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Blinding the Wiretapper: RIS-Enabled User Occultation in the ISAC Era

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arXiv 2025年

作者： Rexhepi, Getuar Rou, Hyeon Seok de Abreu, Giuseppe Thadeu Freitas Alexandropoulos, George C. School of Computer Science and Engineering Constructor University Bremen Campus Ring 1 Bremen28759 Germany Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

An undesirable consequence of the foreseeable proliferation of sophisticated integrated sensing and communications (ISAC) technologies is the enabling of spoofing, by malicious agents, of situational information (such as proximity, direction or location) of legitimate users of wireless systems. In order to mitigate this threat, we present a novel ISAC scheme that, aided by a reconfigurable intelligent surface (RIS), enables the occultation of the positions of user equipment (UE) from wiretappers, while maintaining both sensing and desired communication performance between the UEs and a legitimate base station (BS). To that end, we first formulate an RIS phase-shift optimization problem that jointly maximizes the sum-rate performance of the UEs (communication objective), while minimizing the projection of the wiretapper’s effective channel onto the legitimate channel (hiding objective), thereby disrupting the attempts by a wiretapper of localizing the UEs. Then, in order to efficiently solve the resulting non-convex joint optimization problem, a novel manifold optimization algorithm is derived, whose effectiveness is validated by numerical results, which demonstrate that the proposed approach preserves legitimate ISAC performance while significantly degrading the wiretapper’s sensing capability. Copyright © 2025, The Authors. All rights reserved.

关键词： Sensitive data

A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues

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IEEE Open Journal of the Communications Society

IEEE Open Journal of the Communications Society 2025年

作者： Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen Multimedia University Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya63100 Malaysia University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah21589 Saudi Arabia Al Hussein Technical University School of Computing and Informatics Amman Jordan Bahria University Department of Computer Engineering Karachi Pakistan School of Computer Science and Information Engineering Hubei Engineering University Xiaogan432000 China Abu Dhabi United Arab Emirates

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G. © 2020 IEEE.

关键词： Deep learning

Water Stress Detection in Indoor Farming using Thermal Infrared Imaging 24

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Water Stress Detection in Indoor Farming using Thermal Infra...

Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Georgios Fevgas Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Department of Informatics Democritus University of Thrace Kavala Greece Department of Network and Digital Media Kingston University Surrey United Kingdom Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (纸本)9798400713170

Early water stress detection is crucial for increasing crop yield, optimizing agricultural practices, and improving water management. This research proposes a novel method for detecting early water stress in lettuces using Thermal Infrared (TIR) and RGB images in a controlled environment. The method initially removes the background of the lettuce canopy to eliminate the noise, then clusters the canopy's thermal data to determine temperature variation and applies a yellow pseudo-color to annotate the stressed areas in the RGB images. The logic behind the proposed method is that during water stress, the transpiration of the leaves' stomata is reduced, and the temperature of the leaves increases. The results show that the proposed method finds water stress sooner than visual symptoms expression, providing helpful information for improved crop management and better water use in agriculture.

关键词： Indoor Farming

Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

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Performance Comparison of Interpolation Methods for Enrichin...

9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment

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arXiv 2025年

作者： Wang, Haoran Huang, Jingwei Yang, Lu Deng, Tianchen Zhang, Gaojing Li, Mingrui The School of Engineering and Informatics University of Sussex United Kingdom The Department of Automation Engineering University of Electronic Science and Technology of China China The Department of Automation Shanghai Jiao Tong University China The Department of Computer Science Dalian University of Technology China

3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization. However, the original 3D Gaussian Splatting lacks color representation for inputs in low-light environments. Simply using enhanced images as inputs would lead to issues with multi-view consistency, and current single-view enhancement systems rely on pre-trained data, lacking scene generalization. These problems limit the application of 3D Gaussian Splatting in low-light conditions in the field of robotics, including high-fidelity modeling and feature matching. To address these challenges, we propose an unsupervised multi-view stereoscopic system based on Gaussian Splatting, called Low-Light Gaussian Splatting (LLGS). This system aims to enhance images in low-light environments while reconstructing the scene. Our method introduces a decomposable Gaussian representation called M-Color, which separately characterizes color information for targeted enhancement. Furthermore, we propose an unsupervised optimization method with zero-knowledge priors, using direction-based enhancement to ensure multi-view consistency. Experiments conducted on real-world datasets demonstrate that our system outperforms state-of-the-art methods in both low-light enhancement and 3D Gaussian Splatting. © 2025, CC BY-NC-ND.

关键词： Stereo image processing

What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs

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arXiv 2025年

作者： Yang, Shenzhi Liang, Bin Liu, An Gui, Lin Yao, Xingkai Zhang, Xiaofang School of Computer Science and Technology Soochow University Suzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Informatics King’s College London London United Kingdom

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE (Wu et al., 2023) proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% (22.7%). The code is available via https://***/ShenzhiYang2000/NODESAFE. Copyright © 2025, The Authors. All rights reserved.

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Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

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arXiv 2025年

作者： Zhang, Yanyue He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user’s personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis