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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiyong Zhang Ruyu Liu Chaochao Wang Xianchao Zhang Jianhua Zhang Xiufeng Liu School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets.

关键词： Point cloud compression Visualization Three-dimensional displays Shape Convolution Streaming media Aerodynamics Rendering (computer graphics) Speech processing Streams

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Efficient Denial of Service Attack Detection in IoT using Kolmogorov-Arnold Networks

arXiv

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arXiv 2025年

作者： Kuznetsov, Oleksandr Department of Theoretical and Applied Sciences eCampus University Via Isimbardi 10 CO Novedrate22060 Italy Department of Intelligent Software Systems and Technologies School of Computer Science and Artificial Intelligence V.N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine

The proliferation of Internet of Things (IoT) devices has created a pressing need for efficient security solutions, particularly against Denial of Service (DoS) attacks. While existing detection approaches demonstrate high accuracy, they often require substantial computational resources, making them impractical for IoT deployment. This paper introduces a novel lightweight approach to DoS attack detection based on Kolmogorov-Arnold Networks (KANs). By leveraging spline-based transformations instead of traditional weight matrices, our solution achieves state-of-the-art detection performance while maintaining minimal resource requirements. Experimental evaluation on the CICIDS2017 dataset demonstrates 99.0% detection accuracy with only 0.19 MB memory footprint and 2.00 ms inference time per sample. Compared to existing solutions, KAN reduces memory requirements by up to 98% while maintaining competitive detection rates. The model's linear computational complexity ensures efficient scaling with input size, making it particularly suitable for large-scale IoT deployments. We provide comprehensive performance comparisons with recent approaches and demonstrate effectiveness across various DoS attack patterns. Our solution addresses the critical challenge of implementing sophisticated attack detection on resource-constrained devices, offering a practical approach to enhancing IoT security without compromising computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Network intrusion

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Quantum Error Correction in Green Communications: Towards Sustainable Quantum Networks

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Thakur, Vikram Singh Kumar, Atul Dev, Kapal Department of Electronics Engineering Varanasi221005 India Munster Technological University Department of Computer Science CorkT12 P928 Ireland Department of Institute of Intelligent Systems University of Johannesburg Auckland Park2006 South Africa Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

This work explores the integration of Quantum Error Correction (QEC) techniques within energy-efficient green communication networks, emphasizing their pivotal role in achieving sustainability in next-generation Information and Communication Technology (ICT) systems. Quantum technologies, particularly error correction protocols like surface codes are indispensable for reliable quantum information processing. However, their implementation often incurs substantial energy overhead due to the extensive physical qubit resources required. This research proposes a framework for adapting scalable QEC algorithms, leveraging innovations such as reconfigurable atom arrays, to optimize energy consumption while preserving high fidelity in quantum operations. Surface codes, a cornerstone of QEC, encode logical qubits in two-dimensional lattices of physical qubits, utilizing stabilizer measurements to detect and correct bit-flip and phase-flip errors. The performance of surface codes is influenced by critical parameters such as code distance, which determines fault tolerance, and gate fidelity, which impacts logical error rates. Through a comprehensive analysis of these parameters, this work evaluates the interplay between code distance, qubit array size, and gate fidelity to highlight their collective impact on fault tolerance and computational accuracy. These insights align with global efforts to reduce the environmental footprint of emerging quantum technologies, advancing the principles of green ICT and environmentally conscious innovation. © 2025 IEEE.

关键词： Qubits

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

引用

arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques

arXiv

引用

arXiv 2025年

作者： Jamal, Muhammad Hassan Alazeb, Abdulwahab Bakhsh, Shahid Allah Boulila, Wadii Shah, Syed Aziz Khattak, Aizaz Ahmad Khan, Muhammad Shahbaz Department of Computer Sciences Quaid-i-Azam University Islamabad45320 Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department of Cyber Security National University of Science & Technology Karachi75350 Pakistan College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Centre for Intelligent Healthcare Coventry University CoventryCVl 5FB United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEHlO 5DT United Kingdom

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models. © 2025, CC BY-NC-ND.

关键词： Smoke detectors

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Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

引用

Medical Image Analysis 2025年 103卷 103580页

作者： Manzano-Patrón, J.P. Deistler, Michael Schröder, Cornelius Kypraios, Theodore Gonçalves, Pedro J. Macke, Jakob H. Sotiropoulos, Stamatios N. Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham United Kingdom Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen & Tübingen AI Center Germany School of Mathematical Sciences University of Nottingham United Kingdom Belgium Department of Computer Science and Department of Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI. © 2025 The Authors

关键词： Monte Carlo methods

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

引用

IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

引用

IEEE Transactions on Network science and Engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

arXiv

引用

arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

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REPRESENTATION OF UNCERTAINTY IN computer VISION USING FUZZY-SETS

引用

IEEE TRANSACTIONS ON computerS 1986年第2期35卷 145-156页

作者： HUNTSBERGER, TL RANGARAJAN, C JAYARAMAMURTHY, SN Intelligent Systems Laboratory Department of Computer Science University of South Carolina

Uncertainty in computer vision can arise at various levels. It can occur in the low level in the raw sensor input, and extends all the way through intermediate and higher levels. Ideally, at any level where decisions are being made on the basis of previous processing steps, a computer vision system must have sufficient flexibility for representation of uncertainty in any of these levels.

关键词： computer vision edge detection fuzzy clustering segmentation shape representation

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