期刊：
PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021),2021年:151-155
通讯作者：
Xiao, Kejiang
作者机构：
[Yang, Maotao; Zou, Wei] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.;[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
通讯机构：
[Xiao, Kejiang] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
会议名称：
11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)
会议时间：
JUN 18-20, 2021
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Zou, Wei;Yang, Maotao] State Grid Hunan Elect Power Ltd Co, China Hunan Prov Key Lab Intelligent Elect Measur, Power Supply Serv Ctr, Metrol Ctr, Changsha, Hunan, Peoples R China.^[Xiao, Kejiang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Res Ctr Educ Informationizat, Wuhan, Hubei, Peoples R China.
关键词：
Data processing;Abnormal data;Load Forecasting
摘要：
Electric load forecasting is a very important task, but there are often many abnormal data in the load data (Burrs). This paper proposes a load forecasting method in view of the large number of burrs existing in load forecasting. We first used the preprocessed load data to cluster the courts and got the 7050 and the 3033 these two categories (7050 and 3033 are the numbers of the two categories respectively, here we use the numbers as their indexes). Next, we use two methods the sliding box filter method and the comparison method to remove burrs. After extracting the features, we use XGBoost and LightGBM for load prediction. Finally, we analyzed the courts with large prediction errors.
作者机构：
[Hu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, China;[Su J.] School of Computer Science, Hubei University of Technology, Wuhan, China;[Koroliuk Y.] Chernivtsi Institute of Trade and Economics, Kyiv National University of Trade and Economics, Chernivtsi, Ukraine
会议名称：
3rd International Conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020
会议时间：
21 January 2020 through 22 January 2020
关键词：
Collaborative learning;Interactive learning environments;Predicting of academic performance;Simulations;Teaching strategies
作者机构：
[Fu, Chengcheng] National Engineering Research Center for E-Learning Central, China Normal University, Wuhan, China;[Jiang, Xiaobin; He, Tingting; Jiang, Xingpeng] School of Computer, Central China Normal University, Wuhan, China
会议名称：
2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021
会议名称：
29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)
会议时间：
MAY 22-30, 2021
会议地点：
ELECTR NETWORK
会议主办单位：
[Li, Zengyang;Qi, Xiaoxiao;Yu, Qinyi;Mo, Ran] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.^[Li, Zengyang;Qi, Xiaoxiao;Yu, Qinyi;Mo, Ran] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Liang, Peng] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.^[Yang, Chen] IBO Technol Shenzhen Co Ltd, Shenzhen, Peoples R China.
会议论文集名称：
International Conference on Program Comprehension
摘要：
Modern software systems, such as Spark, are usually written in multiple programming languages (PLs). Besides benefiting from code reuse, such systems can also take advantages of specific PLs to implement certain features, to meet various quality needs, and to improve development efficiency. In this context, a change to such systems may need to modify source files written in different PLs. We define a multi-programming-language commit (MPLC) in a version control system (e.g., Git) as a commit that involves modified source files written in two or more PLs. To our knowledge, the phenomenon of MPLCs in software development has not been explored yet. In light of the potential impact of MPLCs on development difficulty and software quality, we performed an empirical study to understand the state of MPLCs, their change complexity, as well as their impact on open time of issues and bug proneness of source files in real-life software projects. By exploring the MPLCs in 20 non-trivial Apache projects with 205,994 commits, we obtained the following findings: (1) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 80% of the projects goes to a relatively stable level; (2) more than 90% of the MPLCs from all the projects involve source files written in two PLs; (3) the change complexity of MPLCs is significantly higher than that of non-MPLCs in all projects; (4) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 80% of the projects; and (5) source files that have been modified in MPLCs tend to be more bug-prone than source files that have never been modified in MPLCs. These findings provide practitioners with useful insights on the architecture design and quality management of software systems written in multiple PLs.
摘要：
In order to further improve the effect of cooperative learning and promote the discussion and interaction among group members, this paper designs and verifies a grouping strategy. This strategy elicits empathy ability on the basis of homogeneity among groups and heterogeneity within groups. The influence of empathy on cooperative learning is studied. Forty-six fourth grade students who participated in science courses are selected as the research objects. The learner with high empathy ability is chosen as the group leader in the experimental group, while the learner with low empathy ability is chosen as the group leader in the control group. At the same time, statistical analysis and social network analysis method are used to explore the influence of empathy on learning effects and group interaction. It is found that the group of high empathy ability is significantly higher than the group of low empathy ability in group discussion interaction density and learning effect. This also provides a reference to the later development of learners and the future development of cooperative learning.
作者机构：
[Shang, Chaowang; Zheng, Yumin; Li, Jingfei; Chen, Futing; Bao, Shaojun] Faculty of Artificial Intelligence Education, Central China Normal University, China
会议名称：
4th International Conference on Education Technology Management, ICETM 2021
作者机构：
[Liu, Qingtang; Wu, Linjing] School of Educational Information Technology, Central China Normal University, China;Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of;[Swai, Carina Titus] School of Educational Information Technology, Central China Normal University, China<&wdkj&>Information Systems and Technology, The University of Dodoma, Tanzania, United Republic of
会议名称：
13th International Conference on Education Technology and Computers, ICETC 2021
作者：
Chunyan Zeng;Dongliang Zhu;Zhifeng Wang;Yao Yang
期刊：
Advances in Intelligent Systems and Computing,2021年 1263: 372-381 ISSN：2194-5357
通讯作者：
Wang, Z.
作者机构：
[Zhu D.; Zeng C.; Yang Y.] Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, Hubei 430068, China;[Wang Z.] Department of Digital Media Technology, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
[Wang, Z.] D;Department of Digital Media Technology, China
会议名称：
12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020
会议时间：
31 August 2020 through 2 September 2020
会议论文集名称：
Advances in Intelligent Networking and Collaborative Systems
摘要：
Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.
