摘要：
随着信息技术的迅速发展及其对教育领域的不断渗透,教育信息化在教育改革和发展过程中的重要性日益凸显.信息技术与教育的融合经历了"起步"、"应用"、"整合"和"创新"四个阶段.信息技术不仅革新了传统教育模式,而且营造了全新学习环境.我国信息技术与教育的融合发展还处于初步应用整合阶段,推进信息技术与当代教育深度融合应通过解放思想、制度创新、对外开放、创建协同创新中心等途径加速我国教育现代化进程.With the rapid development of ICT and its continuous penetration into education, ICT is becoming increasingly prominent for the national education reform and development. The integration of ICT with education includes four stages : "emergence", "application", "combination", and "innovation". ICT has not only changed traditional teaching methods, but also created a whole new learning environment. However, China is still at primary application and integration stage. In order to integration ICT with education and implement education modernization in China, we should emancipate our minds, innovate educational policies, stick to opening to the outside world, build collaborative and innovative centers, etc.
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.
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
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.
