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.
关键词：
Deep learning for visual perception;object detection, segmentation and categorization
摘要：
Event cameras can perceive pixel-level brightness changes to output asynchronous event streams, and have notable advantages in high temporal resolution, high dynamic range and low power consumption for challenging vision tasks. To apply existing learning models on event data, many researchers integrate sparse events into dense frame-based representations which can work with convolutional neural networks directly. Although these works achieve high performance on event-based classification, their models need lots of parameters to process dense event frames which do not fit with the sparsity of event data. To utilize the sparse nature of events, we propose a voxel-wise graph learning model (
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VMV-GCN</i>
) for spatio-temporal feature learning on event streams. Specifically, we design the volumetric multi-view fusion module (
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VMVF</i>
) to extract spatial and temporal information from views of voxelized event data. Then we take representative event voxels as vertices and use a novel dual-graph construction strategy to connect them. By aggregating neighborhood information based on relationships of vertices, the proposed dynamic neighborhood feature learning module (
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNFL</i>
) can capture discriminative spatio-temporal features on dynamically updated graphs. Experiments show that our method achieves state-of-the-art performance with low model complexity on event-based classification tasks, such as object classification and action recognition.
作者机构：
[Peng, Shixin; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Chen, Xiaohui] State Grid Hunan Elect Power Co Ltd, Informat & Commun Branch, Changsha 410004, Peoples R China.;[Lu, Wei] Air Force Early Warning Acad, Wuhan 430019, Peoples R China.;[Deng, Chao] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China.
通讯机构：
[Chen, JY ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
摘要：
Cellular communication provides an efficient, flexible, long-lived, and reliable communication technology for smart grids to improve the automated analysis, demand response, adoptive control, and coordination between the generator and consumers. With the expansion of wireless networks and the increase of access devices, interference has become a major problem that limits the performance of cellular wireless communication systems for smart grids. Spatial interference alignment (IA) is an effective method to eliminate interference and improve the capacity of wireless communication networks. This paper provides the sufficient conditions of spatial interference alignment operating with limited precoding matrix feedback for a K-user MIMO interference channel. Each receiver feeds the matrix index of the transmitting precoder back to the corresponding transmitter through an interference-free and error-free link. We calculated the number of feedback bits required to achieve the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as N-d & GE;d(i)(M-d(i))log(2)SNR, where M and d(i) denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results were verified by simulations for practical interference alignment schemes relying on limited precoding matrix feedback indices.
作者：
Zhang, Zhaoli;Li, Zhifei;Liu, Hai;Xiong, Neal N.
期刊：
IEEE Transactions on Knowledge and Data Engineering,2022年34(5):2335-2347 ISSN：1041-4347
通讯作者：
Li, ZF
作者机构：
[Li, Zhifei; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Li, ZF ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
作者机构：
[Yuan, Yishuang; Chen, Jingying; Zhang, Kun; Luo, Meijuan; Chen, Qian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Yuan, Yishuang; Chen, Jingying; Zhang, Kun; Luo, Meijuan; Chen, Qian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
通讯机构：
[Chen, JY ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
摘要：
Facial expression processing mainly depends on whether the facial features related to expressions can be fully acquired, and whether the appropriate processing strategies can be adopted according to different conditions. Children with autism spectrum disorder (ASD) have difficulty accurately recognizing facial expressions and responding appropriately, which is regarded as an important cause of their social disorders. This study used eye tracking technology to explore the internal processing mechanism of facial expressions in children with ASD under the influence of spatial frequency and inversion effects for improving their social disorders. The facial expression recognition rate and eye tracking characteristics of children with ASD and typical developing (TD) children on the facial area of interest were recorded and analyzed. The multi-factor mixed experiment results showed that the facial expression recognition rate of children with ASD under various conditions was significantly lower than that of TD children. TD children had more visual attention to the eyes area. However, children with ASD preferred the features of the mouth area, and lacked visual attention and processing of the eyes area. When the face was inverted, TD children had the inversion effect under all three spatial frequency conditions, which was manifested as a significant decrease in expression recognition rate. However, children with ASD only had the inversion effect under the LSF condition, indicating that they mainly used a featural processing method and had the capacity of configural processing under the LSF condition. The eye tracking results showed that when the face was inverted or facial feature information was weakened, both children with ASD and TD children would adjust their facial expression processing strategies accordingly, to increase the visual attention and information processing of their preferred areas. The fixation counts and fixation duration of TD children on the eyes area increased significantly, while the fixation duration of children with ASD on the mouth area increased significantly. The results of this study provided theoretical and practical support for facial expression intervention in children with ASD.
