High-Confidence Computing最新文献_第2页

Digital twin and metaverse-enhanced battery management for electric vehicles 电动汽车的数字孪生和metaverse增强电池管理

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-10-14 DOI: 10.1016/j.hcc.2025.100358

Judith Nkechinyere Njoku , Ebuka Chinaechetam Nkoro , Robin Matthew Medina , Paul Michael Custodio , Cosmas Ifeanyi Nwakanma , Jae-Min Lee , Dong-Seong Kim

{"title":"Digital twin and metaverse-enhanced battery management for electric vehicles","authors":"Judith Nkechinyere Njoku , Ebuka Chinaechetam Nkoro , Robin Matthew Medina , Paul Michael Custodio , Cosmas Ifeanyi Nwakanma , Jae-Min Lee , Dong-Seong Kim","doi":"10.1016/j.hcc.2025.100358","DOIUrl":"10.1016/j.hcc.2025.100358","url":null,"abstract":"<div><div>The Internet of Things (IoT) and cyber–physical systems (CPS) are driving digital transformation and automation. An essential component of CPS is digital twin (DT) technology, which enables real-time synchronization between physical assets and their virtual counterparts. Battery management systems (BMS) in electric vehicles (EVs) face challenges in handling large volumes of sensor data, often leading to reduced accuracy in battery-state estimation. To address these challenges, DTs have been explored to aid real-time diagnosis and monitoring. One critical step toward the success of DTs is to have practical reference architectures. This paper presents proposes a novel six-layer DT architecture tailored for BMS, extending existing CPS/DT-BMS models by integrating high-fidelity electrochemical modeling, robust nonlinear state estimation, and interactive 3D visualization in a Metaverse environment. The architecture is designed with scalability in mind, supporting deployment on lightweight embedded platforms or via cloud-hosted rendering for resource-limited devices. We validate the approach using MATLAB to develop a thermally coupled SPMe-based DT of a lithium-ion NMC battery, synchronized with a virtual battery model in Unreal Engine for immersive visualization. Experimental results demonstrate accurate state-of-charge estimation (RMSE 0.23%) and low-latency real-time monitoring, highlighting the framework’s potential for deployment in large-scale EV BMS applications.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100358"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fast gray-box adversarial example generation algorithm based on FakeBob 基于FakeBob的快速灰盒对抗示例生成算法

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-01 DOI: 10.1016/j.hcc.2025.100337

Jia Zheng , Wanjin Hou , Hua Zhang , Ming Lv , Huiyu Zhou

{"title":"A fast gray-box adversarial example generation algorithm based on FakeBob","authors":"Jia Zheng , Wanjin Hou , Hua Zhang , Ming Lv , Huiyu Zhou","doi":"10.1016/j.hcc.2025.100337","DOIUrl":"10.1016/j.hcc.2025.100337","url":null,"abstract":"<div><div>There are the excessive queries to the targeted model during the generates of gray-box adversarial examples for speaker recognition systems, which result in high costs of attacks. In this paper, a fast generates algorithm of gray-box adversarial example is proposed based on FakeBob, named F-FakeBob. This algorithm introduces a threshold mechanism for optimization to the optimization strategy of gradient. Only when the increasing of the confidence scores of the adversarial example before and after optimizing is less than the threshold, the gradient is recalculated for the next iteration. By reducing the frequency of gradient calculations, the number of queries to the targeted system is decreased. Experiments on three public datasets of speech, TIMIT, Common Voice, and Voxceleb2, are conducted to generate adversarial examples. The targeted speaker recognition models are based on ECAPA-TDNN and TitaNet architectures. The experimental results show that F-FakeBob can achieve a targeted attack success rate of 99.2% and the number of queries are effectively reduced in the adversarial example generates, with an average query reduction of 25.71% compared to FakeBob.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100337"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A privacy-preserving class imbalance mitigation framework for face recognition 一种保护隐私的人脸识别类失衡缓解框架

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-05-21 DOI: 10.1016/j.hcc.2025.100325

