High-Confidence Computing最新文献

Task migration with deadlines using machine learning-based dwell time prediction in vehicular micro clouds 基于机器学习的车辆微云停留时间预测的任务迁移

IF 3.2

High-Confidence Computing Pub Date : 2025-03-03 DOI: 10.1016/j.hcc.2025.100314

Ziqi Zhou , Agon Memedi , Chunghan Lee , Seyhan Ucar , Onur Altintas , Falko Dressler

{"title":"Task migration with deadlines using machine learning-based dwell time prediction in vehicular micro clouds","authors":"Ziqi Zhou , Agon Memedi , Chunghan Lee , Seyhan Ucar , Onur Altintas , Falko Dressler","doi":"10.1016/j.hcc.2025.100314","DOIUrl":"10.1016/j.hcc.2025.100314","url":null,"abstract":"<div><div>Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks. In this context, the concept of vehicular micro clouds (VMCs) has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks. As many tasks in this application domain are time critical, offloading to the cloud is prohibitive. Additionally, task deadlines have to be dealt with. This paper addresses two main challenges. First, we present a task migration algorithm supporting deadlines in vehicular edge computing. The algorithm is following the earliest deadline first model but in presence of dynamic processing resources, <em>i.e</em>, vehicles joining and leaving a VMC. This task offloading is very sensitive to the mobility of vehicles in a VMC, <em>i.e</em>, the so-called dwell time a vehicles spends in the VMC. Thus, secondly, we propose a machine learning-based solution for dwell time prediction. Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC. Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya. Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions, advancing the state of the art in vehicular edge computing.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100314"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891463","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

On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review 大型语言模型（LLM）及其代理的数据隐私保护：文献综述

IF 3.2

High-Confidence Computing Pub Date : 2025-02-28 DOI: 10.1016/j.hcc.2025.100300

Biwei Yan , Kun Li , Minghui Xu , Yueyan Dong , Yue Zhang , Zhaochun Ren , Xiuzhen Cheng

{"title":"On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review","authors":"Biwei Yan , Kun Li , Minghui Xu , Yueyan Dong , Yue Zhang , Zhaochun Ren , Xiuzhen Cheng","doi":"10.1016/j.hcc.2025.100300","DOIUrl":"10.1016/j.hcc.2025.100300","url":null,"abstract":"<div><div>Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100300"},"PeriodicalIF":3.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859303","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 insider threat detection method based on improved Test-Time Training model 一种基于改进Test-Time Training模型的内部威胁检测方法

IF 3.2

High-Confidence Computing Pub Date : 2025-01-14 DOI: 10.1016/j.hcc.2024.100283

Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang

{"title":"An insider threat detection method based on improved Test-Time Training model","authors":"Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang","doi":"10.1016/j.hcc.2024.100283","DOIUrl":"10.1016/j.hcc.2024.100283","url":null,"abstract":"<div><div>As network and information systems become widely adopted across industries, cybersecurity concerns have grown more prominent. Among these concerns, insider threats are considered particularly covert and destructive. Insider threats refer to malicious insiders exploiting privileged access to networks, systems, and data to intentionally compromise organizational security. Detecting these threats is challenging due to the complexity and variability of user behavior data, combined with the subtle and covert nature of insider actions. Traditional detection methods often fail to capture both long-term dependencies and short-term fluctuations in time-series data, which are crucial for identifying anomalous behaviors. To address these issues, this paper introduces the Test-Time Training (TTT) model for the first time in the field of insider threat detection, and proposes a detection method based on the TTT-ECA-ResNet model. First, the dataset is preprocessed. TTT is applied to extract long-term dependencies in features, effectively capturing dynamic sequence changes. The Residual Network, incorporating the Efficient Channel Attention mechanism, is used to extract local feature patterns, capturing relationships between different positions in time-series data. Finally, a Linear layer is employed for more precise detection of insider threats. The proposed approaches were evaluated using the CMU CERT Insider Threat Dataset, achieving an AUC of 98.75% and an F1-score of 96.81%. The experimental results demonstrate the effectiveness of the proposed methods, outperforming other state-of-the-art approaches.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100283"},"PeriodicalIF":3.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422030","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

Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review Kubernetes应用程序在异构CPU架构上的性能基准测试：实验回顾

IF 3.2

High-Confidence Computing Pub Date : 2024-12-18 DOI: 10.1016/j.hcc.2024.100276

Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman

{"title":"Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review","authors":"Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman","doi":"10.1016/j.hcc.2024.100276","DOIUrl":"10.1016/j.hcc.2024.100276","url":null,"abstract":"<div><div>With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, Kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using Kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering Kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing Kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for Kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100276"},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403423","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 “Exploring Personalized Internet of Things (PIoT), social connectivity, and Artificial Social Intelligence (ASI): A survey” [High-Confidence Computing 4 (2024) 100242] “探索个性化物联网（PIoT）、社会连接和人工社会智能（ASI）：一项调查”的勘误[高置信度计算4 (2024)100242]

IF 3.2

High-Confidence Computing Pub Date : 2024-12-01 DOI: 10.1016/j.hcc.2024.100294

Bisma Gulzar , Shabir Ahmad Sofi , Sahil Sholla

引用次数: 0

Three-dimensional dynamic gesture recognition method based on convolutional neural network 基于卷积神经网络的三维动态手势识别方法

IF 3.2

High-Confidence Computing Pub Date : 2024-11-06 DOI: 10.1016/j.hcc.2024.100280

Ji Xi , Weiqi Zhang , Zhe Xu , Saide Zhu , Linlin Tang , Li Zhao

{"title":"Three-dimensional dynamic gesture recognition method based on convolutional neural network","authors":"Ji Xi , Weiqi Zhang , Zhe Xu , Saide Zhu , Linlin Tang , Li Zhao","doi":"10.1016/j.hcc.2024.100280","DOIUrl":"10.1016/j.hcc.2024.100280","url":null,"abstract":"<div><div>With the rapid advancement of virtual reality, dynamic gesture recognition technology has become an indispensable and critical technique for users to achieve human–computer interaction in virtual environments. The recognition of dynamic gestures is a challenging task due to the high degree of freedom and the influence of individual differences and the change of gesture space. To solve the problem of low recognition accuracy of existing networks, an improved dynamic gesture recognition algorithm based on ResNeXt architecture is proposed. The algorithm employs three-dimensional convolution techniques to effectively capture the spatiotemporal features intrinsic to dynamic gestures. Additionally, to enhance the model’s focus and improve its accuracy in identifying dynamic gestures, a lightweight convolutional attention mechanism is introduced. This mechanism not only augments the model’s precision but also facilitates faster convergence during the training phase. In order to further optimize the performance of the model, a deep attention submodule is added to the convolutional attention mechanism module to strengthen the network’s capability in temporal feature extraction. Empirical evaluations on EgoGesture and NvGesture datasets show that the accuracy of the proposed model in dynamic gesture recognition reaches 95.03% and 86.21%, respectively. When operating in RGB mode, the accuracy reached 93.49% and 80.22%, respectively. These results underscore the effectiveness of the proposed algorithm in recognizing dynamic gestures with high accuracy, showcasing its potential for applications in advanced human–computer interaction systems.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100280"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387521","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

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

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

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