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IEEE Networking Letters 2025年第1期7卷 51-55页

作者： Metaferia, Getahun Lemma, Frezewd Adama Science and Technology University Department of Computer Science and Engineering Adama1881 Ethiopia

Software-defined Networking (SDN) is an innovative network architecture tailored to address the modern demands of network virtualization and cloud computing, which require features such as programmability, flexibility, agility, and openness to foster innovation. However, this architecture also brings forth new security challenges, particularly due to the separation of the data plane from the control plane. Our investigation centers on a specific vulnerability termed link fabrication, which can lead to topology poisoning. A compromised network topology can cause substantial disruptions across the entire network infrastructure. Through a systematic survey, we identified that significant research efforts have been directed towards mitigating link fabrication attacks. We classified the existing studies into six categories of vulnerabilities: Host-based, port amnesia, invisible assailant attack, topology freezing, switch-based link fabrication, and link latency. Furthermore, our survey highlights several open challenges in areas such as Programmable dataplane, dedicated attack trees and threat models, active defense and mitigation strategies, as well as controller awareness and machine learning. To address the vulnerabilities identified, we propose the implementation of a distance-bounding protocol concept at the control plane as a potential solution. © 2019 IEEE.

关键词： Fabrication Topology Network topology Control systems Relays Threat modeling Switches Routing Software defined networking Security

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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Power synchronization: taming massive diversified serverless functions under power constraints

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science China(Information sciences) 2025年第3期68卷 47-65页

作者： Du LIU Lu ZHANG Yechen XU Xinkai WANG Lingyu SUN Yifei PU Xiaofeng HOU Chao LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong University AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and Technology

Carbon neutrality has become an important design objective worldwide. However, the on-going shift to cloud-naive era does not necessarily mean energy efficiency. From the perspective of power management, co-hosted serverless functions are difficult to tame. They are lightweight, short-lived applications sensitive to power capping activities. In addition, they exhibit great individual and temporal variability, presenting idiosyncratic performance/power scaling goals that are often at odds with one another. To date, very few proposals exist in terms of tailored power management for serverless platforms. In this work, we introduce power synchronization, a novel yet generic mechanism for managing serverless functions in a power-efficient way. Our insight with power synchronization is that uniform application power behavior enables consistent and uncompromised function operation on shared host machines. We also propose PowerSync, a synchronization-based power management framework that ensures optimal efficiency based on a clear understanding of functions. Our evaluation shows that PowerSync can improve the energy efficiency of functions by up to 16% without performance loss compared to conventional power management strategies.

关键词： serverless computing variability energy efficiency power management power synchronization

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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural Computing and Applications 2025年 1-46页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Liberty: An Economy, Three Fold Collaboration, and Virtualization-aware Resource Management Approach for Multiverse Based Application Execution Over 6G Enhanced Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1804-1844页

作者： Chowdhury, Mahfuzulhoq Computer Science and Engineering Department Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

Metaverse-based virtual worlds can provide users with an immersive digital experience by utilizing extended reality, IoT, 6G communication, and computing technology. Unlike the multiverse, in which users can access one virtual environment-based activity at a time, metaverse applications allow users to engage in different virtual environment-based activities simultaneously. Previous research did not look at three-fold collaboration, time-criticality, economy, energy, and network function virtualization (NFV)-aware resource management for multiverse, metaverse, and non-metaverse applications concurrently. To conquer these issues, this article proposes a three-fold collaboration (usage of host, neighbor user, and service provider-owned resources), time-criticality, NFV, energy, economy, and service delivery delay-aware resource management scheme for multiverse, metaverse, and non-metaverse application execution over 6G networks, taking into account heterogeneous physical and digital workers, NFV, communication resources, application arrival, and deadline. Experimental results show that the proposed liberty scheme outperforms existing methods by at least 22.67% economic cost, 31.28% energy expenditure cost, 44.11% service provider profit, and 8.83% task completion ratio gain. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network function virtualization

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Campaign: A Personalized Offloading, Semantic Communication, Latency-aware Resource Slicing and SFC Orchestration for SDN and NFV Empowered 6G Serverless Computing Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1480-1515页

作者： Chowdhury, Mahfuzulhoq The Computer Science and Engineering Department Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

