作者机构：
[Yang, Zongkai; Liu, Sannyuya; Kong, Xi; Liu, Zhi; Kong, X] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Kong, X ] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.
关键词：
computer-based mind mapping;reflection;cognitive presence;online learning;epistemic network analysis
摘要：
Reflection plays a very important role in the learning process, contributing to improved learning performance and potentially influencing cognitive process. Few studies, however, have used computer-based mind mapping to enhance student reflective activities and examine the relationship between reflection, cognitive presence, and learning outcomes. Therefore, a quasi-experiment was implemented by recruiting students from a big data class at a normal university in central China. The collected data was analyzed by jointly using analysis of covariance, cognitive network analysis, linear regression, and moderating effect analysis. The results were as follows: (a) Students who used computer-based mind mapping performed better on reflection, higher-order cognitive presence, and learning outcomes. (b) The epistemic network analysis showed that students who used computer-based mind mapping had strong connections in higher levels of cognitive presence. (c) Reflection had a positive predictive effect on cognitive presence and learning outcomes, with mind mapping positively moderating the relationship between reflection, cognitive presence, and learning outcomes.
摘要：
Existing discrete cosine transform single-pixel imaging (DCT-SPI) improves the imaging quality, but its number of measurements is twice as the number of pixels of the illumination pattern under full sampling. To reduce the number of measurements, in this letter we propose a single-pixel imaging method called positive discrete cosine transform single-pixel imaging (PDCT-SPI). In the proposed method, only the positive patterns are employed to reconstruct the image. Thus, the number of measurements is reduced by 1/2. Based on the characteristics of Fourier series, the background noise is eliminated by subtracting the average of the detected values to guarantee the imaging quality of PDCT-SPI. Experimental results show that under the same sampling rates, the image quality reconstructed by PDCT-SPI is similar as DCT-SPI, the number of measurements of PDCT-SPI is only half of DCT-SPI.
作者机构：
[Liu, Leyuan; Sun, Jianchi; Gao, Yunqi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Liu, Leyuan; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China<&wdkj&>National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China
关键词：
Clothed 3D human reconstruction;Parametric body model;Single-image 3D reconstruction;Well-aligned model
摘要：
Reconstructing clothed 3D human models from a single image is rather challenging, since the information about the invisible areas of a human being has to be "guessed" by algorithms. To reduce the difficulty, current state-of-the-art methods usually employ a parametric 3D body model to guide the clothed 3D human reconstruction. However, the quality of reconstructed clothed 3D human models heavily depends on the accuracy of the parametric body model. To address this problem, we propose to employ a well-aligned parametric body model to guide single-image clothed 3D human reconstruction. First, the STAR model is adopted as the statistical model to represent the parametric body model, and a two-stage method that combines a regression-based approach and an optimization-based approach is proposed to estimate the pose and shape parameters iteratively. By incorporating the advantages of the statistical models and the parameter estimation method, a well-aligned 3D body model can be recovered from a single input image. Then, a deep neural network that fuses the 3D geometry information of the 3D parametric body model and the visual features extracted from the input image is proposed for reconstructing clothed 3D human models. Training losses that aim to align the reconstructed model with the ground-truth model respectively in the 3D model space and the multi-view 2D re-projection spaces are designed. Quantitative and qualitative experimental results on three public datasets (THuman, BUFF, and LSP) show that our method produces more accurate and robust clothed 3D human reconstructions compared to the state-of-the-art methods.
期刊：
DATA TECHNOLOGIES AND APPLICATIONS,2023年57(3):418-435 ISSN：2514-9288
通讯作者：
Hung, J.-L.
作者机构：
[Zhang, Lizhao] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.;[Li, Hao; Hu, Zhuang; Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.
通讯机构：
[Hung, J.-L.] D;Department of Educational Technology, United States
摘要：
Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans' sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.
作者机构：
[Zhang, Cheng; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.;[Deng, Yongjian] Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Co, Beijing, Peoples R China.;[Li, Youfu; Xie, Bochen] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.
