High-Confidence Computing最新文献_第4页

Learning-based cooperative content caching and sharing for multi-layer vehicular networks 基于学习的多层车辆网络协同内容缓存与共享

IF 3.2

High-Confidence Computing Pub Date : 2024-11-05 DOI: 10.1016/j.hcc.2024.100277

Jun Shi , Yuanzhi Ni , Lin Cai , Zhuocheng Du

{"title":"Learning-based cooperative content caching and sharing for multi-layer vehicular networks","authors":"Jun Shi , Yuanzhi Ni , Lin Cai , Zhuocheng Du","doi":"10.1016/j.hcc.2024.100277","DOIUrl":"10.1016/j.hcc.2024.100277","url":null,"abstract":"<div><div>Caching and sharing the content files are critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100277"},"PeriodicalIF":3.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917787","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 study on an efficient OSS inspection scheme based on encrypted GML 基于加密GML的OSS检测方案研究

IF 3.2

High-Confidence Computing Pub Date : 2024-11-05 DOI: 10.1016/j.hcc.2024.100279

Seok-Joon Jang , Im-Yeong Lee , Daehee Seo , Su-Hyun Kim

{"title":"A study on an efficient OSS inspection scheme based on encrypted GML","authors":"Seok-Joon Jang , Im-Yeong Lee , Daehee Seo , Su-Hyun Kim","doi":"10.1016/j.hcc.2024.100279","DOIUrl":"10.1016/j.hcc.2024.100279","url":null,"abstract":"<div><div>The importance of Open Source Software (OSS) has increased in recent years. OSS is software that is jointly developed and maintained globally through open collaboration and knowledge sharing. OSS plays an important role, especially in the Information Technology (IT) field, by increasing the efficiency of software development and reducing costs. However, licensing issues, security issues, etc., may arise when using OSS. Some services analyze source code and provide OSS-related data to solve these problems, a representative example being Blackduck. Blackduck inspects the entiresource code within the project and provides OSS information and related data included in the whole project. Therefore, there are problems such as inefficiency due to full inspection of the source code and difficulty in determining the exact location where OSS is identified. This paper proposes a scheme to intuitively analyze source code through Graph Modelling Language (GML) conversion to solve these problems. Additionally, encryption is applied to GML to performsecure GML-based OSS inspection. The study explains the process of converting source code to GML and performing OSS inspection. Afterward, we compare the capacity and accuracy of text-based OSS inspection and GML-based OSS inspection. Signcryption is applied to performsafe, GML-based, efficient OSS inspection.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100279"},"PeriodicalIF":3.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891464","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

Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset 基于SAT-6数据集的低分辨率卫星图像联合特征选择与分类

IF 3

High-Confidence Computing Pub Date : 2024-11-05 DOI: 10.1016/j.hcc.2024.100278

Rajalaxmi Padhy, Sanjit Kumar Dash, Jibitesh Mishra

{"title":"Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset","authors":"Rajalaxmi Padhy, Sanjit Kumar Dash, Jibitesh Mishra","doi":"10.1016/j.hcc.2024.100278","DOIUrl":"10.1016/j.hcc.2024.100278","url":null,"abstract":"<div><div>The modern industries of today demand the classification of satellite images, and to use the information obtained from it for their advantage and growth. The extracted information also plays a crucial role in national security and the mapping of geographical locations. The conventional methods often fail to handle the complexities of this process. So, an effective method is required with high accuracy and stability. In this paper, a new methodology named RankEnsembleFS is proposed that addresses the crucial issues of stability and feature aggregation in the context of the SAT-6 dataset. RankEnsembleFS makes use of a two-step process that consists of ranking the features and then selecting the optimal feature subset from the top-ranked features. RankEnsembleFS achieved comparable accuracy results to state-of-the-art models for the SAT-6 dataset while significantly reducing the feature space. This reduction in feature space is important because it reduces computational complexity and enhances the interpretability of the model. Moreover, the proposed method demonstrated good stability in handling changes in data characteristics, which is critical for reliable performance over time and surpasses existing ML ensemble methods in terms of stability, threshold setting, and feature aggregation. In summary, this paper provides compelling evidence that this RankEnsembleFS methodology presents excellent performance and overcomes key issues in feature selection and image classification for the SAT-6 dataset.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100278"},"PeriodicalIF":3.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827308","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

IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization IDL-LTSOJ：基于深度神经网络的缺陷定位智能在线判断系统的研究与实现

IF 3.2

High-Confidence Computing Pub Date : 2024-11-01 DOI: 10.1016/j.hcc.2024.100268

Lihua Song , Ying Han , Yufei Guo , Chenying Cai

{"title":"IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization","authors":"Lihua Song , Ying Han , Yufei Guo , Chenying Cai","doi":"10.1016/j.hcc.2024.100268","DOIUrl":"10.1016/j.hcc.2024.100268","url":null,"abstract":"<div><div>The evolution of artificial intelligence has thrust the Online Judge (OJ) systems into the forefront of research, particularly within programming education, with a focus on enhancing performance and efficiency. Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture, this paper introduces an innovative Integrated Intelligent Defect Localization and Lightweight Task Scheduling Online Judge (IDL-LTSOJ) system. Firstly, to achieve token-level fine-grained defect localization, a Deep Fine-Grained Defect Localization (Deep-FGDL) deep neural network model is developed. By integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), this model extracts fine-grained information from the abstract syntax tree (AST) of code, enabling more accurate defect localization. Subsequently, we propose a lightweight task scheduling architecture to tackle issues, such as limited concurrency in task evaluation and high equipment costs. This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks, substantially enhancing system evaluation efficiency. The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. Moreover, the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes, which represents a significant improvement in evaluation efficiency over centralized evaluation methods.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100268"},"PeriodicalIF":3.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895507","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 novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval 一种新的用于无监督跨模态检索的深度高级概念挖掘连接哈希模型

IF 3.2

High-Confidence Computing Pub Date : 2024-10-29 DOI: 10.1016/j.hcc.2024.100274

Chun-Ru Dong , Jun-Yan Zhang , Feng Zhang , Qiang Hua , Dachuan Xu

{"title":"A novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval","authors":"Chun-Ru Dong , Jun-Yan Zhang , Feng Zhang , Qiang Hua , Dachuan Xu","doi":"10.1016/j.hcc.2024.100274","DOIUrl":"10.1016/j.hcc.2024.100274","url":null,"abstract":"<div><div>Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval; (2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100274"},"PeriodicalIF":3.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917786","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

MCLORA: Maritime ad-hoc communication system based on LORA MCLORA：基于LORA的海上自组织通信系统

IF 3.2

High-Confidence Computing Pub Date : 2024-10-28 DOI: 10.1016/j.hcc.2024.100275

Jie Zhang, Hui Liu, Yusheng He, Wei Gao, Nannan Xu, Chao Liu

{"title":"MCLORA: Maritime ad-hoc communication system based on LORA","authors":"Jie Zhang, Hui Liu, Yusheng He, Wei Gao, Nannan Xu, Chao Liu","doi":"10.1016/j.hcc.2024.100275","DOIUrl":"10.1016/j.hcc.2024.100275","url":null,"abstract":"<div><div>Maritime communication plays a crucial role in fields such as ocean resource exploration and marine environmental monitoring. Existing maritime communication methods either face challenges in equipment deployment or are limited by high power requirements, making sustained operation difficult. The emergence of LoRa presents an opportunity in this regard, with its characteristics of low power consumption and long communication range, meeting the demands for long-term maritime communication. However, LoRa’s underlying implementation is not open-source, and LoRaWAN itself adopts a star topology, limiting communication between nodes. Therefore, we have devised a communication packet header working at the application layer to enable peer-to-peer communication between nodes. Our on-campus field tests have shown that our system can achieve node-to-node communication, networking functionalities, with a packet delivery rate more than 94%, and max data transmission rate can achieve 1027 bps. In the sea test, the communication rate of our node remained basically around 1035 bps due to the absence of objects blocking the line of sight, and packet delivery rate was more than 96%. The byte error rates of all experiments were less than 0.5%.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100275"},"PeriodicalIF":3.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686785","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

Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs 最小化WRSN在多muv和激光充电无人机辅助下的充电任务时间

IF 3.2

High-Confidence Computing Pub Date : 2024-10-05 DOI: 10.1016/j.hcc.2024.100272

Jian Zhang , Chuanwen Luo , Ning Liu , Yi Hong , Zhibo Chen

{"title":"Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs","authors":"Jian Zhang , Chuanwen Luo , Ning Liu , Yi Hong , Zhibo Chen","doi":"10.1016/j.hcc.2024.100272","DOIUrl":"10.1016/j.hcc.2024.100272","url":null,"abstract":"<div><div>This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms is verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100272"},"PeriodicalIF":3.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817179","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

Identity-based threshold (multi) signature with private accountability for privacy-preserving blockchain 基于身份的阈值（多重）签名与隐私保护区块链的私人问责制

IF 3.2

High-Confidence Computing Pub Date : 2024-09-12 DOI: 10.1016/j.hcc.2024.100271

Jing Tian , Yanqi Zhao , Xiaoyi Yang , Xuan Zhao , Ruonan Chen , Yong Yu

{"title":"Identity-based threshold (multi) signature with private accountability for privacy-preserving blockchain","authors":"Jing Tian , Yanqi Zhao , Xiaoyi Yang , Xuan Zhao , Ruonan Chen , Yong Yu","doi":"10.1016/j.hcc.2024.100271","DOIUrl":"10.1016/j.hcc.2024.100271","url":null,"abstract":"<div><div>Identity-based threshold signature (IDTHS) allows a threshold number of signers to generate signatures to improve the deterministic wallet in the blockchain. However, the IDTHS scheme cannot determine the identity of malicious signers in case of misinformation. To solve this challenge, we propose an identity-based threshold (multi) signature with private accountability (for short AIDTHS) for privacy-preserving blockchain. From the public perspective, AIDTHS is completely private and no user knows who participated in generating the signature. At the same time, when there is a problem with the transaction, a trace entity can trace and be accountable to the signers. We formally define the syntax and security model of AIDTHS. To address the issue of identifying malicious signers, we improve upon traditional identity-based threshold signatures by incorporating zero-knowledge proofs as part of the signature and leveraging a tracer holding tracing keys to identify all signers. Additionally, to protect the privacy of signers, the signature is no longer achievable by anyone, which requires a combiner holding the keys to produce a valid signature. We give a concrete construction of AIDTHS and prove its security. Finally, we implement the AIDTHS scheme and compare it with existing schemes. The key distribution algorithm of AIDTHS takes 34.60 <span><math><mrow><mi>μ</mi><mi>s</mi></mrow></math></span> and the signature algorithm takes 13.04 ms. The verification algorithm takes 1 <span><math><mi>s</mi></math></span>, which is one-third of the time the TAPS scheme uses.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100271"},"PeriodicalIF":3.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security 数字孪生网络景观导航：架构、应用、隐私和安全调查

IF 3.2

High-Confidence Computing Pub Date : 2024-09-11 DOI: 10.1016/j.hcc.2024.100269

Akshita Maradapu Vera Venkata Sai , Chenyu Wang , Zhipeng Cai , Yingshu Li

{"title":"Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security","authors":"Akshita Maradapu Vera Venkata Sai , Chenyu Wang , Zhipeng Cai , Yingshu Li","doi":"10.1016/j.hcc.2024.100269","DOIUrl":"10.1016/j.hcc.2024.100269","url":null,"abstract":"<div><div>In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100269"},"PeriodicalIF":3.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum to “An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility” [High-Confid. Comput. 3 (2023) 100153] 对 "利用深度卷积神经网络和搜索位置优化算法改进鲁棒性和不可感知性的有效数字音频水印 "的勘误 [High-Confid. Comput.

IF 3.2

High-Confidence Computing Pub Date : 2024-09-01 DOI: 10.1016/j.hcc.2024.100256

Abhijit J. Patil , Ramesh Shelke

引用次数: 0