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Enhancing 5G NR Downlink Performance through Advanced Waveform Generation Techniques 3

Enhancing 5G NR Downlink Performance through Advanced Wavefo...

3rd Odisha International Conference on electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Samanta, Indu Sekhar Rout, Pravat Kumar Joshi, Sukumar Mohanty, Sarthak Panda, Subhasis Department of Computer Science and Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Electrical Engineering Bhubaneswar India Srinix College of Engineering Department of Electrical Engineering Balasore India

ISBN: (纸本)9798350354379

The 5G New Radio (NR) network rollout calls for innovative methods to satisfy the growing demand for low latency, large data speeds, and consistent connectivity. Focussing on integrating several channels and signals, numerology, and configuration objects inside MATLAB and Simulink environments, this work investigates creating and implementing complex downlink waveform-generating algorithms in 5G NR. The proposed approach optimizes the main components of waveform production and summarises them to improve general system performance. © 2024 IEEE.

关键词： MATLAB

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Kinmen Wind Lion Face Generation Based on the Deep Convolutional Generative Adversarial Network 13

Kinmen Wind Lion Face Generation Based on the Deep Convoluti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wu, Chia-Huan Zhao, Yu-Xiang Chou, Cheng-Hou Hsieh, Yi-Zeng National Taiwan University of Science and Technology Department of Electrical and Computer Engineering Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350355079

Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used in recognition, classification and generation. The results generated by generative adversarial networks have become increasingly realistic and are now being applied to human face generation and reconstruction. Among the various technologies, the deep convolutional generative adversarial network (DCGAN) is more widely used. Based on the trained DCGAN of human and cat faces, this paper collected Kinmen wind lion data by YOLOv3 and crawler technology and transferred the data to the DCGAN of wind lions through network-based transfer learning to generate complete wind lion faces. The experimental process consisted of four parts. The wind lion dataset was directly used to train the DCGAN in the first two parts, and the DCGAN of the human and cat faces was used for transfer learning in the second two parts. According to the results, the training effect of human face-based transfer learning was the best and was the closest to Kinmen's wind lions. © 2024 IEEE.

关键词： Generative adversarial networks

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Educational Data Analysis using Generative AI

Educational Data Analysis using Generative AI

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Berr, Abdul Leelaluk, Sukrit Tang, Cheng Chen, Li Okubo, Fumiya Shimada, Atsushi Department of Electrical Engineering and Computer Science Kyushu University Japan Graduate School of Information Science and Electrical Engineering Kyushu University Japan

With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades. © 2024 CEUR-WS. All rights reserved.

关键词： Students

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Content-aware malicious webpage detection using convolutional neural network

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Multimedia Tools and Applications 2024年第3期83卷 8145-8163页

作者： Chang, Yen-Jen Tsai, Kun-Lin Jiang, Wei-Cheng Liu, Meng-Kun Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Department of Electrical Engineering Tunghai University Taichung City Taiwan

Malicious websites often install malware on user devices to gather user information or to disrupt device operations, violate user privacy, or adversely affect company interests. Many commercial tools are available to prevent malicious webpages from accessing devices;however, current versions of these tools may become useless as soon as a new generation of malware is released. In this study, a content-aware malicious webpage detection (CAMD) method was developed;this CAMD method can verify whether a webpage is malicious by applying a novel webpage contextual visualization process, which retrieves the critical codes of webpages, transforms those codes into one-dimensional grayscale images, and applies convolutional neural networks to detect any malicious webpages. To verify the feasibility of proposed CAMD, 50000 normal and 50000 malicious webpages from the VirusTotal website were used. The results indicated that the proposed CAMD achieved an accuracy of > 98%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Websites

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Entanglement-Controlled Quantum Federated Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18318-18330页

作者： Park, Soohyun Lee, Hyunsoo Jung, Soyi Park, Jihong Bennis, Mehdi Kim, Joongheon The Division of Computer Science Sookmyung Women’s University Seoul04310 Korea Republic of The Department of Electrical and Computer Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Singapore The Centre for Wireless Communications University of Oulu Oulu90014 Finland

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this paper develops a novel depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high entanglement entropy and inter-depth interference. Therefore, the proposed method in this paper mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions. © 2014 IEEE.

关键词： Federated learning

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An Efficient Federated Learning Framework for IoT Intrusion Detection 100

An Efficient Federated Learning Framework for IoT Intrusion ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Yushen Fang, Fang Wang, Boyu Zhang, Lan Western University Department of Computer Science London Canada Western University Department of Electrical and Computer Engineering London Canada Clemson University Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798331517786

The exponential growth of the Internet of Things (IoT) ecosystems has raised significant cybersecurity concerns. Deep learning (DL)-based methods have shown promising performance in detecting potential cyber threats in IoT networks. However, as these methods often involve data centralization, they can pose serious data privacy issues for IoT users and increase the communication burden of local networks. Federated learning (FL), as a distributed learning paradigm, enables privacy-preserving training of IoT intrusion detection models by requiring only model updates from IoT devices. However, the resource-constrained nature of IoT devices can significantly decrease FL training efficiencies, such as increased training latency and delayed convergence speed. Moreover, the data heterogeneous issues of IoT devices can also impact the accuracy and robustness of the trained model. To address these challenges, we propose an efficient FL framework, FedKD-Prox, based on federated proximal (FedProx) and knowledge distillation (KD). To improve the prediction accuracy within a limited time budget, the proposed framework aims to efficiently exploit the computation capability of the IoT trainers, reduce the communication overhead of FL, and alleviate the impact of heterogeneous data issues. The simulation results show that FedKD-Prox achieves higher accuracy and improves the robustness of the trained intrusion detection model. © 2024 IEEE.

关键词： Federated learning

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High-Precision Digital Twin Platform Based on Ray Tracing Simulation 15

High-Precision Digital Twin Platform Based on Ray Tracing Si...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Noh, Yong Jun Choi, Kae Won Sungkyunkwan University Department of Computer Science and Engineering Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350364637

The evolution of wireless networks necessitates so-phisticated optimization strategies to address the challenges posed by heterogeneous traffic arising from various domains. Digital Twin (DT) concept has emerged as an innovative solution for efficiently regulating and managing complex and dynamic networks. DT enables safe and cost-effective network evaluation and optimization across different scenarios. We introduce a high-precision DT platform, capable of producing comprehensive channel datasets for real-world communication environments. This platform not only gathers high variance and large-scale channel data without affecting the quality of service to the real-world users but also shows promise as a novel tool for conducting and assessing various experiments. © 2024 IEEE.

关键词： digital twin ray tracing wireless networks

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LLM-based Skill Diffusion for Zero-shot Policy Adaptation 38

LLM-based Skill Diffusion for Zero-shot Policy Adaptation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kim, Woo Kyung Lee, Youngseok Kim, Jooyoung Woo, Honguk Department of Computer Science and Engineering Sunkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Recent advances in data-driven imitation learning and offline reinforcement learning have highlighted the use of expert data for skill acquisition and the development of hierarchical policies based on these skills. However, these approaches have not significantly advanced in adapting these skills to unseen contexts, which may involve changing environmental conditions or different user requirements. In this paper, we present a novel LLM-based policy adaptation framework LDuS which leverages an LLM to guide the generation process of a skill diffusion model upon contexts specified in language, facilitating zero-shot skill-based policy adaptation to different contexts. To implement the skill diffusion model, we adapt the loss-guided diffusion with a sequential in-painting technique, where target trajectories are conditioned by masking them with past state-action sequences, thereby enabling the robust and controlled generation of skill trajectories in test-time. To have a loss function for a given context, we employ the LLM-based code generation with iterative refinement, by which the code and controlled trajectory are validated to align with the context in a closed-loop manner. Through experiments, we demonstrate the zero-shot adaptability of LDuS to various context types including different specification levels, multi-modality, and varied temporal conditions for several robotic manipulation tasks, outperforming other language-conditioned imitation and planning methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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