会议名称：
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
会议时间：
JUN 17-24, 2023
会议地点：
Vancouver, CANADA
会议主办单位：
[Zhang, Cheng;Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.^[Deng, Yongjian] Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Co, Beijing, Peoples R China.^[Xie, Bochen;Li, Youfu] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.
会议论文集名称：
IEEE Conference on Computer Vision and Pattern Recognition
摘要：
Head pose estimation (HPE) has been widely used in the fields of human machine interaction, self-driving, and attention estimation. However, existing methods cannot deal with extreme head pose randomness and serious occlusions. To address these challenges, we identify three cues from head images, namely, neighborhood similarities, significant facial changes, and critical minority relationships. To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned. Specifically, we design several orientation tokens to explicitly encode the basic orientation regions. Meanwhile, a novel token guide multiloss function is designed to guide the orientation tokens as they learn the desired regional similarities and relationships. We evaluate the proposed method on three challenging benchmark HPE datasets. Experiments show that our method achieves better performance compared with state-of-the-art methods. Our code is publicly available at https://github.com/zc2023/TokenHPE.
期刊：
Asia Pacific Education Review,2023年:1-14 ISSN：1598-1037
通讯作者：
Li, MY
作者机构：
[Yang, Wei; Yang, Xiao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Lu, Chun] Minist Educ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Wuhan, Hubei, Peoples R China.;[Li, Miaoyun] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan, Hubei, Peoples R China.;[Lu, Chun] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China.;[Li, Miaoyun] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan, Hubei, Peoples R China.
通讯机构：
[Li, MY ] ;Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan, Hubei, Peoples R China.
关键词：
Perceived ICT competence;Academic performance;Multilevel analysis;Rural Chinese schools
摘要：
The relationship between Information and Communication Technology (ICT) and academic performance is a controversial issue that has attracted increasing attention from administrators, policymakers, and researchers. The relationship between perceived ICT competence and the academic performance of rural students deserves particular attention. Although a small but growing body of research has examined the relationship between perceived ICT competence and student academic performance, few studies have viewed perceived ICT competence as a multilevel construct. This study aimed to fill this gap by examining the relationship between multilevel perceived ICT competence (i.e., student- and school-level perceived ICT competence) and student academic performance using a sample of 5530 students from 156 schools in rural China. Two-level hierarchical linear modeling results indicated that student- and school-level perceived ICT competence could predict academic performance. Furthermore, school-level perceived ICT competence could moderate the relationship between student-level ICT competence and academic outcomes. Specifically, the role of student-level perceived ICT competence showed heterogeneity across schools. Academic performance was strongly correlated with student-level perceived ICT competence in schools with a low level of perceived ICT competence; in contrast, this outcome was not observed in schools with a high level of perceived ICT competence. The findings suggest that administrators and policymakers in China should pay special attention to rural schools where perceived ICT competence is low and consider providing services for students in these schools to promote educational equity.
期刊：
Innovations in Education and Teaching International,2023年 ISSN：1470-3297
通讯作者：
Du, X.;Dai, M.
作者机构：
[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Columbia, SC USA.;[Dai, Miao; Li, Hao; Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Hubei, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID USA.
