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IEEE Open Journal of the Computer Society
Institute of Electrical and Electronics Engineers, Inc.
IEEE Open Journal of the Computer Society

Institute of Electrical and Electronics Engineers, Inc.

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IEEE Open Journal of the Computer Society/Journal IEEE Open Journal of the Computer SocietyEIESCI
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    2024 List of Reviewers*

    1-3页
      原文链接:
    • NETL
    • NSTL

    Editorial: Gratitude, Reflection, and Celebration: My Tenure as EiC Comes to a Close

    Song Guo
    1-1页
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    • NETL
    • NSTL
    • IEEE

    New Incoming EIC Editorial

    Vincenzo Piuri
    2-3页
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    • NETL
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    • IEEE

    The Metaverse for Industry 5.0 in NextG Communications: Potential Applications and Future Challenges

    Prabadevi BoopathyNatarajan DeepaPraveen Kumar Reddy MaddikuntaNancy Victor...
    4-24页
    查看更多>>摘要：With the advent of new technologies and endeavours for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. The metaverse enables people to communicate with each other by combining the physical world with the virtual world. However, realizing the Metaverse requires symmetric content delivery, low latency, dynamic network control, etc. Industry 5.0 is expected to reform the manufacturing processes through human-robot collaboration and effective utilization of technologies like Artificial intelligence for increased productivity and less maintenance. The metaverse with Industry 5.0 may have tremendous technological integration for a more immersive experience and enhanced productivity. In this review, we present an overview of the metaverse and Industry 5.0, focusing on key technologies that enable the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twins, blockchain, and 6G communication networks. The article then discusses the metaverse's diverse applications across various Industry 5.0 sectors, such as agriculture, supply chain management, healthcare, education, and transportation, illustrated through several research initiatives. Additionally, the article addresses the challenges of implementing the industrial metaverse, proposes potential solutions, and outlines directions for future research.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Emerging Technologies Driving Zero Trust Maturity Across Industries

    Hrishikesh Joshi
    25-36页
    查看更多>>摘要：This study explores the profound impact of emerging technologies on the Zero Trust paradigm and the challenges they present in the evolving cybersecurity landscape. As organizations grapple with increasingly complex threats, the integration of innovative technologies with Zero Trust principles offers both promising solutions and new challenges. The research investigates how artificial intelligence, machine learning, blockchain, quantum computing, and cloud/edge technologies are reshaping the implementation and efficacy of Zero Trust architectures. These technologies enable more sophisticated trust evaluation algorithms, enhanced threat intelligence, and dynamic access control mechanisms, thereby extending the boundaries of traditional Zero Trust models. The rapid pace of innovation introduces complexities in maintaining continuous verification and least-privilege access across hybrid and multi-cloud environments. Furthermore, the integration of AI and machine learning in Zero Trust frameworks raises questions about data privacy, algorithmic bias, and the need for explainable security decisions. The article proposes a methodology for addressing these challenges, emphasizing the need for adaptive Zero Trust strategies that can evolve alongside technological advancements. Through examination of real-world case studies and empirical research, this study provides insights into the practical implications of emerging technologies on Zero Trust implementation. It offers guidance for enterprises on harnessing these technologies to create more resilient, responsive, and effective cybersecurity measures. This research aims to equip organizations with the knowledge and strategies necessary to embrace emerging technologies within a Zero Trust framework, enabling them to navigate the complex interplay between innovation and security in the digital age.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    ECMO: An Efficient and Confidential Outsourcing Protocol for Medical Data

    Xiangyi MengYuefeng DuCong Wang
    37-48页
    查看更多>>摘要：Cloud computing has significantly advanced medical data storage capabilities, enabling healthcare institutions to outsource data management. However, this shift introduces critical security and privacy risks, as sensitive patient information is stored on untrusted third-party servers. Existing cryptographic solutions, such as searchable encryption, offer some security guarantees but struggle with challenges like leakage-based attacks, high computational overhead, and limited scalability. To address these limitations in medical data outsourcing, we present ECMO, a novel protocol that combines an ordered additive secret sharing algorithm with a unique index permutation method. This approach efficiently outsources medical data while safeguarding both the data itself and access patterns from potential leakage. Our experimental results demonstrate ECMO's efficiency and scalability, with a single store operation containing 500 keywords taking only $42.5 \;\mu s$ on average.
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    • NETL
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    • IEEE

    CD-LLMCARS: Cross Domain Fine-Tuned Large Language Model for Context-Aware Recommender Systems

