ICT Express最新文献

英文中文

Prior-free 3D human pose estimation in a video using limb-vectors

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.09.015

Anam Memon , Qasim Arain , Nasrullah Pirzada , Akram Shaikh , Adel Sulaiman , Mana Saleh Al Reshan , Hani Alshahrani , Asadullah Shaikh

Estimating accurate 3D human poses from a monocular video is fundamental to various computer vision tasks. Existing methods exploit 2D-to-3D pose lifting, multiview images, and depth sensors to model spatio-temporal dependencies. However, depth ambiguities, occlusions, and larger temporal receptive fields pose challenges to these approaches. To address this, we propose a novel prior-free DCNN-based 3D human pose estimation method for monocular image sequences using limb vectors. Our method comprises two subnetworks: a limb direction estimator and a limb length estimator. The limb direction estimator utilizes a fully convolutional network to model limb direction vectors across a temporal window. We show that network complexity can be significantly reduced by utilizing dilated convolutional operations and a relatively smaller receptive field while maintaining estimation accuracy. Moreover, the limb length estimator captures stable limb length estimations from a reliable frame set. Our model has shown superior performance compared to existing methods on the Human3.6M and MPI-INF-3DHP datasets.

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引用次数: 0

A review on label cleaning techniques for learning with noisy labels

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.09.007

Jongmin Shin , Jonghyeon Won , Hyun-Suk Lee , Jang-Won Lee

Classification models categorize objects into given classes, guided by training samples with input features and labels. In practice, however, labels can be corrupted by human error or mistakes, known as label noise, which degrades classification accuracy. To address this issue, recently, various works propose the algorithms to clean datasets with label noise. We categorize the algorithms in granular ways, and review the algorithms, such as sample selection, label correction, and select-and-correct algorithms, based on the categorization. In addition, we provide future research directions for cleaning datasets, considering practical challenges, such as class imbalance, class incremental learning, and corrupted input features.

引用次数: 0

Dynamic content-cached satellite selection and routing for power minimization in LEO satellite networks

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.09.004

Jeongmin Seo , Dongho Ham , Jeongho Kwak

Efficient delivery of content to areas where terrestrial Internet service is unavailable can be possible via content caching at low earth orbit (LEO) satellites. Cached content in several LEO satellites must be delivered via inter-satellite links (ISLs) with appropriate routing techniques. Until now, content caching and routing techniques have been optimized independently. To tackle this issue, the optimization of selecting a content-cached satellite and routing is jointly performed, using the example of Earth observation data cached across multiple satellites. In this paper, we first formulate a dynamic power minimization problem constrained by the queue stability of all LEO satellites, where the control variables are the selection of content-cached satellite and routing in every satellite. To solve this long-term time-averaged problem, we leverage Lyapunov optimization framework to transform the original problem into a series of slot-by-slot problems. Moreover, we prove that the average power consumption and the average queue backlog by the proposed algorithm can be upper-bounded via theoretical analysis. Finally, through extensive simulations, we demonstrate that our proposed algorithm surpasses existing independent content-retrieval algorithms in terms of power consumption, queue backlog, and fairness.

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引用次数: 0

Attention Retinex Network(A4R-Net) for face detection under low-light environment

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.09.009

Minsu Kim , Yunho Jung , Seongjoo Lee

The degradation of recognition rates in low-light environments is a critical issue in terms of security when using object and face recognition technologies in various locations. Existing low-light enhancement models have shown limitations in terms of computational cost and performance. However, this paper overcomes these limitations. The experimental results demonstrate that our model achieves the same performance as existing models with 13 times lower computational cost and a face detection performance of 82.2%.

The structure of this paper is as follows: Introduction, which provides the background and explains the limitations of existing models. Proposed Method, which details the structure and working principles of A4R-Net. Experimental Results, which present the evaluation of low-light enhancement performance and the comparison of face detection using YOLOv4 [1]. Conclusion, which discusses the contributions of this research and future research directions.

The source code and dataset is https://github.com/Obiru2698/obiru2698.github.io/

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引用次数: 0

Prediction of permissioned blockchain performance for resource scaling configurations

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.09.003

Seungwoo Jung , Yeonho Yoo , Gyeongsik Yang, Chuck Yoo

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a “black-box,” leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of

\sim

1.9%, which is highly accurate and can be applied in the real-world.

