Mobile Robots 基于ddpg的移动机器人路径规划改进方法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70317

Xianyong Ruan, Du Jiang, Juntong Yun, Bo Tao, Yuanmin Xie, Baojia Chen, Meng Jia, Li Huang

{"title":"Improved DDPG-Based Path Planning for Mobile Robots","authors":"Xianyong Ruan, Du Jiang, Juntong Yun, Bo Tao, Yuanmin Xie, Baojia Chen, Meng Jia, Li Huang","doi":"10.1002/cpe.70317","DOIUrl":"https://doi.org/10.1002/cpe.70317","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid advancement of robotics technology, path planning has attracted extensive research attention. Reinforcement learning, owing to its ability to acquire optimal policies through continuous interaction with the environment, offers a promising solution for path planning in environments with incomplete or unknown information. However, reinforcement learning-based path planning methods often suffer from high training complexity and low utilization of effective samples. To address these issues, this paper proposes an improved deep reinforcement learning (DRL) algorithm. The proposed approach builds upon the deep deterministic policy gradient (DDPG) algorithm and incorporates a short-term goal planning strategy based on local perceptual information, which decomposes the global navigation task into multiple short-term subgoals, thereby reducing task complexity and enhancing learning efficiency. Furthermore, a reward function integrating the artificial potential field (APF) method is designed to improve obstacle avoidance capability. To tackle the low utilization of effective experiences in DDPG, a dual experience pool strategy is introduced to improve experience utilization efficiency and accelerate model training. The parameters for short-term goal selection are optimized through multiple comparative experiments, and the proposed method is evaluated against several DRL-based path planning approaches in a static environment. Experimental results demonstrate that the improved algorithm significantly accelerates convergence. Moreover, dynamic environment simulation experiments verify that the proposed algorithm can effectively avoid moving obstacles and achieve safe navigation to the target position.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

IoTSim-Osmosis-MARL: Towards Multi-Agent Reinforcement Learning Osmotic Computing IoTSim-Osmosis-MARL：迈向多智能体强化学习渗透计算

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI: 10.1002/cpe.70324

Lukasz Kowalski, Tomasz Szydlo

{"title":"IoTSim-Osmosis-MARL: Towards Multi-Agent Reinforcement Learning Osmotic Computing","authors":"Lukasz Kowalski, Tomasz Szydlo","doi":"10.1002/cpe.70324","DOIUrl":"https://doi.org/10.1002/cpe.70324","url":null,"abstract":"<div>\u0000 \u0000 <p>Internet of Things systems exist in various areas of our everyday life. Data from sensors installed in smart cities and homes is processed in edge and cloud computing centers, providing several benefits that improve our lives. The group of devices might cooperate to fulfill desired goals, trying to preserve their resources and handle the failures of the devices. The paper presents the multi-agent reinforcement learning (MARL) extension to the osmotic computing simulation framework, enabling direct interactions between IoT devices. The proposed approach allows IoT devices to operate autonomously and cooperatively in dynamic environments, reducing the need for manual intervention and enabling resilient, energy-efficient sensing coverage. We discuss the multi-agent sensing coverage problem as one directly applicable to the IoT sensing systems. We identify the challenges posed to the framework and analyze management algorithms for cooperating osmotic agents. In the evaluation, we demonstrate that cooperation between devices enables the self-autonomous behavior of IoT systems. A case study yields promising results, showing that the adaptation of sensors may allow them to replace each other by lowering energy usage (up to 25% reduction), increasing global coverage (from 74% to 92%), and preserving battery life by dynamically adjusting their sensing range. Finally, the presented framework is a novel contribution that combines MARL environments with IoT systems simulation, enabling future research in this field.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Hybrid-Update Efficient Federated Learning Method Based on Multi-Teacher Knowledge Distillation in the Internet of Things 物联网中基于多教师知识蒸馏的混合更新高效联邦学习方法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-28 DOI: 10.1002/cpe.70256

Yang Lan, Lixiang Li, Haipeng Peng

{"title":"A Hybrid-Update Efficient Federated Learning Method Based on Multi-Teacher Knowledge Distillation in the Internet of Things","authors":"Yang Lan, Lixiang Li, Haipeng Peng","doi":"10.1002/cpe.70256","DOIUrl":"https://doi.org/10.1002/cpe.70256","url":null,"abstract":"<div>\u0000 \u0000 <p>The emergence of federated learning (FL) provides a new learning paradigm for private protection of data in the Internet of Things (IoT). However, it takes a lot of time for the server to obtain a global model with superior performance, which restricts the development of FL in the IoT. Therefore, this paper proposes a hybrid-update efficient federated learning method based on multi-teacher knowledge distillation in the Internet of Things. Firstly, considering the local training of each client, we design a data separation method of divide and conquer, which transforms data separation into a many-objective solution problem with constraints, and the unseparated data is used to train local models to speed up the training of the local model. Then, to alleviate the adverse effects of the above method, we introduce the knowledge distillation technology, and a multi-teacher model is designed for separated data. The teacher models are trained in advance, and they pass on their respective professional knowledge to the local models during the FL process. In the communication between the clients and the server, we only pass part of the model weights to further improve the overall efficiency of FL. To mitigate the impact of the above process, this paper proposes a hybrid-update federated learning strategy, which divides the update of the global model into federated aggregation update and generative weights update to improve the performance. Finally, we use the MNIST dataset, fashion-MNIST dataset, GTSRB dataset, SVHN dataset, and 20 Newsgroups dataset to simulate non-independent and identically distributed (non-IID) scenarios, and many experiments are performed to verify the effectiveness of the proposed method. Our method improves the overall efficiency of FL and further promotes the development of the IoT.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Bal-IDS: A Robust Network Intrusion Detection System for Enhancing Low-Frequency Attack Detection in IoT Networks Bal-IDS：增强物联网网络低频攻击检测的鲁棒网络入侵检测系统

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-28 DOI: 10.1002/cpe.70306

Jing Li, Mengru Wang, Zhi Yin

{"title":"Bal-IDS: A Robust Network Intrusion Detection System for Enhancing Low-Frequency Attack Detection in IoT Networks","authors":"Jing Li, Mengru Wang, Zhi Yin","doi":"10.1002/cpe.70306","DOIUrl":"https://doi.org/10.1002/cpe.70306","url":null,"abstract":"<div>\u0000 \u0000 <p>Network Intrusion Detection Systems (NIDS) are crucial in safeguarding IoT security. However, due to complex traffic patterns and severe class imbalance, existing intrusion detection methods struggle to detect low-frequency attacks, which are rare and sophisticated. This paper proposes Bal-IDS, a novel NIDS designed to enhance low-frequency attack detection in IoT networks. Bal-IDS employs a parallel architecture that combines an improved one-dimensional Convolutional Neural Network (1DCNN) for spatial feature extraction with Bidirectional Gated Recurrent Units (BiGRU) for temporal feature extraction. These features are dynamically fused using a self-attention mechanism to strengthen representation. A two-stage class balancing method, Sampling-based Equalization Loss (SEL), is designed to address class imbalance. This approach incorporates an adaptive oversampling strategy to mitigate local sample imbalance and utilizes Equalization Loss v2 (EQLv2) to address global gradient imbalance, significantly improving the detection rate for low-frequency attacks while maintaining low computational costs. The effectiveness of Bal-IDS is validated on the NSL-KDD and BoT-IoT datasets, achieving multi-class classification accuracies of 99.88% and 99.96%, respectively, with false alarm rates of 0.08% and 0.03%, surpassing state-of-the-art methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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