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3rd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Multiple granularity network and dynamic label for domain adaptive person re-identification 4

Multiple granularity network and dynamic label for domain ad...

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2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Wang, Xile Zhang, Sihan Song, Junyu Zhang, Miaohui Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China School of Artificial Intelligence Henan University Henan Kaifeng475004 China

The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

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Depth Guided Cross-modal Residual Adaptive Network for RGB-D Salient Object Detection 2

Depth Guided Cross-modal Residual Adaptive Network for RGB-D...

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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhao, Zhengyun Yang, Qingpeng Yang, Shangqin Wang, Jun School of Artificial Intelligence Henan University Kaifeng Henan475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan475004 China

Depth modal features can provide complementary information for salient object detection (SOD). Most of the existing RGB-D SOD methods focus on fully combining RGB and Depth modal features without distinguishing them. In this paper, we propose a new depth guided cross-modal residual adaptive network for RGB-D SOD. We use two independent resnet-50 to extract the features of the two modes respectively. Then the cross-modal channel-wise refinement module is designed to obtain complementary modal information. We design a crossmodal guided module to make complementary modal information guide RGB image feature extraction. Finally, the residual adaptive selection module is used to enhance the spatial mutual concerns between the two modal features to achieve multimodal information fusion. Experimental results show that our method can achieve a more reasonable fusion state of RGB and Depth, and verify the effectiveness of our final saliency model. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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A Review of Detection-Related Multiple Object Tracking in Recent Times

A Review of Detection-Related Multiple Object Tracking in Re...

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International Conference on Advanced Communication Technology (ICACT)

作者： Suya Li Ying Cao Xin Xie Henan University School of Computer and Information Engineering Jinming Campus Kaifeng China School of Computer and Information Engineering Jinming Campus Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

Multi-object tracking (MOT) is garnering more attention due to its widespread application in the area of autonomous driving, human-computer interaction, and intelligent video surveillance. Especially in recent years, MOT has rapidly developed thanks to related technologies such as object detection, which has helped in handling interfering factors such as crowded scene occlusion, small objects, and similar appearances. Among these, Detection-based MOT is the mainstream for accurately forming objects' trajectories. Therefore, according to the analysis of the last three years' research, this paper particularly focuses on discussing the continuous optimization strategies of MOT around the development of object detection at each stage. In addition, this article also introduces the commonly used benchmark datasets and related applications of MOT.

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3d-Usanet：A Spatio-Temporal Coupled Based Transformer for Video Anomaly Detection Method

SSRN

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SSRN 2024年

作者： Mu, Huiyu Wang, Luhui Yin, Hongjian Li, Yonggan Dang, Lanxue Liu, Yang Zuo, Xianyu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Fair Competition Review Transaction Center of Henan Province Zhengzhou450008 China

Video anomaly detection aims to detect incidental and sudden patterns of events in complex scenes, which plays an essential role for maintaining public security and averting potential risks. Unsupervised learning is particularly effective in solving the problem of insufficient annotation of video abnormal behaviour in low resource scenarios. However，most relate methods decompose it into two sub-tasks: spatio anomaly detection (S-VAD) and temporal anomaly detection (T-VAD), which barely consider the coupled correlations and the difficulty of coupling global spatio-temporal information. To address this, we provide a new perspective on constructing a novel dynamic spatio-temporal coupling relationship by designing a 3D U-shape Attention Network (3D-UsANet). On the one hand, we construct a Spatio-Temporal Feature Modelling Transformer (STFMT) module to handle different spatio positions at the same time step. On the other hand, a Channel-wise Scale-aware Fusion Modelling Transformer (CSFMT) module is integrated into 3D-UsANet to overcome the multi-scale heterogeneous divides and disregard irrelevant characteristics, which adaptively captures global semantic dependencies and bridges the spatio-temporal semantic gap. Essentially, we evaluate the performance of 3D-UsANet on UCSD Ped1、UCSD Ped2 and CUHK Avenue datasets with the AUC score 83.50%、96.30%、86.70%，respectively. The extensive experimental results demonstrate the effectiveness of 3D-UsANet in detecting anomaly behaviors. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Few-Shot Node Classification on Attributed Networks Based on Prototypical Network

Few-Shot Node Classification on Attributed Networks Based on...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yaliang Zhao Guangming Zhang Jinke Wang Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

