作者机构：
[Xu, P.; Zhang, M.] Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;[Xu, P.; Zhang, M.] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Peoples R China.;[Xu, P.] Hubei Meteorol Serv Ctr, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, M.] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Peoples R China.
关键词：
node attribute;generative adversarial network;network embedding;policy gradient;bidirectional long short term memory
摘要：
Graph generative adversarial network has achieved remarkable effectiveness, such as link prediction, node classification, user recommendation and node visualization in recent years. Most existing methods mainly focus on how to represent the proximity between nodes according to the structure of the graph. However, the graph nodes also have rich attribute information in social networks, the traditional methods mainly consider the node attributes as auxiliary information incorporate into the embedding representation of the graph to improve the accuracy of node classification and link prediction. In fact, in social networks, these node attributes are often sparse. Due to privacy and other reasons, the attributes of many nodes are difficult to obtain. Inspired by the application of generative adversarial network in image field, we propose an innovative framework to discover node latent attribute. Through experiments, we demonstrate the effectiveness of our proposed methods.
作者：
Hongxia Li;ChengLing Zhao*;Taotao Long;Yan Huang;Fengfang Shu
期刊：
British Journal of Educational Technology,2021年52(6):2263-2277 ISSN：0007-1013
通讯作者：
ChengLing Zhao
作者机构：
[Hongxia Li; ChengLing Zhao; Taotao Long; Yan Huang; Fengfang Shu] School of Educational Information Technology, Central China Normal University, Wuhan, China
通讯机构：
[ChengLing Zhao] S;School of Educational Information Technology, Central China Normal University, Wuhan, China
期刊：
JOURNAL OF COMMUNICATIONS AND NETWORKS,2021年23(4):271-280 ISSN：1229-2370
通讯作者：
Liao, Shengbin
作者机构：
[Liao, Shengbin] Cent China Normal Univ, Natl Engn Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Liao, Shengbin] C;Cent China Normal Univ, Natl Engn Ctr E Learning, Wuhan, Peoples R China.
关键词：
Index Terms;Consensus algorithm;network utility maximization;power control;primary-dual algorithm;wireless sensor networks
摘要：
This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual decomposition-based consistency price algorithm to solve the coupled problem. However, the consistency price algorithm suffers from slow convergence. We then propose a two-step method to address the given issue. The first step is to build up a global consensus problem by introducing slack variables to transform the NUM problem with globally coupled variables into a NUM problem with coupled constraints. The second step is to design a distributed algorithm that combines the first-order gradient/subgradient method and a local consensus algorithm to solve the global consensus problem. The proposed algorithm is a primary algorithm which has faster convergence speed than the consistency price algorithm which is a primary-dual algorithm. Experimental results have demonstrated the effectiveness of our proposed approach.
期刊：
Journal of Educational Computing Research,2021年59(7):1319-1342 ISSN：0735-6331
通讯作者：
Wu, Linjing;Liu, Qingtang
作者机构：
[Wu, Linjing; Liu, Qingtang; Li, Jing; Yang, Weiqing; He, Liming; Zhang, Yaosheng] Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Liu, Qingtang] Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.;[Cheng, Yun] Huang Gang Normal Univ, Sch Educ, Huanggang, Hubei, Peoples R China.
通讯机构：
[Wu, LJ; Liu, QT] C;Cent China Normal Univ, Sch Educ Informat Technol, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
collaborative knowledge building;knowledge contribution;information theory;amount of information;information gain
摘要：
The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice.
期刊：
SN Computer Science,2021年2(2):1-11 ISSN：2662-995X
通讯作者：
Xuan Wang
作者机构：
[Mengting Chen; Xuan Wang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Jixin Wang; Can Zuo; Jun Tian] School of Educational Information Technology, Central China Normal University, Wuhan, China;[Yongpeng Cui] Co-Innovation Center of Informatization and Balanced Development of Basic Education, Central China Normal University, Wuhan, China
通讯机构：
[Xuan Wang] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
College student;UTAUT;Continuous intention;Online course platforms
摘要：
In recent years, online learning model has become the mainstream in higher education. The cooperation between universities and Internet education platforms provides a good learning environment and abundant online elective courses for college students, but the practical teaching effect is not ideal. Therefore, based on the Universal Theory of Acceptance and Use of Technology (UTAUT), this study introduced the perceived cost and content quality to build a model of college students’ continuous intention to use online course platforms, and used structural equation model to study the relationship among the variables. The results showed that effort expectancy and social influence affected continuous intention indirectly via performance expectancy; content quality indirectly affected continuous intention through effort expectancy, performance expectancy and effort expectancy–performance expectancy; perceived cost had a significant negative effect on continuous intention. These research results provide new ideas for the design and development of online course platform.
