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Immersive Embedded Consumer Model Leveraging AI with Zero-Trust Architecture for Cyber-Physical System

IEEE Transactions on Consumer Electronics 2025年

作者： Rehman, Amjad Haseeb, Khalid Kolivand, Hoshang Saba, Tanzila Al-Khasawneh, Mahmoud A. Ahmad, Shabir Ullah, Inam Lab Riyadh12435 Saudi Arabia Liverpool John Moores University School of Computer Science and Mathematics United Kingdom School of Computing and Digital Technologies Staffordshire University Liverpool Staffordshire United Kingdom University City Sharjah 1797 United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Jordan School of Computing Skyline University College Sharjah United Arab Emirates Incheon Startup Park Center of Artificial Intelligence for Medical Instruments Convensia-daero Incheon204 Korea Republic of Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of

The rapid expansion of the wireless network and its combination with the Consumer Internet of Things (CIoT) has introduced significant research challenges for Cyber-Physical Systems (CPS). The distributed nature of these embedded networks provides big data analytics with the integration of sensors, electronic devices, hardware, and network components. They provide diverse functionalities while sensing the observing environment based on the demand of network users with timely feedback, which supports efficient decision-making. However, CIoT devices in CPS increasingly interact with both physical and digital environments, such interaction raises the research challenges of performance optimization, data breaches, and long-run seamless connectivity. In addition, the constraint devices demanded lightweight communication methods to remain robust and enhance network stability. This paper presents an adaptive model that leverages AI-based optimization techniques with efficient data management and utilization of resources in consumer applications. Moreover, the integrated edges provide consistency and resilience of communication services in unpredictable systems, while adopting a Zero-Trust architecture with more trustworthy and secure transmissions. It increases the system's scalability and improves the response time with the limited computing power of devices for real-time processing. Simulation results demonstrated the significant outcomes of the proposed model over the dynamic scenarios for packet delivery ratio by 35.5%, network throughput by 38.7%, energy consumption by 41.6%, and latency by 45% against existing solutions. © 1975-2011 IEEE.

关键词： consumer technologies edge intelligence embedded systems information security technological development

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Towards Understanding Interactive Sonic Gastronomy with Chefs and Diners 25

Towards Understanding Interactive Sonic Gastronomy with Chef...

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

作者： Hongyue Wang Jialin Deng Linjia He Nathalie Overdevest Ryan Wee Yan Wang Phoebe O. Toups Dugas Don Samitha Elvitigala Florian Floyd Mueller Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Diverse-ability Interaction Lab School of Computer Science Bristol United Kingdom Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Mineral Resources Commonwealth Scientific and Industrial Research Organisation Melbourne Australia

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Feature enhancement model with up sampling based cyber threat attack detection and classification on imbalanced dataset in Industrial Internet of Things

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Alexandria Engineering Journal 2025年 128卷 247-258页

作者： Hayam Alamro Fahd N. Al-Wesabi Sultan Alahmari Jawhara Aljabri Shouki A. Ebad Asma Alshuhail Fouad Shoie Alallah Abdulrhman Alshareef Mahir Mohammed Sharif Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia King Abdul Aziz City for Science and Technology (KACST) Cybersecurity Institute Riyadh Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia

With the fast growth and utilization of artificial intelligence (AI) models, the Industrial Internet of Things (IIoT) has remarkably progressed in locating industrial communication and enhancing industrial methods quickly. In Industry 5.0, the hyper-automation method is a technical trend that navigates manufacturing objects to intellectual devices of IIoT, smart, agile software, cloud computing, smart robotics, and embedded modules by reliability and higher intricacy. Machine learning (ML) and deep learning (DL) methods were established for identifying anomalies by understanding the usual behaviour methods of the cyber threat attack and identifying and detecting deviations. This study proposes a Feature Enhancement Model with a White Shark Optimizer-based Cyber Threat Attack Detection and Classification (FEWSO-CTADC) technique on an Imbalanced Dataset in an IIoT environment. The primary focus of the FEWSO-CTADC technique is to enhance the automatic classification and detection of cyber threats in the IIoT environment. Initially, the FEWSO-CTADC technique implements a data preprocessing model to scale the raw information into a uniform format. Next, the SMOTE technique is used to manage the imbalanced class distribution in the attack recognition database. Moreover, the WSO-based feature subset selection is accomplished to select the superior set of features. Finally, the FEWSO-CTADC method utilizes the stacked auto-encoder (SAE) method for attack classification and recognition. Extensive experiments were conducted to demonstrate the improved performance of the FEWSO-CTADC approach, and the results were compared across various methods. The performance validation of the FEWSO-CTADC approach exhibited a superior value of 99.20 % over recent techniques under diverse metrics.

关键词： Cyber threat Stacked auto-encoder White shark optimizer Feature enhancement model Industrial Internet of Things

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DIPOLAR UNIT SIZE IN COUPLED-DIPOLE CALCULATIONS OF THE SCATTERING MATRIX-ELEMENTS

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OPTICS LETTERS 1993年第15期18卷 1211-1213页

作者： HOEKSTRA, AG SLOOT, PMA Parallel Scientific Computing Group department of Computer Systems Faculty of Mathematics and Computer Science University of Amsterdam

The coupled-dipole method is widely used to calculate the light-scattering matrix S from arbitrary particles. An important parameter in the model is the size of the dipolar subunits. Usually a size of approximately 1/10 to approximately 1/20 of the wavelength of the incident light is sufficient for accurate calculations. However, it was noted that accurate S-34 calculations require much smaller dipolar subunits. We show that this conclusion is too pessimistic, by examining the sensitivity of the S-34 elements on surface roughness of spherical particles. Furthermore we give an example of an accurate S-34 calculation with dipolar subunits as large as 1/10 of the wavelength.

关键词： .dipoles surface roughness CD method Coupled Dipole Scattering Matrix dipolar subunits

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Enhanced multiple sclerosis diagnosis by MRI image retrieval using convolutional autoencoders

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Egyptian Informatics Journal 2025年 30卷

作者： R.M. Haggag Eman M. Ali M.E. Khalifa Mohamed Taha Scientific Computing Department Faculty of Computers and Artificial Intelligence Benha University Egypt Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Benha University Egypt

Multiple sclerosis (MS) is an autoimmune disorder characterized by damage to the central nervous system (CNS), leading to neuronal degeneration and affecting over 2.8 million individuals globally. Early and accurate diagnosis of MS is critical, given its significant social and economic consequences. Magnetic resonance imaging (MRI) remains the gold standard for MS diagnosis and monitoring. This study introduces a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages a newly designed Convolutional Autoencoder (CAE) model to improve the diagnostic evaluation of MS-related MRI scans. The proposed system extracts latent features from query and reference MRI images using the CAE. Extensive ablation studies involving nine distance metrics and diverse feature space dimensions identify 64 as the optimal latent feature size and validate the Mahalanobis distance as the superior similarity measure. Evaluated on four publicly available MS MRI datasets, the framework achieves Mean Average Precision (MAP) scores of 91.23%, 98.68%, 99.88%, and 99.69%, respectively, demonstrating enhanced diagnostic accuracy. The system also outperforms existing similar CBMIR frameworks for other diseases in MAP scores and generalizes effectively without requiring extensive preprocessing or segmentation. The primary contribution of this work is the development of a CAE-driven CBMIR system optimized for MS diagnosis, achieving state-of-the-art MAP performance while maintaining an average retrieval latency of 780 ms outperforming the compared systems.

