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

Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model 评估区块链上的加密货币市场风险：使用 ARMA-GARCH-VaR 模型的实证研究

IEEE Open Journal of the Computer Society Pub Date : 2024-02-27 DOI: 10.1109/OJCS.2024.3370603

Yongrong Huang;Huiqing Wang;Zhide Chen;Chen Feng;Kexin Zhu;Xu Yang;Wencheng Yang

{"title":"Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model","authors":"Yongrong Huang;Huiqing Wang;Zhide Chen;Chen Feng;Kexin Zhu;Xu Yang;Wencheng Yang","doi":"10.1109/OJCS.2024.3370603","DOIUrl":"10.1109/OJCS.2024.3370603","url":null,"abstract":"Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"83-94"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10449426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987737","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

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study 揭示东南亚国家恶意软件与盗版软件之间的联系：案例研究

IEEE Open Journal of the Computer Society Pub Date : 2024-02-09 DOI: 10.1109/OJCS.2024.3364576

Asif Iqbal;Muhammad Naveed Aman;Ramkumar Rejendran;Biplab Sikdar

{"title":"Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study","authors":"Asif Iqbal;Muhammad Naveed Aman;Ramkumar Rejendran;Biplab Sikdar","doi":"10.1109/OJCS.2024.3364576","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3364576","url":null,"abstract":"Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peer-to-peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed others. Our findings reveal that adware and Trojans are the most prevalent types of malware, with average infection rates of 34% and 35%, respectively, among our pirated software samples. Notably, our evaluation of AV detection performance highlights variations in sensitivity, ranging from a high of 132% to a low of 30% across all AV engines. Furthermore, upon installing pirated software, the most adversely affected operating system settings are the firewall and user account control configurations. Given the potential for malware to steal information or create malicious backdoors, its high prevalence within pirated software poses a substantial security risk to end users.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"62-72"},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937097","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

CTLA: Compressed Table Look up Algorithm for Open Flow Switch CTLA：开式流量开关的压缩查表算法

IEEE Open Journal of the Computer Society Pub Date : 2024-02-02 DOI: 10.1109/OJCS.2024.3361710

Veeramani Sonai;Indira Bharathi;Muthaiah Uchimucthu;Sountharrajan S;Durga Prasad Bavirisetti

{"title":"CTLA: Compressed Table Look up Algorithm for Open Flow Switch","authors":"Veeramani Sonai;Indira Bharathi;Muthaiah Uchimucthu;Sountharrajan S;Durga Prasad Bavirisetti","doi":"10.1109/OJCS.2024.3361710","DOIUrl":"10.1109/OJCS.2024.3361710","url":null,"abstract":"The size of the TCAM memory grows as more entries are added to the flow table of Open Flow switch. The procedure of looking up an IP address involves finding the longest prefix. In order to keep up with the link speed, the IP lookup operation in the forwarding table should also need to be speed up. TCAM's scalability and storage are constrained by its high power consumption and circuit density. The only time- or space-efficient algorithms for improvement are the subject of several research studies. In order to boost performance even further, this study focuses on time and space efficient algorithms. To strike a balance between speedy data access and efficient storage, this study proposes a combination of compression and a quick look-up mechanism to satisfy the space and speed requirements of the Open Flow switch. As the data is compressed, performance improves because less memory is required to store the look-up table and fewer bits are required to search. The look up complexity of proposed approach is \u0000<inline-formula><tex-math>$O(log,(log;n/2))$</tex-math></inline-formula>\u0000 and average space reduction is 61%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"73-82"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952219","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

Slingshot: Globally Favorable Local Updates for Federated Learning 弹弓：联盟学习的全局有利局部更新

IEEE Open Journal of the Computer Society Pub Date : 2024-01-22 DOI: 10.1109/OJCS.2024.3356599