Serverless computing has shifted cloud server management responsibilities away from end users and towards service providers. Serverless computing offers greater scalability, flexibility, ease of deployment, and cost-effective resource utilization. Previous serverless computing research excluded semantic communication, software-defined networking (SDN) and network function virtualization (NFV)-enabled service function chaining (SFC) orchestration, and minimum latency-aware hybrid worker selection based on user and service provider-retained resource usage, federated learning (FL), and blockchain (BC) operations. To overcome these issues, this article proposes cognitive intelligence, personalized offloading, FL and BC-based semantic and nonsemantic serverless applications with user satisfaction, multiple benefits, minimum hybrid latency cost-aware resource slicing, physical, digital, and NFV worker selection, and an SFC orchestration policy for SDN and NFV-enabled 6G networks that leverage user and service provider retained resource usage. Simulation results indicate that the proposed scheme improves E2E delay by over 40%, user financial gain by 4%, user satisfaction by 8%, and throughput by 6% when compared to existing schemes. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network function virtualization

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MCFusion: Frequency Domain Characteristics Enhancement and Feature Compensation Fusion Network for RGB-T Object Detection

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IEEE Sensors Journal 2025年第11期25卷 20880-20893页

作者： Gao, Yinbo Liao, Zhuhua Liu, Yizhi Yi, Aiping Zhang, Guoqiang Hunan University of Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China Hainan Normal University Department of Information Science and Technology Haikou China

The multi-modal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting conditions such as low light or backlight. However, there has been a lack of focus on the frequency domain feature information of the visible and thermal modalities themselves, as well as the complementarity of cross-modal features. Furthermore, current visible and thermal feature fusion methods only utilize feature information from the current layer, neglecting context information. Therefore, this paper proposes a novel network framework for enhancing frequency domain Characteristics and fusing cross-layer and cross-modal features to compensate for these limitations. This framework introduces two key modules: the Frequency Domain Characteristics Enhancement (FCE) module and the Cross-Layer and Cross-Modal Feature Compensation Fusion (CCF) module. The FCE module consists of two sub-modules. Reduce high-frequency information loss(FCE-RHFL) module operates on the visible modality to reduce high-frequency information loss, utilizing methods such as high-frequency masking and frequency recombination. Meanwhile, Enhance high-frequency information representation(FCE-EHFR) module enhances high-frequency information representation for the thermal modality through convolutions with different kernels and frequency domain enhancement techniques. In the CCF module, cross-modal and cross-layer feature compensation methods are employed to compensate for differences in modalities, followed by capture complementary information across modalities using a query-guided cross-attention mechanism. Finally, we conduct experimental comparisons on the KAIST and FLIR datasets, and the experimental results demonstrate that our method has excellent performance and robust detection results. © 2001-2012 IEEE.

关键词： Frequency domain analysis

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Evaluating LLMs in Persian News Summarization 15

Evaluating LLMs in Persian News Summarization

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15th International Conference on Information and Knowledge technology, IKT 2024

作者： VarastehNezhad, Arya Tavasoli, Reza Masumi, Mostafa Majd, Seyed Soroush Shamsfard, Mehrnoush Department of Computer Engineering University of Tehran Tehran Iran Department of Computer Science University of South Carolina Columbia United States Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798331522254

This study evaluates the performance of eight Large Language Models (LLMs) in Persian news summarization: GPT-4o, Claude-3.5-Sonnet, Gemini-Pro-1.5, Llama-3.1-405B, Command-R, Mistral-Large-2, DeepSeek V2.5, and Gemma-2-9B. We assess these models across five news categories: Economy, International, Sports, technology, and Social, using the pn_summary dataset. Our evaluation employs multiple metrics, including BERTScore and ROUGE, across two input conditions: article-only and article-with-title. Results show that Llama-3.1405b performed best against reference summaries in the article-only setting, achieving the highest BERTScore F1 (50.60) and ROUGE-L (33.96) scores. Notably, including article titles helped models produce summaries which aligned more closely to the reference summary, increasing the average BERTScore F1 from 48.31 to 50.16 across most models. Moreover, when comparing generated summaries to original articles, Mistral-Large-2 led with a BERTScore F1 of 48.09. In category-specific analysis, Mistral-Large-2 consistently outperformed the reference summaries across all news categories, with the most significant improvement in the Economic category. This study provides valuable insights into the current capabilities of LLMs for Persian summarization, highlighting their potential and the impact of input structure on performance. Our findings contribute to the growing body of research on multilingual summarization and have practical implications for Persian language processing applications. © 2024 IEEE.

关键词： Natural language processing systems

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Identification of Human Activity from Video Streaming Smartphone Data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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