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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Trubák, Kamil Štípal, Jiří Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

With the increasing number of end users that are using multimedia services, demand for access network high bitrate systems with sufficient quality of services also increases. However, this might not always be ensured by the telecom operators, as they must perform network optimization concerning Quality of Service (QoS) and multimedia data transmission. In this work, we focused on Gigabit Passive Optical Network (GPON) performance testing with the help of various tools (iPerf, RFC 6349 or ITU-T Y.1564). The Grafana software tool is used to monitor data streams. Measurements were made to limit the downstream bitrate of up to 20 end users at 100 Mbit/s. Based on the repeated measurements, an aggregation curve is modelled indicating the available bitrate with respect to the network load. © 2025 SPIE.

关键词： Internet protocols

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A QoS-aware adaptive cross-layer cyber-attack detection algorithm for vehicular networks

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Vehicular Communications 2025年 54卷

作者： Kaur, Avneet Saiyed, Makhduma F. Al-Anbagi, Irfan Shamim Hossain, M. Faculty of Engineering and Applied Science University of Regina Regina SK Canada Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia

Securing data transmission and detecting cyber-attacks in consumer Vehicular Ad hoc Networks (VANETs) pose significant challenges due to the highly dynamic topology of the network and frequent mobility of nodes. These characteristics enable attackers to exploit vulnerabilities and evade detection, making real-time attack detection complex while maintaining the Quality of Service (QoS). In this paper, we propose an Adaptive Cross-layer Cyber-attack Detection (ACCD) algorithm that dynamically detects and isolates malicious nodes while optimizing traffic routing based on application-specific requirements. The proposed algorithm uses a cross-layer traffic-aware approach to classify data flows into security-critical and delay-sensitive applications, ensuring optimal routing through the selection of Ant Colony Optimization (ACO) and Ad hoc On-Demand Multipath Distance Vector (AOMDV) protocols. The integration of pre-route authentication (PRA) enhances malicious node isolation, reducing the impact of selective forwarding and blocking attacks. Simulation results show that ACCD achieves lower end-to-end delay and higher Packet Delivery Ratio (PDR) while effectively balancing network security and performance. © 2025

关键词： ACO AOMDV Cross layer Cyber-attacks End-to-end delay Intelligent transportation system Reliability VANETs

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Multimedia Services Problems in Hybrid Network 23

Multimedia Services Problems in Hybrid Network

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Štípal, Jiří Trubák, Kamil Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

This work is dealing with actual topic in the field of internet service providers that are solving the question of metallic and optical networks convergence. The aim was to construct an experimental network topology in the form of WAN or LAN topology consisting of GPON network with VDSL2 technology, which led to the creation of a hybrid access network, on which the parameters of multimedia services were then measured. In particular, focusing on the distribution of video streams through SDTV & HDTV (MPEG-2) + HDTV (MPEG-4). As part of the network integrity measurements, tests are performed according to the recommendations of RFC 6349 and ITU-T Y.1564 standard. © 2025 SPIE.

关键词： Multimedia services

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Efficient Resource Allocation Using Service Function Chain in Agricultural Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Yao, Jiamin Qiao, Sibo Xie, Yu Kumari, Saru Lin, Qiao Khan, Fazlullah Shanghai Maritime University College of Information Engineering Shanghai201306 China Tiangong University School of Software Tianjin300387 China Chaudhary Charan Singh University Department of Mathematics Meerut India University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system's responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency. © 1975-2011 IEEE.

关键词： Chains

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Lightweight and Roubust Key Agreement for Securing IIoT-Driven Flexible Manufacturing Systems

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IEEE Internet of Things Journal 2025年

作者： Hammad, Muhammad Badshah, Akhtar Almeer, Mohammed A. Waqas, Muhammad Song, Houbing Chen, Sheng Han, Zhu GIK Institute of Engineering Sciences and Technology Faculty of Mechanical Engineering Topi23640 Pakistan University of Malakand Department of Software Engineering Dir Lower18800 Pakistan University of Bahrain Computer Engineering Department College of Information Technology 32038 Bahrain University of Greenwich Faculty of Engineering and Science Centre for Sustainable Cybersecurity United Kingdom Edith Cowan University School of Engineering PerthWA6027 Australia Department of Information Systems BaltimoreMD United States University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages. © 2025 IEEE.

关键词： Flexible manufacturing systems

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Using Machine Learning to Enhance Personalization in E-Learning

Using Machine Learning to Enhance Personalization in E-Learn...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Sneha S. Palimkar Kannan Kunnathully Srinivasarao Thota Meeta Joshi Afsha Imran Akkalkot Nidal Al Said Computer Engineering and Technology COEP Technological University Pune Maharashtra Veltech School of Law Software Engineering Capital One Virginia USA Faculty of Management Studies Marwadi University Computer Science Engineering College of Engineering Pune Technological University Pune Maharashtra College of Mass Communication Ajman University Ajman UAE

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter.

关键词： Metalearning Electronic learning Machine learning algorithms Accuracy Databases Education Production Natural language processing Classification algorithms Ensemble learning

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Malaysia Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of IT Washington University of Science and Technology (WUST) Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN’s hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model’s potential for accurate real-time air quality forecasting, offering a robust, sustainable solution.

关键词： Accuracy Atmospheric modeling Artificial neural networks Predictive models Air quality Data models Real-time systems Environmental monitoring Forecasting Public healthcare

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Adaptive Prompt Learning With Distilled Connective Knowledge for Implicit Discourse Relation Recognition

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 586-597页

作者： Bang Wang Zhenglin Wang Wei Xiang Yijun Mo School of Software Engineering and Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology (HUST) Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Implicit discourse relation recognition (IDRR) aims at recognizing the discourse relation between two text segments without an explicit connective. Recently, prompt learning has been applied to the IDRR task with great performance improvements over various neural network-based approaches. However, the discrete nature of the state-art-of-art prompting approach requires manual design of templates and answers, a big hurdle for its practical applications. In this paper, we propose a continuous version of prompt learning together with connective knowledge distillation, called AdaptPrompt, to reduce manual design efforts via continuous prompting while further improving performance via knowledge transfer. In particular, we design and train a few virtual tokens to form continuous templates and automatically select the most suitable one by gradient search in the embedding space. We also design an answer-relation mapping rule to generate a few virtual answers as the answer space. Furthermore, we notice the importance of annotated connectives in the training dataset and design a teacher-student architecture for knowledge transfer. Experiments on the up-to-date PDTB Corpus V3.0 validate our design objectives in terms of the better relation recognition performance over the state-of-the-art competitors.

关键词： Training Encoding Adaptation models Text recognition Knowledge engineering Feature extraction Bidirectional control Annotations Vocabulary Vectors

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