作者机构：
[Du, Xu; Zhang, Mingyan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China;[Shelton, Brett E.; Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA
通讯机构：
[Shelton, Brett E.] B;Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.
摘要：
The study proposes two new measures, time and location entropy, to depict students' physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been touted as one of the most attractive features of online learning, the question remains as to the success of students when engaging in online courses through disparate locations and points-in-time. The procedures describe an analysis of 5293 students' spatio-temporal patterns using metadata relating to place and time of access. Grouping into segments that describe their patterns of engagement, results indicate that the high location-high time entropy (i.e. multiple times, multiple locations) was the segment with lowest success when compared with other students. Statistical and modeling results also found that female students tended to learn at fixed or few locations resulting in the highest performance scores on the final exam. The primary implication is that female students tend to be successful because they study in fewer locations, and all students who study at consistent times outperform those with more varied time patterns. Existing brain research supports the findings on gender differences in learning performance and spatio-temporal characteristics.
期刊：
Expert Systems with Applications,2022年207:117680 ISSN：0957-4174
通讯作者：
Sannyuya Liu<&wdkj&>Qing Li
作者机构：
[Liu, Sannyuya; Zou, Rui; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Gao, Lu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Zou, Rui; Gao, Lu; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhang, Kai] Yangtze Univ, Sch Comp Sci, Jingzhou 434025, Peoples R China.;[Jiang, Lulu] Nanhai Expt Sch, Foshan 528299, Peoples R China.
通讯机构：
[Sannyuya Liu; Qing Li] N;National Engineering Laboratory for 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<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
期刊：
Journal of Baltic Science Education,2022年21(1):156-170 ISSN：1648-3898
通讯作者：
Lu, C.
作者机构：
[Xing, Danxia] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Lu, Chun] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
Educational Informatization Strategy Research Base Ministry of Education, Central China Normal University, Hubei, Wuhan, China
关键词：
computational thinking skills;Internet attitude;Internet self-efficacy;Internet use;smart classroom;secondary school students
期刊：
Frontiers in Psychology,2022年13:3735 ISSN：1664-1078
通讯作者：
Wang, M.
作者机构：
[Li, Miaoyun; Wang, Meiqian] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Reading literacy;ICT use;metacognition;ICT use intensity;moderated mediation analysis
摘要：
The use of information and communication technologies (ICT) is increasingly becoming prevalent among students, both at home and school. While inconsistent results were found for student ICT use and reading literacy, this study attempted to explain these ambiguous links with the moderation of ICT use intensity and mediation of metacognition. Three moderated mediation models for each type of ICT use (at home for entertainment activities and for schoolwork, as well as at school) were analyzed using a Hong Kong sample taken from the Programme for International Student Assessment (PISA) 2018 data pertaining to 5180 15-year-old students from 152 schools. A dynamic effect pattern was found for the links of all ICT use types and reading literacy with the increasing intensity of ICT use, which begins with a positive effect followed by a decrease to less positive, then turns to fluctuating negative and finally ends up with a stable negative effect. But the dominant effect varies across ICT use intensity, which result in different overall effects of three ICT use types. In addition, all three aspects of metacognition showed a profound negative mediation on links of intensive and excessive ICT use with reading literacy, and a less positive mediation for limited ICT use. The metacognition of assessing credibility showed a more important role than summarizing, which was followed by understanding and remembering. In light of the findings, the study recommended that more metacognitive scaffolds should be developed for students with intensive or excessive ICT use, so as to alleviate the side effects of ICT use on their reading literacy.
