Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Xie, Yunfang; Zhang, Su; Liu, Yingdi

doi:10.18280/ts.380109

Cited by 27 publications

(18 citation statements)

References 23 publications

(25 reference statements)

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“…erefore, this paper tries to analyze and evaluate the relationship between teaching pressure and self-efficacy of college teachers based on artificial neural network [18][19][20]. Section 2 establishes a grey correlation analysis (GRA) model for the teaching pressure and self-efficacy of college teachers and details the analysis procedure.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of the Relationship between Teaching Pressure and Self-Efficacy of College Teachers Based on Artificial Neural Network

2022

Scientific Programming

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With the reform of the education system, the society today raises higher requirements for college teachers, which cause immense psychological stress among them. To enhance the quality of teachers, it is important to analyze the relationship between teaching pressure and self-efficacy. Therefore, this paper tries to analyze and evaluate the relationship between teaching pressure and self-efficacy of college teachers based on artificial neural network. Firstly, a grey correlation analysis (GRA) model was established for the teaching pressure and self-efficacy of college teachers, and the analysis procedure was detailed. Then, the possible multicollinearity of the GRA model was tested. In addition, a linear regression model was established based on Lasso variable selection model and ridge regression variable selection model, aiming to eliminate the multicollinearity between various teaching pressure factors in the GRA model. Finally, a multilabel learning algorithm was proposed based on neural network and label correlation. In this way, the correlations between the various teaching pressure factors and teachers’ self-efficacy were mined automatically. The proposed model proved valid through experiments.

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Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of the Relationship between Teaching Pressure and Self-Efficacy of College Teachers Based on Artificial Neural Network

2022

Scientific Programming

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“…Liu et al [22] used the basic principles of the GAN network and the results of previous studies to build a deep neural network for predicting future frames as an abnormal behavior detection model. Xie et al [23] presented a deep learning algorithm to evaluate abnormal behavior based on spatiotemporal representation learning. Liu et al [24] adopted the framework of variational abnormal behavior detection to solve the variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents.…”

Section: Introductionmentioning

confidence: 99%

OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

Sun

Guo

et al. 2022

Mathematics

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A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is proposed in this paper. Compared to fixed rules or existing computer-vision methods, the proposed method can more effectively learn from the operators’ behaviors and incorporate their intentions into the monitoring strategy. To the best of our knowledge, this method is the first to apply a monitoring-strategy recommendation model containing a global encoder and a local encoder in monitoring systems. The local encoder can adaptively select important items in the operating sequence to capture the main purpose of the operator, while the global encoder is used to summarize the behavior of the entire sequence. Two experiments are conducted on two data sets. Compared with att-RNN and att-GRU, the joint coding model in experiment 1 improves the Recall@20 by 9.4% and 4.6%, respectively, and improves the MRR@20 by 5.49% and 3.86%, respectively. In experiment 2, compared with att-RNN and att-GRU, the joint coding model improves by 11.8% and 6.2% on Recall@20, and improves by 7.02% and 5.16% on MRR@20, respectively. The results illustrate the effectiveness of the our model in monitoring systems.

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“…Frontier technologies such as cell phone chips and 5G make cell phone no longer a communication gear but a multi-functional mobile terminal that can assist users in learning, shopping, working, and entertaining with diverse application software installed on it [1][2][3][4][5][6][7]. Such technological advancement lays a foundation for the emergence of mobile learning platforms, and mobile learning quickly becomes a learning method in style.…”

Section: Introductionmentioning

confidence: 99%

Features and Influencing Factors of Mobile Learning Behavior of Employees in Accounting Profession

Wang¹,

Dai²,

Pei³

et al. 2022

Int. J. Emerg. Technol. Learn.

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Data mining of the mobile learning behavior of learners can help researchers understand the underlying association rules of such behavior and the internal mechanism of the development of their thinking during the learning process, thereby giving the true and accurate evaluations on the thought and status of mobile learners. However, existing models established for mobile learning behavior recognition cannot give satisfactory enough explanations to the changes in the cognition level of learners, so this paper took employees in accounting profession as subjects to analyze the features and influencing factors of their mobile learning behavior. At first, this paper built a neural network model based on the attention mechanism and used it to classify and recognize the input data of mobile learning behavior via original data learning, and feature extraction and analysis. Then, this paper created evaluation scenarios for subjects participating in mobile learning, designed economic management tasks applicable for actual application scenarios, and proposed several evaluation indexes for assessing their learning performance. After that, the explicit learning behavior of the subjects was taken as the criterion for judging whether their learning performance has achieved the desired learning goals or not. At last, the effectiveness of our analysis model was verified by the experimental results.

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Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Cited by 27 publications

References 23 publications

Analysis and Evaluation of the Relationship between Teaching Pressure and Self-Efficacy of College Teachers Based on Artificial Neural Network

Analysis and Evaluation of the Relationship between Teaching Pressure and Self-Efficacy of College Teachers Based on Artificial Neural Network

OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

Features and Influencing Factors of Mobile Learning Behavior of Employees in Accounting Profession

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