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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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Sonar Target Detection Based on a Dual Channel Attention Convolutional Network 12

Sonar Target Detection Based on a Dual Channel Attention Con...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Liu, Yang Wang, Ruiyi Cao, Kejing Wang, Jiuhao Shi, Zezhao Wang, Yadi Zhou, Yi Shenzhen Research Institute Henan University Henan Province Engineering Research Center of Spatial Information Processing College of Computer and Information Engineering Kaifeng China College of Computer and Information Engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China College of Computer and Information Engineering Henan University Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China College of Artificial Intelligence Henan University Shenzhen Research Institute Henan University Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the complexity and diversity of underwater environment, high-precision and fast target detection is a scientific problem in underwater acoustic information extraction, especially the underwater target detection of sonar images still has a technical bottleneck. With the development of intelligent detection technology, as the state of the art model, target detection model based on deep neural network adopts different scale feature extraction mechanism, which is easy to generate false alarm for important targets and difficult to overcome the contradiction between false detection and missed detection. The attention mechanism can fully learn the features of the target and improve the accuracy of target detection. Considering the characteristics of seabed exploration task and underwater target, we propose a deep convolution network based on dual channel attention mechanism (DCNet), This model can strengthen the features of the target of interest while weakening the irrelevant background noise information, so as to improve the detection accuracy of the target and enhance the detection ability of the underwater target. The experimental results show that the average accuracy of the dual channel attention mechanism can achieve higher accuracy than the original model, and is superior to other target detection models in accuracy and performance. This research has important practical significance for improving the task of underwater target detection of sonar images and has a wide range of engineering application prospects in the detection of underwater acoustic systems. © 2022 IEEE.

关键词： Underwater acoustics

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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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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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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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Oriented Object Detection in Aerial Images with Double Midpoint Vectors

Oriented Object Detection in Aerial Images with Double Midpo...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Qi Hu Xiaopan Chen Caihong Yuan Minghao Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results.

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Negative Sample Mining based Deep Feature Learning for Kinship Verification 21

Negative Sample Mining based Deep Feature Learning for Kinsh...

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Proceedings of the 2021 5th International Conference on Video and Image processing

作者： Xiaoke Zhu Changlong Li Xiaopan Chen Minghao Zheng Yunwei Li Jihui Hu Henan Key Laboratory of Big Data Analysis and Processing Henan University China School of Computer and Information Engineering Henan University China

ISBN: (纸本)9781450385893

Kinship verification is an important and challenging task. The aim of it is to find out whether there is a kin relationship between one pair of facial images. However, there are many challenges for kinship verification. One of the most important challenge is the problem of data imbalance. Specifically, if there exists N positive kinship pairs, we can get N(N-1) negative pairs. Obviously, the number of negative kinship pairs is much larger than the number of positive kinship pairs. How to make full use of positive and negative samples for training is a valuable problem. But most of the existing methods just randomly select the same number of negative sample pairs as the positive sample pairs. What is more, different negative pairs contain different discrimination information. Therefore, finding discriminative sample is also an important problem. In this paper, we propose a simple and effective method, named Negative sample Mining based Deep Feature Learning (NMDFL) to solve the above problems. Specifically, different with most existing methods, we sample N positive kinship pairs and T*N negative pairs, where T>1. Then, they are sent to the train net, and their impacts on network updating are adjusted in proportion to the number of positive and negative samples. At the same time, the corresponding weight of each negative sample is dynamically adjusted according to the training results of each time. Experimental results on the KinFaceW-I and KinFaceW-II datasets proves the effectiveness of our method.

关键词： Deep Feature Learning Image-based Kinship Verification Mining Negative Samples Unbalanced data

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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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