作者机构：
[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Wuhan, Hubei, Peoples R China.;[Wang, Guangshuai; Zhang, Kun; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Tang, Suyun] Guangzhou Univ, Sch Educ, Guangzhou, Guangdong, Peoples R China.;[Wang, Guanghai] Shanghai Jiao Tong Univ, Shanghai Childrens Med Ctr, Dept Dev & Behav Pediat, Pediat Translat Med Inst,Sch Med, 1678 Dongfang Rd, Shanghai 200127, Peoples R China.
通讯机构：
[Jingying Chen] N;[Guanghai Wang] P;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>Pediatric Translational Medicine Institution, Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
作者：
Wu, Di;Zhou, Chi;Liang, Xingfang;Li, Yating;Chen, Min
期刊：
Education and Information Technologies,2022年27(4):5325-5348 ISSN：1360-2357
作者机构：
[Li, Yating; Liang, Xingfang; Zhou, Chi; Chen, Min; Wu, Di] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 LuoYu St, Wuhan 430079, Peoples R China.
关键词：
Innovative behavior;Innovative teaching;Integrating technology into teaching;Rural teacher;Influencing factors
摘要：
The importance of rural teachers’ innovative behavior of integrating technology into teaching (ITT) has been well recognized. Nevertheless, rural teachers’ innovative behavior of ITT is far from satisfactory. In order to promote rural teachers’ innovative behavior of ITT, it is necessary to better understand what factors are related to it. This study developed a research model of factors related to rural teachers’ innovation behavior of ITT based on social cognitive theory (SCT). To verify the model, this study collected surveys from 4090 primary and secondary school teachers in rural areas of China, adopted structural equation modeling to analyze the data. The results indicated that organizational environment, peer support, and information literacy contributed to rural teachers’ innovative behavior of ITT, while technostress hindered rural teachers’ innovative behavior of ITT. In addition, information literacy mediated the effect of organizational environment and peer support on innovative behavior of ITT, and technostress mediated the effect of peer support and information literacy on innovative behavior of ITT. These findings provide valuable information for teacher training and professional development to promote rural teachers’ innovative behavior of ITT.
期刊：
IEEE Transactions on Industrial Informatics,2022年18(1):16-25 ISSN：1551-3203
通讯作者：
Zhang, Kun
作者机构：
[Yang, Zongkai; Guo, Chen; Zhang, Kun; Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Yang, Zongkai; Guo, Chen; Zhang, Kun; Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Honghai] Harbin Inst Technol Shenzhen, State Key Lab Robot & Syst, Shenzhen 518055, Peoples R China.;[Liu, Honghai] Univ Portsmouth, Portsmouth PO1 2UP, Hants, England.
通讯机构：
[Zhang, Kun] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Empathy ability is one of the most important social communication skills in early childhood development. To analyze the children's empathy ability, facial expression analysis (FEA) is an effective way due to its ability to understand children's emotional states. Previous works mainly focus on recognizing the facial expression categories yet fail to estimate expression intensity, the latter of which is more important for fine-grained emotion analysis. To this end, this article first proposes to analyze children's empathy ability with both the categories and the intensities of facial expressions. A novel FEA method based on intensity label distribution learning is presented, which aims to recognize expression categories and estimate their intensity levels in an end-to-end framework. First, the intensity label distribution is generated for each frame in the expression sequence using a linear interpolation estimation and a Gaussian function to address the lack of reasonable annotations for expression intensity. Then, the extended intensity label distribution is presented to automatically encode the expression intensity in a multidimensional expression space, which aims to integrate the expression recognition and intensity estimation into a unified framework as well as boost the expression recognition performance by suppressing the variations in appearance caused by intensity and by emphasizing those variations among weak expressions. Finally, a Siamese-like convolutional neural network is presented to learn the expression model from a pair of frames that includes an expressive frame and its corresponding neutral frame using the extended intensity label distribution as the supervised information, thus effectively eliminating the expression-unrelated information's influence on FEA. Numerous experiments validate that the proposed method is promising in analysis of the differences in empathy ability between typically developing children and children with autism spectrum disorder.