    Adeel Ashraf CheemaMuhammad Shahzad SarfrazUsman HabibQamar Uz Zaman...
    49-59页
    查看更多>>摘要：Recommender systems are essential for providing personalized content across various platforms. However, traditional systems often struggle with limited information, known as the cold start problem, and with accurately interpreting a user's comprehensive preferences, referred to as context. The proposed study, CD-LLMCARS (Cross-Domain fine-tuned Large Language Model for Context-Aware Recommender Systems), presents a novel approach to addressing these issues. CD-LLMCARS leverages the substantial capabilities of the Large Language Model (LLM) Llama 2. Fine-tuning Llama 2 with information from multiple domains can enhance the generation of contextually relevant recommendations that align with a user's preferences in areas such as movies, music, books, and CDs. Techniques such as Low-Rank Adaptation (LoRA) and Half Precision Training (FP16) are both effective and resource-efficient, allowing CD-LLMCARS to perform optimally in cold start scenarios. Extensive testing of CD-LLMCARS indicates outstanding accuracy, particularly in challenging scenarios characterized by limited user history data relevant to the cold start problem. CD-LLMCARS offers precise and pertinent recommendations to users, effectively mitigating the limitations of traditional recommender systems.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Object Re-Identification Based on Federated Incremental Subgradient Proximal Optimization

    Li KangChuanghong ZhaoJianjun Huang
    60-71页
    查看更多>>摘要：Object Re-identification (Object ReID) is one of the key tasks in the field of computer vision. However, traditional centralized ReID methods face challenges related to privacy protection and data storage. Federated learning, as a distributed machine learning framework, can utilize dispersed data for model training without sharing raw data, thereby reducing communication costs and ensuring data privacy. However, the real statistical heterogeneity in federated object re-identification leads to domain shift issues, resulting in decreased performance and generalization ability of the ReID model. Therefore, to address the privacy constraints and real statistical heterogeneity in object re-identification, this article focuses on studying the object re-identification method based on the Federated Incremental Subgradient Proximal(FedISP) framework. FedISP effectively alleviates weight divergence and low communication efficiency issues through incremental sub-gradient proximal methods and ring topology, ensuring stable model convergence and efficient communication. Considering the complexity of ReID scenarios, this article adopts a ViT-based task model to cope with feature skew across clients. Additionally, it defines camera federated scenarios and dataset federated scenarios for problem modeling and analysis. Furthermore, due to the heterogeneous classifiers that clients may have, the approach intergrates personalized layers. In the experiments, instance datasets of two federated scenarios were constructed for model training. The final test results show that FedISP can effectively address the privacy protection and statistical heterogeneity issues faced by ReID.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Time Series Classification of Raw Voice Waveforms for Parkinson's Disease Detection Using Generative Adversarial Network-Driven Data Augmentation

    Marta Rey-ParedesCarlos J. PérezAlfonso Mateos-Caballero
    72-84页
    查看更多>>摘要：Parkinson's disease (PD) is a neurodegenerative disorder that affects more than 10 million people worldwide. Despite its prevalence, the detection of PD remains a complicated task, as no gold standard test has yet been developed to provide an accurate diagnosis. In this context, many recent studies have focused on the automatic detection and progression tracking of PD from voice-related characteristics, being feature engineering the most common approach. This work intends to address an existing research gap by introducing a novel strategy that analyzes raw voice waveforms. Despite recent advancements, one of the significant hurdles is still the lack of extensive and diverse datasets. This article also implements a data augmentation solution. Big Vocoder Slicing Adversarial Network (BigVSAN) is used to generate synthetic voice data that mimics the characteristics of real patients and healthy subjects. For the PD detection task, deep learning models such as ResNet, LSTM-FCN, InceptionTime, and CDIL-CNN are used. The experiments were performed using the speech task of sustained vowel /a/ in the PC-GITA database, which contains the recordings of healthy and PD subjects. CDIL-CNN achieves the best results, improving the accuracy by 15.87% (8.96%) compared to the model that does not use augmented data (from the best method found in the literature that uses voice waveforms). The results of this study indicate that models trained with raw waveforms showcase modest but promising performance, underlying the potential of audio analysis to improve the early detection of PD, providing a non-invasive and potentially remotely applicable method.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures

    Haoxuan LiuVasu SinghMichał FilipiukSiva Kumar Sastry Hari...
    85-96页
    查看更多>>摘要：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 $ $0.2% and $ $0.01% 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.
      原文链接:
    • NETL
    • NSTL
    • IEEE