In today’s society, the interactions between various entities form complicated attributed networks. The node classification task as the core task of analyzing attribute networks has gained a considerable amount of attention from researchers. The existing graph neural network-based node classification approaches all demand lots of labeled data, but a high percentage of node classes in practical scenarios contain only restricted labeled data. So their classification effect is not good. In response to this problem, we put forward an improved graph prototypical network (IGPN) to enhance the few-shot node classification’s accuracy. In particular, we start by obtaining the embedding representations of nodes on the attributed network through a graph attention network. Then combined with the node’s degree information and neighbor information, the importance coefficient of all nodes in the support set is learned. After that, we can obtain the prototype representation of every class. Finally, the node classification can be completed by the distance of the query nodes’ representation from all class prototypes. Our method is preferable to the baseline method on three datasets according to a large number of experiments.

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Research progress on an intelligent production and distribution of the remote sensing products

Research progress on an intelligent production and distribut...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Kong, Dezhen Zhao, Jinhuan Tian, Meng Liu, Yang Henan Province Space Information Processing Engineering Laboratory KaifengHenan475004 China Henan Key Laboratory of Big Data Analysis and Processing KaifengHenan475004 China School of Computer and Information Engineering Henan University KaifengHenan475004 China

ISBN: (纸本)9783030539795

Since the amount of remote sensing data that can be obtained every day has reached ten terabytes, the storage and sharing of massive remote sensing data and the construction of product production application architecture have become imminent research topics. The purpose of this study is to build an efficient system that can meet the needs of massive remote sensing data storage, calculation, and distribution. The article elaborates on the latest research progress of cloud service remote sensing data distribution and intelligent production from the four aspects of remote sensing cloud service development, data distribution technology, cloud service technology, and operation mechanism of remote sensing cloud computing processing model, and proposes cloud service remote sensing key issues facing data distribution, and summarize and prospect related research, with a view to promoting the further development of research on cloud service remote sensing data distribution and intelligent production. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Remote sensing

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A Two-stage Selection Hyper-heuristic Algorithm for the Capacitated Vehicle Routing Problem

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Hou, Yan-e He, Wenwen Wang, Congran Ren, Xinhui Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China

As a classical combinatorial optimization problem, capacitated vehicle routing problem (CVRP) has been continuously studied. However, developing effective approaches for CVRP remains very challengeable. This article presented a two-stage selection hyper-heuristic algorithm for CVRP. The proposed algorithm used the multi-armed bandit algorithm as the high-level selection strategy, which selected a low-level heuristic at each step in the iterative local search framework according to the rewards that it had obtained. For the new neighborhood solution that was found by the selected lowlevel heuristic, a hybrid acceptance strategy was employed to decide whether to accept it. In addition, we also designed a low-level heuristic based on the ruin-and-recreate principle to expand the search space of neighborhood solutions except for the regular neighborhood operators widely used in CVRP. Moreover, the routes of better solutions were also put into a pool of routes. Then a set partitioning procedure model was built based on these routes and then solved by CPLEX to obtain better solution. Computational results on 82 instances demonstrate that the presented algorithm is a satisfactory and competitive approach for CVRP. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Vehicle routing

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A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Hou, Yan-e Gu, Wenbo Wang, Chunxiao Yang, Kang Wang, Yujing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China

School bus routing problem (SBRP) has been studied for decades. Many successful approaches based on heuristics or metaheuristics have been developed for various SBRP problems. However, developing an effective algorithm for SBRP is still a very challenging task. This paper developed a Q-learning-based selection hyper-heuristic to solve basic and open single-school SBRP problems, which both aim to minimize the total travel distance. The proposed algorithm took a Qlearning algorithm as the high-level strategy to select a lowlevel heuristic from a set of low-level heuristics, which are dependent on the problem domain. The selected low-level heuristic was regarded as an action and then executed to improve the current solution. In each stage of the optimization process, the best action with the best cumulative rewards will be chosen to get better results. The presented algorithm was implemented and some experiments were carried out on some Capacitated vehicle routing problem (CVRP) instances and SBRP benchmark instances. Experiment results on two types of instances demonstrate that our proposed hyper-heuristic algorithm is more competition than existing approaches. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Vehicle routing

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Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 2021

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

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2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Yawei Zhang Fangtao Ren Xi Liu Fan Zhang School of computer and information engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC.

关键词： DNA Methylation Machine learning Artificial neural networks Hepatocellular carcinoma

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