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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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Text Detection and Recognition Algorithm for Arbitrary Shapes in Natural Scenes 3

Text Detection and Recognition Algorithm for Arbitrary Shape...

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3rd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Deng, Zhenrong Liu, Zongyang Yang, Rui Huang, Rong Lan, Rushi Guilin University of Electronic Technology Guangxi Key Laboratory of Graphics Intelligent Processing Guilin541004 China Nanning Research Institute Guilin University of Electronic Technology Guilin541004 China Guangxi Jinghang UAV Co. Ltd. Nanning530025 China

ISBN: (纸本)9798350302356

Natural scenes are rich and varied in text presentation and complex in the background. These characteristics are likely to cause problems such as missing text and difficulties in coordinate regression during detection. At the same time, the gradient disappears due to the long text sequence in the recognition stage, thus causing poor correlation before and after the text. In order to address the issue, this research suggests an end-To-end scene text detection and identification model that emphasizes spatial image data and long-range text relevance. The model uses the ABCNet network as the underlying structure. In the detection stage, this paper uses ResNet to extract image features, feature fusion with iterative FPN, convolution using convolutional blocks with coordinate information, and finally, focus the coordinate information by coordinated attention mechanism. This paper uses a combination of Transformer and LSTM coding blocks in the recognition phase to encode the detection region. It uses Transformer to obtain textual long-sequence relations, LSTM to enhance short-range information, and a lightweight attention mechanism to decode. The experimental findings demonstrate that the suggested model outperforms more widely used approaches in terms of text localisation and recognition. © 2023 IEEE.

关键词： Convolution

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Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization 16th

Attribute Selection Method Based on Artificial Bee Colony Al...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Ji, Yuxuan Ye, Jun Yang, Zhenyu Ao, Jiaxin Wang, Lei Nanchang Institute of Technology Jiangxi Nanchang330000 China Key Laboratory of Water Information Cooperative Perception and Intelligent Processing of Jiangxi Province Jiangxi Nanchang330000 China

ISBN: (纸本)9789811912559

At present, the existing attribute reduction algorithm combining artificial bee colony and neighborhood rough set basically uses the attribute dependence and the number of attribute subsets as parameters to construct the fitness function, while ignoring the role of heuristic information. As a result, the number of bee colony iterations increases, and the convergence speed is slow. Aiming at this kind of problem, an improved method is proposed. First, a discernibility matrix under the neighborhood rough set is defined;secondly, an attribute importance measurement method of the discernibility matrix under the neighborhood decision system is proposed;The attribute importance of the domain discrimination matrix constructs a new fitness function for the heuristic factor;finally, an attribute selection algorithm for artificial bee colony algorithm and neighborhood discrimination matrix importance optimization is designed. Compared with the original algorithm, the new method reduces the number of generations, accelerates the convergence speed, and retains the minimum attribute reduction collected during each iteration, and multiple minimum attribute reductions can be obtained. The experimental results on the UCI data set prove the feasibility and effectiveness of the algorithm. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Koebe Type Theorems and Pre-Schwarzian of Harmonic K-quasiconformal Mappings,and Their Applications

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Acta Mathematica Sinica,English Series 2022年第11期38卷 1965-1980页

作者： Shao Lin CHEN Saminathan PONNUSAMY College of Mathematics and Statistics Hengyang Normal UniversityHengyang 421002P.R.China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002P.R.China Department of Mathematics Indian Institute of Technology MadrasChennai-600036India

In this article,we first establish an asymptotically sharp Koebe type covering theorem for harmonic K-quasiconformal *** we use it to obtain an asymptotically Koebe type distortion theorem,a coefficients estimate,a Lipschitz characteristic and a linear measure distortion theorem of harmonic K-quasiconformal *** last,we give some characterizations of the radial John disks with the help of pre-Schwarzian of harmonic mappings.

关键词： Harmonic K-quasiconformal mapping Koebe type covering theorem Koebe type distortion theorem Radial John disk

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Influence of Si buffer layer on the crystal quality of SiGe films in Ge/Si/SiGe heterostructures: A molecular dynamics investigation

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Surface Science 2025年 761卷

作者： Xie, Jianan Lin, Tao Wang, Cailin College of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

SiGe materials have become a research hotspot due to their important applications in semiconductor devices, especially in optoelectronic and high-speed electronic devices. In this study, based on molecular dynamics simulations, the influence of the Si buffer layer on the quality of films in Ge/Si/SiGe heterostructures is investigated. By simulating the growth process of the Ge/Si/SiGe heterostructure, a deposition model based on Ge(100) substrates is established. Inspired by the concept of reverse gradient buffer layers, Si buffer layers are directly grown on Ge substrates, followed by the deposition of SiGe films. This study primarily investigates the effects of the growth temperature and deposition thickness of the Si buffer layer on the quality of SiGe films. Based on the deposition parameters identified as suitable under the current simulation conditions (620 °C, 9.7 nm), the influence of the buffer layer on SiGe films with varying Ge compositions is further analyzed. The results show that the dislocations and stacking faults formed in the Si buffer layer effectively relieve the stress caused by lattice mismatch, thus improving the crystal quality of the subsequent SiGe films. This study provides theoretical insights into the Ge/Si/SiGe heterostructure film growth process, which helps enhance the quality of SiGe films and expands their applications in semiconductor devices. © 2025

关键词： Lattice mismatch

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Molecular dynamics study of the effects of deposition parameters on Si0.5Ge0.5 film growth on Ge(100) substrate

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Computational Materials Science 2025年 256卷

With the widespread application of SiGe films in the semiconductor field, precisely controlling the growth process to obtain high-quality films has become an important research direction. This study focuses on the growth of Si0.5Ge0.5 films on Ge (100) substrates and investigates the effects of different deposition parameters (such as deposition interval, incident energy, incident angle, and substrate off-angle) on film quality. Through molecular dynamics simulations, the effects of these deposition parameters on the crystal structure, dislocation density, and surface roughness of the films are systematically analyzed. The results show that shorter deposition intervals lead to faster atomic deposition, increasing the proportion of disordered atoms;longer deposition intervals effectively reduce the proportion of disordered atoms, but excessively long intervals may increase surface roughness and form island-like structures. Additionally, appropriate incident energy, incident angle, and substrate off-angle can significantly optimize the film quality, with the surface roughness being minimized when the incident energy is 1 eV. © 2025 Elsevier B.V.