关键词： Multiple sclerosis Convolutional autoencoder Content-based medical image retrieval CBMIR CAE MS

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Clustering-Based Model Reduction for Glioma Graph Networks

Clustering-Based Model Reduction for Glioma Graph Networks

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Mlinarić, P. Sokolikj, Z. Talla, G. Meyer-Baese, U. Stadlbauer, A. Malberg, H. Na, C.-H. Juetten, K. Wiestler, B. Meyer-Baese, A. Department of Mathematics Virginia Tech United States Department of Scientific Computing Florida State University TallahasseeFL United States Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL United States Department of Neurosurgery Friedrich-Alexander University of Erlangen-Nürnberg Germany Institute for Biomedical Engineering Technical University of Dresden Germany Aachen Germany Departments of Neuroradiology & Radiation Oncology TUM School of Medicine Health Technical University of Munich Germany Institute for Advanced Study Technical University of Munich Germany Department of Neurosurgery RWTH Aachen University Aachen52074 Germany Department of Computer Science Florida State University TallahasseeFL United States

ISBN: (纸本)9781510685987

We analyze structural and functional connectivity graphs for both healthy and glioma subjects by clustering-based model reduction to obtain a detailed understanding of these complex graphs. The reduced-order network is obtained by dividing the connected brain regions or nodes into disjoint clusters. Further, each cluster is replaced by a single new node in a reduced-size graph network. Several partitions are analyzed and evaluated based on model reduction errors. © 2025 SPIE.

关键词： Bayesian networks

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Sparse dual-graph regularized quadratic dimensionality reduction algorithm based on subspace learning

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Digital Signal Processing: A Review Journal 2025年 164卷

作者： Ma, Ziping Huang, Yulei Ma, Jinlin Li, Huirong School of Mathematics and Information Science North Minzu University Yinchuan750030 China School of Mathematics and Statistics Ningxia University Yinchuan750030 China School of Computer Science and Engineering North Minzu University Yinchuan750030 China School of Mathematics and Computer Application Shangluo University Shangluo726000 China Ningxia Collaborative Innovation Center for Scientific Computing and Intelligent Information Processing North Minzu University Yinchuan750030 China

Nonnegative matrix factorization (NMF) has been attracting more and more attention due to its significant performance in dimensionality reduction. NMF performs dimensionality reduction by mapping high-dimensional data into low-dimensional space, which might lead to the uncontrollability, i.e. quantity and quality, of dimensions in dimensionality reduction. To address this issue, we innovatively propose a novel sparse dual-graph regularized quadratic dimensionality reduction (SDQDR) algorithm by embedding feature selection mechanism to semi-supervised NMF, which are fused as a whole rather than the simple combinations of NMF and feature selection in existing methods. The proposed SDQDR not only inherits the advantages of semi-supervised NMF but also demonstrates some special and important properties, such as controllability of dimensionality reduction, higher clustering performance, and significant dimensionality reduction with low time cost, etc. Then, to reinforce the sparsity structure and retain the manifold structures of both the data and the feature space simultaneously, 1-2,1/2 sparse constraint and dual graph regularization are incorporated into the proposed framework. Finally, the objective function, convergence proof, and iterative update rules are presented. In comparison with 15 state-of-the-art algorithms, relevant results demonstrate the proposed method is competitive, especially in terms of the efficiency of dimensionality reduction. © 2025 Elsevier Inc.

关键词： Non-negative matrix factorization

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The Silent Majority: Demystifying Memorization Effect in the Presence of Spurious Correlations

arXiv

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arXiv 2025年

作者： You, Chenyu Dai, Haocheng Min, Yifei Sekhon, Jasjeet S. Joshi, Sarang Duncan, James S. Department of Electrical & Computer Engineering Yale University New HavenCT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Department of Statistics and Data Science Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States

Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalanced test performance across minority and majority groups. In this work, we take a closer look at the fundamental cause of such imbalanced performance through the lens of memorization, which refers to the ability to predict accurately on atypical examples (minority groups) in the training set but failing in achieving the same accuracy in the testing set. This paper systematically shows the ubiquitous existence of spurious features in a small set of neurons within the network, providing the first-ever evidence that memorization may contribute to imbalanced group performance. Through three experimental sources of converging empirical evidence, we find the property of a small subset of neurons or channels in memorizing minority group information. Inspired by these findings, we articulate the hypothesis: the imbalanced group performance is a byproduct of "noisy" spurious memorization confined to a small set of neurons. To further substantiate this hypothesis, we show that eliminating these unnecessary spurious memorization patterns via a novel framework during training can significantly affect the model performance on minority groups. Our experimental results across various architectures and benchmarks offer new insights on how neural networks encode core and spurious knowledge, laying the groundwork for future research in demystifying robustness to spurious correlation. © 2025, CC BY.

关键词： Neurons

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Sensing With Communication Signals: From Information Theory to Signal Processing

arXiv

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arXiv 2025年

作者： Liu, Fan Liu, Ya-Feng Cui, Yuanhao Masouros, Christos Xu, Jie Han, Tony Xiao Buzzi, Stefano Eldar, Yonina C. Jin, Shi National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Guangdong518172 China Huawei Technologies Co. Ltd China Department of Electrical and Information Engineering University of Cassino and Southern Lazio CassinoI-03043 Italy Department of Electronics Information and Bioengineering Politecnico di Milano MilanoI-20133 Italy ParmaI-43124 Italy Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This tutorial paper provides a comprehensive technical overview of the fundamental theory and signal processing methodologies for ISAC transmission with random communication signals. We begin by introducing the deterministic-random tradeoff (DRT) between S&C from an information-theoretic perspective, emphasizing the need for specialized signal processing techniques tailored to random ISAC signals. Building on this foundation, we review the core signal models and processing pipelines for communication-centric ISAC systems, and analyze the average squared auto-correlation function (ACF) of random ISAC signals, which serves as a fundamental performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for the three key components of communication-centric ISAC systems: modulation schemes, constellation design, and pulse shaping filters. The goal is to either enhance sensing performance without compromising communication efficiency or to establish a scalable tradeoff between the two. We then extend our analysis from a single-antenna ISAC system to its multi-antenna counterpart, discussing recent advancements in multiinput multi-output (MIMO) precoding techniques specifically designed for random ISAC signals. We conclude by highlighting several open challenges and future research directions in the field of sensing with communication signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal modulation

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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI

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scientific Reports 2025年第1期15卷 1-14页

作者： Hussain, Sayyed Shahid Shah, Pir Masoom Dawood, Hussain Degang, Xu Alshamayleh, Ahmad Khan, Muhammad Adnan Ghazal, Taher M. School of Automation Central South University Changsha 410083 China School of Computer Science and Engineering Central South University Changsha 410010 China Department of Computer Science Bacha Khan University Charsadda Pakistan School of Computing Skyline University College Sharjah United Arab Emirates Department of Data Science and Artificial Intelligence Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13120 South Korea Riphah School of Computing and Innovation Riphah International University Lahore Campus Lahore 54000 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Networks and Cybersecurity Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson’s disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the vision transformer (ViT) excels at capturing global features through its self-attention mechanism, but it compromises local feature representations. Additionally, the varying magnitude of MR data poses a challenge for ViT, potentially leading to the gradient vanishing problem. To address these limitations, this paper proposed a novel framework that combines the Swin-Transformer and CNN to capture both local and global features effectively. To mitigate the gradient vanishing issue in ViT, we used skipped connections and cosine attention mechanism in VIT that preserves the output distribution regardless of input magnitude variations. The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. Subsequently, the extracted features from both blocks are concatenated and fed to the dense block for decision-making. The proposed model achieved promesing results of 96%, 97%, 95%, and 95% in terms of accuracy, sensitivity, specificity, and area under the curve, respectively. © The Author(s) 2025.

关键词： CNN Cosine similarity MRI Parkinson’s disease Swin-transformer

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