作者机构：
National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China;National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China;School of Computer Science, Wuhan University, Wuhan, People’s Republic of China;[Jingying Chen; Junlin Hu; Kun Zhang; Xiao Zeng; Yuhao Ma; Wenrui Lu; Kai Zhang] National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China;[Guangshuai Wang] National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>School of Computer Science, Wuhan University, Wuhan, People’s Republic of China
通讯机构：
[Kun Zhang; Guangshuai Wang] N;National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>School of Computer Science, Wuhan University, Wuhan, People’s Republic of China
作者机构：
[Peng Shixin; Chen Kai; Tian Tian; Chen Jingying] National Engineering Research Center for E-Learning, National Engineering Laboratory for Educational Big Data, Central China Normal University, Hubei, 430079, China
通讯机构：
[Chen Jingying] N;National Engineering Research Center for E-Learning, National Engineering Laboratory for Educational Big Data, Central China Normal University, Hubei, 430079, China
摘要：
Although speech emotion recognition is challenging, it has broad application prospects in human-computer interaction. Building a system that can accurately and stably recognize emotions from human languages can provide a better user experience. However, the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition, and they do not effectively simulate the inter-modality dynamics in speech emotion recognition tasks. This paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion recognition. The proposed method consists of three parts: two high-level feature extractors for text and audio modalities, and an autoencoder-based feature fusion. For audio modality, we propose a structure called Temporal Global Feature Extractor (TGFE) to extract the high-level features of the time-frequency domain relationship from the original speech signal. Considering that text lacks frequency information, we use only a Bidirectional Long Short-Term Memory network (BLSTM) and attention mechanism to simulate an intra-modal dynamic. Once these steps have been accomplished, the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion classification. We conducted extensive experiments on three public benchmark datasets to evaluate our method. The results on Interactive Emotional Motion Capture (IEMOCAP) and Multimodal EmotionLines Dataset (MELD) outperform the existing method. Additionally, the results of CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) are competitive. Furthermore, experimental results show that compared to unimodal information and autoencoder-based feature level fusion, the joint multimodal information (audio and text) improves the overall performance and can achieve greater accuracy than simple feature concatenation.
期刊：
Education and Information Technologies,2022年27(6):8751-8770 ISSN：1360-2357
通讯作者：
Yating Li
作者机构：
[Zhou, Chi; Chen, Min; Wang, Yiming] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Li, Yating] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yating Li] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China
关键词：
Teacher burnout;School ICT construction;Teacher information literacy;Mediation analysis;fsQCA;Educational informatization
摘要：
Understanding the factors related to teacher burnout can support school administrators and teachers in optimizing the direction of school development and reducing teacher burnout. This study investigated the impact of school information and communication technology (ICT) construction and teacher information literacy on teacher burnout and explored the combined effects of the above factors by using a structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Through the analysis of 7,979 primary and secondary school teachers, the study found that the construction of high-quality school hardware facilities, software facilities and advanced ICT-related policies can reduce teacher burnout and affirmed the intermediary role of teacher information literacy. Some development suggestions on the collaborative improvement in schools and teachers have been put forward to provide ideas for reducing teacher burnout in the intelligent era.
作者机构：
[Rong, Wenting; He, Zili; Zhao, Liang; Yang, Qiaolai; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Rong, Wenting; He, Zili; Dai, Zhicheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Liang Zhao; Zhicheng Dai] N;National Engineering Research Center for E-Learning, Central China Normal University, WuHan 430079, PR China<&wdkj&>National Engineering Research Center for Educational Big Data, Central China Normal University, WuHan 430079, PR China
关键词：
Head pose estimation;Standard luminance;Center offset loss;Border adjustment;Feature fusion
摘要：
Head pose estimation (HPE) is widely used in attention detection, behavior analysis, and expression recognition. Nevertheless, in some complex scenes (such as facial occlusion, large head deflection angle, and multi-person in one scene), HPE still has the problem of low estimation accuracy. To solve this problem, we propose a dual position feature fusion method for estimating head pose. First, the RGB input is replaced with a standard luminance, which reduces the effect of extraneous light factors. Subsequently, the center offset loss is used to detect the head and body position, and dynamic adjustment strategy is used to deflate the border, aiming to not only obtain the best confidence level but also improve the capability of multi-person HPE. Finally, the esti-mate results under head position and body position are fused to further reduce the estimate loss. We tested our approach on the popular public AFLW2000, BIWI, and UPNA datasets, the results show the superiority of our approach in solving the occlusion, deflection, and multi-person scene problems.
