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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68555 条记录，以下是131-140 订阅

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

引用

18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Blockchain-Enabled Medical Supply Chain Architecture for Rapid Response in Hajj 2nd

Blockchain-Enabled Medical Supply Chain Architecture for Rap...

引用

2nd international conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Albalwy, Faisal Department of Cybersecurity College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

ISBN: (纸本)9789819776023

The annual Hajj pilgrimage presents a unique logistical challenge for healthcare institutions, with the need to efficiently manage medical supplies for millions of participants. Traditional methods often fall short in transparency, efficiency, and rapid response. This paper proposes a novel Blockchain-Enabled Medical Supply Chain Architecture for Rapid Response in Hajj, a framework specifically tailored to address the special requirements of this large-scale event. Leveraging the decentralized nature of blockchain technology, this solution integrates smart contracts, Internet of Things (IoT) sensors, and mobile applications to automate supply chain operations and enable real-time monitoring. The proposed architecture emphasizes secure data management, transparent tracking, and automation, facilitating quick reactions to medical emergencies. Future work involves the implementation and comprehensive evaluation of this architecture, with a focus on real-world efficiency, responsiveness, and alignment with healthcare regulations. By modernizing the Hajj medical supply chain, this innovation aims to enhance pilgrim safety and set a benchmark for large-scale event healthcare logistics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Supply chain management

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Dilated convolution model for lightweight neural network 2nd

Dilated convolution model for lightweight neural network

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Gupta, Sachin Goyal, Priya Vyas, Bhuman Shabaz, Mohammad Bala, Suchitra Sameen, Aws Zuhair Department of CSE Maharaja Agrasen Institute of Technology India Department Computer Science and Engineering Manipal University Jaipur India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts And Science College India College of Medical Techniques Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9781032738659

Lightweight convolutional neural networks are being studied further in order to better deploy deep convolutional neural networks to edge devices, minimize the number of model parameters in deep neural networks, and reduce network complexity. On accomplish the lightweight goal, the full convolution is performed to the remaining network structure. To increase the light effect, it is merged with point-by-point convolution to build an improved dilated convolution lightweight approach. The enhanced dilated convolution is coupled with conventional convolution to lessen the attenuation of accuracy, and a lightweight fusion dilated convolution is presented. The improved dilated convolution light approach offers the most substantial lightweight benefit, according to the data. Furthermore, the lightweight fusion dilated convolution approach decreases the amount of model parameters and offers the best speed-accuracy trade-off. © 2025 The Author(s).

关键词： Convolution

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Event-Triggered Secondary Control Strategy of DC Microgrid in Islanded Mode 26

Event-Triggered Secondary Control Strategy of DC Microgrid i...

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26th international conference on Industrial technology, ICIT 2025

作者： Li, Haibin Luo, Wensheng Li, Haofei Jin, Zhongqi Li, Chaoyi School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331521950

In recent years, with the introduction and widespread application of distributed generation technology, the importance of microgrids has continued to increase. One of the current research directions is how to improve the control strategies of microgrids. Traditional microgrid control strategies use droop control, which also faces issues of voltage deviation and current equalization deviation. This paper addresses these issues by proposing a distributed secondary control based on event-triggering to compensate for them. Only at the event-triggering moments, the estimated voltage values are calculated to replace the actual values, reducing communication between converters and voltage deviation among converters. This paper validates through simulation that the event-triggered distributed secondary control strategy effectively ensures stable output voltage and reduces the output current deviation problem. © 2025 IEEE.

关键词： Distributed parameter control systems

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Research on marine litter identification model based on convolutional neural network 24

Research on marine litter identification model based on conv...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Huang, Xin Zheng, Zhiguo Liu, Qianyu College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The problem of marine litter is becoming more and more serious, which poses a great threat to the marine ecological environment. The research of marine litter identification and classification problem is imminent, and the identification and classification of images are especially critical. The advantage of convolutional neural network, one of the deep learning models, lies in its ability to automatically learn the features in the image, thus improving the accuracy of image classification. Therefore, the study of marine litter recognition model based on convolutional neural network is of some significance. Collecting and pre-processing multiple types of marine litter image data, extracting features in terms of colour, shape and texture, constructing a marine litter recognition model based on convolutional neural network, training and analysing the comparison results, effectively verifying the accuracy and precision of the model. The proposed model has high accuracy and stability in marine litter identification, which can provide effective technical support for marine environmental protection as well as a technical basis for the protection of marine ecosystems. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Unraveling Urban Traffic Congestion Patterns in Bangladesh 11

