期刊：
Artificial Intelligence in Medicine,2023年145:102677 ISSN：0933-3657
通讯作者：
Jiang, XP
作者机构：
[Fu, Chengcheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Jiang, Xingpeng; Fu, Chengcheng; He, Tingting] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Fu, Chengcheng; van Harmelen, Frank; Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands.;[Fu, Chengcheng; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Natl Language Resources Monitor Res Ctr Network Me, Wuhan, Peoples R China.;[Huang, Zhisheng] Tongji Univ, Sch Med, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr, Shanghai, Peoples R China.
通讯机构：
[Jiang, XP ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
关键词：
Food;Gut microbiota;Knowledge graph;Mental health
作者：
Du, Xu;Dai, Miao;Tang, Hengtao;Hung, Jui-Long;Li, Hao;...
期刊：
Journal of Computing in Higher Education,2023年35(2):272-295 ISSN：1042-1726
通讯作者：
Hengtao Tang
作者机构：
[Dai, Miao; Hung, Jui-Long; Li, Hao; Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Columbia, SC 29208 USA.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID USA.;[Zheng, Jinqiu] Guangdong Med Univ, Dongguan 523808, Guangdong, Peoples R China.
通讯机构：
[Hengtao Tang] D;Department of Educational Studies, University of South Carolina, Columbia, SC, United States
关键词：
Cognitive load;Collaborative problem solving;Computer networking;Virtual experimentation;Online learning
期刊：
LIBRARY HI TECH,2023年41(4):1039-1062 ISSN：0737-8831
作者机构：
[Li, Yating] National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China;[Zhou, Chi; Wu, Di; Chen, Min] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Teachers’ information literacy;Information literacy evaluation;Online information behavior;Online learning and teaching platform;Process evaluation;Supervised learning models
摘要：
Purpose
Advances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).
Design/methodology/approach
First, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.
Findings
The results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.
Originality/value
The evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.
期刊：
Journal of Autism and Developmental Disorders,2023年53(6):2314-2327 ISSN：0162-3257
通讯作者：
Jingying Chen
作者机构：
[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.;[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Liao, Mengyi] Pingdingshan Univ, Coll Comp Sci & Technol, Pingdingshan 467000, Henan, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, People’s Republic of China
期刊：
Multimedia Tools and Applications,2023年82(9):14091-14105 ISSN：1380-7501
通讯作者：
Shixin Peng
作者机构：
[Peng, Shixin; Tan, Lei; Chen, Chang; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Shixin Peng] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Person re-identification;Cross modality;Channel decoupling
摘要：
Cross-modality person re-identification (CM-ReID) is a very challenging problem due to the discrepancy in data distributions between visible and near-infrared modalities. To obtain a robust sharing feature representation, existing methods mainly focus on image generation or feature constrain to decrease the modality discrepancy, which ignores the large gap between mixed-spectral visible images and single-spectral near-infrared images. In this paper, we address the problem by decoupling the mixed-spectral visible images into three single-spectral subspaces R, G, and B. By aligning the spectrum, we noted that even using a single spectral image instead of the VIS images could result in a better performance. Based on the above observation, we further introduce a clear and effective three-path channel decoupling network (CDNet) for combining the three spectral images. Extensive experiments implemented on the benchmark CM-ReID datasets, SYSU-MM01 and RegDB indicated that our method achieved state-of-the-art performance and outperformed existing approaches by a large margin. On the RegDB dataset, the absolute gain of our method in terms of rank-1 and mAP is well over 15.4% and 8.5%, respectively, compared with the state-of-the-art methods.
作者：
Su, Zhu;Li, Yue;Liu, Zhi;Sun, Jianwen;Yang, Zongkai;...
期刊：
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT,2023年71(5):1941-1963 ISSN：1042-1629
通讯作者：
Liu, S
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, S ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi; Peng, Xian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Zhang, Ning] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.
通讯机构：
[Peng, X.] N;National Engineering Research Center for Educational Big Data, China
作者：
He, Xiuling;Fang, Jing;Cheng, Hercy N. H.;Men, Qibin;Li, Yangyang
期刊：
Education and Information Technologies,2023年28(9):11401-11422 ISSN：1360-2357
通讯作者：
Jing Fang
作者机构：
[He, Xiuling; Men, Qibin] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Hubei, Peoples R China.;[Fang, Jing; Li, Yangyang] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Hubei, Peoples R China.;[Cheng, Hercy N. H.] Taipei Med Univ, Ctr Gen Educ, Taipei City, Taiwan.
