IEEE Open Journal of the Computer Society最新文献_第10页

Utility-Oriented Computation Scheduling for Energy-Efficient Mobile Edge Computing Networks 面向效用的节能移动边缘计算网络计算调度

IEEE Open Journal of the Computer Society Pub Date : 2022-11-04 DOI: 10.1109/OJCS.2022.3219025

Ran Bi;Yiwei Sun;Yuexin He;Ting Peng;Meng Han;Guozhen Tan

{"title":"Utility-Oriented Computation Scheduling for Energy-Efficient Mobile Edge Computing Networks","authors":"Ran Bi;Yiwei Sun;Yuexin He;Ting Peng;Meng Han;Guozhen Tan","doi":"10.1109/OJCS.2022.3219025","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3219025","url":null,"abstract":"As a new computing paradigm, mobile edge computing (MEC) enables users to execute computation-intensive tasks at the network edge nodes (ENs) through computation offloading. Energy consumption of computation offloading is envisioned as a significant metric to satisfy the high quality of experience (QoE). In multi-ENs MEC networks, computation scheduling and power control of each user is tightly coupled with task offloading. Moreover, due to the stochastic task arrivals and unknown wireless channel conditions, it is challenging to allocate resources for efficient offloading without prior information of tasks and channels. In this paper, we propose an individualized utility metric of each user. We formulate the problem of computation scheduling and power control of each user as a stochastic optimization problem. We aim to maximize the long-term averaged utility quality of all users by jointly optimizing the computation scheduling, task-partition factor and power control. We use Lyapunov optimization technique to convert the long-term stochastic problem into a series of deterministic sub-problems in each time slot. We propose an online algorithm for utility quality maximization (OAUQM). The asymptotic optimality and queue stability of our algorithm are analyzed. Experimental simulations are conducted to evaluate the performance of the proposed algorithm against the benchmark offloading algorithms in terms of utility quality and energy consumption.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"260-270"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09940189.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67792320","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: 1

Efficient Video Privacy Protection Against Malicious Face Recognition Models 针对恶意人脸识别模型的高效视频隐私保护

IEEE Open Journal of the Computer Society Pub Date : 2022-11-01 DOI: 10.1109/OJCS.2022.3218559

Enting Guo;Peng Li;Shui Yu;Hao Wang

引用次数: 1

When Digital Economy Meets Web3.0: Applications and Challenges 当数字经济与Web3.0相遇：应用与挑战

IEEE Open Journal of the Computer Society Pub Date : 2022-10-27 DOI: 10.1109/OJCS.2022.3217565

Chuan Chen;Lei Zhang;Yihao Li;Tianchi Liao;Siran Zhao;Zibin Zheng;Huawei Huang;Jiajing Wu

{"title":"When Digital Economy Meets Web3.0: Applications and Challenges","authors":"Chuan Chen;Lei Zhang;Yihao Li;Tianchi Liao;Siran Zhao;Zibin Zheng;Huawei Huang;Jiajing Wu","doi":"10.1109/OJCS.2022.3217565","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3217565","url":null,"abstract":"With the continuous development of web technology, Web3.0 has attracted a considerable amount of attention due to its unique decentralized characteristics. The digital economy is an important driver of high-quality economic development and is currently in a rapid development stage. In the digital economy scenario, the centralized nature of the Internet and other characteristics usually bring about security issues such as infringement and privacy leakage. Therefore, it is necessary to investigate how to use Web3.0 technologies to solve the pain points encountered in the development of the digital economy by fully exploring the critical technologies of digital economy and Web3.0. In this paper, we discuss the aspects of Web3.0 that should be integrated with the digital economy to better find the entry point to solve the problems by examining the latest advances of Web3.0 in machine learning, finance, and data management. We hope this research will inspire those who are involved in both academia and industry, and finally help to build a favourable ecology for the digital economy.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"233-245"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09931409.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67792127","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: 26

Cardinality Estimation in Inner Product Space 内积空间中的基数估计

IEEE Open Journal of the Computer Society Pub Date : 2022-10-17 DOI: 10.1109/OJCS.2022.3215206

Kohei Hirata;Daichi Amagata;Takahiro Hara

引用次数: 4

An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection 基于人类语义知识的注意力神经网络及其在点击诱饵检测中的应用

IEEE Open Journal of the Computer Society Pub Date : 2022-10-12 DOI: 10.1109/OJCS.2022.3213791

Feng Wei;Uyen Trang Nguyen

{"title":"An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection","authors":"Feng Wei;Uyen Trang Nguyen","doi":"10.1109/OJCS.2022.3213791","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3213791","url":null,"abstract":"Clickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. In this article, we propose a novel attention-based neural network for the task of clickbait detection. To the best of our knowledge, our work is the first that incorporates human semantic knowledge into an artificial neural network, and uses linguistic knowledge graphs to guide attention mechanisms for the clickbait detection task. Extensive experimental results show that the proposed model outperforms existing state-of-the-art clickbait classifiers, even when training data is limited. The proposed model also performs better or comparably to powerful pretrained models, namely, BERT, RoBERTa, and XLNet, while being much more lightweight. Furthermore, we conducted experiments to demonstrate that the use of human semantic knowledge can significantly enhance the performance of pretrained models in the semisupervised domain such as BERT, RoBERTa, and XLNet.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"217-232"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09917322.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67792125","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

Forecasting TCP's Rate to Speed up Slow Start 预测TCP速率以加快慢速启动

IEEE Open Journal of the Computer Society Pub Date : 2022-09-22 DOI: 10.1109/OJCS.2022.3208701