Unraveling Urban Traffic Congestion Patterns in Bangladesh

引用

11th international conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Hasan, Md. Babul Sarker, Manash Faculty of Computer Sc. and Engr. Patuakhali Sc. and Tech. University Dumki Patuakhali Bangladesh Institute of Information Technology University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789897587450

This research presents a comprehensive study on divisional traffic analysis and clustering in Bangladesh, leveraging Google Maps and image processing techniques for traffic intensity data collection across all divisions from January 2023 to June 2023. A total of 1,39,008 snapshots were captured at 15-minute intervals, yielding a detailed traffic dataset. We conducted an in-depth analysis of the collected time series data, focusing on its decomposition into trend, seasonal, and random components (Y = T * S * R). To enhance clustering accuracy, we proposed a modification technique by dividing traffic intensity (Y) by the random fluctuations (R) to minimize random noise in the data preprocessing stage. We implemented Modified Hierarchical Clustering with Dynamic Time Warping (DTW) for clustering, demonstrating superior similarities-pattern extraction compared to traditional hierarchical clustering. Our results identified four distinct traffic clusters. This study provides insights into regional traffic behaviors and offers a robust approach to clustering traffic data, contributing to Bangladesh’s more effective traffic management strategies. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Information management

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***: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification 31

***: Infusing Large Language Models with Causal Knowledge fo...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Cai, Ruichu Yu, Shengyin Zhang, Jiahao Chen, Wei Xu, Boyan Zhang, Keli School of Computer Science Guangdong University of Technology China Huawei Noah's Ark Lab China

ISBN: (纸本)9798891761964

Despite the demonstrated potential of large language models (LLMs) in diverse NLP tasks, their causal reasoning capability appears inadequate when evaluated within the context of the Event Causality Identification (ECI) task. The ECI tasks pose significant complexity for LLMs and necessitate comprehensive causal priors for accurate identification. To improve the performance of LLMs for causal reasoning, we propose a multi-agent Decomposed reasoning framework for Event Causality Identification, designated as ***. In the discovery stage, *** incorporates specialized agents such as Causal Explorer and Mediator Detector, which capture implicit causality and indirect causality more effectively. In the reasoning stage, *** introduces the agents Direct Reasoner and Indirect Reasoner, which leverage the knowledge of the generalized causal structure specific to the ECI. Extensive evaluations demonstrate the state-of-the-art performance of *** compared with baselines based on LLMs and supervised training. Our implementation will be open-sourced at https://***/DMIRLAB-Group/***. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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TDMF: Text-Guided Denoising and Interactive Medical Image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 IEEE.

关键词： denoising Medical image fusion text-guided fusion

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Power allocation and voltage control strategies for islanded microgrids 24

Power allocation and voltage control strategies for islanded...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Zhang, Shuai Zhang, Jihong Zhang, Xin Inner Mongolia University of Science and Technology Inner Mongolia Autonomous Region China

ISBN: (纸本)9798400710681

Addressing the bus voltage stabilization issue arising from renewable energy fluctuation and load stochasticity in DC microgrids, we have developed an advanced hybrid energy storage dual-loop control strategy. This strategy integrates predictive control with feed-forward regulation to significantly enhance system performance. In the design of this strategy, a fuzzy PI controller is employed to establish the outer power loop, complemented by a current inner loop without differential beat control, ensuring precise and stable control. The strategy meticulously divides PV output and load variations into average power, error power, and transient power components. By leveraging the unique differentiated capabilities of the hybrid energy storage system, these power components are precisely compensated for, achieving stable bus voltage. To verify the effectiveness of this strategy, MATLAB/Simulink simulation experiments were conducted. The experimental results demonstrate that this strategy excels in balancing bus voltage, optimizing the distribution of stored power, and enhancing the overall energy utilization of the system. Consequently, it not only meets national standards for voltage fluctuation but also ensures robust and reliable system operation. The successful application of this strategy undoubtedly provides powerful technical support and guarantee for the stable operation of DC microgrids. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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