摘要：
Reading as a key skill in learning has always received much attention. However, in traditional reading, students passively accept the knowledge taught by teachers, reading still remains superficial, lacking the experience of personal emotions, in-depth thinking and the cultivation of creative thinking, thus lacking deep dialogue between people and texts. In addition, the psychological state of the reading process is difficult to capture, and can only be detected from the final score, thus lacking a deep dialogue between people. In recent years, the deep integration of information technology and education has brought vitality to education. In order to break the predicament of reading teaching. This paper explores a visual reading teaching model supported by a smart environment, which aims to strengthen the deep dialogue of multiple objects in the reading course, so as to achieve the purpose of deep reading.
摘要：
Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.
期刊：
Lecture Notes in Computer Science,2019年11854:116-127 ISSN：0302-9743
通讯作者：
Song, Wu
作者机构：
[Yu, Xinguo; Song, Wu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Song, Wu] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
9th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议时间：
NOV 18-22, 2019
会议地点：
Sydney, AUSTRALIA
会议主办单位：
[Song, Wu;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Exam invigilation video;Video scene classification;Convolutional neural network
摘要：
This paper presents a double channel 3D convolution neural network to classify the exam scenes of invigilation videos. The first channel is based on the C3D convolution neural network, which is the status-of-arts method of the video scene classification. The structure of this channel is redesigned for classifying the exam-room scenes of invigilation videos. Another channel is based on the two-stream convolution neural network using the optical flow graph sequence as its input. This channel uses the data from the optical flow of video to improve the performance of the video scene classification. The formed double channel 3D convolution neural network has appropriate size of convolution kernel and pooling kernel design. Experiments show that the proposed neural network can classify the exam-room scenes of invigilation videos faster and more accurately than the existing methods.
作者机构：
[Wu, Di; Zhu, Sha; Yu, Liqin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Dept Curriculum & Instruct, Oswego, NY USA.;[Yang, Harrison Hao] Cent China Normal Univ, Dean Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.;[MacLeod, Jason] DYouville Univ, Buffalo, NY USA.
通讯机构：
[Wu, Di] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
关键词：
Information literacy;Teenage students;Influential factors;Social cognitive theory
作者：
Shi, Yinghui;Yang, Huiyun;Zhang, Jingman;Wang, Shimeng;Yang, Harrison Hao*
期刊：
2019 INTERNATIONAL SYMPOSIUM ON EDUCATIONAL TECHNOLOGY (ISET 2019),2019年:276-280
通讯作者：
Yang, Harrison Hao
作者机构：
[Shi, Yinghui; Zhang, Jingman; Yang, Harrison Hao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Yang, Huiyun] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Wang, Shimeng] Cent China Normal Univ, Collaborat & Innovat Ctr Educ Technol, Wuhan, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 13126 USA.
通讯机构：
[Yang, Harrison Hao] C;[Yang, Harrison Hao] S;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;SUNY Coll Oswego, Sch Educ, Oswego, NY 13126 USA.
会议名称：
International Symposium on Educational Technology (ISET)
会议时间：
JUL 02-04, 2019
会议地点：
Univ Hradec Kralove, Hradec Kralove, CZECH REPUBLIC
摘要：
Research to date has been controversial with regard to the effectiveness of interactive whiteboard-based classroom instructions on student learning outcomes. The purpose of the present study was to identify high-quality empirical publications that examine the learning outcomes of students and to utilize meta-analyses to determine the overall effectiveness of interactive whiteboard-based instructions. A total of 23 studies were included in this systematic review. The calculated effect size showed that interactive whiteboard-based instructions can positively influence students' cognitive learning outcomes compared to traditional lectures. Moderator variable analysis suggests that the pedagogical approach and the year of publication significantly moderate the effectiveness of interactive whiteboard-based classroom instruction. These results indicate that the interactive whiteboard-based instructional model proves mature reliable Idler several years of application in educational environments, and it helps students to improve their cognitive learning across all currently available interdisciplinary research reports. The interactive whiteboard-based was also found to he more effective when instructors integrate active or collaborative pedagogical approaches.
期刊：
International Journal of Pattern Recognition and Artificial Intelligence,2019年33(7):1940006 ISSN：0218-0014
通讯作者：
Zhang, Fayong
作者机构：
[Zhang, Fayong; Fang, Fang; Liu, Yuanyuan; Li, Xingmei] China Univ Geosci, Fac Informat Engn, Wuhan, Hubei, Peoples R China.;[Zeng, Zhizhong; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhang, Fayong] C;China Univ Geosci, Fac Informat Engn, Wuhan, Hubei, Peoples R China.
摘要：
Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.
作者机构：
[Lu, Wei; Wen, Xiaoqiao; Wang, Yongliang] Air Force Early Warning Acad, Wuhan 430072, Hubei, Peoples R China.;[Hua, Xiaoqiang] Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhong, Liang] China Univ Geosci, Dept Commun Syst, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Lu, Wei] A;Air Force Early Warning Acad, Wuhan 430072, Hubei, Peoples R China.
作者机构：
[Li, Guangqiang; Huo, Yanzhu; Huang, Ao] Wuhan Univ Sci & Technol, State Key Lab Refractories & Met, Wuhan 430081, Hubei, Peoples R China.;[Yang, Juan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yang, Juan] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词：
big data;data mining;electrical conductivity;oxide melts
摘要：
Electrical conductivity is one of the most basic physical(-)chemical properties of oxide-based melts and plays an important role in the materials and metallurgical industries. Especially with the metallurgical melt, molten slag, existing research studies related to slag conductivity mainly used traditional experimental measurement approaches. Meanwhile, the idea of data-driven decision making has been widely used in many fields instead of expert experience. Therefore, this study proposed an innovative approach based on big data mining methods to investigate the computational simulation and prediction of electrical conductivity. Specific mechanisms are discussed to explain the findings of our proposed approach. Experimental results show slag conductivity can be predicted through constructing predictive models, and the Gradient Boosting Decision Tree (GBDT) model is the best prediction model with 90% accuracy and more than 88% sensitivity. The robustness result of the GBDT model demonstrates the reliability of prediction outcomes. It is concluded that the conductivity of slag systems is mainly affected by TiO(2), FeO, SiO(2), and CaO. TiO(2) and FeO are positively correlated with conductivity, while SiO(2) and CaO have negative correlations with conductivity.
