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2021 11th International Conference on Information Technology in Medicine and Education (ITME)最新文献

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Practice and Thinking of “Data Structure and Programming” Based on Blended Teaching 基于混合式教学的《数据结构与程序设计》实践与思考
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00127
Tang Yanqin, Chen Weiwei, Wu Yongfen, Yuan En, S. Lei, Zhang Wenyu
{"title":"Practice and Thinking of “Data Structure and Programming” Based on Blended Teaching","authors":"Tang Yanqin, Chen Weiwei, Wu Yongfen, Yuan En, S. Lei, Zhang Wenyu","doi":"10.1109/ITME53901.2021.00127","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00127","url":null,"abstract":"In order to improve the programming ability of students, teachers are actively seeking various new methods for research and practice. Based on the “Data Structure and Program Design” course, we have carried out the exploration and practice of blended teaching, formulated instructional design based on OBE theory, constructed the teaching mode of online preview before class, offline class + online test in class, and online test after class. Practice shows that these measures improve the students' ability to analyze, express and solve problems.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"412 1","pages":"600-604"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77229782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Online Teaching System Combining Information Feedback and Teaching Evaluation 信息反馈与教学评价相结合的在线教学系统
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00099
Wei Li, Hong Wang
{"title":"Online Teaching System Combining Information Feedback and Teaching Evaluation","authors":"Wei Li, Hong Wang","doi":"10.1109/ITME53901.2021.00099","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00099","url":null,"abstract":"Traditional teaching model is not efficient, as teachers cannot quickly change the focus of teaching based on students' misunderstandings, and students cannot promptly reflect the problems encountered to teachers. Fortunately, the rapid development of information technology brings us many new concepts, such as video teaching, live teaching, and big educational data. They profoundly change the traditional teaching methods and further promote the development of e-Education. Therefore, this article proposes an online course teaching system that combines information feedback and teaching evaluation. Specially, students feedback their questions to teachers through the feedback module which collects students' questions to generate teaching evaluations for this class. At the same time, teachers can check the teaching effects of this class through the evaluation module, reply to students' questions, and adjust subsequent teaching content. Our proposed model enables teachers to grasp the key points of teaching and improves students' learning efficiency. Finally, we implemented the system with Java Web technology and applied the system to the actual teaching process. The experimental results show that the combination of information feedback and teaching evaluation can significantly improve the teaching effect.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"17 1","pages":"465-469"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80339451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SAD: A novel method for ensemble outlier detection with dynamic prediction label 基于动态预测标签的集成异常点检测新方法
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00060
Xining Huang, Zhenchang Zhang, Jiaxiang Lin, DanDan Bai
{"title":"SAD: A novel method for ensemble outlier detection with dynamic prediction label","authors":"Xining Huang, Zhenchang Zhang, Jiaxiang Lin, DanDan Bai","doi":"10.1109/ITME53901.2021.00060","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00060","url":null,"abstract":"Majority voting outlier detection is a traditional method that has been widely used in many fields. It uses the strategy of majority vote to make a prediction, which makes it perform poorly in acc index sometimes. In this paper, a method called second anomaly detection (SAD) is proposed, to detect the connection of outlier scores between each other and decide the advantage strength of a sample when defining the outlierness, which is expressed as $a$ factor, then the prediction label of a sample is ascertained according to the a value. Finally, SAD is compared with several majority voting anomaly detection algorithms in accuracy performance, such as iForest, HBOS, AutoEncoder, it is shown that the proposed algorithm SAD is effective.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"24 1","pages":"257-260"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84625628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Neural Network-Based Prescription of Chinese Herbal Medicines 基于神经网络的中草药处方
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00084
Wen Zhao, Weikai Lu, Changen Zhou, Zuoyong Li, Haoyi Fan, Xuejuan Lin, Zhaoyang Yang, Candong Li