Jialiang Liu;Huawei Huang;Chun Wang;Sicong Zhou;Ruixin Li;Zibin Zheng

{"title":"Slingshot: Globally Favorable Local Updates for Federated Learning","authors":"Jialiang Liu;Huawei Huang;Chun Wang;Sicong Zhou;Ruixin Li;Zibin Zheng","doi":"10.1109/OJCS.2024.3356599","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3356599","url":null,"abstract":"Federated Learning (FL), as a promising distributed learning paradigm, is proposed to solve the contradiction between the data hunger of modern machine learning and the increasingly stringent need for data privacy. However, clients naturally present different distributions of their local data and inconsistent local optima, which leads to poor model performance of FL. Many previous methods focus on mitigating objective inconsistency. Although local objective consistency can be guaranteed when the number of communication rounds is infinite, we should notice that the accumulation of global drift and the limitation on the potential of local updates are non-negligible in those previous methods. In this article, we study a new framework for data-heterogeneity FL, in which the local updates in clients towards the global optimum can accelerate FL. We propose a new approach called \u0000<italic>Slingshot</i>\u0000. Slingshot's design goals are twofold, i.e., i) to retain the potential of local updates, and ii) to combine local and global trends. Experimental results show that \u0000<italic>Slingshot</i>\u0000 helps local updates become more globally favorable and outperforms other popular methods under various FL settings. For example, on CIFAR10, \u0000<italic>Slingshot</i>\u0000 achieves 46.52% improvement in test accuracy and 48.21× speedup for a lightweight neural network named \u0000<italic>SqueezeNet</i>\u0000.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"39-49"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715219","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

Low Area and Low Power FPGA Implementation of a DBSCAN-Based RF Modulation Classifier 基于 DBSCAN 的射频调制分类器的低面积、低功耗 FPGA 实现

IEEE Open Journal of the Computer Society Pub Date : 2024-01-18 DOI: 10.1109/OJCS.2024.3355693

Bill Gavin;Tiantai Deng;Edward Ball

引用次数: 0

IEEE Open Journal of the Computer Society Information for Authors IEEE 计算机学会公开期刊作者须知

IEEE Open Journal of the Computer Society Pub Date : 2023-12-15 DOI: 10.1109/OJCS.2023.3239731

引用次数: 0

Prediction of Customer Behavior Changing via a Hybrid Approach 通过混合方法预测客户行为变化

IEEE Open Journal of the Computer Society Pub Date : 2023-11-29 DOI: 10.1109/OJCS.2023.3336904

Nien-Ting Lee;Hau-Chen Lee;Joseph Hsin;Shih-Hau Fang

{"title":"Prediction of Customer Behavior Changing via a Hybrid Approach","authors":"Nien-Ting Lee;Hau-Chen Lee;Joseph Hsin;Shih-Hau Fang","doi":"10.1109/OJCS.2023.3336904","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3336904","url":null,"abstract":"This study proposes a hybrid approach to predict customer churn by combining statistic approaches and machine learning models. Unlike traditional methods, where churn is defined by a fixed period of time, the proposed algorithm uses the probability of customer alive derived from the statistical model to dynamically determine the churn line. After observing customer churn through clustering over time, the proposed method segmented customers into four behaviors: new, short-term, high-value, and churn, and selected machine learning models to predict the churned customers. This combination reduces the risk to be misjudged as churn for customers with longer consumption cycles. Two public datasets were used to evaluate the hybrid approach, an online retail of U.K. gift sellers and the largest E-Commerce of Pakistan. Based on the top three learning models, the recall ranged from 0.56 to 0.72 in the former while that ranged from 0.91 to 0.95 in the latter. Results show that the proposed approach enables companies to retain important customers earlier by predicting customer churn. The proposed hybrid method requires less data than existing methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"27-38"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060318","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 Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model 基于三维物体检测的实时人体动作识别模型

IEEE Open Journal of the Computer Society Pub Date : 2023-11-20 DOI: 10.1109/OJCS.2023.3334528

Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong

{"title":"A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model","authors":"Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong","doi":"10.1109/OJCS.2023.3334528","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3334528","url":null,"abstract":"Computer vision technologies have greatly improved in the last few years. Many problems have been solved using deep learning merged with more computational power. Action recognition is one of society's problems that must be addressed. Human Action Recognition (HAR) may be adopted for intelligent video surveillance systems, and the government may use the same for monitoring crimes and security purposes. This paper proposes a deep learning-based HAR model, i.e., a 3-dimensional Convolutional Network with multiplicative LSTM. The suggested model makes it easier to comprehend the tasks that an individual or team of individuals completes. The four-phase proposed model consists of a 3D Convolutional neural network (3DCNN) combined with an LSTM multiplicative recurrent network and Yolov6 for real-time object detection. The four stages of the proposed model are data fusion, feature extraction, object identification, and skeleton articulation approaches. The NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets are some used to train the model. The suggested model surpasses other cutting-edge models by reaching an accuracy of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on the abovementioned datasets. Other state-of-the-art (SOTA) methods compared in this study are traditional CNN, Yolov6, and CNN with BiLSTM. The results verify that actions are classified more accurately by the proposed model that combines all these techniques compared to existing ones.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"14-26"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060146","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

Benchmark for Personalized Federated Learning 个性化联合学习的基准

IEEE Open Journal of the Computer Society Pub Date : 2023-11-13 DOI: 10.1109/OJCS.2023.3332351

Koji Matsuda;Yuya Sasaki;Chuan Xiao;Makoto Onizuka

{"title":"Benchmark for Personalized Federated Learning","authors":"Koji Matsuda;Yuya Sasaki;Chuan Xiao;Makoto Onizuka","doi":"10.1109/OJCS.2023.3332351","DOIUrl":"10.1109/OJCS.2023.3332351","url":null,"abstract":"Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets. Since data distributions may differ across clients, data heterogeneity is a challenging issue in federated learning. To address this issue, numerous federated learning methods have been proposed to build personalized models for clients, referred to as personalized federated learning. Nevertheless, no studies comprehensively investigate the performance of personalized federated learning methods in various experimental settings such as datasets and client settings. Therefore, in this article, we aim to benchmark the performance of existing personalized federated learning methods in various settings. We first survey the experimental settings in existing studies. We then benchmark the performance of existing methods through comprehensive experiments to reveal their characteristics in computer vision and natural language processing tasks which are the most popular tasks based on our survey. Our experimental study shows that (i) large data heterogeneity often leads to highly accurate predictions and (ii) standard federated learning methods (e.g. FedAvg) with fine-tuning often outperform personalized federated learning methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"2-13"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135612465","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

Data Extraction and Question Answering on Chart Images Towards Accessibility and Data Interpretation 面向可及性和数据解释的图表图像数据提取与问答

IEEE Open Journal of the Computer Society Pub Date : 2023-10-31 DOI: 10.1109/OJCS.2023.3328767

Shahira K C;Pulkit Joshi;Lijiya A

{"title":"Data Extraction and Question Answering on Chart Images Towards Accessibility and Data Interpretation","authors":"Shahira K C;Pulkit Joshi;Lijiya A","doi":"10.1109/OJCS.2023.3328767","DOIUrl":"10.1109/OJCS.2023.3328767","url":null,"abstract":"Graphical representations such as chart images are integral to web pages and documents. Automating data extraction from charts is possible by reverse-engineering the visualization pipeline. This study proposes a framework that automates data extraction from bar charts and integrates it with question-answering. The framework employs an object detector to recognize visual cues in the image, followed by text recognition. Mask-RCNN for plot element detection achieves a mean average precision of 95.04% at a threshold of 0.5 which decreases as the Intersection over Union (IoU) threshold increases. A contour approximation-based approach is proposed for extracting the bar coordinates, even at a higher IoU of 0.9. The textual and visual cues are associated with the legend text and preview, and the chart data is finally extracted in tabular format. We introduce an extension to the TAPAS model, called TAPAS++, by incorporating new operations and table question answering is done using TAPAS++ model. The chart summary or description is also produced in an audio format. In the future, this approach could be expanded to enable interactive question answering on charts by accepting audio inquiries from individuals with visual impairments and do more complex reasoning using Large Language Models.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"314-325"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10302417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263025","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