检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Lou, Cuijuan Zhou, Quan Li, Ranyang Liu, Wufeng Li, Yan Yuchi, Ming Ding, Mingyue Key Laboratory of Grain Information Processing and Control Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Department of Automation School of Automation Wuhan University of Technology Wuhan430070 China Department of Biomedical Engineering School of Life Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education Medical Ultrasound Laboratory Huazhong University of Science and Technology Wuhan430074 China

Noise suppression and resolution improvement are important issues in high-echo target ultrasound imaging detection. To address this problem, this paper proposes a phased array focused ultrasound imaging method based on an adaptive spectral broadening filter. The proposed method first standardizes the received signals after fixed-point transmission by subtracting the mean of the received aperture signals and dividing by the standard deviation of the received aperture signals. Then, spectralbroadening was achieved through covariance and temporal autocorrelation coefficient calculations. Finally, the variance factor proposed for the first time is calculated as a weight from standardized received data after fixed-point transmission focusing to obtain the final scanline data. To evaluate the performance of the proposed method in high-echo target ultrasound imaging detection, experimental phantom images and two views of the interventricular septum including the parasternal long-axisviews and the apical four-chamber views, are reconstructed. In three scenarios, some finer structures of the targets in the images by the proposed method can be more easily identified compared to delay and sum (DAS), square-neighborhood robust signal mean-to-standard-deviation factor (SN-RSMSF), and coherence factor beamforming (CF). For the experimental phantom data, the image resolution measured by -20dB mainlobe width of the proposed method can reach 14.2 percents of DAS, 18.3 percents of SN-RSMSF and 18.3 percents of CF. The proposed method also outperforms the other three methods in terms of noise suppression. The proposed method can be used to detect diseases that involve high-echo targets such as stones, heart valves, and calcifications. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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Access control of blockchain based on dual-policy attribute-based encryption 22

Access control of blockchain based on dual-policy attribute-...

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22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Han, Daojun Chen, Jinyu Zhang, Lei Shen, Yatian Wang, Xueheng Gao, Yihua Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728176499

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through 'pseudo-Anonymity'. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center;instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally,

关键词： Blockchain

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Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks

arXiv

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arXiv 2022年

作者： Lu, Ziyi Yu, Na Wang, Xuehe The School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Leveraging Contaminated datasets to Learn Clean-data Distribution with Purified Generative Adversarial Networks

arXiv

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arXiv 2023年

作者： Tian, Bowen Su, Qinliang Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China

Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many variants of GAN have been proposed. However, existing GANs are almost established on the assumption that the training dataset is clean. But in many real-world applications, this may not hold, that is, the training dataset may be contaminated by a proportion of undesired instances. When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution). To learn the target distribution from contaminated datasets, two purified generative adversarial networks (PuriGAN) are developed, in which the discriminators are augmented with the capability to distinguish between target and contaminated instances by leveraging an extra dataset solely composed of contamination instances. We prove that under some mild conditions, the proposed PuriGANs are guaranteed to converge to the distribution of desired instances. Experimental results on several datasets demonstrate that the proposed PuriGANs are able to generate much better images from the desired distribution than comparable baselines when trained on contaminated datasets. In addition, we also demonstrate the usefulness of PuriGAN on downstream applications by applying it to the tasks of semi-supervised anomaly detection on contaminated datasets and PU-learning. Experimental results show that PuriGAN is able to deliver the best performance over comparable baselines on both tasks1 Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment

arXiv

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arXiv 2023年

作者： Liu, Jiahao Wu, Jiang Chen, Jinyu Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others’ models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Cross Initialization for Personalized Text-to-Image Generation

arXiv

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arXiv 2023年

作者： Pang, Lianyu Yin, Jian Xie, Haoran Wang, Qiping Li, Qing Mao, Xudong Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China Linnan University Hong Kong East China Normal University China The Hong Kong Polytechnic University Hong Kong

Recently, there has been a surge in face personalization techniques, benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these, a notable method is Textual Inversion, which generates personalized images by inverting given images into textual embeddings. However, methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study, we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods, we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting, thereby compromising the editability. Driven by this observation, we introduce a novel initialization method, termed Cross Initialization, that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5,000 to 320. Furthermore, we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization, this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably, in our experiments, Cross Initialization is the only method that successfully edits an individual’s facial expression. Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability. Code will be available at https://***/lyuPang/CrossInitialization. Copyright © 2023, The Authors. All rights

关键词： Embeddings

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BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

arXiv

引用

arXiv 2023年

作者： Yang, Yunchao Zhou, Yipeng Hu, Miao Wu, Di Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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'Pre-train, prompt' Framework to Boost Graph Neural Networks Performance in EEG analysis

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Cui, Can-Ming Chen, Hong-Yi Chen, Man-Sheng Li, Jiahong Tong, Zhaopeng Fang, Chanmei Wang, Chang-Dong Cai, Yuexin Department of Otolaryngology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangdong Guangzhou510000 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510000 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Electroencephalography (EEG) is a vital noninvasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex nonEuclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a 'pre-train, prompt' framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the 'pre-train, prompt' paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited. © 2013 IEEE.

关键词： Contrastive Learning

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Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

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