期刊：
Learning and Instruction,2022年77:101520 ISSN：0959-4752
通讯作者：
Jiumin Yang
作者机构：
[Pi, Zhongling] Key Laboratory of Modern Teaching Technology (Ministry of Education), Shaanxi Normal University, No. 199 Chang'an Road, Yanta District, Shaanxi Xi'an, Shaanxi Province 710062, China;[Zhu, Fangfang] National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, No. 152 Luoyu Road, Hongshan District, Wuhan, Hubei Province 430079, China;[Zhang, Yi; Yang, Jiumin; Chen, Louqi] Faculty of Artificial Intelligence in Education, Central China Normal University, No. 152 Luoyu Road, Hongshan District, Wuhan, Hubei Province 430079, China
通讯机构：
[Jiumin Yang] F;Faculty of Artificial Intelligence in Education, Central China Normal University, No. 152 Luoyu Road, Hongshan District, Wuhan, 430079, Hubei Province, China
关键词：
Beat gesture;Complexity of visual learning material;Head nod;Rhythmic movements;Video lecture
作者：
Hai Liu;Tingting Liu;Yu Chen;Zhaoli Zhang;You-Fu Li
期刊：
IEEE Transactions on Multimedia,2022年:1-12 ISSN：1520-9210
作者机构：
School of Education, Hubei University, No. 368 Youyi Road, Wuhan, Hubei, China;Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong;[Hai Liu; Yu Chen; Zhaoli Zhang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;City University of Hong Kong Shenzhen Research Institute, Shenzhen, China;[Tingting Liu] School of Education, Hubei University, No. 368 Youyi Road, Wuhan, Hubei, China<&wdkj&>Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong
摘要：
Human pose estimation (HPE) has many wide applications such as multimedia processing, behavior understanding and human-computer interaction. Most previous studies have encountered many constraints, such as restricted scenarios and RGB inputs. To mitigate constraints to estimating the human poses in general scenarios, we present an efficient human pose estimation model (i.e., EHPE) with joint direction cues and Gaussian coordinate encoding. Specifically, we propose an anisotropic Gaussian coordinate coding method to describe the skeleton direction cues among adjacent keypoints. To the best of our knowledge, this is the first time that the skeleton direction cues is introduced to the heatmap encoding in HPE task. Then, a multi-loss function is proposed to constrain the output to prevent the overfitting. The Kullback-Leibler divergence is introduced to measure the predication label and its ground truth one. The performance of EHPE is evaluated on two HPE datasets: MS COCO and MPII. Experimental results demonstrate that EHPE can obtain robust results, and it significantly outperforms existing state-of-the-art HPE methods. Lastly, we extend the experiments on infrared images captured by our research group. The experiments achieved the impressive results regardless of insufficient color and texture information. Author
摘要：
Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.
摘要：
Knowledge graphs are multi-relational data that contain massive entities and relations. As an effective graph representation technique based on deep learning, graph neural network has reported outstand-ing performance for modeling knowledge graphs in recent studies. However, previous graph neural network-based models have not fully considered the heterogeneity of knowledge graphs. Furthermore, the attention mechanism has demonstrated its great potential in many areas. In this paper, a novel heterogeneous graph neural network framework based on a hierarchical attention mechanism is proposed, including entity-level, relation-level, and self-level attentions. Thus, the proposed model can selectively aggregate informative features and weights them adequately. Then the learned embeddings of entities and relations can be utilized for the downstream tasks. Extensive experimental results on various heterogeneous graph tasks demonstrate the superior performance of the proposed model compared to several state-of-the-art methods. (C) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.;[Chen, Hao] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.;[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Li, Youfu] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
会议名称：
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
会议时间：
JUN 18-24, 2022
会议地点：
New Orleans, LA
会议主办单位：
[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.^[Chen, Hao] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.^[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Li, Youfu] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
会议论文集名称：
IEEE Conference on Computer Vision and Pattern Recognition
摘要：
Event cameras attract researchers' attention due to their low power consumption, high dynamic range, and extremely high temporal resolution. Learning models on event-based object classification have recently achieved massive success by accumulating sparse events into dense frames to apply traditional 2D learning methods. Yet, these approaches necessitate heavy-weight models and are with high computational complexity due to the redundant information introduced by the sparse-to-dense conversion, limiting the potential of event cameras on real-life applications. This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models. To this end, we introduce a novel graph representation for event data to exploit their sparsity better and customize a lightweight voxel graph convolutional neural network (EV-VGCNN) for event-based classification. Specifically, (1) using voxel-wise vertices rather than previous point-wise inputs to explicitly exploit regional 2D semantics of event streams while keeping the sparsity; (2) proposing a multi-scale feature relational layer (MFRL) to extract spatial and motion cues from each vertex discriminatively concerning its distances to neighbors. Comprehensive experiments show that our model can advance state-of-the-art classification accuracy with extremely low model complexity (merely 0.84M parameters).