Amani Aldahiri , Ibrahim Khalil , Mohammad Saidur Rahman , Mohammed Atiquzzaman

{"title":"A privacy-preserving class imbalance mitigation framework for face recognition","authors":"Amani Aldahiri , Ibrahim Khalil , Mohammad Saidur Rahman , Mohammed Atiquzzaman","doi":"10.1016/j.hcc.2025.100325","DOIUrl":"10.1016/j.hcc.2025.100325","url":null,"abstract":"<div><div>AI-powered face recognition has become essential to various IoT applications, including home automation, security systems, and personalized services. While these systems offer significant advancements, they still face critical challenges related to accuracy and privacy. One major issue is class imbalance, which is common in face recognition systems where certain demographic groups are underrepresented. This imbalance results in biased models, compromising the accuracy and fairness of these systems. Furthermore, traditional centralized training methods can expose sensitive facial data, raising serious privacy concerns. Federated Learning (FL) has emerged as a solution to improve model training by enabling collaboration across devices without sharing sensitive data. However, it also worsens the issue of data heterogeneity. This paper proposes a Hierarchical Federated Learning (HFL) framework to address class imbalance while preserving privacy. By aggregating local models at different hierarchical levels, the framework mitigates data imbalance and enhances fairness in face recognition systems. Additionally, a privacy-preserving mechanism based on Secure Multi-Party Computation (SMPC) is implemented to ensure data security during the training process.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100325"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Localitycache: Toward efficient straggler tolerance in LRC-coded storage via caching local parity blocks Localitycache：通过缓存本地奇偶校验块，在lrc编码存储中实现高效的离散容错

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-01 DOI: 10.1016/j.hcc.2025.100339

Ximeng Chen , Si Wu , Yinlong Xu

{"title":"Localitycache: Toward efficient straggler tolerance in LRC-coded storage via caching local parity blocks","authors":"Ximeng Chen , Si Wu , Yinlong Xu","doi":"10.1016/j.hcc.2025.100339","DOIUrl":"10.1016/j.hcc.2025.100339","url":null,"abstract":"<div><div>Modern distributed storage systems increasingly employ Locally Repairable Codes (LRCs) to provide reliable, low-cost data storage with high repair efficiency. However, the presence of stragglers, i.e., nodes that unpredictably slow down, can significantly impact access latency. Traditional approaches for handling stragglers, such as detection, blacklisting, or speculative execution, are often insufficient for efficient straggler tolerance. In this paper, we show how an in-memory caching strategy coupled with LRCs can bypass stragglers without relying on precise straggler detection. We propose LocalityCache, a novel in-memory caching mechanism designed for LRC-coded distributed storage systems, which effectively mitigates the impact of stragglers by caching local parity blocks. We provide theoretical guarantees for LocalityCache and show that caching local parity blocks minimizes the likelihood of encountering stragglers. Additionally, we devise optimized workflows for write, read, and repair operations under LocalityCache to ensure system efficiency. We implement LocalityCache in a distributed key–value store prototype atop Redis. Our extensive testbed evaluations show that LocalityCache can significantly reduce read latency of the baselines by up to 73.6% in the presence of stragglers.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100339"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A low-complexity decorrelation method for PHY-based key generation 一种基于物理的密钥生成的低复杂度去相关方法

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-04-30 DOI: 10.1016/j.hcc.2025.100323

Gokhan Sahin

{"title":"A low-complexity decorrelation method for PHY-based key generation","authors":"Gokhan Sahin","doi":"10.1016/j.hcc.2025.100323","DOIUrl":"10.1016/j.hcc.2025.100323","url":null,"abstract":"<div><div>Physical layer characteristics of a wireless channel, which can be measured independently by the two communicating parties and yield near-identical results due to the channel reciprocity property, have been shown to be an important source of shared secrecy generation. Various types of channel state information (CSI), such as the received signal strength (RSS) and the channel impulse response (CIR) can be utilized for this purpose. Through periodic probing of the CSI, a continuous cycle of secret bit generation and key renewal can be maintained. However, many forms of wireless CSI inherently have substantial temporal correlation, which may hinder the secrecy generation process. Accordingly, various autocorrelation reduction (decorrelation) methods have been proposed, using either sub-sampling approaches that discard potentially valuable CSI data, or, computationally expensive transform domain approaches such as the Discrete Cosine Transform (DCT), Karhunen–Loeve Transform (KLT), and principal component analysis (PCA) that may not be feasible or desirable for resource and energy constrained devices. This paper proposes a low-complexity method for reducing the autocorrelation of the CSI measurements through a reordering of the data based on integer sequences such as the Fibonacci sequences, and applies it to various types of CSI data that represent both the individual path level CIR and the aggregate level multipath gain or RSS. We evaluate the performance of the method in three standard multipath ITU channel models. Fibonacci sequences are observed to be an effective means of decorrelating the channel measurement data, thereby eliminating the need for computationally intensive methods.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100323"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure three-party clustering with identifying malicious behavior 通过识别恶意行为来保护三方集群

