作者机构：
[Sun, Chengzhang; Dai, Zhicheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Zhao, Liang] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Li, Zhi] Huazhong Univ Sci & Technol, Patent Ctr, Wuhan 430074, Peoples R China.
通讯机构：
[Zhao, L.] N;National Engineering Laboratory for Educational Big Data, China
关键词：
Education;Indexes;Information and communication technology;Analytic hierarchy process;Solid modeling;Licenses;Training;Smart learning environments;assessment;analytic hierarchy process;back propagation;higher education
摘要：
The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request. (c) 2021 Elsevier B.V. All rights reserved.
作者机构：
[Liu, Leyuan; Huo, Jiao; Jiang, Rubin; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Leyuan; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab 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 430079, China<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.
期刊：
Behaviour & Information Technology,2021年40(3):260-270 ISSN：0144-929X
通讯作者：
Zhao, Qingbai;Zhou, Zhijin;Chen, Jingying
作者机构：
[Zhao, Qingbai; Zhou, Zhijin; Li, Songqing; Xu, Sheng; Chen, Xianke; Chen, Shi] Cent China Normal Univ, Sch Psychol, Minist Educ, Key Lab Adolescent Cyberpsychol & Behav, Wuhan 430079, Hubei, Peoples R China.;[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Hubei, Peoples R China.;[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhao, QB; Zhou, ZJ; Chen, Jingying] C;Cent China Normal Univ, Sch Psychol, Minist Educ, Key Lab Adolescent Cyberpsychol & Behav, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
关键词：
Individual creativity support system;idea generation;associative distance;stimulus acquisition mode
摘要：
Automated discovery of geometric theorems has attracted considerable attention from the research community. In this paper, a new method is proposed to discover geometric theorems automatically. This method first generates vector equations based on given geometric relations about a geometric figure and then transforms the vector equations into a system of homogeneous linear equations; after computing the determinants of the coefficient matrices corresponding to the system of equations, the elimination method is applied to obtain a large number of geometric relationships. The test on more than 200 geometric problems shows that the geometric relationships discovered automatically by the proposed method are of obvious geometric meaning.
作者机构：
[Dai, Zhicheng; Zhang, Qianqian] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Zhao, Liang; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Zhu, Xiaoliang] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
关键词：
Education;Licenses;Internet of Things;Data visualization;Australia;Market research;Information and communication technology;Internet of Things (IoT);education;bibliometric analysis;visualization;CiteSpace
作者：
Du, Xui;Yang, Juan*;Shelton, Brett E.;Hung, Jui-Long;Zhang, Mingyan
期刊：
Behaviour & Information Technology,2021年40(1):49-62 ISSN：0144-929X
通讯作者：
Yang, Juan
作者机构：
[Yang, Juan; Zhang, Mingyan; Du, Xui] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Hung, Jui-Long; Shelton, Brett E.] Boise State Univ, Dept Educ Technol, Boise, ID 83725 USA.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, 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：
Systematic meta-review;learning analytics;educational data mining;big data;prediction of performance;learner modelling
摘要：
As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching and learning. While often combined with educational data mining (EDM), crucial distinctions exist for LA that mandate a separate review. This study aims to conduct a systematic meta-review of LA for mining key information that could assist in describing new and helpful directions to this field of inquiry. Within 901 LA articles analyzed, eight reviews were identified and synthesised to identify and determine consistencies and gaps. Results show that LA is at the stage of early majority and has attracted great research efforts from other fields. The majority of LA publications were focused on proposing LA concepts or frameworks and conducting proof-of-concept analysis rather than conducting actual data analysis. Collecting small datasets for LA research is predominant, especially in K-12 field. Finally, four major LA research topics, including prediction of performance, decision support for teachers and learners, detection of behavioural patterns & learner modelling and dropout prediction, were identified and discussed deeply. The future research of LA is also outlined for purpose of better understanding and optimising learning as well as learning contexts.
作者机构：
[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Diwakar, Mithun; Nagarajan, Srikantan S.; Cai, Chang; Cai, C] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.;[Hashemi, Ali; Haufe, Stefan] Charit Univ Med Berlin, Berlin Ctr Adv Neuroimagin, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Elect Engn & Comp Sci Fac, Machine Learning Grp, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Inst Math, Berlin, Germany.
通讯机构：
[Cai, Chang] C;[Cai, C; Nagarajan, SS] U;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.
摘要：
Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.
摘要：
Head pose estimation (HPE) under active infrared (IR) illumination has attracted much attention in the fields of computer vision and machine learning. However, IRHPE often suffers from the problems of low-quality IR images and ambiguous head pose. To tackle these issues, we propose a novel nonuniform Gaussian-label distribution learning network (NGDNet) for the HPE task. First, we reveal the essential properties from two different perspectives: 1) two head pose images change differently in pitch and yaw directions with the same angle increasing on the central pose; 2) the IR head pose variation first increases and then decreases in the pitch direction. Subsequently, the first property indicates the pose image label as a nonuniform label distribution (Gaussian function) with different long and short axes. The second property is leveraged to determine the distribution size in accordance with the similarities of adjacent hand poses. Lastly, the proposed NGDNet is verified on a new IRHPE dataset, which is built by our research group. Experimental results on several datasets demonstrate the effectiveness of the proposed model. Compared with conventional algorithms, our NGDNet model achieves state-of-the-art performance with 77.39% on IRHPE, 99.08% on CAS-PEAL-R1, and 87.41% on Pointing & rsquo;04. Our code is publicly available at https://github.com/TingtingSL/NGDNet. (c) 2021 Elsevier B.V. All rights reserved.
