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SSRN 2023年

作者： Zhuang, Tianming Qin, Zhiguang You, Li Deng, Erqiang Ding, Yi Cao, Mingsheng Guo, Yingkun Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China Department of Molecular Genetics Erasmus University Medical Center Rotterdam Netherlands Department of Radiology Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects Ministry of Education West China Second University Hospital Sichuan University Sichuan Chengdu610072 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

The electrocardiogram diagnosis played an important role in early arrhythmia prevention and cardiovascular disease detection. How to analysis and detect the electrocardiogram automatically becomes a challenging task in clinical practice. Convolutional neural network based approaches have been widely applied in the field of automatic arrhythmia detection and analysis. However, the existing methods fails to preserve the abundant and essential latent representatives from multiple dimensions. To address these problems, a dual spatial-channel aggregation module is proposed, which expects to concentrate on the prominent target information along spatial and channel dimension of electrocardiogram signals. Moreover, a multi-scale enhancement module with channel-wise gating mechanism is also proposed to enlarge the receptive resolution and further to improve the learning ability of long-range dependencies from the global information. Furthermore, these two proposed modules are integrated as a "plug-and-play" block that can be easily adopted as a drop-in replacement for any 3×3 convolution layers in convolutional neural networ based architectures. In order to evaluate the effectiveness and generalization of the proposed block, it is plugged in a series of convolutional neural network based frameworks and are evaluated on two benchmark datasets, MIT-BIH and PTB. The extensive experimental results show that the proposed "plug-and-play" block can significantly improve the arrhythmia classification performance. Moreover, compared to other state-of-the-art methods, the framework plugged with the proposed block can achieve a competitive classification performance, proving the feasibility and advancement of the proposed dual attention-based multiscale feature learning block. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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Vertical Federated Learning: Challenges, Methodologies and Experiments

arXiv

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arXiv 2022年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Wei, Sha Wu, Fan Chen, Guihai Ranbaduge, Thilina School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Ministry of Education China Data61 CSIRO SydneyNSW2015 Australia Beijing100191 China The Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, as well as the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different types of clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems from four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Ar-Manet: A Low-Quality Image Restoration Method Based on Multi-Feature Fusion

SSRN

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SSRN 2024年

作者： Zhang, Yugui Zhou, Qi Jia, Tingting Ke, Zunwang Jia, YiZhen Wang, Gang Yang, Guang Du, Minghua Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Hunan RAMON Science & Technology Co. Ltd. Changsha410138 China Department of Stomatology The First Medical Centre Chinese PLA General Hospital Beijing100853 China School of Software Xinjiang University Urumqi830091 China School of Computing and Data Engineering NingboTech University Ningbo315100 China National Heart and Lung Institute Imperial CollegeLondon LondonSW7 2BX United Kingdom The Royal Brompton Hospital LondonSW3 6NP United Kingdom Medical School of Chinese PLA China

Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, taking care of both overexposure and underexposure problems. On this basis, we improve the U-Net model by introducing the Strengthen-Operate-Subtract (SOS) feature enhancement module and a multi-scale aggregate back-projection module to achieve finer image restoration and improve the propagation of feature information. In addition, we introduce an atrous residual dense block at the highest scale to expand the receptive field of the network and efficiently capture local information to improve the quality of the exposure-corrected images. These innovations significantly optimize the effect of image exposure correction and improve the problems of loss of image details, oversaturation of regional contrast, and imbalance of color naturalness in existing techniques. Through extensive and in-depth analytical evaluations, we have validated the effectiveness of our model, which also demonstrates excellent performance compared with the most recent methods on the test database. © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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Algorithms for Multi-Functional Complex Network Model

Algorithms for Multi-Functional Complex Network Model

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IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Min Yuan Sheng Bin Chi-Cheng Chen Gengxin Sun Qingdao University School of Data Science and Software Engineering No.308 Ningxia Rd Qingdao Shandong China

ISBN: (数字)9781728187129

ISBN: (纸本)9781728187136

Different from previous complex network models based on graph theory, multi-functional complex network model (MFCN) only uses nodes to represent, and the content of graph theory is no longer applicable in it. In order to better apply MFCN, the storage structure, construction algorithm and traversal algorithm are defined and verified in the paper. The advantages of MFCN over the previous complex network models in constructing network are discussed.

