High-Confidence Computing最新文献_第2页

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

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

Trusted access control mechanism for data with blockchain-assisted attribute encryption 区块链辅助属性加密数据可信访问控制机制

IF 3.2

High-Confidence Computing Pub Date : 2024-08-31 DOI: 10.1016/j.hcc.2024.100265

Chang Liu , Dong Wang , Da Li , Shaoyong Guo , Wenjing Li , Xuesong Qiu

{"title":"Trusted access control mechanism for data with blockchain-assisted attribute encryption","authors":"Chang Liu , Dong Wang , Da Li , Shaoyong Guo , Wenjing Li , Xuesong Qiu","doi":"10.1016/j.hcc.2024.100265","DOIUrl":"10.1016/j.hcc.2024.100265","url":null,"abstract":"<div><div>In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100265"},"PeriodicalIF":3.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859302","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

Deep reinforcement learning based resource provisioning for federated edge learning 基于深度强化学习的联邦边缘学习资源配置

IF 3.2

High-Confidence Computing Pub Date : 2024-08-30 DOI: 10.1016/j.hcc.2024.100264

Xingyun Chen, Junjie Pang, Tonghui Sun

{"title":"Deep reinforcement learning based resource provisioning for federated edge learning","authors":"Xingyun Chen, Junjie Pang, Tonghui Sun","doi":"10.1016/j.hcc.2024.100264","DOIUrl":"10.1016/j.hcc.2024.100264","url":null,"abstract":"<div><div>With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100264"},"PeriodicalIF":3.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859301","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

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates 基于运行时验证和需求更新的智能配电系统动态评估方法

IF 3.2

High-Confidence Computing Pub Date : 2024-07-09 DOI: 10.1016/j.hcc.2024.100255

Yunshuo Li , Xiangjun Duan , Yuanyuan Xu , Cheng Zhao

{"title":"Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates","authors":"Yunshuo Li , Xiangjun Duan , Yuanyuan Xu , Cheng Zhao","doi":"10.1016/j.hcc.2024.100255","DOIUrl":"10.1016/j.hcc.2024.100255","url":null,"abstract":"<div><div>The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100255"},"PeriodicalIF":3.2,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691678","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

An overview of machine unlearning 机器非学习概述

IF 3.2

High-Confidence Computing Pub Date : 2024-07-06 DOI: 10.1016/j.hcc.2024.100254

Chunxiao Li , Haipeng Jiang , Jiankang Chen , Yu Zhao , Shuxuan Fu , Fangming Jing , Yu Guo

引用次数: 0