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Cross-Layer Management Framework for Enhancing XR-Based System Security in Zero-Trust Wireless Communications

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2011-2024页

作者： Ghourab, Esraa M. Azab, Mohamed Gracanin, Denis Al-Qutayri, Mahmoud Muhaidat, Sami Khalifa University 6G Center Department of Computer Science Abu Dhabi127788 United Arab Emirates Virginia Military Institute Department of Computer and Information Sciences VA United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Khalifa University 6G Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms. © 1983-2012 IEEE.

关键词： Resource allocation

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Image segmentation via two-step deep variational priors

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Pattern Recognition Letters 2025年 195卷 44-50页

作者： Tan, Lu Tai, Xue-Cheng Li, Ling Liu, Wan-Quan Chan, Raymond H. Hong, Dan-Feng School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Bergen Norway Curtin University Perth Australia School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Operations and Risk Management and School of Data Science Lingnan University Tuen Mun Hong Kong Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization dependency. Moreover, it possesses a refinement process designed to handle challenging scenarios, such as images containing obscured, damaged, or absent objects, or those with complex backgrounds. Our proposed approach consists of two main procedures, i.e., selective segmentation and shape transformation. The first procedure works as a stem in a totally unsupervised way. A convolutional neural network (CNN) based architecture is properly incorporated into the selective weighting constrained variational segmentation model. The second procedure is to further refine the outputs. This part can be achieved in two ways: one direction is to establish a joint model with the semantic shape constraint. The other technical direction is to make the shape descriptor separated from the joint model and work as an individual unit. In the proposed approach, the minimization problem is transformed from iterative minimization for each variable to automatically minimizing the loss function by learning the generator network parameters. This also leads to a good inductive bias associated with classic variational methods. Extensive experiments have demonstrated the significant advantages. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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Simulating Fake News Dissemination on Twitter with Multivariate Hawkes Processes

Simulating Fake News Dissemination on Twitter with Multivari...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Jiang, Yichen Porter, Michael D. University of Virginia Department of Engineering Systems and Environment Charlottesville United States University of Virginia School of Data Science Department of Engineering Systems and Environment Charlottesville United States

ISBN: (纸本)9781665480451

Disinformation and misinformation, spread over online social media, can be harmful to society. While there are a number of models that predict or explain the dynamic behavior of fake news dissemination over social media, the limited availability of datasets hinders the ability to fully test these models under a wide range of possible conditions. Simulation, however, offers a low-cost approach to evaluate models over many different scenarios. This paper proposes a novel approach for simulating fake news dissemination process on Twitter. Our approach combines a Multivariate Hawkes Point Processes with concepts from Agent-Based models, to generate realistic data that incorporates the core elements of Twitter including user networks, tweet type, user stance toward the news event, and time. The flexible and efficient simulation approach can capture a wide range of realistic behavior through a rich set of tuning parameters. We show how closely the simulated data can replicate real fake news events. © 2022 IEEE.

关键词： Computational methods

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English Offensive Text Detection using CNN based Bi-GRU model 2

English Offensive Text Detection using CNN based Bi-GRU mode...

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2nd International Conference on information and Communication Technology, ICICT 2024

作者： Roy, Tonmoy Islam, Md Robiul Miazee, Asif Ahammad Antara, Anika Al Amin Hossain, Sunjim Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary Virginia United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

ISBN: (纸本)9798331508227

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models. ©2024 IEEE.

关键词： Tweets

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GENSIMO - A Generic Framework for Modelling Social Insurance systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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Reevaluating data Partitioning for Emotion Detection in EmoWOZ

arXiv

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arXiv 2023年

作者： Mostafavi, Moeen Porter, Michael D. Systems and Information Engineering University of Virginia CharlottesvilleVA United States Data Science & Systems and Information Engineering University of Virginia CharlottesvilleVA United States

This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and unevenly distributed across the partitions, which causes sub-optimal performance and poor comparison of models. We propose a stratified sampling scheme based on emotion tags to address this issue, improve the dataset's distribution, and reduce dataset shift. We also introduce a special technique to handle conversation (sequential) data with many emotional tags. Using our proposed sampling method, models built upon EmoWoz can perform better, making it a more reliable resource for training conversational agents with emotional intelligence. We recommend that future researchers use this new partitioning to ensure consistent and accurate performance evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Paired Robotic Devices with Subtle Expression of Sadness for Enriching Social Connectedness 15th

Paired Robotic Devices with Subtle Expression of Sadness f...

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15th International Conference on Social Robotics, ICSR 2023

作者： Uchida, Misako Nunez, Eleuda Hassan, Modar Hirokawa, Masakazu Suzuki, Kenji Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan Data Science Laboratories NEC Corporation Kawasaki Japan

ISBN: (纸本)9789819987146

Various factors contribute to feelings of loneliness and compromised emotional well-being. One potential strategy to address this issue involves creating systems that enhance social connectedness between users at distance. In the literature, many of these systems utilize physically embodied devices and foster a sense of presence and intimacy. However, there has been limited exploration of targeting and sharing negative emotions. This study introduces a novel robotic device with a caricatured appearance named BlueBot, capable of subtle expressions of sadness. This study outlines the technical aspects and design principles underpinning touch-based non-verbal communication. Additionally, we present findings from a pilot test and in-the-wild field trial, describing users’ responses and behaviors to investigate the impact on social connectedness. Our observations indicate that the developed system offers a nuanced approach that not only heightens awareness of the other individual’s presence but also enhances emotional sensitivity. Several design insights for future similar studies are derived from our research. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robotics

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Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C

Vital Measurements of Hospitalized COVID-19 Patients as a Pr...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jiang, Sihang Loomba, Johanna Sharma, Suchetha Brown, Donald University of Virginia Department of Engineering of Systems and Environment United States University of Virginia United States University of Virginia School of Data Science United States

ISBN: (纸本)9781665468190

It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements. © 2022 IEEE.

关键词： Machine learning

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