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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Succinct Perfect Zero-knowledge for MIP

arXiv

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arXiv 2025年

作者： Fu, Honghao Zhang, Xingjian Concordia Institute for Information Systems Engineering Concordia University Montreal Canada QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Hong Kong

In the recent breakthrough result [MS24], Slofstra and Mastel show that there is a two-player one-round perfect zero-knowledge MIP∗ protocol for RE. We build on their result to show that there exists a succinct two-player one-round perfect zero-knowledge MIP∗ protocol for RE with polylog question size and O(1) answer size, or with O(1) question size and polylog answer size. To prove our result, we analyze the four central compression techniques underlying the MIP∗ = RE proof [JNV+20] — question reduction, oracularization, answer reduction, and parallel repetition — and show that they all preserve the perfect (as well as statistical and computational) zero-knowledge properties of the original protocol. Furthermore, we complete the study of the conversion between constraint-constraint and constraint-variable binary constraint system (BCS) nonlocal games, which provide a quantum information characterization of MIP∗ protocols. While Paddock [Pad22] established that any near-perfect strategy for a constraint-variable game can be mapped to a constraint-constraint version, we prove the converse, fully establishing their equivalence. © 2025, CC0.

关键词： Constraint theory

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LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1098-1111页

作者： Shao, Hao-Chiang Chen, Guan-Yu Lin, Yu-Hsien Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu National Chung Hsing University Institute of Data Science and Information Computing Taichung402202 Taiwan Novatek Microelectronics Corporation Hsinchu300092 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan United Microelectronics Corporation Product Engineering Department Hsinchu300094 Taiwan United Microelectronics Corporation Development of Smart Manufacturing Hsinchu300094 Taiwan

Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on 1) the variation of possible circuit shape deformation estimated by the lithography simulator and 2) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms the previous state-of-the-art methods on real-world data. © 2024 IEEE.

关键词： Integrated circuit layout

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Fast Image matting based on matting pyramids and surrogate models 2

Fast Image matting based on matting pyramids and surrogate m...

引用

2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Liang, Yihui Gou, Hongshan Liu, Guisong Huang, Sibo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Zhongshan China Guiyang Institute of Humanities and Technology College of Big Data and Information Engineering Guiyang China Southwestern University of Finance and Economics School of Computing and Artificial Intelligence Chengdu China Huizhou University Network and Information Centre Huizhou China

ISBN: (纸本)9798350328004

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time. © 2023 IEEE.

关键词： Pixels

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Copy-Move Detection in Optical Microscopy: A Segmentation Network and A dataset

arXiv

引用

arXiv 2024年

作者： Shao, Hao-Chiang Liao, Yuan-Rong Tseng, Tse-Yu Chuo, Yen-Liang Lin, Fong-Yi Institute of Data Science and Information Computing National Chung Hsing University Taiwan SOE Technology Inc. Taiwan Ltd. Taiwan

With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen attacks, rendering standard object-detection-based approaches less effective. To address this, we reformulate the problem of detecting biomedical copy-move forgery regions as an intra-image co-saliency detection task and propose CMSeg-Net, a copy-move forgery segmentation network capable of identifying unseen duplicated areas. Built on a multi-resolution encoder-decoder architecture, CMSeg-Net incorporates self-correlation and correlation-assisted spatial-attention modules to detect intra-image regional similarities within feature tensors at each observation scale. This design helps distinguish even small copy-move targets in complex microscopic images from other similar objects. Furthermore, we created a copy-move forgery dataset of optical microscopic images, named FakeParaEgg, using open data from the ICIP 2022 Challenge to support CMSeg-Net’s development and verify its performance. Extensive experiments demonstrate that our approach outperforms previous state-of-the-art methods on the FakeParaEgg dataset and other open copy-move detection datasets, including CASIA-CMFD, CoMoFoD, and CMF. The FakeParaEgg dataset, our source code, and the CMF dataset with our manually defined segmentation ground truths are available at https://***/YoursEver/FakeParaEgg. © 2024, CC BY.

关键词： Image segmentation

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Frequency Diverse Array-enabled RIS-aided Integrated Sensing and Communication

引用

IEEE Transactions on Wireless Communications 2025年

作者： Yang, Hanyu Gong, Shiqi Liu, Heng Xing, Chengwen Zhao, Nan Niyato, Dusit Beijing Institute of Technology School of Information and Electronics Beijing100081 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Integrated sensing and communication (ISAC) has been envisioned as a prospective technology to enable ubiquitous sensing and communications in next-generation wireless networks. In contrast to existing works on reconfigurable intelligent surface (RIS) aided ISAC systems using conventional phased arrays (PAs), this paper investigates a frequency diverse array (FDA)-enabled RIS-aided ISAC system, where the FDA aims to provide a distance-angle-dependent beampattern to effectively suppress the clutter, and RIS is employed to establish high-quality links between the BS and users/target. We aim to maximize sum rate by jointly optimizing the BS transmit beamforming vectors, the covariance matrix of the dedicated radar signal, the RIS phase shift matrix, the FDA frequency offsets and the radar receive equalizer, while guaranteeing the required signal-to-clutter-plus-noise ratio (SCNR) of the radar echo signal. To tackle this challenging problem, we first theoretically prove that the dedicated radar signal is unnecessary for enhancing target sensing performance, based on which the original problem is much simplified. Then, we turn our attention to the single-user single-target (SUST) scenario to demonstrate that the FDA-RIS-aided ISAC system always achieves a higher SCNR than its PA-RIS-aided counterpart. Moreover, it is revealed that the SCNR increment exhibits linear growth with the BS transmit power and the number of BS receive antennas. In order to effectively solve this simplified problem, we leverage the fractional programming (FP) theory and subsequently develop an efficient alternating optimization (AO) algorithm based on symmetric alternating direction method of multipliers (SADMM) and successive convex approximation (SCA) techniques. Numerical results demonstrate the superior performance of our proposed algorithm in terms of sum rate and radar SCNR. © 2002-2012 IEEE.

关键词： Beam forming networks

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control

Over-the-Air Federated Edge Learning with Error-Feedback One...

引用

International Conference on Computer and Communications (ICCC)

作者： Yuding Liu Dongzhu Liu Richeng Jin Guangxu Zhu Qingjiang Shi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Computing Science University of Glasgow Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. The existing one-bit digital over-the-air aggregation scheme features one-bit gradient quantization at edge devices and majority voting-based decoding at edge server, and therefore offers enhanced communication efficiency. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation, while the aggregation errors caused by channel fading in Air-FEEL further impair the performance. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) with an optimized power control policy. To this end, we first derive the impact of aggregation errors on the convergence of FEEL with EFOBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The analytical results and optimized power control policy are validated by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the vanilla one-bit quantization scheme without error feedback or optimized power control policy.

关键词： Performance evaluation Fading channels Training Quantization (signal) Wireless networks Atmospheric modeling Power control Channel estimation Training data Convergence

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

arXiv

引用

arXiv 2024年

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle routing

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System 1

Development of a Sensor-Based Wearable Fetal and Maternal He...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Jayalath, Yugashi Maduranga, M.W.P. Tilwari, Valmik Wijayarathna, Wmsrb Wanniarachchi, Waam Sharma, Debashree School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Sri Lanka Technology Campus Faculty of Computing and IT Department of Data Science Padukka Sri Lanka Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350364590

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device. © 2024 IEEE.

关键词： Electronic health record

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