作者机构：
[Xie, Hekun; Zhang, Hao; Huang, Tao; Geng, Jing] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Li, Zhi; Yang, Huali] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Yang, H.] N;National Engineering Research Center for E-Learning, China
期刊：
Arabian Journal of Geosciences,2021年14(15):1-16 ISSN：1866-7511
通讯作者：
Zhe Wang
作者机构：
[Li, Jingjing] Huazhong Agr Univ, Coll Life Sci & Technol, Wuhan 430070, Hubei, Peoples R China.;[Li, Jingjing] Hubei Inst Water Resources Survey & Design, Wuhan 430070, Hubei, Peoples R China.;[Wang, Zhe] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Wang, Zhe] Wuhan Univ Sci & Technol, Sch Literature Law & Econ, Wuhan 430065, Hubei, Peoples R China.
通讯机构：
[Zhe Wang] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Literature, Law and Economics, Wuhan University of Science and Technology, Wuhan, China
摘要：
Water is called the “source of life”; it is the foundation of our human existence, and it is also an essential resource in the development of the country. Water pollution mainly includes two aspects. The first is that the water quality is polluted to a certain extent. Although our country has successively introduced many policies and measures to improve the water environment and reduce water pollution in recent years, people’s treatment of the water environment and the degree of attention is still not in place. The other is due to the extensive use of various pesticides and chemical fertilizers in the process of land planting and plant planting, resulting in a decrease in the water storage and drainage capacity of the soil. The quality of water resources has a certain degree of influence on the utilization efficiency of water resources. The full use of water resources refers to the use of water resources as raw materials or production costs into the production and operation process. While promoting urban development, the overall coordinated development of cities in adjacent or similar areas, especially cities with similar or the same water area, should be carried out, so that the development of each city and the development of the water area can coexist harmoniously. The theory of green development lies in the water. The definition in the efficient use of resources is to use the smallest possible investment in water resources in exchange for maximum benefits. This article organically combines the allocation of water resources with the protection and pollution control of the ecological and natural environment, so as to study the utilization of water resources in cities, and provide corresponding theoretical support for the improvement of the ecological environment of water resources and the increasing cleanliness.
作者机构：
[Chen, Qianjun; He, Tingting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Ding, Yongchang; Liu, Jie; Chen, Qianjun; Liu, Chang] Hubei Univ, Coll Life Sci, State Key Lab Biocatalysis & Enzyme Engn China, Wuhan 430062, Hubei, Peoples R China.;[Liu, Jie] Hubei Univ, Fac Resources & Environm Sci, Wuhan 430062, Hubei, Peoples R China.;[He, Tingting] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, J.] S;State Key Laboratory of Biocatalysis and Enzyme Engineering of China, China
关键词：
Image recognition;Transfer learning;Data models;Feature extraction;Deep learning;Training;Image color analysis;Deep learning;transfer learning;convolutional neural network;spider sex identification
摘要：
The rapid and accurate identification of spider sex is the first step in spider image recognition. The traditional artificial method used to identify the sex of mature spiders is mainly based on their genital structures (male palps or female epigynum) and highly dependent on the professional background of the identifiers. This article uses computer-based deep learning and transfer learning to identify the sex of spider, explores the design and application of convolutional neural networks in deep learning for spider sex recognition from images, and establishes a neural network model that displays excellent performance in experiments. In addition to optimizing the network model, we select appropriate hyperparameters to improve the accuracy of recognition and reduce the influence of human factors in the identification process. Through a comparison of multiple sets of experiments based on existing sample data collected in the laboratory, we find that the transfer learning method based on Xception can obtain better prediction accuracy than ResNet-152. After data augmentation, the optimization of a combined activation function and the fine-tuning of frozen layers, the prediction accuracy reaches 98.02%, and for an actual measurement of independent samples, the recognition accuracy reaches 92.38%. Therefore, the proposed method can basically replace manual identification and provide a reference for the artificial intelligence-based identification of spider species. Additionally, the model results indicate that male and female dimorphism may exist beyond the non-genital characteristics of spiders.
摘要：
As a typical technique of optical three-dimensional (3D) shape measurement, laser scanning can provide good measurement accuracy by using simple and low-cost optical configuration. The performance of 3D laser scanning greatly depends on the center detection of the laser stripe. In general, laser stripe detection algorithm expects the intensity of the laser stripe remaining moderate and stable. To deal with the negative impact of dramatic change in the intensity of the laser stripe, a high dynamic range (HDR) laser scanning technique with concise algorithm and simple hardware configuration is proposed in this paper. The Bayer filter in the sensor chip of a color camera is exploited to provide different intensity responses to the laser. Then the sub-images of the laser stripe, which correspond to different color channels and have different intensity levels, can be decomposed from the raw image captured by the color camera. A dedicated algorithm is proposed to achieve HDR laser stripe detection, which collects coordinates with the best quality from different sub-images. Finally, 3D surface of improved quality can be reconstructed with the detected laser stripe. The proposed HDR laser scanning technique can be achieved from single-shot raw image by trading pixel resolution for time efficiency. The validity of the proposed method is demonstrated in comparative experiments. (C) 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
作者机构：
[Duan, Chao] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Li, Kaiqi; Li, Qing; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Qing Li] N;National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods.
