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Performance Analysis of ISAC With Active Multi-functional RISs in Rician Fading Channels

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Parihar, Abhinav Singh Singh, Keshav Alexandropoulos, George C. Ding, Zhiguo Li, Chih-Peng National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilissia Athens15784 Greece University of Illinois Chicago Department of Electrical and Computer Engineering IL60601 United States The University of Manchester School of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

In this work, we study the performance of an integrated sensing and communications (ISAC) system utilizing both a simultaneous transmitting and receiving (STAR) reconfigurable intelligent surface (RIS) and a solely reflective RIS under Rician fading channels. The base station is assumed to have multiple antennas, and with the assistance of active tunable reflections and refractions from the STAR-RIS, performs non-orthogonal multiple access (NOMA) data communications to multiple users, while the conventional RIS contributes to the detection of targets lying in its vicinity via its amplified reflections. We present novel analytical expressions for performance metrics of the considered system, including the outage probability, ergodic rate, system throughput, and sensing rate, as well as conduct an asymptotic analysis to emphasize the balance between sensing and communication capabilities. Our numerical analytical results demonstrate that the considered active RISs can simultaneously enhance both signal strength and sensing performance, which is particularly pronounced in low to medium signal-to-noise ratio conditions, outperforming passive RIS configurations. We have also performed comparisons between the considered NOMA scheme and an orthogonal one, unveiling the superiority of the former in terms of the above-mentioned performance metrics. The effects of system parameters, such as the transmission power, Rician factor, and channel estimation errors, on the overall network performance were investigated through extensive Monte Carlo simulations, validating our analytical findings. © 2015 IEEE.

关键词： Channel estimation

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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IEEE Transactions on Services Computing 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

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Self-Supervised Hyperbolic Spectro-Temporal Graph Convolution Network for Early 3D Behavior Prediction

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Peng Lai, Qin Li, Haibo Zhao, Chong Wang, Qicong Meng, Hongying Xiamen University Department of Computer Science and Technology School of Informatics Xiamen361102 China Brunel University London Department of Electronic and Electrical Engineering UxbridgeUB8 3PH United Kingdom

3D human behavior is a highly nonlinear spatiotemporal interaction process. Therefore, early behavior prediction is a challenging task, especially prediction with low observation rates in unsupervised mode. To this end, we propose a novel self-supervised early 3D behavior prediction frame-work that learns graph structures on hyperbolic manifold. Firstly, we employ the sequence construction of multi-dynamic key information to enlarge the key details of spatio-temporal behavior sequences, addressing the high redundancy between frames of spatio-temporal interaction. Secondly, for capturing dependencies among long-distance joints, we explore a unique graph Laplacian on hyperbolic manifold to perceive the subtle local difference within frames. Finally, we leverage the learned spatio-temporal features under different observation rates for progressive contrast, forming self-supervised signals. This facilitates the extraction of more discriminative global and local spatio-temporal information from early behavior sequences in unsupervised mode. Extensive experiments on three behavior datasets have demonstrated the superiority of our approach at low to medium observation rates. © 2016 IEEE.

关键词： Self-supervised learning

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Finding Optimal Sensor Placements for Traffic Data Reconstruction Using QR Pivoting and Deep Reinforcement Learning

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Transportation Research Procedia 2025年 82卷 2885-2897页

作者： Hoang Dat Pham Sharath MN Ahmed Ashraf Babak Mehran Manitoba Learning and AI Research Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada R3T5V6 Urban Mobility and Transportation Informatics Group Department of Civil Engineering University of Manitoba Winnipeg Canada R3T5V6

This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic information from sparsely placed sensors, connected vehicles, or other state-of-the-art methods. We first present a baseline method based matrix factorization of the eigen-vector basis matrix, followed by column pivoting. Moreover, we propose a reinforcement learning framework to improve the ranking method when the traffic data is used for the purpose of routing. This study utilizes dynamic traffic data that is observed and estimated from simulation.

关键词： Traffic Data Data Reconstruction Sensor Placement Matrix Factorization Reinforcement Learning

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Judging Phishing Under Uncertainty: How Do Users Handle Inaccurate Automated Advice? 25

Judging Phishing Under Uncertainty: How Do Users Handle Inac...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Tarini Saka Kalliopi Vakali Adam D G Jenkins Nadin Kokciyan Kami Vaniea School of Informatics University of Edinburgh Edinburgh United Kingdom University of Edinburgh Edinburgh United Kingdom School of Informatics King's College London London United Kingdom Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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On the Rate-Exponent Region of Integrated Sensing and Communications With Variable-Length Coding

arXiv

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arXiv 2025年

作者： Papoutsidakis, Ioannis Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

This paper considers the achievable rate-exponent region of integrated sensing and communication systems in the presence of variable-length coding with feedback. This scheme is fundamentally different from earlier studies, as the coding methods that utilize feedback impose different constraints on the codewords. The focus herein is specifically on the Gaussian channel, where three achievable regions are analytically derived and numerically evaluated. In contrast to a setting without feedback, we show that a trade-off exists between the operations of sensing and communications. Copyright © 2025, The Authors. All rights reserved.

关键词： Codes (symbols)

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SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs

arXiv

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arXiv 2025年

作者： Abdaljalil, Samir Kurban, Hasan Sharma, Parichit Serpedin, Erchin Atat, Rachad Electrical and Computer Engineering Texas A&M University College StationTX United States College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Luddy School of Informatics BloomingtonIN United States Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs—commonly known as "hallucinations." Among existing mitigation strategies, uncertainty-based methods are particularly attractive due to their ease of implementation, independence from external data, and compatibility with standard LLMs. In this work, we introduce a novel and scalable uncertainty-based semantic clustering framework for automated hallucination detection. Our approach leverages sentence embeddings and hierarchical clustering alongside a newly proposed inconsistency measure, SINdex, to yield more homogeneous clusters and more accurate detection of hallucination phenomena across various LLMs. Evaluations on prominent open- and closed-book QA datasets demonstrate that our method achieves AUROC improvements of up to 9.3% over state-of-the-art techniques. Extensive ablation studies further validate the effectiveness of each component in our framework. © 2025, CC BY.

关键词： Semantics

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Multi-task Domain Adaptation for Computation Offloading in Edge-intelligence Networks

arXiv

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arXiv 2025年

作者： Han, Runxin Yang, Bo Yu, Zhiwen Cao, Xuelin Alexandropoulos, George C. Yuen, Chau School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States School of Electrical and Electronics Engineering Nanyang Technological University Singapore

In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as MultiTask Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multitask learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Computation offloading

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Metaverse for Education: Developments, Challenges, and Future Direction

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computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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