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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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SAE-EDA: Sparse AutoEncoder-Based Feature Extraction and Exponential Discriminant Analysis for Robust Face Recognition 16th

SAE-EDA: Sparse AutoEncoder-Based Feature Extraction and Ex...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Maafiri, Ayyad Himeur, Yassine Chougdali, Khalid Atalla, Shadi Mansoor, Wathiq Ziti, Soumia Ibn Tofail University Kenitra Morocco College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Intelligent Processing and Security of Systems Research Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9789819635245

Despite its advances, face recognition (FR) technology confronts significant challenges, especially in adverse conditions such as varying lighting, facial expressions, poses, and aging effects, which necessitate the development of more robust and accurate recognition algorithms. The importance of face recognition for social robotics lies in its ability to facilitate personalized and secure interactions, enhancing the robot’s social engagement and functionality. This research introduces a novel FR methodology integrating statistical learning and shallow neural networks to refine facial feature representation and recognition. Our approach, which combines a Sparse Auto-Encoder with Exponential Discriminant Analysis, is evaluated across renowned face databases (GT, AR, LFW), demonstrating superior recognition rates and outperforming existing state-of-the-art techniques. Specifically, our system achieved recognition accuracy of 91.94% on the AR dataset under sunglass occlusion, 90.04% under scarf occlusion, and an impressive 99.74% on the LFW database, highlighting its efficacy and resilience against challenging conditions. This work not only advances the field of facial recognition but also underscores the potential of integrated machine learning approaches in addressing complex real-world challenges in digital authentication and security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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The Balance Model for V2X System with Variable Infrastructure

The Balance Model for V2X System with Variable Infrastructur...

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2025 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Plotnikov, Pavel V. Tambovtsev, Gleb I. Vladyko, Andrei G. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Information Technology and Software Engineering Saint Petersburg Russia The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Radioelectronic Systems and Robotics Saint Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the optimization problem and seeks a balance equation between key performance-influencing parameters in a Vehicle-to-Everything (V2X) network. A novel mathematical optimization model is proposed, focusing on the interaction between On-Board Units (OBUs) and both stationary and mobile Roadside Units (RSUs/mRSUs). The optimization problem is expressed through a multi-objective function considering critical parameters such as data transmission latency, energy consumption, and resource availability. The study demonstrates how RSU density and mobility impact key network performance indicators, including system stability and data loss percentage during peak hours. This work provides a structured analytical approach to optimizing Intelligent Transportation System (ITS) infrastructure, ensuring more reliable and efficient communication between vehicles. © 2025 IEEE.

关键词： Motor transportation

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models 2

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Nia, Nooshin Nooraei Shirzad, Nasratullah Osemeha, Nwabueze Kenneth Near East University Department of Computer Information Systems Nicosia99138 Cyprus Artificial Intelligence Near East University Nicosia Cyprus Near East University Software Engineering Department Nicosia Cyprus

ISBN: (纸本)9798331505745

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM. © 2025 IEEE.

关键词： Recurrent neural networks

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Performance Analysis of the MUSIC Algorithm in Presence of Correlated Noise

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IEEE Access 2025年 13卷 91957-91971页

作者： Ham, Hyeong Woo Kwon, Hyuk-In Lee, Joon-Ho Hanwha Systems Naval Radar System Team Yongin17121 Korea Republic of Sejong University Department of Aerospace Systems Engineering Seoul05006 Korea Republic of Sejong University Department of Information and Communication Engineering Seoul05006 Korea Republic of

Passive direction finding has been studied over the past decades and theoretic performance prediction is a fundamental problem. The performance of the MUSIC algorithm for Direction-of-arrival (DOA) estimation under measurement uncertainty is analyzed. In the conventional MUSIC algorithm performance analysis, noise is assumed to be uncorrelated. In this paper, the noise correlation between antenna elements is considered according to the distance between antenna elements. The Mean Squared Error (MSE) obtained through Monte-Carlo simulations is compared with the MSE derived from analytical expressions. The primary contribution of this paper is to reduce the computational complexity of the performance analysis of the MUSIC algorithm compared to the existing Monte-Carlo simulation-based performance analysis. To our knowledge, no previous studies have derived a closed-form analytical performance analysis of the algorithm in the presence of correlated noise. © 2013 IEEE.

关键词： Monte Carlo methods

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IPL Data Analysis by Web Scraping and Using PowerBI

IPL Data Analysis by Web Scraping and Using PowerBI

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Mogith P Mothiswar T B G Mohammad Abubak J. Jagannathan N. Balaganesh Software Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information.

关键词： Visualization Technological innovation Fans Data analysis Key performance indicator Data visualization Market research Knowledge discovery Cleaning

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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PIXHELL: When Pixels Learn to Scream

arXiv

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arXiv 2025年

作者： Guri, Mordechai Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Offensive-Cyber Research Lab

This paper presents a technique for generating sound by leveraging the electrical properties of liquid crystal displays (LCDs). The phenomenon occurs due to vibrational noise produced by capacitors within the LCD panel during rapid pixel state transitions. By modulating these transitions through specially crafted bitmap patterns projected onto the screen, we demonstrate how weak yet audible acoustic signals can be generated directly from the display. We designed, implemented, evaluated, and tested a system that repurposes the LCD as a sound-emitting device. Potential applications for this technique include low-power auditory feedback systems, short-range device communication, air-gap covert channels, secure auditory signaling, and innovative approaches to human-computer interaction. Copyright © 2025, The Authors. All rights reserved.

关键词： Liquid crystal displays

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Impact of Artificial Intelligence on Decision-Making in information systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in Computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models information systems

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