作者机构：
[Liu, Leyuan; Zhang, Li; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
26th IEEE International Conference on Image Processing (ICIP)
会议时间：
SEP 22-25, 2019
会议地点：
Taipei, TAIWAN
会议主办单位：
[Liu, Leyuan;Zhang, Li;Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Image Processing ICIP
关键词：
Frontal face synthesis;Face normalization;GAN;Face recognition
摘要：
This paper proposes a Progressive Pose-Normalization Generative Adversarial Network (PPN-GAN) for frontal face synthesis and face recognition. The key idea is to normalize a profile face progressively: starting from inferring an intermediate face that has a small view difference to the profile face, and then increasing the view difference step by step, until the frontal view of the profile face is recovered. In addition to the progressive strategy, an additional identity discriminator and identity-aware losses in both the image and feature spaces are also incorporated into the GAN for identity preserving. Experimental results show that our method not only produces compelling perceptual results but also outperforms the state-of-the-art methods on face recognition under large-pose.
摘要：
微博作为时下热门的社交网络平台,针对其所产生的评论文本进行情感分析已经成为人工智能领域的一个研究热点。考虑到虚假评论会降低情感分析的准确度,从评论用户的状态和行为出发,提出一种基于用户状态与行为的可信度评价体系,用于提取虚假评论特征。结合该特征与PU（Positive and unlabeled）学习算法进行虚假评论识别;运用SVM分类器和随机梯度下降回归模型对去除虚假评论的文本进行主观句分类与情感分析。实验表明,进行虚假评论识别后的情感分析准确率、召回率分别达到0.88和0.89,比传统方法具有更高的分析效能。
作者机构：
[Zhang C.; He X.; Fang J.; Chen Z.; Wu K.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China
会议名称：
International Conference on Intelligent Computing, Communication and Devices, ICCD 2017
会议时间：
9 December 2017 through 10 December 2017
关键词：
Dynamic time warping;Gesture segmentation;Real-time gesture recognition;RealSense;Teaching gesture design
作者机构：
[Liu, Yanshen; Liu, Sanya; Wu, Huiting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Liu, Yi] Cent China Normal Univ, Educ Informatizat Res Ctr Hubei, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Wu, Huiting] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
期刊：
IEEE-ASME Transactions on Mechatronics,2019年24(1):384-394 ISSN：1083-4435
通讯作者：
Liu, Hai
作者机构：
[Liu, Sannyuya; Liu, Tingting; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Liu, Tingting] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA.;[Li, Youfu; Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
通讯机构：
[Liu, Hai] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词：
FTIR imaging spectrometers;instrumentation;mechatronics industry;optical data processing;robot vision;wavelet transforms
摘要：
Fourier transform infrared (FTIR) imaging spectrometers are often corrupted by the problems of band overlap and random noise during the infrared spectrum acquisition process. Such noise would degrade the quality of the acquired infrared spectrum, limiting the precision of the subsequent processing. In this paper, we present a novel blind reconstruction method with wavelet transform regularizations for infrared spectrum obtained from the aging instrument. Inspired by the finding that the wavelet coefficient distribution of the clean spectrum is sparser than that of the degraded spectrum, a blind reconstruction model for infrared spectrum is proposed in this paper to regularize the distribution of the degraded spectrum by total variation regularization. This method outperforms when suppressing random noise and preserving the spectral structure details. In addition, an effective optimization scheme is introduced in overcoming the issue of formulated optimization. The instrument response function and latent spectrum can be simultaneously estimated through the proposed method that can efficiently mitigate the effects caused by instrument degradation. Finally, extensive experiments on simulated and real noisy infrared spectra are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones. Thus, the reconstructed spectrum will better serve the feature extraction and educational robot infrared vision sensing in industrial applications.
期刊：
Journal of Educational Computing Research,2019年58(1):63-86 ISSN：0735-6331
通讯作者：
Liu, Hai
作者机构：
[Liu, Sannyuya; Li, Zhenhua; Cao, Taihe; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Li, Zhenhua] China West Normal Univ, Network & Informat Management Ctr, Nanchong, Peoples R China.;[Liu, Hai] Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, Hai] N;Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
摘要：
Online learning engagement detection is a fundamental problem in educational information technology. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real time. To improve the accuracy of learning engagement detection, we have collected two aspects of students’ behavior data: face data (using adaptive weighted Local Gray Code Patterns for facial expression recognition) and mouse interaction. In this article, we propose a novel learning engagement detection algorithm based on the collected data (students’ behavior), which come from the cameras and the mouse in the online learning environment. The cameras were utilized to capture students’ face images, while the mouse movement data were captured simultaneously. In the process of image data labeling, we built two datasets for classifier training and testing. One took the mouse movement data as a reference, while the other did not. We performed experiments on two datasets using several methods and found that the classifier trained by the former dataset had a better performance, and its recognition rate is higher than that of the latter one (94.60% vs. 91.51%).
作者机构：
[Liu, Sannyuya; Zhao, Liang; Yan, Zhonghua] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Liu, Sannyuya; Zhao, Liang; Yan, Zhonghua] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.;[Cheng, Xiufeng] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yan, Zhonghua] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
