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40th IEEE Conference on Computer Communications, INFOCOM 2021

作者： Zhong, Zhicong Zhou, Yipeng Wu, Di Chen, Xu Chen, Min Li, Chao Sheng, Quan Z. Sun Yat-Sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Department of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Tencent Technology Co. Ltd Shenzhen China

ISBN: (纸本)9780738112817

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple PSes. However, it is unclear whether PFL can really achieve a faster convergence rate or not. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we conduct theoretical analysis on the convergence rate of P-FedAvg, and derive the optimal weights for each PS to mix parameters with its neighbors. We also examine how the overlay topology formed by PSes affects the convergence rate and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly improve convergence rates than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems. © 2021 IEEE.

关键词： Network topology

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CATIRI:An Efficient Method for Content-and-Text Based Image Retrieval

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Journal of Computer Science & Technology 2019年第2期34卷 287-304页

作者： Mengqi Zeng Bin Yao Zhi-Jie Wang Yanyan Shen Feifei Li Jianfeng Zhang Hao Lin Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871 China School of Computing University of Utah Salt Lake City 84112 U.S.A. Alibaba Group Hangzhou 311121 China

The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods,and thus it has attracted much attention *** retrieval based on such a combination is usually called the content-and-text based image retrieval(CTBIR).Nevertheless,existing studies in CTBIR mainly make efforts on improving the retrieval *** the best of our knowledge,little attention has been focused on how to enhance the retrieval ***,image data is widespread and expanding rapidly in our daily ***,it is important and interesting to investigate the retrieval *** this end,this paper presents an efficient image retrieval method named CATIRI(content-and-text based image retrieval using indexing).CATIRI follows a three-phase solution framework that develops a new indexing structure called *** MHIM-tree seamlessly integrates several elements including Manhattan Hashing,Inverted index,and *** use our MHIM-tree wisely in the query,we present a set of important metrics and reveal their inherent *** on them,we develop a top-k query algorithm for *** results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.

关键词： image retrieval text-and-visual feature indexing top-k

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Corrigendum to “Classification of transgenic corn varieties using terahertz spectroscopy and convolutional neural network” [J. Food Compos. Anal. 145 (2025) 107771]

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Journal of Food Composition and analysis 2025年

作者： Yuying Jiang Xixi Wen Hongyi Ge Guangming Li Hao Chen Mengdie Jiang Qingcheng Sun Shilei Wei Peng Li Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Ma, Youzhong Zhang, Ruiling Zhang, Yongxin School of Information and Technology Luoyang Normal University Luoyang471934 China Key Laboratory of E-commerce Big Data Processing and Analysis of Henan Province Luoyang471934 China

ISBN: (纸本)9783030309510

Top-k similarity join on high-dimensional data plays an important role in many applications. The traditional tree-like index based approaches can’t deal with large scale high-dimensional data efficiently because of "curse of dimensionality". So in this paper, we firstly propose an approach to construct the similarity distribution histogram using stratified sampling method, then to estimate the similarity threshold according to the number of the required returned results, finally we propose a novel Top-k similarity join algorithm based on similarity distribution histogram. We conduct comprehensive experiments and the experimental results show that our proposed approaches has good efficiency and scalability. © 2019, Springer Nature Switzerland AG.

关键词： Graphic methods

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Classification of transgenic corn varieties using terahertz spectroscopy and convolutional neural network

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Journal of Food Composition and analysis 2025年 145卷

作者： Jiang, Yuying Wen, Xixi Ge, Hongyi Li, Guangming Chen, Hao Jiang, Mengdie Sun, Qingcheng Wei, Shilei Li, Peng Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China

Corn is the largest grain crop in China. With the increasing use of transgenic corn in agricultural production, accurately identifying transgenic corn varieties has become crucial for ensuring food safety and quality. This study combines terahertz time-domain spectroscopy (THz-TDS) technology with a convolutional neural network (CNN) to classify five transgenic corn varieties. First, the time-domain spectra of transgenic corn were collected, and the frequency-domain spectra and absorption coefficient spectra were calculated. Next, abnormal data were removed using the isolation forest algorithm. The data were then preprocessed using the first derivative (FD) and principal component analysis (PCA) to extract effective spectral features. Based on these features, classification models were constructed using support vector machine (PCA-SVM), grid search optimized SVM (PCA-GS-SVM), particle swarm optimization SVM (PCA-PSO-SVM), and hippopotamus optimization SVM (PCA-HO-SVM). Additionally, a one-dimensional CNN (1D-CNN) model was built using the full-band spectral data. The results indicate that the performance of the 1D-CNN model with FD preprocessing was significantly improved, with an accuracy of 96.68 % and a misclassification rate of only 3.32 %. The study effectively classifies transgenic corn with high accuracy, showing potential for widespread application in quality detection. © 2025 Elsevier Inc.

