作者机构：
[Chen, Mao; Liu, Zhi; Liu, Sanya; Tang, Xiangyang; Min, Lei] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Tang, Xiangyang] School of Mathematics and Statistics, Central China Normal University, Wuhan, China
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Community detection;Complex network;Interference node;Local community
作者：
Chen, Jingying*;Chen, Dan;Li, Xiaoli;Zhang, Kun
期刊：
IEEE Transactions on Industrial Informatics,2014年10(1):323-330 ISSN：1551-3203
通讯作者：
Chen, Jingying
作者机构：
[Zhang, Kun; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Chen, Dan] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China.;[Li, Xiaoli] Beijing Normal Univ, Natl Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.
通讯机构：
[Chen, Jingying] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
关键词：
Behavior detection;intelligent systems;multimodal sensory information;social communication skills
摘要：
How to improve social communication skills for children, especially those with social communication difficulties such as attention deficit/hyperactivity disorder, has long been a challenge faced by researchers and therapists. Recent research indicates that computer-assisted approaches may be effective in addressing this issue. This study aimed to understand children's behaviors and then provide appropriate support to improve their social communication skills. We have established an intelligent system, inside which a child can freely play interactive social skills games with virtual characters. The virtual characters can adjust their own behaviors by adapting to the child's cognitive state (e.g., focus of attention) and affective state (e.g., happiness or surprise). The child's behavior is identified in real-time by recognition of multimodal sensory information, which includes head pose and eye gaze estimation, gesture detection, and affective state detection supported by a series of algorithms proposed in this study. Furthermore, this intelligent system has been enabled in a nonintrusive manner using a novel approach of multicamera surveillance to provide the child with natural interaction with the system. Experimental results show the system can estimate a user's attention and affective states with correctness rates of 93% and 91.3%, respectively. The results obtained suggest that the methods have strong potential as alternative methods for sensing human behavior and providing appropriate support.
期刊：
IEEE Transactions on NanoBioscience,2014年13(2):89-96 ISSN：1536-1241
通讯作者：
Li, Peng
作者机构：
[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Li, Peng; Zhang, Ming; Wang, Yan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhao, Junmin] Henan Univ Urban Construct, Pingdingshan 467036, Peoples R China.
通讯机构：
[Li, Peng] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Algorithm CACE;connected affinity;effective and accurately;overlapping functional modules
摘要：
A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.
作者机构：
[Su, Zhiming; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Chen, Jingying] Cent China Normal Univ, CICET, Wuhan, Peoples R China.;[Chen, Haiqing] Hankou Coll, Wuhan, Peoples R China.
会议名称：
7th International Congress on Image and Signal Processing (CISP)
会议时间：
OCT 14-16, 2014
会议地点：
Dalian, PEOPLES R CHINA
会议主办单位：
[Su, Zhiming;Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Chen, Jingying] Cent China Normal Univ, CICET, Wuhan, Peoples R China.^[Chen, Haiqing] Hankou Coll, Wuhan, Peoples R China.
摘要：
A dynamic facial expression recognition method based on the auto-regressive (AR) models using combined features of both shape and texture features is proposed in this paper. The AR model is effective to model complicated facial motions. In this work, six AR models are first learned for six basic expressions based on the fusion of shape and texture features of the difference between the neutral image and expressive face image. The difference tends to focus the facial parts that are changed from the neutral to expressive face and eliminate the influence of identity of the facial image. The shape features are facial feature point displacements between the normalized neutral and expressive face images while the texture features are local texture. Then the AR models are used to generate the predicted sequence which is compared with the actual sequence. The corresponding expression is inferred from the most similar predicted sequence to the actual one. Finally a line segment based method is proposed to compute the similarity between the predicted and actual expression sequences. The experiments have been conducted based on the extended Cohn-Kanade database. Encouraging results suggest a strong potential for dynamic facial expression.