Ralf Lübben

{"title":"Forecasting TCP's Rate to Speed up Slow Start","authors":"Ralf Lübben","doi":"10.1109/OJCS.2022.3208701","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3208701","url":null,"abstract":"Selection of the optimal transmission rate in packet-switched best-effort networks is challenging. Typically, senders do not have any information about the end-to-end path and should not congest the connection but at once fully utilize it. The accomplishment of these goals lead to congestion control protocols such as TCP Reno, TCP Cubic, or TCP BBR that adapt the sending rate according to extensive measurements of the path characteristics by monitoring packets and related acknowledgments. To improve and speed up this adaptation, we propose and evaluate a machine learning approach for the prediction of sending rates from measurements of metrics provided by the TCP stack. For the prediction a neural network is trained and evaluated. The prediction is implemented in the TCP stack to speed up TCP slow start. For a customizable and performant implementation the extended Berkeley packet filter is used to extract relevant data from the kernel space TCP stack, to forward the monitoring data to a user space data rate prediction, and to feed the prediction result back to the stack. Results from a online experiment show improvement in flow completion time of up to 30%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"185-194"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09899695.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68100585","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: 1

Fusion of Building Information Modeling and Blockchain for Metaverse: A Survey 建筑信息建模与元宇宙区块链的融合研究综述

IEEE Open Journal of the Computer Society Pub Date : 2022-09-15 DOI: 10.1109/OJCS.2022.3206494

Huakun Huang;Xiangbin Zeng;Lingjun Zhao;Chen Qiu;Huijun Wu;Lisheng Fan

{"title":"Fusion of Building Information Modeling and Blockchain for Metaverse: A Survey","authors":"Huakun Huang;Xiangbin Zeng;Lingjun Zhao;Chen Qiu;Huijun Wu;Lisheng Fan","doi":"10.1109/OJCS.2022.3206494","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3206494","url":null,"abstract":"Metaverse and blockchain, as the latest buzzwords, have attracted great attention from industry and academia. They will inevitably promote technological innovation in the field of building information modeling (BIM) in the future. BIM organizes various building information into a whole by establishing a virtual three-dimensional model of architectural engineering using digital technology. The metaverse seamlessly integrates the real world and the virtual world, and conducts rich activities such as creation, display, and trading. Therefore, through the exploration of the metaverse, it will be possible to build an exciting digital world and transform the physical world better. Meanwhile, introducing the blockchain technology could ensure the fairness and security of resource transactions, data storage, and other activities. In this survey, we delve into the metaverse and blockchain empowerment by studying BIM components, metaverse applications in virtual world construction, and the latest research on blockchain. We also discuss how BIM technology and blockchain can be integrated with metaverse. The collaborations between academia and industry would be certainly required for further development and interdisciplinary research on the metaverse and the integration of blockchain into BIM. We hope to see our survey help researchers, engineers and educators build an open, fair and rational future BIM ecosystem.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"195-207"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09893188.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67792123","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: 21

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge 医疗保健合作联合学习：边缘的多模式新冠肺炎诊断

IEEE Open Journal of the Computer Society Pub Date : 2022-09-14 DOI: 10.1109/OJCS.2022.3206407

Adnan Qayyum;Kashif Ahmad;Muhammad Ahtazaz Ahsan;Ala Al-Fuqaha;Junaid Qadir

{"title":"Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge","authors":"Adnan Qayyum;Kashif Ahmad;Muhammad Ahtazaz Ahsan;Ala Al-Fuqaha;Junaid Qadir","doi":"10.1109/OJCS.2022.3206407","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3206407","url":null,"abstract":"Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"172-184"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09891834.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68100586","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: 95

AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks 云端协作网络中人工智能驱动的节能内容任务卸载

IEEE Open Journal of the Computer Society Pub Date : 2022-09-14 DOI: 10.1109/OJCS.2022.3206446

Chao Fang;Xiangheng Meng;Zhaoming Hu;Fangmin Xu;Deze Zeng;Mianxiong Dong;Wei Ni

引用次数: 1

PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-Model Transmission Systems PowerFDNet：基于深度学习的交流输电系统隐式虚假数据注入攻击检测

IEEE Open Journal of the Computer Society Pub Date : 2022-08-18 DOI: 10.1109/OJCS.2022.3199755

Xuefei Yin;Yanming Zhu;Yi Xie;Jiankun Hu

{"title":"PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-Model Transmission Systems","authors":"Xuefei Yin;Yanming Zhu;Yi Xie;Jiankun Hu","doi":"10.1109/OJCS.2022.3199755","DOIUrl":"https://doi.org/10.1109/OJCS.2022.3199755","url":null,"abstract":"Smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and transmission lines. To address this issue, we propose a spatiotemporal deep network, PowerFDNet, for the SFDIA detection in AC-model power grids. The PowerFDNet consists of two sub-architectures: spatial architecture (SA) and temporal architecture (TA). The SA is aimed at extracting representations of bus/line measurements and modeling the spatial structure based on their representations. The TA is aimed at modeling the temporal structure of a sequence of measurements. Therefore, the proposed PowerFDNet can effectively model the spatiotemporal structure of measurements. Case studies on the detection of SFDIAs on the benchmark smart grids show that the PowerFDNet achieved significant improvement compared with the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented lightweight prototype of size 52 MB is implemented and tested for mobile devices, which demonstrates the potential applications on mobile devices. The trained model will be available at [Online]. Available: \u0000<uri>https://github.com/FrankYinXF/PowerFDNet</uri>\u0000.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"3 ","pages":"149-161"},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/9682503/09861714.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67792120","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: 2