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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark, and Lessons Learned

IEEE Transactions on Field Robotics

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IEEE Transactions on Field Robotics 2025年 2卷 270-287页

作者： Olivier Gamache Jean-Michel Fortin Matěj Boxan François Pomerleau Philippe Giguère Department of Computer Science and Software Engineering Université Laval Québec QC Canada

Reproducibility is a cornerstone of scientific progress, as it enables fair comparisons between algorithms through the development of detailed solutions and datasets. However, standard datasets often present limitations, particularly due to the fixed nature of input data sensors, which makes it difficult to compare methods that actively adjust sensor parameters to suit environmental conditions. This is the case with automatic-exposure (AE) methods, which rely on environmental factors to influence the image acquisition process. As a result, AE methods have traditionally been benchmarked in an online manner, rendering experiments nonreproducible. Building on our previous work, we propose a methodology that utilizes an emulator capable of generating images at any exposure time. This approach leverages BorealHDR, a unique multiexposure stereo dataset, along with its new extension, in which data were acquired along a repeated trajectory at different times of the day to assess the impact of changing illumination. In total, BorealHDR covers 13.4km over 59 trajectories in challenging lighting conditions. The dataset also includes lidar-inertial odometry-based maps with pose estimation for each image frame, as well as global navigation satellite system (GNSS) data for comparison. We demonstrate that by using images acquired at various exposure times, we can emulate realistic images with a root-mean-square error (RMSE) below 1.78% compared to ground truth images. Using this offline approach, we benchmarked eight AE methods, concluding that the classical AE method remains the field’s best performer. To further support reproducibility, we provide in-depth details on the development of our backpack acquisition platform, including hardware, electrical components, and performance specifications. In addition, we share valuable lessons learned from deploying the backpack over more than 25 km across various environments. Our code and dataset are available online at this link: https:/

关键词： Cameras Trajectory Simultaneous localization and mapping Visualization Benchmark testing Reproducibility of results Lighting Heuristic algorithms Global navigation satellite system Emulation

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An Online Integrated Development Environment for Automated Programming Assessment Systems 17

An Online Integrated Development Environment for Automated P...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9789897587467

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

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DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning

DocTTT: Test-Time Training for Handwritten Document Recognit...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Wenhao Gu Li Gu Ziqiang Wang Ching Yee Suen Yang Wang Department of Computer Science and Software Engineering Concordia University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder (MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets.

关键词： Training Metalearning Adaptation models Handwriting recognition Visualization Technological innovation computer vision Text recognition Computational modeling Layout

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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A Review on Feature Selection Techniques for Anxiety Classification from Physiological Signals 1

A Review on Feature Selection Techniques for Anxiety Classif...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798331518578

Anxiety disorders significantly impact individuals' quality of life and are traditionally assessed using subjective methods, which often lack accuracy. Recent advancements have enabled the use of physiological signals for anxiety classification, providing a more objective approach. However, the multidomain nature of these signals results in high-dimensional feature sets, necessitating effective feature selection. Therefore, this paper reviews various feature selection techniques-filter, wrapper, embedded, and hybrid-used in physiological-based anxiety classification, analyzing the advantages and limitations. It also highlights key challenges and suggests future research directions. Furthermore, this review introduces a proposed study utilizing a novel Corr-FGA hybrid feature selection algorithm to develop an efficient anxiety classification framework. The proposed framework achieves the highest accuracy of 88.93% using 8 features. This review aims to guide researchers in developing a robust, efficient anxiety classification model while providing insights that can help streamline feature selection and optimize machine learning model performance in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Capturing Temporal Components for Time Series Classification 27th

Capturing Temporal Components for Time Series Classificatio...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Vavilthota, Venkata Ragavendra Ramanathan, Ranjith Aakur, Sathyanarayanan N. Department of Computer Science Oklahoma State University StillwaterOK74078 United States Department of Animal and Food Sciences Oklahoma State University StillwaterOK74078 United States Department of Computer Science and Software Engineering Auburn University Auburn36849 United States

ISBN: (纸本)9783031781063

Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a compositional representation learning approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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