通讯机构：
[Dai, M.; Du, X.] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China
期刊：
Journal of King Saud University - Computer and Information Sciences,2023年35(7):101605 ISSN：1319-1578
通讯作者：
Zhang, Q
作者机构：
[Wang, Xiaoguang; Wang, Shutong] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.;[Wang, Shutong] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Zhao, Anran] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.;[Lai, Chenghang] Fudan Univ, Sch Comp Sci, Shanghai 200438, Peoples R China.;[Zhang, Qi; Zhang, Q] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, Q ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Facial expression recognition;Graph convolutional network;Geometry cue;Uncertainty;Emotion label distribution learning
摘要：
Facial expression recognition (FER) task in the wild is challenging due to some uncertainties, such as the ambiguity of facial expressions, subjective annotations, and low-quality facial images. A novel model for FER in-the-wild datasets is proposed in this study to solve these uncertainties. The overview of the proposed method is as follows. First, the facial images are grouped into high and low uncertainties by the pre-trained network. The graph convolutional network (GCN) framework is then used for the facial images with low uncertainty to obtain geometry cues, including the relationship among action units (AUs) and the implicit connection between AUs and expressions, which help predict the probability of the underlying emotional label. The emotion label distribution is produced by combining the predicted latent label probability and the given label. For the facial images with high uncertainty, k-nearest neighbor graphs are built to determine the k facial images in the low uncertainty group with the highest similarity to the given facial image. The emotion label distribution of the given image is then replaced by fusing the emotion label distribution based on the distances between the given image and its adjacent images. Finally, the constructed emotion label distribution facilitates training in a straightforward manner using a convolutional neural network framework to identify facial expressions. Experimental results on RAF-DB, FERPlus, AffectNet, and SFEW2.0 datasets demonstrate that the proposed method achieved superior performance compared to state-of-the-art approaches. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
期刊：
Current Psychology,2023年42(27):23687-23697 ISSN：1046-1310
通讯作者：
Zhongling Pi
作者机构：
[Yang, Jiumin; Liu, Caixia; Zhang, Yi] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Wu, Changcheng] Cent China Normal Univ, Natl Engn Res Ctr Learning, Artificial Intelligence Educ Div, Wuhan 430079, Peoples R China.;[Wu, Changcheng] Sichuan Normal Univ, Coll Comp Sci, Chengdu 610101, Peoples R China.;[Pi, Zhongling] Shaanxi Normal Univ, Minist Educ, Key Lab Modern Teaching Technol, 199 South Changan Rd, Xian 710062, Shaanxi, Peoples R China.
通讯机构：
[Pi, Z.] K;Key Laboratory of Modern Teaching Technology (Ministry of Education), Shaanxi Normal University, No. 199 South Chang’an Road, Yanta District, Shaanxi Province, Xi’an, China
期刊：
Information Processing & Management,2023年60(1):103106 ISSN：0306-4573
通讯作者：
Jing Wang
作者机构：
[Yang, Shuoqiu; Li, Hao; Hu, Zhuang; Du, Xu; Wang, Jing] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Yang, Shuoqiu; Li, Hao; Hu, Zhuang; Du, Xu; Wang, Jing] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Jing Wang] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
关键词：
Bi-hypergraph network;Intelligent education;Knowledge hypergraph;Teaching image annotation;Visual-knowledge features fusion;Visual-knowledge inconsistency
期刊：
Neural Computing and Applications,2023年35(11):8343-8356 ISSN：0941-0643
通讯作者：
Baolin Yi
作者机构：
[Shen, Xiaoxuan; Wang, Wei; Zhang, Huanyu; Li, Zhifei; Yi, Baolin] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Baolin Yi] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Link prediction;Knowledge graph embedding;Convolution neural network;Feature interaction;Complex relations
摘要：
Most knowledge graphs(KGs) are large and incomplete graph-structure database, which can be completed by predicting miss links according to the existing knowledge. The mainstream method is knowledge graph embedding (KGE) which is designed to learn low dimensional embedding of entities and relations. However, knowledge graph embedding still faces two major issues: (1) How to generate more expressive embeddings? (2) How to solve semantic polysemy of entities in different relations? In this paper, we propose a novel KG embedding model, RIECN (Relation-based Interactive Embedding Convolutional Network), which achieves high-quality performance and shows some advancements in modeling complex relations. In RIECN, FIR (Feature Interaction Reshaping) method is introduced to increase the feature interactions between entity and relation embeddings to generate more expressive feature maps. In addition, a new method of generating relation-based dynamic convolution filters, RDCF, is proposed. RDCF generates specific relation and hybird-size convolution filters, which enriches the feature maps of each entity improving the accuracy of link prediction task especially in complex relations scenario. We tested the performance of our model on five benchmark datasets. The experimental results show that the RIECN model significantly outperforms recent state-of-the-art models by 0.1–3.2% and 1.1–3.7%, in terms of MMR metric and Hit@1 metric, respectively.