{"title":"Neural Network-Based Prescription of Chinese Herbal Medicines","authors":"Wen Zhao, Weikai Lu, Changen Zhou, Zuoyong Li, Haoyi Fan, Xuejuan Lin, Zhaoyang Yang, Candong Li","doi":"10.1109/ITME53901.2021.00084","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00084","url":null,"abstract":"Objective: To develop a neural network model that recommends traditional Chinese medicine (TCM) herbal prescriptions. Methods: We constructed a new dataset of diagnosis and treatment knowledge from the Treatise on Febrile Diseases. Based on TCM's logical principles of “syndrome differentiation” and “state recognition”, a back-propagation neural network model is proposed that simulates clinical diagnosis and treatment. Results: The proposed model is a four-layer BP neural network. Experiments on the constructed dataset show that the proposed method achieved the best precision, recall, and F1-scores. Conclusion: The proposed method provides much more accurate herbal prescription recommendations than logistic regression.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"51 1","pages":"390-393"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85096634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Analysis of Intelligent Personalized Learning Mode in Big Data Era 大数据时代智能个性化学习模式分析
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00116
Wang Haipeng, Tang Tiantian, M. Zhongyang, Zheng Yuanjie, Wang Hong, Jia Weikuan, Guo Qiang
{"title":"Analysis of Intelligent Personalized Learning Mode in Big Data Era","authors":"Wang Haipeng, Tang Tiantian, M. Zhongyang, Zheng Yuanjie, Wang Hong, Jia Weikuan, Guo Qiang","doi":"10.1109/ITME53901.2021.00116","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00116","url":null,"abstract":"With the advent of the era of big data, a new generation of intelligent information processing technology develop rapidly and vigorously, which has greatly promoted the innovative reform in the concept of education and teaching. The aim of this research is to promote learning efficiency and teaching precision through using big data technology and intelligent means. An intelligent personalized learning mode is built, which mainly including four aspects: academic analysis, intelligent push, individual feedback, multiple evaluations. The mode can conduct in-depth mining and analysis of student data, enrich students' off-class learning resources, intelligently push students' individual learning feedback in real time, and conduct multiple evaluations for each student. Consequently the mode completely changing the deficiency of the traditional learning mode, including one-sided cognition of each students, insufficient learning resources, lack of real-time feedback and single learning evaluation. The mode can form an intelligent and efficient personalized learning environment based on making the overall learning process quantifiable, real-time feedback, and evaluable.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"21 1","pages":"548-551"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78410240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition 基于知识蒸馏的轻量级自适应图卷积网络用于骨架动作识别
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00045
Zhongwei Qiu, Hongbo Zhang, Qing Lei, Jixiang Du
{"title":"Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition","authors":"Zhongwei Qiu, Hongbo Zhang, Qing Lei, Jixiang Du","doi":"10.1109/ITME53901.2021.00045","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00045","url":null,"abstract":"Skeleton-based human action recognition has received extensive attention due to its easy access to human skeleton data. However, the current mainstream skeleton-based action recognition methods have more or less the problem of overlarge parameters, which makes it difficult for these methods to meet the requirements of timeliness and accuracy. To solve this problem, we improve attention-enhanced adaptive graph convolutional neural network (AAGCN) to obtain a high-precision improved AAGCN (IAAGCN), and use it as teacher model to conduct knowledge distillation of our lightweight IAAGCN (LIAAGCN). The results of the tests on the NTU-RGBD dataset are validated by knowledge distillation to allow LIAAGCN to maintain good accuracy while keeping the parameters small.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"16 1","pages":"180-184"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74689267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on the implementation path and practice of data driven university governance modernization—Taking Shandong Youth College of Political Science as an example 数据驱动大学治理现代化的实施路径与实践研究——以山东青年政治学院为例
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00106
Zhiyong Wang, Ran Huang
{"title":"Research on the implementation path and practice of data driven university governance modernization—Taking Shandong Youth College of Political Science as an example","authors":"Zhiyong Wang, Ran Huang","doi":"10.1109/ITME53901.2021.00106","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00106","url":null,"abstract":"From the perspective of data-driven technology, this paper analyzed the practical challenges faced by colleges and universities in the process of realizing the modernization of educational governance, and summarized the implementation path and technical framework from practice, So as to provide a useful reference for colleges and universities to realize the governance modernization. Through research and summary, the implementation path mainly consist of three important components: selecting a reasonable platform architecture, improving data governance services and continuously promoting data governance operations. Finally,take Shandong Youth College of Political Science as an example to carry out practical research and display the case results of data driven governance modernization. It's proved that the implementation path of data driven university governance modernization proposed in this paper is effective.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"24 1","pages":"500-504"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81744892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
