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

Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization 基于修正艾奇逊-艾特肯函数贝叶斯优化的组合爆炸决策树多规则挖掘算法

IEEE Open Journal of the Computer Society Pub Date : 2024-04-30 DOI: 10.1109/OJCS.2024.3394928

Yuto Omae;Masaya Mori;Yohei Kakimoto

{"title":"Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization","authors":"Yuto Omae;Masaya Mori;Yohei Kakimoto","doi":"10.1109/OJCS.2024.3394928","DOIUrl":"10.1109/OJCS.2024.3394928","url":null,"abstract":"Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if–then rules. However, as decision trees are constructed by an algorithm that achieves clear classification with minimum necessary rules, the trees possess the drawback of extracting only minimum rules, even when various latent rules exist in data. Approaches that construct multiple trees using randomly selected feature subsets do exist. However, the number of trees that can be constructed remains at the same scale because the number of feature subsets is a combinatorial explosion. Additionally, when multiple trees are constructed, numerous rules are generated, of which several are untrustworthy and/or highly similar. Therefore, we propose “MAABO-MT” and “GS-MRM” algorithms that strategically construct trees with high estimation performance among all possible trees with small computational complexity and extract only reliable and non-similar rules, respectively. Experiments are conducted using several open datasets to analyze the effectiveness of the proposed method. The results confirm that MAABO-MT can discover reliable rules at a lower computational cost than other methods that rely on randomness. Furthermore, the proposed method is confirmed to provide deeper insights than single decision trees commonly used in previous studies. Therefore, MAABO-MT and GS-MRM can efficiently extract rules from combinatorially exploded decision trees.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"215-226"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840476","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

Affective Computing and the Road to an Emotionally Intelligent Metaverse 情感计算与情感智能元宇宙之路

IEEE Open Journal of the Computer Society Pub Date : 2024-04-18 DOI: 10.1109/OJCS.2024.3389462

Farrukh Pervez;Moazzam Shoukat;Muhammad Usama;Moid Sandhu;Siddique Latif;Junaid Qadir

{"title":"Affective Computing and the Road to an Emotionally Intelligent Metaverse","authors":"Farrukh Pervez;Moazzam Shoukat;Muhammad Usama;Moid Sandhu;Siddique Latif;Junaid Qadir","doi":"10.1109/OJCS.2024.3389462","DOIUrl":"10.1109/OJCS.2024.3389462","url":null,"abstract":"The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and context-aware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"195-214"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626556","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

Optimal Neighborhood Contexts in Explainable AI: An Explanandum-Based Evaluation 可解释人工智能中的最佳邻域语境：基于解释备忘录的评估

IEEE Open Journal of the Computer Society Pub Date : 2024-04-18 DOI: 10.1109/OJCS.2024.3389781

Urja Pawar;Donna O'Shea;Ruairi O'Reilly;Maebh Costello;Christian Beder

{"title":"Optimal Neighborhood Contexts in Explainable AI: An Explanandum-Based Evaluation","authors":"Urja Pawar;Donna O'Shea;Ruairi O'Reilly;Maebh Costello;Christian Beder","doi":"10.1109/OJCS.2024.3389781","DOIUrl":"10.1109/OJCS.2024.3389781","url":null,"abstract":"Over the years, several frameworks have been proposed in the domain of Explainable AI (XAI), however their practical applicability and utility need to be clarified. The neighbourhood contexts are shown to significantly impact the explanations generated by XAI frameworks, thus directly affecting their utility in addressing specific questions, or “explananda”. This work introduces a methodology that use a comprehensive range of neighbourhood contexts to evaluate and enhance the utility of specific XAI techniques, particularly Feature Importance and CounterFactuals. In this evaluation, two explananda are targeted. The first one examines whether features' collection should be halted as per the AI model based on the sufficiency of the current set of information. Here, the information refers to the features present in the data used to train the AI-based system. The second one explores what is the most effective information (features) that should be collected next to ensure that the AI outputs the same classification as it would have generated with all the information present. These questions serve as a platform to demonstrate our methodology's ability to assess the impact of customised neighbourhood contexts on the utility of XAI.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"181-194"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626718","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