摘要：
Contemporary society expects learners to synthesize large amounts of available information and take advantage of interdisciplinary knowledge to tackle complex, real-world issues. STEAM education aims to cultivate students' ability to solve such problems through interdisciplinary thinking but is often represented by courses that are merely disjointed arrays of school subjects. On the other hand, Maker education harnesses society's enthusiasm for technological innovation and creativity but overlooks the scientific principles that underpin these processes. This research presents a novel elementary school course informed by the interdisciplinary principles of STEAM, integrated with Maker's focus on technology and creativity. The course design also utilized engineering design as a meta-thematic framework. A total of 164 third-grade pupils participated in the research, with responses analyzed using descriptive statistical methods. The findings indicated that the integrated design of the course promoted pupils' learning motivation, self-efficacy, and acquisition of interdisciplinary knowledge. These effects were not gender-specific and demonstrate the potential applicability of a STEAM/Maker integrated approach to curriculum design in other settings.
摘要：
Spatial position consistency and occlusion consistency are two important problems in augmented reality systems. In this paper, we proposed a novel method that can address the registration problem and occlusion problem simultaneously by using an RGB-D camera. First, to solve the image alignment errors caused by the imaging mode of the RGB-D camera, we developed a depth map inpainting method that combines the FMM and RGB-D information. Second, we established an automatic method to judge the close-range mode based on the depth histogram to solve the registration failure problem caused by hardware limitations. In the close-range mode, the registration method combining the fast ICP and ORB was adopted to calculate the camera pose. Third, we developed an occlusion handling method based on the geometric analysis of the scene. Several experiments were performed to validate the performance of the proposed method. The experimental results indicate that our method can obtain stable and accurate registration and occlusion handling results in both the close-range and non-close-range modes. Moreover, the mutual occlusion problem can be handled effectively, and the proposed method can satisfy the real-time requirements of augmented reality systems.
作者机构：
[Min, Qiusha; Zhou, Zhongwei; Li, Ziyi] School of Educational Information Technology, Central China Normal University, No.152 Luoyu Road, Wuhan;Hubei, China;[Min, Qiusha; Zhou, Zhongwei; Li, Ziyi] Hubei, China
会议名称：
2021 5th International Conference on Management Engineering, Software Engineering and Service Sciences, ICMSS 2021
作者：
Zhao, Yue;Peng, Jiangtao;Wei, Yantao;Peng, Qinmu;Mou, Yi
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2021年18(10):1836-1840 ISSN：1545-598X
通讯作者：
Peng, J.
作者机构：
[Zhao, Yue] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.;[Peng, Jiangtao] Hubei Univ, Fac Math & Stat, Hubei Key Lab Appl Math, Wuhan 430062, Peoples R China.;[Wei, Yantao] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;[Peng, Qinmu] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China.;[Mou, Yi] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
通讯机构：
[Peng, J.] H;Hubei Key Laboratory of Applied Mathematics, China
关键词：
Feature extraction;Training;Hyperspectral imaging;Learning systems;Computational complexity;Electronic mail;Hyperspectral image (HSI) classification;multiple-feature latent space (MFLS) learning;spatial information
摘要：
Considering that multiple features can improve the classification performance as they contain diversity information of images, a multiple-feature latent space learning-based method is proposed for hyperspectral image (HSI) classification in this letter. In the proposed method, a latent space that contains diversity information of multiple features and transformation matrices between the latent space and features are both learned. Moreover, spatial information is used for labeling unlabeled samples in the classification. Experimental results on the Indian Pines and University of Pavia data sets demonstrate the effectiveness of the proposed method.