OFHR: Online Streaming Feature Selection With Hierarchical Structure Based on Relief 基于浮雕的分层结构在线流媒体特征选择
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00038
Chenxi Wang, Xiaoqing Zhang, Jinkun Chen, Yu Mao, Shaozi Li, Yaojin Lin
{"title":"OFHR: Online Streaming Feature Selection With Hierarchical Structure Based on Relief","authors":"Chenxi Wang, Xiaoqing Zhang, Jinkun Chen, Yu Mao, Shaozi Li, Yaojin Lin","doi":"10.1109/ITME53901.2021.00038","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00038","url":null,"abstract":"Hierarchical classification learning, an emerging classification task in machine learning, is an essential topic. In which various feature selection algorithms have been proposed to select informative features for hierarchical classification. How-ever, existing hierarchical feature selection algorithms consider that the feature space of data is completely obtained in advance, and neglect the uncertainty and dynamism, i.e., feature arrives dynamically in an online manner. In this paper, we present an online streaming feature selection framework with hierarchical structure. First, we apply the closeness matrix between internal nodes to the Relief algorithm, which can calculate the weights of the dynamic features. Second, significant features are dynamically selected for each internal node by considering the hierarchical relationships and feature weights between nodes in the tree structure. Moreover, we perform redundant analysis of features by calculating the covariance between features, and then obtain a superior online feature subset for each internal node. Finally, the proposed algorithm is compared with six online streaming feature selection methods on six hierarchical data sets. The experimental results prove that our algorithm can improve the classification accuracy of the classifier by 10% compared to the suboptimal algorithms, which indicates that the algorithm outperforms other comparative algorithms in hierarchical data sets.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"1 1","pages":"140-145"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82962993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using Visualization to Teach an Introductory Programming Course with Python 使用可视化来教授Python编程入门课程
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00109
Zhiqi Xu, Xuewen Shen, Shengyou Lin, Fan Zhang
{"title":"Using Visualization to Teach an Introductory Programming Course with Python","authors":"Zhiqi Xu, Xuewen Shen, Shengyou Lin, Fan Zhang","doi":"10.1109/ITME53901.2021.00109","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00109","url":null,"abstract":"More and more colleges have offered introductory programming courses for students from different majors, aiming to cultivate students' computational thinking skills. However, teaching introductory programming courses, especially to freshmen, remains a challenging endeavor despite a lot of research and experiments. In this paper we presented our innovative teaching strategy and its implementation both with the utilization of visualization in an introductory Python programming course. The results from our comparative teaching experiments show that visualization could benefit students a lot in learning Python programming and improving their computational thinking abilities.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"315 1","pages":"514-518"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91083402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
3D Forest-tree Modeling Approach Based on Loading Segment Models 基于加载段模型的三维森林树木建模方法
2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00022
Cui Zeyu, Huaiqing Zhang, Nianfu Zhu, Tingdong Yang, Liu Yang, Yuanqing Zuo, Zhang Jing, Hua-Lin Zhang, Lin-lin Wang
{"title":"3D Forest-tree Modeling Approach Based on Loading Segment Models","authors":"Cui Zeyu, Huaiqing Zhang, Nianfu Zhu, Tingdong Yang, Liu Yang, Yuanqing Zuo, Zhang Jing, Hua-Lin Zhang, Lin-lin Wang","doi":"10.1109/ITME53901.2021.00022","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00022","url":null,"abstract":"For the difficulty of tree polymorphism 3D modeling in the stand, the paper explored a 3D forest-tree-modeling approach based on loading trunk model and branch models. The approach is combined with the characteristics of tree branch structure that calculate the branch matching points of the intersection between the branch model and the crown curve to construct the tree branch structure. In addition, branch models are adjusted to eliminate the overlapping of branch models when the adjacent trees had overlapping crowns. The 3D model of forest-tree was constructed in accordance with the growth law and morphological characteristics of forest-tree. The results showed that this approach can use a small amount of measurement data to simulate forest-tree crown of sample plot or stand.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"53 1","pages":"55-59"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91331318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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