期刊：
Mathematical Problems in Engineering,2014年2014 ISSN：1024-123X
通讯作者：
Wang, Yan
作者机构：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Mao, Mingzhi] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[He, Tingting; Hu, Xiaohua] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Wang, Yan] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
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：
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.
摘要：
A blind deconvolution algorithm with modified Tikhonov regularization is introduced. To improve the spectral resolution, spectral structure information is incorporated into regularization by using the adaptive term to distinguish the spectral structure from other regions. The proposed algorithm can effectively suppress Poisson noise as well as preserve the spectral structure and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Comparative results on simulated and real degraded Raman spectra are reported. The recovered Raman spectra can easily extract the spectral features and interpret the unknown chemical mixture.
摘要：
In statistical text emotion recognition, semi-supervised learning that can leverage plenty of unlabeled data has drawn much attention in recent years. However, the quality of the training data is typically influenced by some mislabeled samples. In this paper, we present a novel co-training method, namely adaptive multi-view selection (AMVS), to improve labeling accuracy of unlabeled samples for semi-supervised emotion recognition. In particular, two importance distributions are proposed to construct multiple discriminative feature views. One is the distribution of feature emotional strengths, and the other is the importance distribution of view dimensionality. On the basis of these two distributions, several feature views are iteratively selected from the original feature space in a cascaded way, and corresponding base classifiers are trained on these views to build a dynamic and robust ensemble. The experimental results on the real-life dataset consisting of moods posts demonstrate the proposed AMVS outperforms conventional multi-view semi-supervised emotion recognition methods, and that abundant emotional discriminative features could be fully exploited in view selection process. In statistical text emotion recognition, semi-supervised learning that can leverage plenty of unlabeled data has drawn much attention in recent years. However, the quality of the training data is typically influenced by some mislabeled samples. In this paper, we present a novel co-training method, namely adaptive multi-view selection (AMVS), to improve labeling accuracy of unlabeled samples for semi-supervised emotion recognition. In particular, two importance distributions are proposed to construct multiple discriminative feature views. One is the distribution of feature emotional strengths, and the other is the importance distribution of view dimensionality. On the basis of these two distributions, several feature views are iteratively selected from the original feature space in a cascaded way, and corresponding base classifiers are trained on these views to build a dynamic and robust ensemble. The experimental results on the real-life dataset consisting of moods posts demonstrate the proposed AMVS outperforms conventional multi-view semi-supervised emotion recognition methods, and that abundant emotional discriminative features could be fully exploited in view selection process.
作者机构：
[Chen, Mao; Liu, Zhi; Liu, Sanya; Min, Lei] National Engineering Research Center for E-Learning, Huazhong Normal University, No. 152, Luoyu Road, Wuhan 430079, China
通讯机构：
[Chen, M.] N;National Engineering Research Center for E-Learning, Huazhong Normal University, No. 152, Luoyu Road, Wuhan 430079, China
关键词：
Community detection;Core community;Label propagation;Similarity strength
