The Recognition of Teacher Behavior Based on Multimodal Information Fusion

Wu, Dongli; Chen, Jia; Deng, Wei; Wei, Yantao; Luo, Heng; Wei, Yangyu

doi:10.1155/2020/8269683

Cited by 20 publications

(20 citation statements)

References 23 publications

(24 reference statements)

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“…erefore, the processing of big data has strong technical integrity and intersectionality. From base to top, the whole innovation stack fundamentally incorporates foundation and asset levels, huge information framework programming layer (counting circulated capacity and equal PCs), investigation calculation layer, and large information application layer [16,17]. Table 1 describes the main technical aspects and technical contents of the big data processing technology stack.…”

Section: Big Data Processing Technology Stack and Its Processingmentioning

confidence: 99%

Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

Yang

2022

Mobile Information Systems

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The so-called multimodal information refers to the information from different information sources on different or the same side of the same description target. These pieces of information are different in terms of storage structure, representation, semantic connotation, credibility, and emphasis, but there is a certain inevitable connection between them. This paper aims to study how to analyze and study the low carbon of enterprises with the help of multimodal information fusion based on the background of big data and construct the evolutionary neural network of the improved adaptive genetic algorithm. This paper puts forward the problem of low carbon in enterprises, which is based on multimodal information fusion, and then elaborates on the concept and related algorithms of multimodal information fusion. Information fusion has carried out case design and analysis. Through the research and analysis of enterprise low carbon and self-adaptive algorithm, it can be obtained that the neural network has reached the threshold of 3.95 after iterating for nearly 60 generations, and stopped iterating to obtain the best individual. The evolutionary neural network in this paper reaches stability after a small number of iterations and can basically achieve a certain low carbonization.

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Section: Big Data Processing Technology Stack and Its Processingmentioning

confidence: 99%

Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

Yang

2022

Mobile Information Systems

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“…For multimodal image fusion methods, a lot of research has been done for this purpose in recent years [25][26][27][28][29][30]. In 2013, Nummenmaa et al of Aalto University [24] invited a total of 700 people from all over the world and showed them movies, stories, etc.…”

Section: Multimodal Fusion Microexpression Recognition Methodmentioning

confidence: 99%

Research on a Microexpression Recognition Technology Based on Multimodal Fusion

et al. 2021

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Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexpression recognition work. In this paper, we propose a microexpression recognition method based on multimodal fusion through a comparative study of traditional microexpression recognition algorithms such as LBP algorithm and CNN and LSTM deep learning algorithms. The method couples the separate microexpression image information with the corresponding body temperature information to establish a multimodal fusion microexpression database. This paper firstly introduces how to build a multimodal fusion microexpression database in a laboratory environment, secondly compares the recognition accuracy of LBP, LSTM, and CNN + LSTM networks for microexpressions, and finally selects the superior CNN + LSTM network in the comparison results for model training and testing on the test set under separate microexpression database and multimodal fusion database. The experimental results show that a microexpression recognition method based on multimodal fusion designed in this paper is more accurate than unimodal recognition in multimodal recognition after feature fusion, and its recognition rate reaches 75.1%, which proves that the method is feasible and effective in improving microexpression recognition rate and has good practical value.

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“…In online education, there was also variation between the different studies. The work presented in Wu et al (2020) stood out for focusing on the teacher and for analyzing teacher gestures, behavior, and especially body position/pose, as well as proposing a general model of human joint positions which they used to model teacher movement in an open online classroom. Some studies, such as Brodny (2017), fused various video sources and student data from the platform.…”

Section: Multimodal Datamentioning

confidence: 99%

“…There were also a number of studies in the later or decision fusion category, based on fusing the predictions made by the different classifiers constructed from the different data sources. Three studies used the “ensembles” approach to fuse the decisions (Chango, Cerezo, & Romero, 2021; Chango, Cerezo, Sanchez‐Santillan, et al, 2021; Wu et al, 2020). There were also four studies which based decision fusion on the decision made by the classifier with the best predictive ability (N. L. Henderson, Rowe, Mott, & Lester, 2019; N. Henderson et al, 2020; Peng & Nagao, 2021; Monkaresi et al, 2017).…”

Section: Data Fusion Techniques In Multimodal La/edmmentioning

confidence: 99%

A review on data fusion in multimodal learning analytics and educational data mining

Chango

Lara²,

Cerezo

et al. 2022

WIREs Data Min & Knowl

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The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.

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The Recognition of Teacher Behavior Based on Multimodal Information Fusion

Cited by 20 publications

References 23 publications

Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

Research on a Microexpression Recognition Technology Based on Multimodal Fusion

A review on data fusion in multimodal learning analytics and educational data mining

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