关键词： Crystal atomic structure

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Comparing the difference in enhancement of kokumi-tastingγ-glutamyl peptides on basic taste via molecular modeling approaches and sensory evaluation

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Food Science and Human Wellness 2022年第6期11卷 1573-1579页

作者： Juan Yang Jing Guo Ruijie Mai Hao Dong Chun Cui Xiaofang Zeng Weidong Bai College of Light Industry and Food Technology Guangdong Provincial Key Laboratory of Lingnan Specialty Food Science and TechnologyAcademy of Contemporary Agricultural Engineering InnovationsZhongkai University of Agriculture and EngineeringGuangzhou 510225China Key Laboratory of Green Processing and Intelligent Manufacturing of Lingnan Specialty Food Ministry of AgricultureGuangzhou 510225China School of Food Science and Engineering South China University of TechnologyGuangzhou 510640China

γ-Glutamyl peptides can enhance basic taste sensations such as saltiness,sweetness,and umaminess,while the molecular mechanism and the difference in taste enhancement remain ***,two complex conformations:taste type 1 receptor 1(T1 R1)-MSG and taste type 1 receptor 2(T1 R2)-sucrose were constructed to form binding *** peptides showed affinity for the two receptors,but a higher affi nity scores and more binding amino acid residues for the T1 R1-MSG receptor,implying that they may exhibit a higher umami-enhancing ***,γ-glutamyl alanine(γ-EA)displayed the highest affi nity for the two receptors through mobilizing multiple amino acid residues to form hydrophobic and hydrogen bonds,indicating it had the highest enhancement for umaminess and sweetness among these *** evaluation demonstrated the enhancement ofγ-EA on umaminess was superior to that of ***,γ-glutamyl peptides could enhance basic taste sensation via activating taste receptor,and exhibited a highest umami-enhancing effect.

关键词： γ-Glutamyl peptides Taste-enhancing effect Molecular mechanism Taste type 1 receptor 1 Taste type 1 receptor 2

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Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1233-1247页

作者： Yiming Lei Haiping Zhu Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200433China with Huawei Shanghai 200120China Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China IEEE

The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)***,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better *** improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning *** merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)***,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding ***,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction ***,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature *** training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian *** results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.

关键词： Terms-Convolutional neural network(CNNs) medical image classification meta-learning ordinal regression random forest

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A New Encryption Scheme Based on RSA Encryption Algorithm and Chen Chaotic System 12th

A New Encryption Scheme Based on RSA Encryption Algorithm an...

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12th International Conference on Computer Engineering and Networks, CENet 2022

作者： He, Chenchen Chen, Zhong College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

Nowadays the security of images is obviously more important. Some scholars have applied Rivest-Shamir-Adleman (RSA) algorithm and Chen chaotic system to image encryption. However, the application of the two methods in image encryption can still be explored. In this paper, it proposes an encryption scheme which uses asymmetric cipher RSA encryption algorithm and Chen chaotic system to scramble and diffuse the image. In this encryption scheme, firstly, it applies the RSA algorithm to obtain the public key and uses the specified formula to calculate the initial value of the Chen chaotic system. Secondly, the four chaotic sequences generated by Chen chaotic system are applied to the scrambling stage and the diffusion stage of the image process respectively. In the scrambling phase, it uses Arnold scrambling. It performs forward XOR diffusion and then performs reverse XOR diffusion in the diffusion stage. Finally, the encryption process is all completed and the encrypted image is obtained. Moreover, after appropriate experiments, the encryption algorithm is proved to have a good guarantee for the security of the image. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diffusion

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Fine grained image recognition algorithm based on CNN-Transformer and paired interaction

Fine grained image recognition algorithm based on CNN-Transf...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Yu Wang Hong Zhang Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

A new fine-grained image recognition algorithm (P-ResViT, Paired Interaction Res50-ViT) based on CNN-Transformer is proposed to address the issue of existing fine-grained image recognition methods based on convolutional neural networks (CNN) lacking attention mechanisms and difficulty in accurately locating key regions, as well as weak ability to extract contextual information and global features. This algorithm introduces paired interaction attention mechanisms to supplement external attention. This algorithm can utilize the self attention mechanism of the Vision Transformer and the external attention mechanism formed by paired image inputs during training to achieve precise positioning, while preserving the inductive bias and local feature extraction capabilities of CNN. Firstly, the fine-tuning improved ResNet-50 completes local feature extraction and obtains high-resolution feature maps. Secondly, the Transformer encodes the tokenized image blocks of the feature map as input sequences for extracting global context. Finally, the Transformer completes global feature extraction and outputs the classification sequence. In addition, two external attention mechanisms were introduced by inputting image pairs during training, including collaborative attention mechanisms for similar images and cross attention mechanisms for heterogeneous images, fully utilizing the correlation information between images. After experimental evaluation, the P-ResViT proposed in this article has reached advanced levels in three fine-grained image datasets CUB, CAR, and AIR.

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