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Proceedings of the 2025 ACM International Conference on Interactive Media Experiences

作者： Ville Mäkelä Kartik Chinda Irtika Khan Stratford School of Interaction Design and Business University of Waterloo Waterloo Ontario Canada Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Computer Engineering Ramrao Adik Institute of Technology Dr. D.Y Patil University Mumbai Maharashtra India

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Low-error Reconstruction of Directional Functions with Spherical Harmonics

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Vlnas, Michal Milet, Tomas Zemcik, Pavel Brno University of Technology Department of Computer Graphics and Multimedia Faculty of Information Technology Czech Republic Lappeenranta-Lahti University of Technology School of Engineering Science Lappeenranta 53850 Finland

This paper proposes a novel approach for the low-error reconstruction of directional functions with spherical harmonics. We introduce a modified version of Spherical Gaussians with adaptive narrowness and amplitude to represent the input data in an intermediate form. This representation is then projected into spherical harmonics using a closed-form analytical solution. Because of the spectral properties of the proposed representation, the amount of ringing artifacts is reduced, and the overall precision of the reconstructed function is improved. The proposed method is more precise comparing to existing methods. The presented solution can be used in several graphical applications, as discussed in this paper. For example, the method is suitable for sparse models such as indirect illumination or reflectance functions. © 1995-2012 IEEE.

关键词： directional functions light models low-error reconstruction ringing spherical harmonics spherical radial basis functions visualization

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FRACTIONAL DISCRETE HELLY FOR PAIRS IN A FAMILY OF BOXES

arXiv

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arXiv 2025年

作者： Eom, Taehyun Kim, Minki Lee, Eon R&Bd Foundations Chonnam National University Gwangju Korea Republic of Department of Mathematical Sciences Gwangju Institute of Science and Technology Gwangju Korea Republic of School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of

Given a point set S in Rd, a family of sets is S-intersecting if its members have a point in common in S. Recently, Edwards and Soberón proved a fractional version of Halman’s theorem for axis-parallel boxes, showing that every finite family F of axis-parallel boxes in Rd with positive density of S-intersecting (d + 1)-tuples contains an S-intersecting subfamily of size linear in |F|. We prove that qualitatively the same conclusion can be achieved if the density of S-intersecting pairs is sufficiently *** Codes 52A35 Copyright © 2025, The Authors. All rights reserved.

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Rotatable Antenna Enabled Wireless Communication System with Visual Recognition: A Prototype Implementation

arXiv

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arXiv 2025年

作者： Dai, Liang Zheng, Beixiong Tan, Yanhua Zhu, Lipeng Chen, Fangjiong Zhang, Rui School of Microelectronics South China University of Technology Guangzhou511442 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Rotatable antenna (RA) is an emerging technology that has great potential to exploit additional spatial degrees of freedom (DoFs) by flexibly altering the three-dimensional (3D) orientation/boresight of each antenna. In this demonstration, we present a prototype of the RA-enabled wireless communication system with a visual recognition module to evaluate the performance gains provided by the RA in practical environments. In particular, a mechanically-driven RA is developed by integrating a digital servo motor, a directional antenna, and a microcontroller, which enables the dynamic adjustment of the RA orientation. Moreover, the orientation adjustment of the RA is guided by the user’s direction information provided by the visual recognition module, thereby significantly enhancing system response speed and self-orientation accuracy. The experimental results demonstrate that the RA-enabled communication system achieves significant improvement in communication coverage performance compared to the conventional fixed antenna system. Copyright © 2025, The Authors. All rights reserved.

关键词： Servomotors

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 34

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Backdoor Attacks Federated Graph Learning

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DRacv: Detecting and auto-repairing vulnerabilities in role-based access control in web application

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Journal of Network and computer Applications 2025年 240卷

作者： Xu, Ke Zhang, Bing Li, Jingyue He, Haitao Ren, Rong Ren, Jiadong School of Information Science and Engineering Yanshan University No. 438 West Section of Hebei Street Hebei Qinhuangdao06600 China The Department of Computer science Norwegian University of Science and Technology Trondheim 7030 Norway The Key Laboratory for Software Engineering of Hebei Province Yanshan University Hebei Qinhuangdao06600 China

Traditional methods for analyzing Broken Access Control (BAC) vulnerabilities have limitations regarding low coverage of access control rules, high false positive rate (FPR). Additionally, state-of-the-art strategies for repairing BAC vulnerabilities utilizing statement-level replacement as a repair method may introduce new logical errors. To address these challenges, we propose a novel approach called DRacv (Detect and Repair Access Control Vulnerabilities) to identify and auto-repair vulnerabilities in Role-Based Access Control (RBAC) mode used in web applications. To detect vulnerabilities, DRacv first constructs a Fine-grained Global Multi-attribute Architectural Navigation Graph model (FG-MANG) for web applications through dynamic execution and static analysis, which characterizes full relationships between roles, privileges, and accessible page resources. Based on access control rules extracted from FG-MANG, DRacv generates targeted attack payloads to detect BAC vulnerabilities, significantly reducing FPR and eliminating redundant attack payloads. To auto-repair the identified vulnerabilities, DRacv first precisely extracts access control privilege parameters, validation functions, and contextual statements to construct the patch code templates. These templates generate user- and role-level verification patch codes for different users and roles. Instead of changing the vulnerable code, the patch codes behave like firewalls. They are added as separate files and invoked by the web page with vulnerability to defend against access control compromises. DRacv was evaluated on 12 popular open-source web applications in PHP and JAVA. From the applications, DRacv identified 35 vulnerabilities (11 were new) with only one false positive, achieving an FPR of 2.78%. We also compared DRacv's detection results with state-of-the-art studies. Results show that DRacv outperforms those studies regarding the number of vulnerabilities detected and FPR. Among the 35 vulnerabilities