关键词： Complex networks Economics Graph theory Conferences Biomedical engineering Medical services Sustainable development

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Research on Robustness of Coupling Networks Based on Multi-subnet Composite Complex Network Model

Research on Robustness of Coupling Networks Based on Multi-s...

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IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Ning Jia Gengxin Sun Chi-Cheng Chen Sheng Bin Qingdao University School of Data Science and Software Engineering No.308 Ningxia Rd Qingdao Shandong China

ISBN: (数字)9781728187129

ISBN: (纸本)9781728187136

In order to solve the cascading failure problem of real networks, it is important to study the robustness of coupled networks. In this paper, a multi-subnet complex complex network model is used to build a cascading failure model on the coupled network. The influence of network size, adjustable parameters, and coupling factors on the robustness of the coupled network is mainly studied. Research shows that when both subnets are BA networks, the greater the contribution of the coupling relationship to the initial load of the nodes, and adopt heterogeneous coupling, networks will more robust.

关键词： Couplings Robustness Power system faults Power system protection Load modeling Medical services Conferences

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Matrix Decomposition Recommendation Algorithm Based on Multiple Social Relationships

Matrix Decomposition Recommendation Algorithm Based on Multi...

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IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Cuijuan Gong Gengxin Sun Chi-Cheng Chen Sheng Bin Qingdao University School of Data Science and Software Engineering No.308 Ningxia Rd Qingdao Shandong China

ISBN: (数字)9781728187129

ISBN: (纸本)9781728187136

A real society has multiple social relationships between users, but existing social network recommendation algorithms often only introduce a social relationship into the recommendation system. This paper introduces a variety of social relationships into the recommendation system based on a multi-subnet composite complex network model. Based on the analysis of the experimental results on the Epinions dataset, a recommendation algorithm introducing multiple social relationships has a significantly higher recommendation accuracy than a recommendation algorithm.

关键词： Matrix decomposition Social network services Collaboration Filtering Conferences Biomedical engineering Medical services

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

引用

作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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A Geometric Strategy Algorithm for Orthogonal Pro jection onto a Parametric Surface

引用

Journal of Computer science & Technology 2019年第6期34卷 1279-1293页

作者： Xiaowu Li Zhinan Wu Feng Pan Juan Liang Jiafeng Zhang Linke Hou College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematics and Computer Science Yichun UniversityYichun 336000China School of Software Engineering South China University of TechnologyGuangzhou 510006China Department of Science Taiyuan Institute of TechnologyTaiyuan 030008China Center for Economic Research Shandong UniversityJinan 250100China

In this paper, we investigate how to compute the minimum distance between a point and a parametric surface, and then to return the nearest point (foot point) on the surface as well as its corresponding parameter, which is also called the point projection problem of a parametric surface. The geometric strategy algorithm (hereafter GSA) presented consists of two parts as follows. The normal curvature to a given parametric surface is used to find the corresponding foot point firstly, and then the Taylor's expansion of the parametric surface is employed to compute parameter increments and to get the iteration formula to calculate the orthogonal projection point of test point to the parametric surface. Our geometric strategy algorithm is essentially dependent on the geometric property of the normal curvature, and performs better than existing methods in two ways. Firstly, GSA converges faster than existing methods, such as the method to turn the problem into a root-finding of nonlinear system, subdividing methods, clipping methods, geometric methods (tangent vector and geometric curvature) and hybrid second-order method, etc. Specially, it converges faster than the classical Newton's iterative method. Secondly, GSA is independent of the initial iterative value, which we prove in Theorem 1. Many numerical examples confirm GSA's robustness and efficiency.

关键词： point projection problem point inversion problem normal curvature normal curvature sphere convergence analysis

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