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-07 DOI: 10.1016/j.hcc.2025.100342

Qingcai Luo , Hui Li , Xiaolin Yang , Hong Qin

{"title":"Secure three-party clustering with identifying malicious behavior","authors":"Qingcai Luo , Hui Li , Xiaolin Yang , Hong Qin","doi":"10.1016/j.hcc.2025.100342","DOIUrl":"10.1016/j.hcc.2025.100342","url":null,"abstract":"<div><div>Clustering algorithms are pivotal tools in data science and machine learning, offering a diverse array of applications ranging from customer segmentation to anomaly detection. With the development of cloud computing and outsourced computing, the adoption of clustering techniques has significantly accelerated. Despite the remarkable benefits of cloud computing, outsourcing sensitive data to remote cloud servers introduces considerable privacy and security concerns. Specifically, there is a risk that cloud service providers may engage in malicious behavior, such as data tampering. In response to these concerns, we propose four basic protocols based on vector space secret sharing, including secure Euclidean distance protocol, comparison protocol, minimum protocol, and division protocol. By applying these protocols, we construct a secure clustering scheme that can identify malicious behaviors. We thoroughly demonstrate the security of each underlying protocol as well as the overall clustering scheme. To validate the practicality and effectiveness of our approach, we conduct experiments on standard datasets. The results show that our clustering scheme performs efficiently while maintaining strong security guarantees.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100342"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147600091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LwRustIP: Memory-safe and efficient embedded networking stack with ownership semantics LwRustIP：具有所有权语义的内存安全和高效的嵌入式网络堆栈

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-09-09 DOI: 10.1016/j.hcc.2025.100351

Guangyong Shang , Guangpeng Qi , Jianing Ren , Xianqi Jin , Wanjiang Shen , Junchao Li , Xiuzhen Cheng , Runyu Pan

{"title":"LwRustIP: Memory-safe and efficient embedded networking stack with ownership semantics","authors":"Guangyong Shang , Guangpeng Qi , Jianing Ren , Xianqi Jin , Wanjiang Shen , Junchao Li , Xiuzhen Cheng , Runyu Pan","doi":"10.1016/j.hcc.2025.100351","DOIUrl":"10.1016/j.hcc.2025.100351","url":null,"abstract":"<div><div>As modern embedded systems are increasingly network connected, their protocol stacks expose themselves as a surface that is frequently attacked. While C-based implementations such as <em>LwIP</em> are efficient, their lack of memory safety induces critical vulnerabilities such as buffer overflows, dangling pointers, and use-after-free, leading to remote code execution or privilege escalation. In this paper, we present <span>LwRustIP</span>, a memory-safe embedded networking stack reimplemented in Rust and compatible with <em>LwIP</em>. We also share our development experience. <span>LwRustIP</span> replaces unsafe linked-list memory management with a custom allocator that honors the Rust ownership semantics, leverages zero-copy techniques for inter-layer packet handoffs, and applies lock-free object pools for concurrent buffer management. These design choices ensure memory safety while maintaining performance comparable to traditional C-based implementations. We deploy <span>LwRustIP</span> on ARM-based embedded platforms and evaluate its correctness, performance, and memory safety. Experimental results show that <span>LwRustIP</span> achieves memory safety without incurring measurable performance overhead compared to the original C-based implementation. Our experience highlights the practical challenges and benefits of using Rust for low-level system components and offers guidance for future efforts in memory-safe reengineering of legacy C codebases.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100351"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain-enabled privacy protection scheme for IoT digital identity management 基于区块链的物联网数字身份管理隐私保护方案

IF 3

High-Confidence Computing Pub Date : 2025-12-01 Epub Date: 2025-03-25 DOI: 10.1016/j.hcc.2025.100320