作者机构：
[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.;[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
会议名称：
IEEE International Performance, Computing, and Communications Conference (IPCCC)
会议时间：
NOV 11-13, 2022
会议地点：
Austin, TX
会议主办单位：
[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.^[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
会议论文集名称：
IEEE International Performance Computing and Communications Conference (IPCCC)
期刊：
Education and Information Technologies,2022年27(9):13067-13087 ISSN：1360-2357
通讯作者：
Yating Li
作者机构：
[Li, Yating; Liu, Yanqiu; Chen, Min; Li, Zhaoang] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China.;[Chen, Min] Minist Educ, Res Ctr Sci & Technol Promoting Educ Innovat & De, Strateg Res Base, Wuhan 430079, Hubei, Peoples R China.;[Chen, Min] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yating Li] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Teachers' information literacy;Principals' information leadership;Organizational climate;ICT implementation strategies;Intermediary chain analysis
摘要：
Teacher information literacy is an important aspect of teachers' professional development and is affected by the school environment. From the perspective of principals, this study discusses the impact of principals' information leadership (PIL), organizational climate (OC), and ICT implementation strategies (IMS) on teachers' information literacy (TIL) and further analyzes the complex system of TIL and the relationship between these factors. Through chain intermediary analysis, this study shows that teachers' information literacy is not only directly affected by their principals' information leadership but also indirectly affected by the chain intermediary effect of organizational climate and ICT implementation strategies. It provides insights into how to cultivate teachers' information literacy in the school system and puts forward some optimization paths to create the best conditions for improving teachers' information literacy.
期刊：
Journal of Educational Computing Research,2022年60(5):1130-1165 ISSN：0735-6331
作者机构：
[Zhang, Mingyan] Zhejiang Normal Univ, Coll Teacher Educ, Jinhua, Zhejiang, Peoples R China.;[Li, Hao; Du, Xu; Liu, Mengfan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, 1910 Univ Dr, Boise, ID 83725 USA.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Columbia, SC USA.
关键词：
Long Short-Term Memory autoencoder;learning pattern;time-series turning points;learning performance
摘要：
Journal of Educational Computing Research, Volume 60, Issue 5, Page 1130-1165, September 2022. <br/>In online learning, students’ learning behavior might change as the course progresses. How students adjust learning behaviors aligned with course requirements reflects their self-regulated learning strategies. Analyzing students’ learning patterns can help instructors understand how the course design or activities shape students’ learning behaviors, including their learning beliefs and motivation, and facilitate teaching decision makings accordingly. This study aims to propose a scientific analytic method to understand students’ self-regulated learning (SRL) patterns. The whole process includes the following four steps: (1) encoding behavioral patterns; (2) detecting turning points and chunking behavioral patterns; (3) grouping similar patterns; and (4) interpreting results. A case study with 4604 K-12 students from 476 courses was conducted to validate the proposed method. Five successful patterns, three at-risk patterns, and three average patterns were identified. The case study indicated that successful students showed at least one of the following characteristics: (1) Balanced, (2) Proactive and Balanced, and (3) Balanced with one highly engaged behavior. The at-risk students showed the following characteristics: (1) Oscillatory and (2) Low Engaged. Patterns which led to successful or at-risk conditions are compared and connected with corresponding SRL strategies. Practical and research implications are discussed in the article as well.
作者：
Li, Zhifei;Liu, Hai*;Zhang, Zhaoli;Liu, Tingting;Xiong, Neal N.
期刊：
IEEE Transactions on Neural Networks and Learning Systems,2022年33(8):3961-3973 ISSN：2162-237X
通讯作者：
Liu, Hai
作者机构：
[Zhang, Zhaoli; Li, Zhifei; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Tingting] Hubei Univ, Sch Educ, Wuhan 430062, Peoples R China.;[Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Xiong, Neal N.] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
通讯机构：
[Liu, Hai] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.
期刊：
IEEE ROBOTICS AND AUTOMATION LETTERS,2022年7(2):1976-1983 ISSN：2377-3766
通讯作者：
Li, YF
作者机构：
[Li, Youfu; Xie, Bochen; Deng, Yongjian] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.;[Shao, Zhanpeng] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China.;[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Li, YF ] C;City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