An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks 基于未来车载网络综合传感与通信的智能路径损耗预测方法

IEEE Open Journal of the Computer Society Pub Date : 2024-04-09 DOI: 10.1109/OJCS.2024.3386733

Zixiang Wei;Bomin Mao;Hongzhi Guo;Yijie Xun;Jiajia Liu;Nei Kato

{"title":"An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks","authors":"Zixiang Wei;Bomin Mao;Hongzhi Guo;Yijie Xun;Jiajia Liu;Nei Kato","doi":"10.1109/OJCS.2024.3386733","DOIUrl":"10.1109/OJCS.2024.3386733","url":null,"abstract":"The developments of communication technologies, Internet of Things (IoT), and Artificial Intelligence (AI) have significantly accelerated the advancement of Intelligent Transportation Systems (ITS) and Autonomous Driving (AD) in recent years. The exchange of sensed information by widely deployed radars, cameras, and other sensors on vehicles and roadside infrastructure can improve the traffic awareness of drivers and pedestrians. However, wireless data transmission in vehicular networks is challenged by highly dynamic path loss due to utilized frequency bands, weather conditions, traffic overheads, and geographical conditions. In this paper, we propose an Integrated Sensing and Communication System (ISAC) based path loss prediction approach to improve the knowledge of wireless data transmissions in vehicular networks, which utilizes multi-modal data collected by millimeter-wave (mmWave) radars, laser radars, and cameras to forecast the end-to-end path loss distribution. By leveraging a generative adversarial network for parameter initialization coupled with fine-tuning through supervised learning, the model's accuracy can be significantly improved. To increase the model's scalability, the effects of weather conditions, geographical conditions, traffic overheads, and frequency bands are all analyzed. According to the simulation results, our model achieves excellent accuracy with Mean Squared Error (MSE) of the predicted path loss distribution below \u0000<inline-formula><tex-math>$3e^{-3}$</tex-math></inline-formula>\u0000 across five different scenarios.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"170-180"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593286","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

Anomaly Detection in the Key-Management Interoperability Protocol Using Metadata 利用元数据在密钥管理互操作性协议中进行异常检测

IEEE Open Journal of the Computer Society Pub Date : 2024-04-09 DOI: 10.1109/OJCS.2024.3386715

Mir Ali Rezazadeh Baee;Leonie Simpson;Warren Armstrong

{"title":"Anomaly Detection in the Key-Management Interoperability Protocol Using Metadata","authors":"Mir Ali Rezazadeh Baee;Leonie Simpson;Warren Armstrong","doi":"10.1109/OJCS.2024.3386715","DOIUrl":"10.1109/OJCS.2024.3386715","url":null,"abstract":"Large scale enterprise networks often use Enterprise Key-Management (EKM) platforms for unified management of cryptographic keys. In such a system, requests and responses commonly use the Key Management Interoperability Protocol (KMIP) format. The KMIP client and server use Transport Layer Security (TLS) to negotiate a mutually-authenti cated connection. Although KMIP traffic is encrypted, monitoring traffic and usage patterns of EKM Systems (EKMS) may enable detection of anomalous (possibly malicious) activity in the enterprise network that is notdetectable by other means. Metadata analysis of enterprise system traffic has been widely studied (for example at the TLS protocol level). However, KMIP metadata in EKMS has not been used for anomaly detection. In this paper, wepresent a framework for automated outlier rejection and anomaly detection. This involves investigati on of KMIP metadata, determining characteristics to extract for dataset generation, and looking for patt erns from which behaviors can be inferred. For automated labeling and detection, a deep learning-based model is applied to thegenerated datasets: Long Short-Term Memory (LSTM) auto-encoder neural networks with specific parameters. As aproof of concept, we simulated an enterprise environment, collected relevant KMIP metadata, and deployed this framework. Although our implementati on used Quintessence Labs EKMS, the framework we proposed is vendorneutral. The experimental results (Precision, Recall, F1 = 1.0) demonstrate that our framework can accurately detectall anomalous enterprise network activities. This approach could be integrated with other enterprise information toenhance detection capabilities. Our proposal can be used as a general-purpose framework for anomaly detecti on and diagnosis.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"156-169"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593715","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