Hao Yu , Guijuan Wang , Anming Dong , Yubing Han , Yawei Wang , Jiguo Yu

{"title":"Blockchain-enabled privacy protection scheme for IoT digital identity management","authors":"Hao Yu , Guijuan Wang , Anming Dong , Yubing Han , Yawei Wang , Jiguo Yu","doi":"10.1016/j.hcc.2025.100320","DOIUrl":"10.1016/j.hcc.2025.100320","url":null,"abstract":"<div><div>With the growth of the Internet of Things (IoT), millions of users, devices, and applications compose a complex and heterogeneous network, which increases the complexity of digital identity management. Traditional centralized digital identity management systems (DIMS) confront single points of failure and privacy leakages. The emergence of blockchain technology presents an opportunity for DIMS to handle the single point of failure problem associated with centralized architectures. However, the transparency inherent in blockchain technology still exposes DIMS to privacy leakages. In this paper, we propose the privacy-protected IoT DIMS (PPID), a novel blockchain-based distributed identity system to protect the privacy of on-chain identity data. The PPID achieves the unlinkability of identity-credential-verification. Specifically, the PPID adopts the Zero Knowledge Proof (ZKP) algorithm and Shamir secret sharing (SSS) to safeguard privacy security, resist replay attacks, and ensure data integrity. Finally, we evaluate the performance of ZKP computation in PPID, as well as the transaction fees of smart contract on the Ethereum blockchain.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 4","pages":"Article 100320"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating decentralized federated learning via momentum GD with heterogeneous delays 通过具有异构延迟的动量GD加速分散联邦学习

IF 3

High-Confidence Computing Pub Date : 2025-12-01 Epub Date: 2025-02-26 DOI: 10.1016/j.hcc.2025.100310

Na Li , Hangguan Shan , Meiyan Song , Yong Zhou , Zhongyuan Zhao , Howard H. Yang , Fen Hou

{"title":"Accelerating decentralized federated learning via momentum GD with heterogeneous delays","authors":"Na Li , Hangguan Shan , Meiyan Song , Yong Zhou , Zhongyuan Zhao , Howard H. Yang , Fen Hou","doi":"10.1016/j.hcc.2025.100310","DOIUrl":"10.1016/j.hcc.2025.100310","url":null,"abstract":"<div><div>Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 4","pages":"Article 100310"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Block-chain abnormal transaction detection method based on generative adversarial network and autoencoder 基于生成对抗网络和自编码器的区块链异常交易检测方法

IF 3

High-Confidence Computing Pub Date : 2025-12-01 Epub Date: 2025-03-03 DOI: 10.1016/j.hcc.2025.100313

Ao Xiong , Chenbin Qiao , Wenjing Li , Dong Wang , Da Li , Bo Gao , Weixian Wang

{"title":"Block-chain abnormal transaction detection method based on generative adversarial network and autoencoder","authors":"Ao Xiong , Chenbin Qiao , Wenjing Li , Dong Wang , Da Li , Bo Gao , Weixian Wang","doi":"10.1016/j.hcc.2025.100313","DOIUrl":"10.1016/j.hcc.2025.100313","url":null,"abstract":"<div><div>Anomaly detection in blockchain transactions faces several challenges, the most prominent being the imbalance between positive and negative samples. Most transaction data are normal, with only a small fraction of anomalous data. Additionally, blockchain transaction datasets tend to be small and often incomplete, which complicates the process of anomaly detection. When using simple AI models, selecting the appropriate model and tuning parameters becomes difficult, resulting in poor performance. To address these issues, this paper proposes GANAnomaly, an anomaly detection model based on Generative Adversarial Networks (GANs) and Autoencoders. The model consists of three components: a data generation model, an encoding model, and a detection model. Firstly, the Wasserstein GAN (WGAN) is employed as the data generation model. The generated data is then used to train an encoding model that performs feature extraction and dimensionality reduction. Finally, the trained encoder serves as the feature extractor for the detection model. This approach leverages GANs to mitigate the challenges of low data volume and data imbalance, while the encoder extracts relevant features and reduces dimensionality. Experimental results demonstrate that the proposed anomaly detection model outperforms traditional methods by more accurately identifying anomalous blockchain transactions, reducing the false positive rate, and improving both accuracy and efficiency.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 4","pages":"Article 100313"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0