引用次数: 0

Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities 深度神经网络和信任管理方法确保可持续智慧城市中智能交通数据的安全

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.006

Sohrab Khan , Sheharyar Khan , Adel Sulaiman , Mana Saleh Al Reshan , Hani Alshahrani , Asadullah Shaikh

Smart transportation, powered by IoT, transforms mobility with interconnected sensors and devices collecting real-time data on traffic, vehicle locations, and passenger needs. This fosters a safer and more sustainable transportation ecosystem, optimizing traffic flow and enhancing public transit efficiency. However, security and privacy challenges emerge in smart transportation systems. Our proposed solution involves a deep neural network (DNN) model trained on extensive datasets from sustainable cities, incorporating historical information like traffic patterns and sensor readings. This model identifies potential malicious nodes, achieving a 90% accuracy rate in predicting threats such as Denial of Service 88%, Whitewash attacks 80%, and Brute Force attacks 75%. This high precision ensures the security and privacy of passenger vehicle data and routes.

由物联网驱动的智能交通通过互联传感器和设备收集有关交通、车辆位置和乘客需求的实时数据，改变了交通方式。这促进了更安全、更可持续的交通生态系统，优化了交通流量，提高了公共交通效率。然而，智能交通系统也面临着安全和隐私方面的挑战。我们提出的解决方案涉及一个深度神经网络（DNN）模型，该模型在来自可持续发展城市的大量数据集上进行训练，并结合了交通模式和传感器读数等历史信息。该模型可识别潜在的恶意节点，在预测拒绝服务 88%、洗白攻击 80% 和暴力攻击 75% 等威胁方面的准确率高达 90%。这种高精确度确保了客运车辆数据和路线的安全性和隐私性。

引用次数: 0

The digital pheromone: Building digital identity of smartphone users based on time-varying multivariates 数字信息素：基于时变多变量构建智能手机用户的数字身份

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.07.008

Anizah Abu Bakar, Azizul Rahman Mohd Shariff, Chan Jia Huei, Suzi Iryanti Fadilah

The increasing reliance on smartphones necessitates secure, accurate user identification methods. This study introduces “digital pheromones”, a novel digital identity creation method based on smartphone user behaviors. Represented by a 56-bit binary number, this identity is derived from quantization and concatenation of 14 time-varying smartphone variables. Motivated by the need for enhanced security and privacy, the study employs statistical analysis to measure user behavior dispersion, showing that wider dispersion variables facilitate faster user uniqueness at lower quantization levels. The results demonstrate 100% uniqueness, ensuring no two users share the same 56-bit identity. This research offers a robust framework for future advancements in smartphone user authentication and identification systems.

随着人们对智能手机的依赖程度越来越高，有必要采用安全、准确的用户身份识别方法。本研究介绍了一种基于智能手机用户行为的新型数字身份创建方法--"数字信息素"。该身份由一个 56 位二进制数表示，由 14 个随时间变化的智能手机变量量化和串联而成。出于增强安全性和隐私性的需要，这项研究采用统计分析来测量用户行为的离散性，结果表明，在较低的量化水平下，较宽的离散变量有助于更快地实现用户唯一性。研究结果显示了 100% 的唯一性，确保没有两个用户共享相同的 56 位身份。这项研究为智能手机用户身份验证和识别系统的未来发展提供了一个强大的框架。

引用次数: 0

Hybrid Approach with Membership-Density Based Oversampling for handling multi-class imbalance in Internet Traffic Identification with overlapping and noise 基于成员密度超采样的混合方法，用于处理具有重叠和噪声的互联网流量识别中的多类不平衡问题

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.04.007

Internet Traffic identification is a crucial method for monitoring Internet application activities and is essential for Internet management and security. Internet traffic data typically displays imbalanced distributions. The uneven distribution of instances in each class indicates the class imbalance problem. This problem can cause a decrease in classification performance because the classifier assumes the dataset has a balanced class distribution. Internet Traffic Identification dataset is often accompanied by overlapping and noise. The hybrid approach to handling class imbalances involving data-level and ensemble-based approaches is usually chosen to overcome this problem. Data-level with oversampling using SMOTE is the choice because of its ability to synthesize new samples for minority classes. However, SMOTE-generated samples tend to be noisy and overlap with the majority of samples. This research proposes the application of a Hybrid Approach with Membership-density-based Oversampling to tackle this challenge. This research emphasizes the importance of applying membership degrees in determining samples that will group samples into safe, overlapping, and noisy areas. Then, top samples will be selected based on density ratio, stability, and score for safe and overlapping safe areas. The study findings that the proposed method effectively addresses multi-class imbalances in six Internet Traffic Identification datasets, yielding slightly improved average accuracy,

F_{b} M e a s u r,

and class balance accuracy results compared to other testing methods, though the difference is not statistically significant. The noise and overlapping scenes experiments demonstrate that the average accuracy obtained is superior, showing a considerable difference compared to all test methods.

