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On Strong Secrecy for Multiple Access Channels With States and Causal CSI

IEEE Transactions on Information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning 3

Intelligent Traffic Management System using Multi-Agent Rein...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shaheen, Dalaali Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Cse Coimbatore India

ISBN: (纸本)9798331509675

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control. © 2025 IEEE.

关键词： Traffic congestion

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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 574-585页

作者： Hameed, Saad Qolomany, Basheer Belhaouari, Samir Brahim Abdallah, Mohamed Qadir, Junaid Al-Fuqaha, Ala Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha5825 Qatar Howard University Department of Medicine College of Medicine WashingtonDC20059 United States Qatar University Department of Computer Science and Engineering Doha5825 Qatar

Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence for deep learning hyperparameter tuning. The proposed LLM-enhanced PSO method addresses the difficulties of efficiency and convergence by using LLMs (particularly ChatGPT-3.5 and Llama3) to improve PSO performance, allowing for faster achievement of target objectives. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions offered by LLMs. Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. Depending on the application, computational complexity is lowered by 20% to 60% compared to traditional PSO methods. Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications. © 2020 IEEE.

关键词： Deep learning

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Enhancing Radiology Report Interpretation with Large-Scale Language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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A Robust Autoencoder HBC Transceiver With CGAN-Based Channel Modeling

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IEEE Sensors Journal 2025年第9期25卷 15935-15949页

作者： Ali, Abdelhay Abdelrahman, Amr N. Celik, Abdulkadir Fouda, Mohammed E. Eltawil, Ahmed M. King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia Compumacy for Artificial Intelligence Solutions Cairo22303-2839 Egypt

Human body communication (HBC) offers a promising alternative for efficient and secure data transmission in wearable healthcare systems by leveraging the body’s conductive properties. Using the conductive properties of the human body, HBC offers significant advantages over conventional radio frequency wireless communication methods, including ultralow power consumption and minimal interference. However, HBC systems face key challenges in energy efficiency, data rate optimization, channel adaptability, and accurate body channel modeling. In this article, we present a novel dual-mode HBC transceiver architecture designed to overcome these challenges by integrating autoencoder-based signal processing with generative adversarial network (GAN)-driven channel modeling framework to enhance communication reliability. Operating in both broadband and narrowband modes, the transceiver dynamically adjusts its data rate and power efficiency based on application-specific demands. The design process involves first developing a conditional GAN (CGAN)-based channel model from real HBC measurements, and then using this model to train an autoencoder-based transceiver architecture. Our CGAN framework generates realistic synthetic channel responses for training, enabling the autoencoder to learn optimal encoding and decoding strategies that are robust to channel variations. Subsequently, we developed a low-power hardware architecture that supports flexible data rates of the proposed design while ensuring robust performance in diverse scenarios. This systematic approach provides key advantages: improved channel modeling accuracy achieving a 0.9 correlation coefficient between generated and real channels and mean squared error (mse) of 0.0071, reduced hardware complexity through elimination of digital-to-analog converter (DAC)/analog-to-digital converter (ADC), and flexible operation with dual-mode support. Operating at a clock speed of 42 MHz in the narrowband mode, the transceiver achie

关键词： Radio transceivers

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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Rapid Amplification of Single-Stranded DNA by On-Bead Plasmonic PCR in an Automated Microfluidic System 38

Rapid Amplification of Single-Stranded DNA by On-Bead Plasmo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Anni Bu, Yang Liu, Yuze Lee, Yung Ching Yobas, Levent The Hong Kong University of Science and Technology Division of Bioengineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331508890

This paper presents a novel automated microfluidic system for rapid plasmonic on-beads polymerase chain reaction (PCR). The highly integrated system automates essential PCR workflows, including sample loading, thermal cycling, purification washes, and elution utilizing pneumatic valves. Additionally, gold nanorods are introduced into the PCR system to leverage their efficient photothermal conversion properties to accelerate the PCR reaction. The platform completes 20 PCR cycles within 12 minutes to amplify single-stranded DNA (ssDNA), demonstrating potential for various rapid ssDNA PCR applications, such as aptamer selection and generation. © 2025 IEEE.

关键词： Microfluidics

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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"There is a Damn Hello on the Social Media!" Insights into Public Risk Communication in the Digital Age 18th

"There is a Damn Hello on the Social Media!" Insights into P...

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18th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2024

作者： Große, Christine Division of Computer Science Electrical and Space Engineering Department of Digital Services and Systems Luleå University of Technology Luleå971 87 Sweden

ISBN: (纸本)9783031725586

The ability to handle threats, such as disinformation, manipulation of public opinion, and disruption of critical supplies, is becoming increasingly important, thus, necessitating, among other strategies, efforts to establish a proper risk communication to the public. This paper addresses the need for more empirical research in this area to contribute to the development of an in-depth understanding of public risk communication that includes information-related threats and cyber issues. The study involves officials of three public organizations entrusted with safety and security in society: the police, the rescue service, and the county administrative board of a county in the middle of Sweden. The results detail the recognition of risks to be communicated, the organization of the communication process, the messages that these actors seek to bring forth, and to whom as well as challenges of public risk communication in the digital era. The findings indicate that information-related and cyber risks are increasingly essential to consider as an additional layer of public communication. Two implications emerged as particularly important: (1) all communication about risks and crises must consider the systemic risk of mis- and disinformation, and (2) tailored communication about the risks interrelated with disinformation should use human-centered, dialogue-based, and moderated approaches. Further research can focus on associated challenges, considering the distribution of responsibilities, inter-organizational information sharing and cooperation, and the possibly stochastic effects on critical (information) infrastructures and, ultimately, societal values. © IFIP International Federation for Information Processing 2025.

关键词： Stochastic systems

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Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3723-3735页

作者： Niu, Haoran Omitaomu, Olufemi A. Langston, Michael A. Grady, Stephen K. Olama, Mohammad Ozmen, Ozgur Klasky, Hilda B. Laurio, Angela Ward, Merry Nebeker, Jonathan Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37830 United States The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Department of Veterans Affairs WashingtonDC United States

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures. © 2013 IEEE.

关键词： Electronic health record

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