Anomaly Detection in Key-Management Activities Using Metadata: A Case Study and Framework 使用元数据进行密钥管理活动中的异常检测：案例研究与框架

IEEE Open Journal of the Computer Society Pub Date : 2024-03-30 DOI: 10.1109/OJCS.2024.3407547

Mir Ali Rezazadeh Baee;Leonie Simpson;Warren Armstrong

{"title":"Anomaly Detection in Key-Management Activities Using Metadata: A Case Study and Framework","authors":"Mir Ali Rezazadeh Baee;Leonie Simpson;Warren Armstrong","doi":"10.1109/OJCS.2024.3407547","DOIUrl":"10.1109/OJCS.2024.3407547","url":null,"abstract":"Large scale enterprise networks often use Enterprise Key-Management (EKM) platforms for unified management of cryptographic keys. Monitoring access and usage patterns of EKM Systems (EKMS) may enable detection of anomalous (possibly malicious) activity in the enterprise network that is not detectable by other means. Analysis of enterprise system logs has been widely studied (for example at the operating system level). However, to the best of our knowledge, EKMS metadata has not been used for anomaly detection. In this article we present a framework for anomaly detection based on EKMS metadata. The framework involves automated outlier rejection, normal heuristics collection, automated anomaly detection, and system notification and integration with other security tools. This is developed through investigation of EKMS metadata, determining characteristics to extract for dataset generation, and looking for patterns from which behaviors can be inferred. For automated labeling and detection, a deep learning-based model is applied to the generated datasets: Long Short-Term Memory (LSTM) auto-encoder neural networks with specific parameters. This generates heuristics based on categories of behavior. As a proof of concept, we simulated an enterprise environment, collected the EKMS metadata, and deployed this framework. Our implementation used QuintessenceLabs EKMS. However, the framework is vendor neutral. The results demonstrate that our framework can accurately detect all anomalous enterprise network activities. This approach could be integrated with other enterprise information to enhance detection capabilities. Further, our proposal can be used as a general-purpose framework for anomaly detection and diagnosis.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"315-328"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189524","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

4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network 4D 脚：利用注意力增强型动态同步图卷积 LSTM 网络记录行走的脚形

IEEE Open Journal of the Computer Society Pub Date : 2024-03-29 DOI: 10.1109/OJCS.2024.3406645

FARZAM TAJDARI;TOON HUYSMANS;XINHE YAO;JUN XU;MARYAM ZEBARJADI;YU SONG

{"title":"4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network","authors":"FARZAM TAJDARI;TOON HUYSMANS;XINHE YAO;JUN XU;MARYAM ZEBARJADI;YU SONG","doi":"10.1109/OJCS.2024.3406645","DOIUrl":"10.1109/OJCS.2024.3406645","url":null,"abstract":"4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D-shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. Here, we introduce a generic framework able to 1) find and align dynamic features in the 3D-scans captured by each camera using the nonrigid-iterative-closest-farthest-points algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTM-based-network capable of aligning 3D-scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a high-quality 3D-mesh model using a non-rigid registration method. With a newly developed 4D-foot-scanner, we validate the framework and create the first open-access data-set, namely the 4D-feet. It includes 4D-shapes (15 fps) of the right and left feet of 58 participants (116 feet including 5147 3D-frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D-scans.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"343-355"},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189622","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 Ultra-Low Cost and Accurate AMC Algorithm and Its Hardware Implementation 超低成本、精确的 AMC 算法及其硬件实现

