IEEE Open Journal of the Computer Society最新文献_第7页

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

An 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

A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language 葡萄牙语自然语言处理中的词义消歧方法

IEEE Open Journal of the Computer Society Pub Date : 2024-03-20 DOI: 10.1109/OJCS.2024.3396518

Clovis Holanda do Nascimento;Vinicius Cardoso Garcia;Ricardo de Andrade Araújo

{"title":"A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language","authors":"Clovis Holanda do Nascimento;Vinicius Cardoso Garcia;Ricardo de Andrade Araújo","doi":"10.1109/OJCS.2024.3396518","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3396518","url":null,"abstract":"Natural language processing (NLP) and artificial intelligence (AI) have advanced significantly in recent years, enabling the development of various tasks, such as machine translation, text summarization, sentiment analysis, and speech analysis. However, there are still challenges to overcome, such as natural language ambiguity. One of the problems caused by ambiguity is the difficulty of determining the proper meaning of a word in a specific context. For example, the word “mouse” can mean a computer peripheral or an animal, depending on the context. This limitation can lead to an incorrect semantic interpretation of the processed sentence. In recent years, language models (LMs) have provided a new impetus to NLP and AI, including in the task of word sense disambiguation (WSD). LMs are capable of learning and generating texts as they are trained on large amounts of data. However, in the Portuguese language, there are still few studies on WSD using LMs. Given this scenario, this article presents a method for WSD for the Portuguese language. To do this, it uses the BERTimbau language model, which is specific to the Portuguese. The results will be evaluated using the metrics established in the literature.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"268-277"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091244","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

ShadowBug: Enhanced Synthetic Fuzzing Benchmark Generation ShadowBug：增强型合成模糊基准生成

IEEE Open Journal of the Computer Society Pub Date : 2024-03-19 DOI: 10.1109/OJCS.2024.3378384

Zhengxiang Zhou;Cong Wang

{"title":"ShadowBug: Enhanced Synthetic Fuzzing Benchmark Generation","authors":"Zhengxiang Zhou;Cong Wang","doi":"10.1109/OJCS.2024.3378384","DOIUrl":"10.1109/OJCS.2024.3378384","url":null,"abstract":"Fuzzers have proven to be a vital tool in identifying vulnerabilities. As an area of active research, there is a constant drive to improve fuzzers, and it is equally important to improve benchmarks used to evaluate their performance alongside evolving heuristics. Current research has primarily focused on using CVE bugs as benchmarks, with synthetic benchmarks receiving less attention due to concerns about overfitting specific fuzzing heuristics. In this paper, we introduce ShadowBug, a new methodology that generates enhanced synthetic bugs. In contrast to existing synthetic benchmarks, our approach involves well-arranged bugs that fit specific distributions by quantifying the constraint-solving difficulty of each block. We also uncover implicit constraints of real-world bugs that prior research has overlooked and develop an integer-overflow-based transformation from normal constraints to their implicit forms. We construct a synthetic benchmark and evaluate it against five prominent fuzzers. The experiments reveal that 391 out of 466 bugs were detected, which confirms the practicality and effectiveness of our methodology. Additionally, we introduce a finer-grained evaluation metric called “bug difficulty,” which sheds more light on their heuristic strengths with regard to constraint-solving and bug exploitation. The results of our study have practical implications for future fuzzer evaluation methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"95-106"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165738","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 Novel NFT Solution for Assets Digitization and Authentication in Cyber-Physical Systems: Blueprint and Evaluation 网络物理系统中资产数字化和身份验证的新型 NFT 解决方案：蓝图与评估

IEEE Open Journal of the Computer Society Pub Date : 2024-03-18 DOI: 10.1109/OJCS.2024.3378424

Usman Khalil;Mueen Uddin;Owais Ahmed Malik;Ong Wee Hong

{"title":"A Novel NFT Solution for Assets Digitization and Authentication in Cyber-Physical Systems: Blueprint and Evaluation","authors":"Usman Khalil;Mueen Uddin;Owais Ahmed Malik;Ong Wee Hong","doi":"10.1109/OJCS.2024.3378424","DOIUrl":"10.1109/OJCS.2024.3378424","url":null,"abstract":"The blueprint of the proposed Decentralized Smart City of Things (DSCoT) has been presented with smart contracts development and deployment for robust security of resources in the context of cyber-physical systems (CPS) for smart cities. Since non-fungibility provided by the ERC721 standard for the cyber-physical systems (CPSs) components such as the admin, user, and IoT-enabled smart device/s in literature is explicitly missing, the proposed DSCoT devised the functionality of identification and authentication of the assets. The proposed identification and authentication mechanism in cyber-physical systems (CPSs) employs smart contracts to generate an authentication access code based on extended non-fungible tokens (NFTs), which are used to authorize access to the corresponding assets. The evaluation and development of the extended NFT protocol for cyber-physical systems have been presented with the public and private blockchain deployments for evaluation comparison. The comparison demonstrated up to 96.69% promising results in terms of execution cost, efficiency, and time complexity compared to other proposed NFT-based solutions.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"131-143"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165630","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

ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures ALBERTA：变压器架构中基于算法的抗错能力

IEEE Open Journal of the Computer Society Pub Date : 2024-03-14 DOI: 10.1109/OJCS.2024.3400696

