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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Bai, Haiyang Zhu, Xiaoke Dong, Lingyuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

The evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a Programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency. © 2023 IEEE.

关键词： Students

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LSGCN: Long short-term traffic prediction with graph convolutional networks 29

LSGCN: Long short-term traffic prediction with graph convolu...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Huang, Rongzhou Huang, Chuyin Liu, Yubao Dai, Genan Kong, Weiyang Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9780999241165

Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Convolution

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors 9

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

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9th International Conference on Computer and Communications, ICCC 2023

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

ISBN: (纸本)9798350317251

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. © 2023 IEEE.

关键词： Object detection

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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation 22

Multimodal Identity Recognition of Spiking Neural Network Ba...

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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Liu, Yang Zhang, Hongxia Li, Yahui Wang, Quanqiang Liu, Cheng Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security. © 2024 IEEE.

关键词： Face recognition

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Application of CPU Parallel Computing in MATLAB Environment for analysis of Anti-Saturation and Gain Effects in PID Controller 13

Application of CPU Parallel Computing in MATLAB Environment ...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Huang, Ruirui Wu, Zhenhui Hou, Yandong Cheng, Qianshuai Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Technology and Media University of Henan Kaifeng Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

With the continuous increase in data size and model complexity, the computational workload has grown rapidly, posing a significant challenge to the capabilities of computer data processing and simulation calculations. Therefore, parallel programming based on multicore and cluster architectures has become one of the mainstream technologies to enhance program execution efficiency and numerical computation efficiency. The theoretical foundations of parallel program computing have been applied in various aspects of engineering applications and theoretical simulations. In this paper, a new parallel PID anti-integral saturation controller is designed for a second-order closed-loop control system of unmanned aerial vehicles (UAVs). It compares and analyzes the runtime, execution efficiency, and speedup ratio of parallel programs and general serial programs under the same scenario. The experimental results demonstrate that parallel computing significantly improves the simulation program's efficiency for PID controller anti-saturation control systems under identical scenarios, exhibiting a high speedup ratio on the existing computing platform. Additionally, this study consolidates common issues encountered in MATLAB parallel program design, offering valuable insights into overcoming challenges in this domain. © 2024 IEEE.

关键词： MATLAB

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PG-YOLO: A Lightweight Vehicle Detection Method for Drones 22

PG-YOLO: A Lightweight Vehicle Detection Method for Drones

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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Liu, Yang Fan, Tengyu Mei, Kun Zhou, Liming Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

With the rapid development of drone technology, object detection technology is widely applied in the field of drone intelligent transportation. In response to the problems of low accuracy and high computational complexity in vehicle detection tasks on resource limited drones, this paper proposes a lightweight and efficient improved model PG-YOLO for drone vehicle detection based on the YOLOv7-tiny model. Firstly, replace the LeakyReLU activation function with the SiLU activation function to compensate for the lack of nonlinear expression in the LeakyReLU activation function, and improve the convergence speed and generalization of the model during training;Secondly, the feature extraction and fusion modules were optimized to enhance the network's perception and capture capabilities of the target, improve detection accuracy, and reduce the number of parameters;Then, lightweight convolution PConv and GSConv are introduced, and a lightweight module PPC based on PConv is proposed to improve the network's Neck layer, reducing the number of parameters and computation while maintain detection performance;Finally, the CBAM attention mechanism is introduced to improve the detection performance for the problem of dense vehicle targets. The improved network achieved an average detection accuracy of 60.1%, an increase of 12.1 percentage points compared to the original network, a reduction of 73.6% in parameter count, a reduction of 15.9% in computational complexity, and a detection speed of 154 FPS. Compared to the original network, the network is more lightweight and achieves higher detection accuracy, which can more effectively perform unmanned aerial vehicle detection tasks. © 2024 IEEE.

关键词： Drones

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An efficient backbone network for target detection in aerial images 4

An efficient backbone network for target detection in aerial...

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

作者： Zhang, Sihan Liu, Yaping Wang, Xile Zhang, Miaohui School of Artificial Intelligence Henan University Henan Kaifeng475004 China School of Mathematics and Statistics Henan University Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China

Target detection in aerial images by high altitude unmanned aerial vehicles is one of the hot research topics. A detection that is efficient and accurate is of a high value in the military and civilian fields. However, due to the irregular distribution of targets and their various scales shown in aerial images, it is difficult for existing backbone networks to effectively extract target features based on deep learning. To address these problems, in the paper, an efficient backbone network called AerNet is proposed, to which a local feature enhancement module (LFEM) is added to fully extract discriminative features of aerial targets via multi-scale convolutional layers. The network AerNet consists of 55 layers, which can keep high spatial resolution in deeper layers and maintain a large receptive field. Experimental results show that our AerNet has achieved a satisfying detection performance on the DOTA benchmark. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Benchmarking

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Knowledge graph embedding via multiplicative interaction 18

Knowledge graph embedding via multiplicative interaction

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2nd International Conference on Innovation in Artificial Intelligence, ICIAI 2018

作者： Huang, Zichao Li, Bo Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9781450363457

Knowledge graphs are playing a crucial role in many machine learning applications. Since most of the knowledge graphs are far from complete, many knowledge graph completion models have been proposed. TransE and its extended models all model knowledge graphs with additive interaction. DistMult demonstrates that multiplicative interaction is more effective for modeling knowledge graphs. However, DistMult performs poorly on 1-to-N, N-to-1 and N-to-N relations. Besides, it does not consider the case that an entity could be a head entity or a tail entity which should be modeled separately. We propose a more fine-grained knowledge graph embedding model called MultE, which models knowledge graphs with multiplicative interaction. In MultE, an entity would have one representation when serving as head entity and another representation when serving as tail entity. For improving performance on 1-to-N, N-to-1 and N-to-N relations, MultE considers all valid tail(or head) entities during training and treats the prediction task as a classification problem. Experiment results show that MultE obtains consistent improvements compared with DistMult and achieves state-of-theart performance. © 2018 Association for Computing Machinery.

关键词： Knowledge graph

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Multi-UAVs cooperative task assignment and path planning scheme

Multi-UAVs cooperative task assignment and path planning sch...

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2021 International Conference on Computer Network Security and Software Engineering, CNSSE 2021

作者： Du, Xiaoyu Guo, Qicheng Li, Hui Zhang, Yanyu International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan University Kaifeng Henan475000 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan475000 China Henan University Minsheng College Kaifeng Henan475000 China

The task assignment and path planning problems of multiple unmanned aerial vehicles (multi-UAVs) cooperative assistance roadside units (RSUs) for data collection are optimization problems with the goal of minimizing time and energy consumption. This paper proposes a hierarchical optimization scheme for multi-UAVs collaborative assistance RSUs data collection. This solution solves the problems that the number of UAVs needs to be set in advance, the convergence speed is slow when the number of tasks increases, and it is easy to fall into a local optimal solution, and the convergence accuracy is poor. First, the solution uses the K-means algorithm to allocate tasks and group RSUs to find the right number of UAVs to perform the task. Then, this paper proposes a hybrid optimization algorithm based on bionic learning for path planning. Finally, we set up a reasonable evaluation mechanism and conducted simulation experiments. The algorithm in this paper is compared with genetic algorithm, gray wolf algorithm and whale algorithm, the results show that the total cost of the task obtained by the proposed algorithm is the lowest, the algorithm stability is better, and the convergence accuracy is the highest. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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