通讯机构：
[Jing Fang] N;National Engineering Research Center for E-learning, Central China Normal University, Wuhan City, China
作者：
Chen, Min;Liu, Yanqiu;Yang, Harrison Hao;Li, Yating;Zhou, Chi
期刊：
Education and Information Technologies,2023年28(11):15011-15030 ISSN：1360-2357
通讯作者：
Chi Zhou
作者机构：
[Zhou, Chi; Chen, Min] Cent China Normal Univ, Educ Informatizat Strategy Res Base Minist Educ, Wuhan 430079, Hubei, Peoples R China.;[Li, Yating; Chen, Min] Cent China Normal Univ, Technol Comm Minist Educ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Ctr Strateg Studies Sci, Wuhan 430079, Hubei, Peoples R China.;[Liu, Yanqiu] Cent China Normal Univ, Key Res Inst Humanities & Social Sci Hubei Prov, Hubei Res Ctr Educ Informatizat Dev, Wuhan 430079, Hubei, Peoples R China.;[Li, Yating; Zhou, Chi; Liu, Yanqiu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Hubei, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 60543 USA.
通讯机构：
[Chi Zhou] E;Educational Informatization Strategy Research Base of Ministry of Education, Central China Normal University, Wuhan, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Online teacher professional development;Teacher participation;Participation frequency;Participation quality;Lag sequential analysis
期刊：
Information Processing & Management,2023年60(4):103350 ISSN：0306-4573
通讯作者：
Li, DTC;Shi, FB
作者机构：
[Zheng, Chao; Wang, Jian; Li, Duantengchuan; Wang, Jingxiong; Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Zhang, Qi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Shi, Fobo; Shi, FB] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Cai, Yuefeng] ZTE Corp, Wuhan 430223, Peoples R China.;[Wang, Xiaoguang; Zhang, Zhen] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Li, DTC ] W;[Shi, FB ] C;Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要：
Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively.
作者机构：
[Zhu, Songkai; Shen, Xiaoxuan; He, Xiuling; Fang, Jing; Li, Yangyang] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; He, Xiuling; Fang, Jing; Li, Yangyang] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Xiuling He] N;National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China
摘要：
Programming online judges (POJs) are widely used to train programming skills, and exercise recom-mendation algorithms in POJs have attracted wide attention. The current programming recommen-dation algorithms cannot make full use of the feedback of user-item pairs and cannot effectively express students' mastery of exercises. Therefore, we propose a dual-track feedback aggregation recommendation model for programming training (DTFARec). In this model, multiple types of feedback fusion mechanism (MTFFM) and dual-track method (DTM) are proposed to solve this problem and can better express students' mastery of exercises. The MTFFM uses an attention mechanism to learn different feedback information, and the DTM is able to fuse information from both feedback and interactive aspects. The experimental results on a real-world dataset show that the model has better recommendation performance than the best performing benchmark and that our method can effectively model students' mastery of exercises.(c) 2022 Elsevier B.V. All rights reserved.
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：
IEEE Transactions on Industrial Informatics,2023年:1-11 ISSN：1551-3203
通讯作者：
Yang, B;Liu, H
作者机构：
[Yang, Bing; Liu, Tingting] Hubei Univ, Sch Educ, 368 Youyi Rd, Wuhan 430062, Hubei, Peoples R China.;[Yang, Bing; Liu, Tingting] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.;[Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Yang, B ] H;[Liu, H ] C;Hubei Univ, Sch Educ, 368 Youyi Rd, Wuhan 430062, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
2D Human pose estimation (HPE) has been widely used in the many fields such as behavioral understanding, identity authentication, and industrial automatic manufacturing. Most of the previous studies have encountered many constraints, such as restricted scenarios and strict inputs. To solve this problem, we present a simple yet effective HPE network called limb direction cues-aware network (LDCNet) with limb direction cues and differentiated Cauchy labels, which can efficiently suppress uncertainties and prevent deep networks from over-fitting uncertain keypoint positions. In particular, LDCNet suppresses the uncertainties from two aspects. (1) A differentiated Cauchy coordinate encoding method is designed to reveal the limb direction information among adjacent keypoints. (2) Jeffreys divergence is introduced as loss function to measure the prediction heatmap and ground-truth one. Positions of keypoints are perceived at the limb direction based deep network in an end-to-end manner. An extensive study on two benchmark data sets (i.e., MS COCO and MPII) illustrates the superiority of the proposed LDCNet model over state- of-the-art approaches.
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.
期刊：
IEEE Systems, Man, and Cybernetics Magazine,2023年9(1):25-36 ISSN：2380-1298
作者机构：
[Yuxin Liu; Jinsong Gui] School of Computer Science and Engineering, Central South University, Changsha, China;[N. Xiong] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hu Bei Province, China
摘要：
There will exist a growing interest in deploying data-intensive and content-rich applications on mobile smart devices. Also, ultrareliable and low-latency communications will be the critical requirements for obtaining good quality of experience for users of smart devices. However, the existing cellular architectures hardly provide a rich and stable spectrum supply to support ultrareliable and low-latency communications. Although future wireless networks are expected to effectively exploit the terahertz frequency band, it is difficult to obtain stable, ultrareliable, and low-latency communications due to the immaturity of both propagation models and radio interface technologies in such a high-frequency band. Therefore, this article introduces cognitive network brokers based on a data-driven cognitive network architecture to integrate and make full use of various resources to provide good network services for users, including an engine for spectrum and device cognition and an engine for cognitive network service construction.
