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15th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Zhang, Jie Feng, Junhong Chen, Guoqiang Yang, Xiani School of Computer Science and Engineering Guangxi Universities Key Lab of Complex System Optimization and Big Data Processing Yulin Normal University YulinGuangxi537000 China School of Computer and Information Engineering Henan University KaifengHenan475004 China

ISBN: (纸本)9789811397097

As artificial bee colony algorithm is sensitive to the initial solutions, and is easy to fall into local optimum and premature convergence, this study presents a novel artificial bee colony algorithm based on uniform design to acquire the better initial solutions. It introduces an initialization method with uniform design to replace random initialization, and selects the better ones of those initial bees generated by the initialization method as the initial bee colony. This study also introduces a crossover operator based on uniform design, which can search evenly the solutions in the small vector space formed by two parents. This can increase searching efficiency and accuracy. The best two of the offsprings generated by the crossover operator based on uniform design are taken as new offsprings, and they are compared with their parents to determine whether to update their patents or not. The crossover operator can ensure that the proposed algorithm searches uniformly the solution space. Experimental results performed on several frequently used test functions demonstrate that the proposed algorithm has more outstanding performance and better global searching ability than standard artificial bee colony algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Vector spaces

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Influence of Elevation and Orbit Interpolation on the Accuracy of R-D Location Model 6

Influence of Elevation and Orbit Interpolation on the Accura...

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

作者： Fan, Minghu Zuo, Xianyu Tian, Junfeng Key Laboratory of Big Data Analysis and Processing of Henan Province College of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781728129129

R-D model is the most commonly location model in geometric processing of SAR images. How to improve its location accuracy has been one of the focuses in application. Based on the in-depth analysis of the solving process of R-D equation, two kinds of influencing factors are selected to study. They are elevation and satellite orbit interpolation method. Then, aiming at the pixel location of high-resolution spaceborne SAR images, three kinds of DEM and two orbit interpolation algorithms were studied. The experimental results show that these two kinds of factors mainly affect the location accuracy of range direction, both the horizontal and vertical resolution of elevation have great influence on location accuracy, the quartic polynomial orbit interpolation can meet requirements of high accuracy location, and further improvement of the location accuracy need other auxiliary means. © 2019 IEEE.

关键词： Synthetic aperture radar

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Parallel Geometric Correction for Single Spaceborne SAR Image 6

Parallel Geometric Correction for Single Spaceborne SAR Imag...

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

ISBN: (纸本)9781728129129

The fast processing of SAR images is of great significance for SAR applications, and parallel computing is the most common solution. The paper studied the parallel computing algorithm for the geometric correction of single spaceborne SAR image, which is based on a classical serial geometric correction algorithm. After fully analyzing the parallel features of the algorithm, a CPU-oriented and a GPU-oriented global parallel correction algorithm were designed. Then, two spaceborne SAR images were used to test the speedup performance of the two algorithms. The experimental results show that the two parallel algorithms can greatly improve the speed of the geometric correction of spaceborne SAR image, and the performance of CPU-oriented parallel algorithm is mainly related to the number of CPU/cores, while the performance of GPU-oriented parallel algorithm is mainly related to the data granularity, and the latter has very high speedup ratio and is an efficient method for geometric correction of spaceborne SAR image. © 2019 IEEE.

关键词： Synthetic aperture radar

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Spatial fuzzy C-means clustering and deep belief network for change detection in synthetic aperture radar images 5

Spatial fuzzy C-means clustering and deep belief network for...

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IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan 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 Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Membership functions

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Breaking the curse of space explosion: Towards effcient NAS with curriculum search 37

Breaking the curse of space explosion: Towards effcient NAS ...

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37th International Conference on Machine Learning, ICML 2020

作者： Guo, Yong Chen, Yaofo Zheng, Yin Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China Weixin Group Tencent China Tencent AI Lab Tencent China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9781713821120

Neural architecture search (NAS) has become an important approach to automatically fnd effective architectures. To cover all possible good architectures, we need to search in an extremely large search space with billions of candidate architectures. More critically, given a large search space, we may face a very challenging issue of space explosion. However, due to the limitation of computational resources, we can only sample a very small proportion of the architectures, which provides insuffcient information for the training. As a result, existing methods may often produce suboptimal architectures. To alleviate this issue, we propose a curriculum search method that starts from a small search space and gradually incorporates the learned knowledge to guide the search in a large space. With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method signifcantly improves the search effciency and fnds better architectures than existing NAS methods. Extensive experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of the proposed method. Copyright 2020 by the author(s).

关键词： Curricula

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Spatiotemporal Evolution analysis of NO 2 Column Density before and after COVID-19 Pandemic in henan Province Based on SI-APSTE Model

Research Square

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Research Square 2021年

作者： Liu, Yang Zhao, Jinhuan Song, Kunlin Cheng, Cheng Li, Shenshen Cai, Kun Henan Engineering Laboratory of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction. © 2021, CC BY.

关键词： Air pollution

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Efficient processing of top-k frequent spatial keyword queries

Research Square

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Research Square 2021年

作者： Xu, Tao Xu, Aopeng Mango, Joseph Liu, Pengfei Ma, Xiaqing Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Key Research Institute of Yellow River Civilization and Sustainable Development Henan University Kaifeng475004 China

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions. © 2021, CC BY.

关键词： big data

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SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances

SASA-Net: A spatial-aware self-attention mechanism for build...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tiansu Gong Fusong Ju Shiwei Sun Dongbo Bu Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Zhongke Big Data Academy Zhengzhou Henan China

ISBN: (纸本)9781665429825

Protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning *** of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential ***, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue’s pose according to all other residues’ features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://*** gongtiansu/SASA-Net/

关键词： Proteins Deep learning Protein engineering Solid modeling Three-dimensional displays Conferences Neural networks

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GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder

arXiv

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arXiv 2024年

作者： Chen, Junzhou Wen, Xuan Zhang, Ronghui Ren, Bingtao Wu, Di Xu, Zhigang Wang, Danwei The Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing100191 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China The School of Information Engineering Chang’an University Shaanxi Xi’an710064 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning;and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, OfficeHome, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA’s critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation. © 2024, CC BY-NC-SA.

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PWG-IDS: An intrusion detection model for solving class imbalance in IIoT networks using generative adversarial networks

arXiv

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arXiv 2021年

作者： Zhang, Lei Jiang, Shuaimin Shen, Xiajiong Gupta, Brij B. Tian, Zhihong Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng47500 China Institute of Data and Knowledge Engineering Henan University Kaifeng47500 China School of Computer and Information Engineering Henan University Kaifeng47500 China National Institute of Technology Kurukshetra India Asia University Taichung Taiwan The Department of Cyberspace Insitute of Advanced Technology GuangZhou University GuangZhou510006 China

With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Intrusion detection

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