Haoxuan Liu;Vasu Singh;Michał Filipiuk;Siva Kumar Sastry Hari

{"title":"ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures","authors":"Haoxuan Liu;Vasu Singh;Michał Filipiuk;Siva Kumar Sastry Hari","doi":"10.1109/OJCS.2024.3400696","DOIUrl":"10.1109/OJCS.2024.3400696","url":null,"abstract":"Vision Transformers are being increasingly deployed in safety-critical applications that demand high reliability. Ensuring the correct execution of these models in GPUs is critical, despite the potential for transient hardware errors. We propose a novel algorithm-based resilience framework called ALBERTA that allows us to perform end-to-end resilience analysis and protection of transformer-based architectures. First, our work develops an efficient process of computing and ranking the resilience of transformers layers. Due to the large size of transformer models, applying traditional network redundancy to a subset of the most vulnerable layers provides high error coverage albeit with impractically high overhead. We address this shortcoming by providing a software-directed, checksum-based error detection technique aimed at protecting the most vulnerable general matrix multiply (GEMM) layers in the transformer models that use either floating-point or integer arithmetic. Results show that our approach achieves over 99% coverage for errors (single bit-flip fault model) that result in a mismatch with \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u00000.2% and \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u00000.01% computation and memory overheads, respectively. Lastly, we present the applicability of our framework in various modern GPU architectures under different numerical precisions. We introduce an efficient self-correction mechanism for resolving erroneous detection with an average of less than 2% overhead per error.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"85-96"},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060659","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

Secure Storage of Crypto Wallet Seed Phrase Using ECC and Splitting Technique 使用 ECC 和分割技术安全存储加密钱包种子短语

IEEE Open Journal of the Computer Society Pub Date : 2024-03-09 DOI: 10.1109/OJCS.2024.3398794

Syeda Tayyaba Bukhari;Muhammad Umar Janjua;Junaid Qadir

{"title":"Secure Storage of Crypto Wallet Seed Phrase Using ECC and Splitting Technique","authors":"Syeda Tayyaba Bukhari;Muhammad Umar Janjua;Junaid Qadir","doi":"10.1109/OJCS.2024.3398794","DOIUrl":"10.1109/OJCS.2024.3398794","url":null,"abstract":"Blockchain technology enables users to control and record their cryptocurrency transactions through the use of digital wallets. As the use of blockchain technology and cryptocurrency wallets continues to grow in popularity, the potential for attacks on these wallets increases, as attackers seek to gain access to the large sums of cryptocurrency they contain. To mitigate these risks, it is important to conduct thorough security evaluations of wallets and implement strong protective measures. In recent years, there have been several incidents involving significant losses of cryptocurrency in crypto-wallets, and in this research, a comprehensive evaluation of seed phrase and password attack methods found in the published literature was conducted, and the topic was advanced by addressing the question of whether seed phrases are hackable. The research aims to use the elliptic-curve cryptography (ECC) encryption algorithm for storing the seed phrase online by encrypting the seed phrase and using the splitting technique to store the crypto wallet seed phrase. It was concluded that it is only possible to hack a seed phrase if a significant portion of it is already known, but even this would require a significant amount of time and computational power.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"278-289"},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140928213","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

Advancing 6G Network Performance: AI/ML Framework for Proactive Management and Dynamic Optimal Routing 提升 6G 网络性能：用于主动管理和动态优化路由的 AI/ML 框架

IEEE Open Journal of the Computer Society Pub Date : 2024-03-08 DOI: 10.1109/OJCS.2024.3398540

Petro Mushidi Tshakwanda;Sisay Tadesse Arzo;Michael Devetsikiotis

{"title":"Advancing 6G Network Performance: AI/ML Framework for Proactive Management and Dynamic Optimal Routing","authors":"Petro Mushidi Tshakwanda;Sisay Tadesse Arzo;Michael Devetsikiotis","doi":"10.1109/OJCS.2024.3398540","DOIUrl":"10.1109/OJCS.2024.3398540","url":null,"abstract":"As 6G networks proliferate, they generate vast volumes of data and engage diverse devices, pushing the boundaries of traditional network management techniques. The limitations of these techniques underpin the need for a revolutionary shift towards AI/ML-based frameworks. This article introduces a transformative approach using our novel Speed-optimized LSTM (SP-LSTM) model, an embodiment of this crucial paradigm shift. We present a proactive strategy integrating predictive analytics and dynamic routing, underpinning efficient resource utilization and optimal network performance. This innovative, two-tiered system combines SP-LSTM networks and Reinforcement Learning (RL) for forecasting and dynamic routing. SP-LSTM models, boasting superior speed, predict potential network congestion, enabling preemptive action, while RL capitalizes on these forecasts to optimize routing and uphold network performance. This cutting-edge framework, driven by continuous learning and adaptation, mirrors the evolving nature of 6G networks, meeting the stringent requirements for ultra-low latency, ultra-reliability, and heterogeneity management. The expedited training and prediction times of SP-LSTM are game-changers, particularly in dynamic network environments where time is of the essence. Our work marks a significant stride towards integrating AI/ML in future network management, highlighting AI/ML's exceptional capacity to outperform conventional algorithms and drive innovative performance in 6G network management.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"303-314"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942005","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