关键词： Open source software

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Multi-Scale Retinex Unfolding Network for Low-Light Image Enhancement

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IEEE Transactions on Multimedia 2025年

作者： Wang, Huake Hou, Xingsong Li, Jutao Yan, Yadi Sun, Wenke Zeng, Xin Zhang, Kaibing Cao, Xiangyong Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Xi'an Jiaotong University Department of Biomedical Engineering School of Life Science and Technology Xi'an710049 China CRRC Qingdao Sifang Rolling Stock Research Institute Co. Ltd. Qingdao266000 China Zhengzhou Vocational College of Finance And Taxation Zhengzhou450000 China Xi'an Polytechnic University Shaanxi Key Laboratory of Clothing Intelligence the School of Computer Science Xi'an710048 China Xi'an Jiaotong University School of Computer Science and Technology Xi'an710049 China

Retinex theory-based low-light image enhancement methods have received increasing attention and achieved tremendous advancements. However, there still exist two seldom-explored issues: 1) The above methods only formally simulate the Retinex decomposition, resulting in lacking explicit interpretability. 2) They usually are performed in single-scale space, leading to suboptimal enhancement results. In this paper, we propose an interpretable Multi-scale Retinex Unfolding Network (MRUNet) for low-light image enhancement, which can tackle both of the aforementioned issues simultaneously. Specifically, we formulate low-light image enhancement as a multi-scale Retinex optimization problem and design an iteration minimization solution to solve it. The optimization solution is further unfolded to fabricate MRUNet, which is empowered with clear physical significance and multi-scale prior knowledge in favor of image enhancement. However, it will aggravate model size and efficiency when exploiting multiple proximal mapping networks to extract multi-scale prior from multi-scale inputs. To surmount the issue, we propose a Scale-Aware Proximal mapping Module (SAPM), which efficiently collect multi-scale prior knowledge via the weight sharing strategy. In SAPM, we tailor a scale-aware transformer to model the specific scale-similarity among different scales. Extensive experiments manifest that MRUNet surpasses other Retinex-based low-light image enhancement methods on multiple benchmarks. Our code and pretrained model can be found in https://***/WHK-Huake/MRUNet. © 2025 IEEE.

关键词： Mapping

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Multi-Agent Collaboration Mechanisms: A Survey of LLMs

arXiv

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arXiv 2025年

作者： Tran, Khanh-Tung Dao, Dung Nguyen, Minh-Duong Pham, Quoc-Viet O’Sullivan, Barry Nguyen, Hoang D. School of Computer Science and Information Technology University College Cork Cork Ireland Department of Information Convergence Engineering Pusan National University Busan Korea Republic of School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02PN40 Ireland

With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence. Copyright © 2025, The Authors. All rights reserved.

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Design of High Isolation and Low Actuation Voltage Micro Cantilever RF-MEMS Switch for Enhanced Performance in mm-Wave Applications

Design of High Isolation and Low Actuation Voltage Micro Can...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Farid Khamoueitouli Javad Yavandhasani Murat Kaya Yapici School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Engineering and Natural Sciences Sabanci University Istanbul Türkiye Department of Electrical and Computer Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

One of the significant challenges in advancing RFMEMS switch technology is achieving low actuation voltage and high capacitance ratio, simultaneously. Reducing the structural spring constant and integrating Metal-Insulator-Metal (MIM) capacitors have been recognized as optimal strategies for lowering actuation voltage; while significantly improving the capacitance ratio in RF-MEMS switches. This study aims to design and development of an RF-MEMS shunt switch in which a cantilever microbeam with a MIM capacitor is integrated to enhance the RF performance in mm-wave frequency band. Simulation using COMSOL®, revealed that the actuation voltage, von mises stress, first resonance frequency mode, switch mass, and the spring constant of the designed switch is about $2.3 ~\mathrm{V}, 5.16 \text{MPa}, 1352 ~\text{Hz}$ , $1.62 \mu ~\mathrm{g}$ , and $0.017 ~\mathrm{N}. \mathrm{m}^{-1}$ , respectively; which are promising results. The switch's performance was evaluated across a wide frequency range from 1 to 100 GHz using ANSYS HFSS® simulation The simulation results indicate excellent radio frequency characteristics, i.e. isolation of -52 dB across the frequency range of 70 GHz to 75 GHz, with an insertion loss of -1 dB at 73 GHz and capacitance ratio of 142. These results highlight the favorable RF performance of the proposed switch, which is suitable for next-generation mm-wave applications.

关键词： Radio frequency Resonant frequency Switches Voltage Insertion loss Capacitance MIM capacitors Resonance Springs Stress

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation Measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms Grid computing Mathematical models Robustness Faces

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