作者机构：
[Liu, Tingting; Chen, Zengzhao; Liu, Hai; Zhang, Zhaoli; Chen, Yingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Liu, Tingting] Carnegie Mellon Univ, Sch Comp Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 USA.;[Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
会议名称：
2nd International Conference on Advances in Image Processing (ICAIP) / 2nd International Conference on Software Engineering and Development (ICSED
会议时间：
JUN 16-18, 2018
会议地点：
Chengdu, PEOPLES R CHINA
会议主办单位：
[Liu, Tingting;Chen, Zengzhao;Liu, Hai;Zhang, Zhaoli;Chen, Yingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Liu, Tingting] Carnegie Mellon Univ, Sch Comp Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 USA.^[Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
摘要：
Facial expression recognition(FER)is an important means of detecting human emotions and is widely applied in many fields,such as affective computing and human-computer interaction.Currently,several methods for FER heavily rely on large amounts of manually labeled data,which are costly and not available in real-world applications.To address this problem,this paper proposes a semi-supervised method based on the deep difference features.First,a cascaded structure is introduced to the original safe semi-supervised SVM(S4VM)to solve the multi-classification task.Then,multiple deep different features are fed to the cascaded S4VM to train the six basic facial expressions using the information of the unlabeled data safely.Extensive experiments show that the proposed method achieved encouraging results on public databases even when using a small labeled sample set.
作者机构：
[Sun, Jianwen; Liu, Sannyuya; Liu, Zhi; Yang, Chongyang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Ruedian, Sylvio] Humboldt Univ, Dept Comp Sci, Berlin, Germany.
会议名称：
7th International Conference of Educational Innovation through Technology (EITT)
会议时间：
DEC 11-14, 2018
会议地点：
Massey Univ, Sch Humanities, Auckland, NEW ZEALAND
会议主办单位：
Massey Univ, Sch Humanities
会议论文集名称：
Proceedings of the International Conference of Educational Innovation through Technology
关键词：
Small Private Online Courses (SPOCs);discussion forum;topic model;emotion analysis;evolutionary trends
摘要：
Small private online courses (SPOCs) have drawn widespread attention due to their adaptability to blended learning in formal education. As a type of interactive tool, many SPOC forums have stored rich textual data including focused learning content and feedback. However, currently, this information is used mainly for measuring students' activity levels instead of academic emotion and their evolutionary trends throughout a teaching period. This paper presents an unsupervised forum understanding model, namely the temporal emotion-topic model (TETM), to model time jointly with emotions and topics. This model is applied to track the evolutionary trends of pairs of {emotion, topic}, i.e., emot-topics. Especially, for each emotion, the most significant topic can be extracted and tracked across a semester. Finally, we investigate the emot-topics differences of different achievement levels of students, and examine the dynamics of emot-topics over course weeks to gain some insights about the relationship between time, emotion and learning achievement. The tracking of emot-topics could be valuable if users are informed of what students are concerned about and how these topics evolve in courses.
期刊：
2018 International Symposium on Educational Technology (ISET),2018年:8-12
通讯作者：
Xu, Jian
作者机构：
[Xu, Jian; He, RanRan; Zhang, Dan; Zhou, Peng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Xu, Jian] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
International Symposium on Educational Technology (ISET)
会议时间：
JUL 31-AUG 02, 2018
会议地点：
Kansai Univ, Osaka, JAPAN
会议主办单位：
Kansai Univ
关键词：
Learning Cyber Space;usage intention;teachers of primary school;regression
摘要：
The trend of using Learning Cyber Space is now rapidly expanding in China, which is expected to change the teaching and learning mode in primary and secondary school. Teachers' usage plays a key role in promoting the LCS. However, the teachers' registering rate of LCS is relatively high while the actual usage rate is low. The purpose of this study is to verified factors including learning resource, user-interface and function, perceived ease of use that affected primary school teachers' use of LCS. The result shown that user-interface and function and perceived ease of use have significant influence on the primary school teachers' use of LCS. Perceived ease of use was found as the most significant factor for usage of primary school teachers' LCS. Learning resource, however, has little impact on the primary school teachers' use of LCS. Implications of this finding are also discussed, which could provide some suggestions for the future development of LCS.
作者机构：
[Hung, Jui-Long; Shelton, Brett E.] Boise State Univ, Boise, ID 83725 USA.;[Yang, Juan; Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
会议名称：
1st International Conference of Innovative Technologies and Learning (ICITL)
会议时间：
AUG 27-30, 2018
会议地点：
Portoroz, SLOVENIA
会议主办单位：
[Shelton, Brett E.;Hung, Jui-Long] Boise State Univ, Boise, ID 83725 USA.^[Yang, Juan;Du, Xu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Learning analytics;Academic at-risk factors;Academic success factors;Ensemble model
摘要：
This study proposes an analytic approach which combines two predictive models (the predictive model of successful students and the predictive model of at-risk students) to enhance prediction performance for use under the constraints of limited data collection. A case study was conducted to examine the effects of the model combination approach. Eight variables were collected from a data warehouse and the Learning Management System. The best model was selected based on the lowest misclassification rate in the validation dataset. The confusion matrix compares the model's performance with the following parameters: accuracy, misclassification, and sensitivity. The results show the new combination approach can capture more at-risk students than the singular predictive model, and is only suitable for the ensemble predictive algorithms.
摘要：
With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user's courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.
期刊：
Lecture Notes in Computer Science,2018年10955:93-99 ISSN：0302-9743
通讯作者：
Zhang, Yue
作者机构：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Yue] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhang, Yue] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称：
14th International Conference on Intelligent Computing (ICIC)
会议时间：
AUG 15-18, 2018
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.^[Zhang, Yue;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
摘要：
In an actual electronic health record (EHR), patient notes are written with terse language and clinical jargons. However, most Pseudo Relevance Feedback (PRF) technique methods do not take into account the significant degree of candidate term in feedback documents and the co-occurrence relationship between a candidate term and a query term simultaneously. In this paper, we study how to incorporate proximity information into the Rocchio's model, and propose a HAL-based Rocchio's model, called HRoc. A new concept of term proximity feedback weight is introduced to model in the query expansion. Then, we propose three normalization methods to incorporate proximity information. Experimental results on 2016 TREC Clinical Support Medicine collections show that our proposed models are effective and generally superior to the state-of-the-art relevance feedback models.
摘要：
Radio-frequency Identification (RFID) grouping proof protocol is widely used in medical healthcare industry, transportation industry, crime forensics and so on,it is a research focus in the field of information security. The RFID grouping proof protocol is to prove that some tags belong to the same group and exist simultaneously. To improve the applicability of the RFID grouping proof protocol in low cost tag applications, this paper proposes a new scalable lightweight RFID grouping proof protocol. Tags in the proposed protocol only generate pseudorandom numbers and execute exclusive-or(XOR) operations. An anti-collision algorithm based on adaptive 4-ary pruning query tree (A4PQT) is used to identify the response message of tags. Updates to secret information in tags are kept synchronized with the verifier during the entire grouping proof process. Based on these innovations, the proposed protocol resolves the scalability issue for low-cost tag systems and improves the efficiency and security of the authentication that is generated by the grouping proof. Compared with other state-of-the art protocols, it is shows that the proposed protocol requires lower tag-side computational complexity, thereby achieving an effective balance between protocol security and efficiency.