互联网流量识别是监控互联网应用活动的重要方法，对互联网管理和安全至关重要。互联网流量数据通常呈现不平衡分布。每个类别中实例的不均匀分布表明存在类别不平衡问题。这个问题会导致分类性能下降，因为分类器假定数据集具有均衡的类分布。互联网流量识别数据集往往伴随着重叠和噪声。处理类不平衡的混合方法通常包括数据级方法和基于集合的方法，以克服这一问题。选择使用 SMOTE 进行超采样的数据级方法，是因为它能为少数类别合成新样本。然而，SMOTE 生成的样本往往存在噪声，并与大多数样本重叠。本研究提出了一种基于成员密度的过度采样混合方法来应对这一挑战。本研究强调在确定样本时应用成员度的重要性，这将把样本归类为安全、重叠和嘈杂区域。然后，将根据密度比、稳定性以及安全区域和重叠安全区域的得分来选择顶级样本。研究发现，所提出的方法能有效解决六个互联网流量识别数据集中的多类不平衡问题，与其他测试方法相比，平均准确率、FbMeasur 和类平衡准确率略有提高，但差异在统计学上并不显著。噪声和重叠场景实验表明，所获得的平均准确率更胜一筹，与所有测试方法相比都有相当大的差异。

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引用次数: 0

Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond 未来无线网络中的边缘计算：对 6G 及其后的全面评估和展望

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.08.007

Mustafa Ergen , Bilal Saoud , Ibraheem Shayea , Ayman A. El-Saleh , Onur Ergen , Feride Inan , Mehmet Fatih Tuysuz

Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards.

未来互联网的目标是成为一个中立的网内存储和计算平台，这对实现 6G 及更高版本的无线用例至关重要。以信息为中心的网络和边缘计算是推动这一愿景的关键范式，可在异构网络中提供快速响应的多样化服务。这种方法需要有效的协调，以便在非同质和分布式计算环境中近乎实时地动态利用链路、存储和计算等资源。此外，网络还必须了解资源可用性和声誉信息，以管理未知和部分观测到的动态系统，确保所需的体验质量（QoE）。本文全面评估了边缘计算技术，首先介绍了其架构框架。我们考察了资源分配、计算授权、数据管理和网络管理等重要方面的当代研究，并强调了现有的研究空白。此外，我们还探讨了边缘计算与 5G 之间的协同作用，并讨论了通过边缘计算增强解决方案的 6G 进展。我们的研究强调了将边缘计算纳入未来考虑的重要性，特别是在可持续能源和标准方面。

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引用次数: 0

A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge–cloud environments 用于边缘云环境中实时异常检测决策支持的 ConvLSTMBNN-DT 和 GPT-4 混合方法

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2024-10-01 DOI: 10.1016/j.icte.2024.07.007

Radityo Fajar Pamungkas, Ida Bagus Krishna Yoga Utama, Khairi Hindriyandhito, Yeong Min Jang

Anomaly detection is a critical requirement across diverse domains to promptly identify abnormal behavior. Conventional approaches often face limitations with uninterpretable anomaly detection results, impeding efficient decision-making processes. This paper introduces a novel hybrid approach, the convolutional LSTM Bayesian neural network with nonparametric dynamic thresholding (ConvLSTMBNN-DT) for prediction-based anomaly detection. In addition, the model integrates fine-tuned generative pre-training version 4 (GPT-4) to provide human-interpretable explanations in edge–cloud environments. The proposed method demonstrates exceptional performance, achieving an average

F 1 - s c o r e

of 0.91 and an area under the receiver operating characteristic curve (

A U C

) of 0.86. Additionally, it effectively offers comprehensible decision-support explanations.

异常检测是不同领域及时识别异常行为的关键要求。传统方法往往面临无法解读异常检测结果的限制，阻碍了高效的决策过程。本文介绍了一种新颖的混合方法，即具有非参数动态阈值的卷积 LSTM 贝叶斯神经网络（ConvLSTMBNN-DT），用于基于预测的异常检测。此外，该模型还集成了微调生成预训练第 4 版（GPT-4），以便在边缘云环境中提供人类可理解的解释。所提出的方法表现出卓越的性能，平均 F1 分数达到 0.91，接收器工作特征曲线下面积 (AUC) 为 0.86。此外，它还有效地提供了可理解的决策支持解释。

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