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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality through Explainable AI and Large Language Models

IEEE Access 2025年 13卷 84689-84712页

作者： Kundu, Ripan Kumar Hoque, Khaza Anuarul University of Missouri Department of Electrical Engineering Computer Science ColumbiaMO United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Enhancing cyber threat detection with an improved artificial neural network model

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Data science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

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Electromagnetic Interference on Unmanned Aerial Vehicles (UAVs): A Case Study of High Power Transmission Line Impacts

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7501-7513页

作者： Rad, Shervin Salehi Zheng, Zilong Kheirollahi, Reza Mostafa, Amr Zhao, Shuyan Wang, Yao Chevinly, Javad Nadi, Elias Bensala, Tarik Zhang, Hua Friedman, Gary Lu, Fei Department of Electrical and Computer Engineering Drexel University Philadelphia PA USA Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA

Nowadays, unmanned aerial vehicles (UAVs) play a significant role in transmission line inspection. This paper focuses on the inertial measurement unit (IMU) as a critical component to study impacts of electromagnetic interference. A case study is conducted by selecting 500 kV/25 kA 60Hz transmission lines as sources of strong electromagnetic fields and investigate their influences on an IMU unit and operation of an UAV. An experimental testing methodology is proposed to explore impacts of magnetic and electric fields separately. A high-power inverter based testing platform is implemented to excite a 2.2-kA planar coil and a 7.5-kA solenoid coil to generate magnetic fields and emulate high current impacts from transmission lines. Meanwhile, a pair of metal plates are adopted to generate a 2 kV/m electric field and study the high voltage impacts. Multiple long-time continuous testings of the DJI Marvic 2 drone have been conducted in the generated magnetic and electric fields. It validates that the onboard magnetometer is sensitive to magnetic fields, while other sensors remain immune to both magnetic and electric fields, at least up to 3100 μT and 2 kV/m, respectively. © 2015 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Deep Learning based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

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IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Irtaza, Syed Ali Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat Oman Institute of Space Technology Electrical Engineering and Computer Science Department Islamabad Pakistan FAST National University of Computer and Emerging Sciences Islamabad Pakistan

In this paper, we have proposed a novel deep-learning model to process electrocardiogram (ECG) signals from single-lead ECG device. This is achieved by using a hybrid of CNN (convolutional neural network) and LSTM (long short-term memory) neural netwrok, where CNN extracts localized features from ECG signals, while the LSTM network extract temporal relationships. Moreover, a filtering-based method is proposed to generate synthetic data to overcome the data imbalance posed by the publicly available PhysioNet/CinC Challenge 2017 dataset. Specifically, three distinct ECG filters: Neurokit, BioSPPy, and EngZeeMod are used to perform data augmentation. As depicted by the simulation results, the proposed model exhibits superior performance with an F1 score of 99.56% providing a gain of 102.25 dB in terms of model size and performance gain of 1.06% outperforming existing state-of-the-art method enabling cost-effective real-time diagnosis with an inference time of 0.48×10-3 secs. Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

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A Novel Lightweight Joint Source-Channel Coding Design in Semantic Communications

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IEEE Internet of Things Journal 2025年第11期12卷 18447-18450页

作者： Yu, Xianhua Li, Dong Zhang, Ning Shen, Xuemin Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Windsor Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and non-local mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multi-scale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments. © 2014 IEEE.

关键词： Channel coding

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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

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Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

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IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Scalable Plug-and-Play Robotic Fabrics Based on Kilobot Modules

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IEEE Robotics and Automation Letters 2025年第7期10卷 6832-6839页

作者： Obilikpa, Stanley C. Talamali, Mohamed S. Miyauchi, Genki Oyekan, John Gros, Roderich The University of Sheffield School of Electrical and Electronic Engineering United Kingdom The University of York Department of Computer Science United Kingdom Technical University of Darmstadt Department of Computer Science Germany

This paper presents a framework for producing robotic fabrics using square lattice formations of interlinked Kilobot modules. The framework supports: (i) fabrics of arbitrary size and shape;(ii) different types of deformable links, namely springs and rods;(iii) easy plug-and-play reconfigurability. Two decentralized straight motion controllers are tested with robotic fabrics comprising up to 81 physical modules: an open-loop controller and a controller from the literature that responds to deformations within the fabric. For spring-based robotic fabrics, the deformation-correcting controller performs best overall, whereas for rod-based robotic fabrics, it is outperformed by the open-loop controller. A decentralized turning motion controller is formally derived and examined for either type of fabric, revealing the ability of the robotic fabrics to move along a curved trajectory using open-loop control. Finally, robotic fabrics are shown to perform basic object manipulation tasks. Robotic fabrics that deploy themselves based on distributed, embodied intelligence could pave the way for novel applications, from patching broken pipes to medical uses within the human body. © 2016 IEEE.

关键词： Modular robots

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