期刊：
Measurement Science And Technology,2023年34(1):015108 ISSN：0957-0233
通讯作者：
Zhicheng Dai
作者机构：
[He, Lamei; Li, Xiaonian] Longdong Univ, Sch Informat Engn, Qingyang, Gansu, Peoples R China.;[Dai, Zhicheng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhicheng Dai] N;National Engineering Research Center for E-learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, People's Republic of China
摘要：
There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the positioning range. It solves the problem of the interference of irrelevant fingerprints. At the same time, a fault-tolerant mechanism is introduced to adjust the circular domain dynamically to ensure that the coarse positioning circular domain contains high similarity reference points and improve the fault tolerance of the coarse positioning. This method consists of offline and online phases. In the offline phase, the values of the received signal strength from Bluetooth low energy are preprocessed using a Gaussian filter to construct a fingerprint database. In the online phase, irrelevant fingerprints are filtered out by using the coarse positioning method. The filtered fingerprints are then matched with a testing point by the K-nearest neighbor algorithm, and the weighted centroids of the nearest reference points are solved. Finally, the coordinate of the testing point is obtained. The experimental results show that this method can effectively improve indoor positioning accuracy when compared with the traditional K-nearest neighbor. The average positioning error of the proposed method is 0.844 m.
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：
IEEE Transactions on Medical Imaging,2023年42(3):762-773 ISSN：0278-0062
通讯作者：
Cai, C;Wu, W
作者机构：
[Cai, Chang; Cai, C] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Kang, Huicong] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Neurol, Wuhan 430079, Hubei, Peoples R China.;[Hashemi, Ali] Tech Univ Berlin, Uncertainty Inverse Modeling & Machine Learning Gr, D-10587 Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Inst Software Engn & Theoret Comp Sci, Fac Elect Engn & Comp Sci 4, Machine Learning Grp, D-10587 Berlin, Germany.;[Chen, Dan] Wuhan Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Wu, W ] A;[Cai, C ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Alto Neurosci Inc, Los Altos, CA 94022 USA.
摘要：
Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI). We then derive three Bayesian inference algorithms for this generative model (expectation-maximization (EBI-EM), convex bounding (EBI-Convex) and fixed-point (EBI-Mackay)) to simultaneously estimate the hyperparameters of the prior distribution for brain source activity and sensor noise. A comprehensive performance evaluation for these three algorithms is performed. Simulations consistently show that the performance of EBI-Convex and EBI-Mackay updates is superior to that of EBI-EM. In contrast to the EBI-EM algorithm, both EBI-Convex and EBI-Mackay updates are quite robust to initialization, and are computationally efficient with fast convergence in the presence of both Gaussian and real brain noise. We also demonstrate that EBI-Convex and EBI-Mackay update algorithms can reconstruct complex brain activity with only a few trials of sensor data, and for resting-state data, achieving significant improvement in source reconstruction and noise learning for electromagnetic brain imaging.
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
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
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：
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：
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.
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：
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：
[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：
[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.
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：
Journal of King Saud University - Computer and Information Sciences,2023年35(7):101594 ISSN：1319-1578
通讯作者：
Li, H
作者机构：
[Yang, Shuoqiu; Li, H; Li, Hao; Du, Xu; Wang, Jing] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educationgal Big Data, Wuhan 430079, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.
通讯机构：
[Li, H ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which real-izes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.(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：
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.