关键词： Classification Convolutional neural network Support vector machine Terahertz spectroscopy Transgenic corn

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analysis of the Causes of Cyanobacteria Bloom:A Review

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Journal of Resources and Ecology 2020年第4期11卷 405-413页

作者： ZHOU Bing CAI Xiaoli WANG Sen YANG Xinxin Key Laboratory of Analysis and Processing of Big Data Kaifeng 475004HenanChina School of Computer and Information Engineering Henan UniversityKaifeng 475004HenanChina School of Information Management Wuhan UniversityWuhan 430072China

Among water blooms,cyanobacteria bloom occurs over the widest range and is much more harmful than other *** occurrence in inland water bodies is affected by many factors,such as meteorology,hydrology,and human ***,the study of the causes of cyanobacterial bloom has become a major focus of *** China Knowledge Network Journal database contains 143 papers from China and abroad from the years 2004 to 2019 that are relevant to the study of cyanobacteria *** begin by analyzing keywords in these studies and creating a keyword distribution map which indicates the factors related to the *** on parameters such as the frequency of words appearing in the text,the full text of each of the 143 papers is analyzed to form a word cloud created by a program written in Python *** irrelevant terms are eliminated,the word cloud map can reveal potential factors that were not identified by keywords *** completing this macro analysis,we examined approximately 100 related papers from the China Knowledge Network Journal database and Web of Science database published from 2014 to ***,we summarize the main reasons for the outbreak of water *** factors causing blooms can be divided into natural factors and human *** the natural factors are illumination,water temperature and nutrient salt *** human factors are generally related to large-scale water conservancy *** paper analyzes and summarizes these factors,and provides a reference to aid in the prevention and treatment of algal *** information in the paper has a certain practical significance for the protection of water environments.

关键词： algae bloom eutrophication literature measurement temperature

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A Lightweight Neural Network Localization Algorithm for Structureless Wireless Sensor Networks 13th

A Lightweight Neural Network Localization Algorithm for Stru...

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13th China Conference on Wireless Sensor Networks, CWSN 2019

作者： Gao, Rong Yang, Zhongheng Wu, Hejun Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789811517846

This paper studies distributed range-based localization in arbitrarily deployed wireless ad hoc networks. Existing range-based localization approaches depend on specially deployed anchors or require dense network deployment. Our algorithm is a distributed paradigm that only requires local information of each node. Therefore, it is applicable to the resource-limited embedded sensors. Specifically, our algorithm performs a three-stage optimization through coarse-grained, middle-grained, and fine-grained levels. We designed an efficient but accurate neural network to learn the hidden relations between the distances of nodes and their positions. Simulations show that our proposed algorithm works in many more types of network deployments than the existing approaches. Furthermore, our algorithm achieves the highest localization accuracy on average. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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A heterogeneous graph with factual, temporal and logical knowledge for question answering over dynamic contexts

arXiv

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arXiv 2020年

作者： Zhong, Wanjun Tang, Duyu Duan, Nan Zhou, Ming Wang, Jiahai Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Microsoft Research

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner. Experimental results on a benchmark dataset show that the injection of various types of knowledge improves a strong neural network baseline. An additional benefit of our approach is that the graph itself naturally serves as a rational behind the decision making. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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First Principle Calculation of Electromagnetic Mechanism for Fe_2Si Bulk Material

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Journal of Wuhan University of Technology(Materials Science) 2019年第1期34卷 64-68页

作者： LI Ruijie YANG Yinye College of Big Data and Information Engineering Guizhou University Special and Key Laboratory of Guizhou Provincial Higher Education for Photoelectric Information Analysis and Processing Guizhou Minzu University School of Materials Science and Engineering Guizhou Minzu University

The electronic structure and the magnetic properties of Fe_2Si bulk have been calculated by the first-principle density function theory method. The band structure shows that the hexagonal Fe^2 Si bulk is ferromagnetic which is a metal structure under spin-up, and a semiconductor with the band gap of 0.518 eV under spin-down. The density of states shows the Fe^1 3d–spin and Fe^3 3d–spin in the electronic system are the main factors that is the source of the ferromagnetic properties of Fe_2Si bulk. The electronic structure Si-ions is 3s23p6 and that of Fe-ions is e_g^2↑e_g^(*1)↓t_(2g)~3↑. The molecular magnetic moment of Fe_2Si is 2.00 μB. The potential diagram of Fe_2Si bulk shows the formation of covalent and ionic bonds between the Fe atom and the Si atom, it leads to the center charge of Fe is polarized and off center position. These special properties of Fe_2Si bulk are mainly caused by d-d exchange and p-d hybridization. The results offer a certain reference for the magnetic semiconductor Fe_2Si material.

关键词： Fe2Si electronic structure magnetic property first-principle

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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