IEEE Open Journal of the Computer Society Pub Date : 2024-03-26 DOI: 10.1109/OJCS.2024.3381827

Yuqin Zhao;Tiantai Deng;Bill Gavin;Edward A. Ball;Luke Seed

引用次数: 0

Training and Serving System of Foundation Models: A Comprehensive Survey 基础模型培训和服务系统：全面调查

IEEE Open Journal of the Computer Society Pub Date : 2024-03-22 DOI: 10.1109/OJCS.2024.3380828

Jiahang Zhou;Yanyu Chen;Zicong Hong;Wuhui Chen;Yue Yu;Tao Zhang;Hui Wang;Chuanfu Zhang;Zibin Zheng

{"title":"Training and Serving System of Foundation Models: A Comprehensive Survey","authors":"Jiahang Zhou;Yanyu Chen;Zicong Hong;Wuhui Chen;Yue Yu;Tao Zhang;Hui Wang;Chuanfu Zhang;Zibin Zheng","doi":"10.1109/OJCS.2024.3380828","DOIUrl":"10.1109/OJCS.2024.3380828","url":null,"abstract":"Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-\u0000<inline-formula><tex-math>$Sigma$</tex-math></inline-formula>\u0000) have demonstrated extraordinary performance in key technological areas, such as natural language processing and visual recognition, and have become the mainstream trend of artificial general intelligence. This has led more and more major technology giants to dedicate significant human and financial resources to actively develop their foundation model systems, which drives continuous growth of these models' parameters. As a result, the training and serving of these models have posed significant challenges, including substantial computing power, memory consumption, bandwidth demands, etc. Therefore, employing efficient training and serving strategies becomes particularly crucial. Many researchers have actively explored and proposed effective methods. So, a comprehensive survey of them is essential for system developers and researchers. This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage. Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems. Through comprehensive discussion and analysis, it hopes to provide a solid theoretical basis and practical guidance for future research and applications, promoting continuous innovation and development in foundation model systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"107-119"},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10478189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205251","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 Lightweight Visual Font Style Recognition With Quantized Convolutional Autoencoder 用量化卷积自动编码器识别轻量级可视字体样式

IEEE Open Journal of the Computer Society Pub Date : 2024-03-20 DOI: 10.1109/OJCS.2024.3378709

Moshiur Rahman Tonmoy;Abdul Fattah Rakib;Rashik Rahman;Md. Akhtaruzzaman Adnan;M. F. Mridha;Jie Huang;Jungpil Shin

{"title":"A Lightweight Visual Font Style Recognition With Quantized Convolutional Autoencoder","authors":"Moshiur Rahman Tonmoy;Abdul Fattah Rakib;Rashik Rahman;Md. Akhtaruzzaman Adnan;M. F. Mridha;Jie Huang;Jungpil Shin","doi":"10.1109/OJCS.2024.3378709","DOIUrl":"10.1109/OJCS.2024.3378709","url":null,"abstract":"Font style recognition plays a vital role in the field of computer vision, particularly in document and pattern analysis, and image processing. In the industry context, this recognition of font styles holds immense importance for professionals such as graphic designers, front-end developers, and UI-UX developers. In recent times, font style recognition using Computer Vision has made significant progress, especially in English. Very few works have been done for other languages as well. However, the existing models are computationally costly, time-consuming, and not diversified. In this work, we propose a state-of-the-art model to recognize Bangla fonts from images using a quantized Convolutional Autoencoder (Q-CAE) approach. The compressed model takes around 58 KB of memory only which makes it suitable for not only high-end but also low-end computational edge devices. We have also created a synthetic data set consisting of 10 distinct Bangla font styles and a total of 60,000 images for conducting this study as no dedicated dataset is available publicly. Experimental outcomes demonstrate that the proposed method can perform better than existing methods, gaining an overall accuracy of \u0000<bold>99.95%</b>\u0000 without quantization and \u0000<bold>99.85%</b>\u0000 after quantization.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"120-130"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10475431","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205089","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