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| 1 | Exploiting multi-channels deep convolutional neural networks for multivariate time series classification显示文摘 | Yi ZHENG QiLIU Enhong CHEN Yong GE J. Leon ZHAO | 2016 | Frontiers of Computer Science2016，10，1： | 20 |
| 2 | Blind recognition of k/n rate convolutional encoders from noisy observation显示文摘Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context.This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix(WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate(BER) regions. Moreover, it is more computationally efficient with the use of the block matrix(BM) method. | Li Huang Wengu Chen Enhong Chen Hong Chen | 2017 | Journal of Systems Engineering and Electronics2017，28，2： | 13 |
| 3 | Scutellaria baicalensis stem-leaf total flavonoid reduces neuronal apoptosis induced by amyloid beta-peptide (25-35)显示文摘Scutellaria baicalensis stem-leaf total flavonoid might attenuate learning/memory impairment and neuronal loss in rats induced by amyloid beta-peptide. This study aimed to explore the effects of Scutellaria baicalensis stem-leaf total flavonoid on amyloid beta-peptide-induced neuronal apoptosis and the expression of apoptosis-related proteins in the rat hippocampus. Male Wistar rats were given intragastric administration of Scutellaria baicalensis stem-leaf total flavonoid, 50 or 100 mg/kg, once per day. On day 8 after administration, 10 μg amyloid beta-peptide (25-35) was injected into the bilateral hippocampus of rats to induce neuronal apoptosis. On day 20, hippocampal tissue was harvested and probed with the terminal deoxyribonucleotidyl transferase-mediated biotin-16-dUTP nick-end labeling assay. Scutellaria baicalensis stem-leaf total flavonoid at 50 and 100 mg/kg reduced neuronal apoptosis induced by amyloid beta-peptide (25-35 in the rat hippocampus. Immunohistochemistry and western blot assay revealed that expression of the pro-apoptotic protein Bax, cytochrome c and caspase-3 was significantly diminished by 50 and 100 mg/kg Scutellaria baicalensis stem-leaf total flavonoid, while expression of the anti-apoptotic protein Bcl-2 was increased. Moreover, 100 mg/kg Scutellaria baicalensis stem-leaf total flavonoid had a more dramatic effect than the lower dosage. These experimental findings indicate that Scutellaria baicalensis stem-leaf total flavonoid dose-dependently attenuates neuronal apoptosis induced by amyloid beta-peptide in the hippocampus, and it might mediate this by regulating the expression of Bax, cytochrome c, caspase-3 and Bcl-2. | Ruiting Wang Xingbin Shen Enhong Xing Lihua Guan Lisheng Xin | 2013 | Neural Regeneration Research2013，8，12： | 8 |
| 4 | Leveraging proficiency and preference for online Karaoke recommendation显示文摘Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these platforms.In the meantime,the system automatically evaluates user proficiency according to their singing behavior.Recommending approximate songs to users can initialize singers5 participation and improve users,loyalty to these platforms.However,this is not an easy task due to the unique characteristics of these platforms.First,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still open.Second,the sparsity of the user-song interaction behavior may greatly impact the recommendation task.To solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing data.Specifically,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are leveraged.Then we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating matrix.Finally,extensive experimental results on a real-world dataset show the effectiveness of our proposed model. | Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA | 2020 | Frontiers of Computer Science2020，14，2： | 3 |
| 5 | Understanding the mechanism of social tie in the propagation process of social network with communication channel显示文摘The propagation of information in online social networks plays a critical role in modern life,and thus has been studied broadly.Researchers have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors.However,the research on the mechanism how social ties between users play roles in propagation process is still limited.Specifically,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a neighbor.To this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)model.The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie.Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users. | Kai LI Guangyi LV Zhefeng WANG Qi LIU Enhong CHEN Lisheng QIAO | 2019 | Frontiers of Computer Science2019，13，6： | 2 |
| 6 | Construction and validation of a prelimina- ry Chinese version of the Wake Forest Physician Trust Scale 显示文摘 | Enhong Dong Ying Liang Wei Liu | 2014 | Medical Science Monitor2014，20，： | 1 |
| 7 | A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimiza-tion Technique Coupled with Artificial Neural Network 显示文摘 | WU Jiansheng CHEN Enhong | 2009 | Lecture Notes in Computer Science2009，5553，3： | 1 |
| 8 | Simultaneous determination of geniposide chlorogenic acid crocin1 and rutin in crude and processed Fructus Gardeniae extracts by high performance liquid chromatography显示文摘 | Enhong Ouyang Chengrong Zhang Xiaomeng Li | 2011 | Pharmacogn Mag2011，7，28： | 1 |
| 9 | Enhancing collaborative filtering by user interests expansion via personalized ranking显示文摘 | Liu Qi Chen Enhong Xiong Hui | 2012 | IEEE Trans on Systems Man and Cybernetics-B2012，42，1： | 1 |
| 10 | Learning to detect subway arrivals for passengers on a train显示文摘传统的放的使用技术例如 GPS 并且无线的本地放,依靠内在的基础结构。在地铁环境,如此的放的系统不管多么不为放的任务是可得到的,例如为在火车的旅客的火车到达的察觉。一条其他的途径是利用在地铁骑手的移动设备可得到的上下文的信息检测火车到达。到这个目的,我们建议利用从地铁骑手的移动设备提取到精确检测火车到达的多重上下文的特征。跟随这根线,我们首先调查可能有效从 3D 加速表和 GSM 无线电根据观察检测火车到达的潜在的上下文的特征。而且,我们建议探索最大的熵(MaxEnt ) 为由学习在上下文的特征和火车到达之间的关联训练一个火车到达察觉者的模型。最后,我们在在北京地铁系统从二根主要地铁线收集的几个真实世界的数据集合上执行广泛的实验。试验性的结果验证建议途径的有效性和效率。 | Kuifei YU Hengshu ZHU Huanhuan CAO Baoxian ZHANG Enhong CHEN Jilei TIAN Jinghai RAO | 2014 | Frontiers of Computer Science2014，8，2： | 1 |
| 11 | Research on the Lapping Uniformity of Noneonstant Eccentricity Plane Lapping显示文摘 | HENG Jia-jin ZHOU Zhao-zhong YUAN Ju-long ZHAO enhong | 2005 | Journal of Harbin Institute of Technology2005，，12： | 1 |
| 12 | A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network显示文摘 | WU Jiansheng CHEN Enhong | 2009 | Lecture Notes in Computer Science2009，5553，3： | 1 |
| 13 | Enhancing collabo-rative filtering by user interests expansion via personalizedranking 显示文摘 | Liu Qi Chen Enhong Xiong Hui | 2012 | IEEE Trans on Systems Man and Cybemet-ics-B2012，42，： | 1 |
| 14 | Enhancing col- laborative filtering by user interest expansion via personal- ized ranking 显示文摘 | Liu Qi Chen Enhong Xiong Hui | 2012 | IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics2012，42，1： | 1 |
| 15 | Capturing correlations of multiple labels: a generative probabilistic model for multi-label text data 显示文摘 | MA Haiping CHEN Enhong XU Linli | 2012 | Neurocomouting2012，92，： | 1 |
| 16 | A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimization technique coupled with artificial neural network显示文摘 | JIANSHENG WU ENHONG CHEN | 2011 | Lecture Note in Computer Science2011，5553，3： | 1 |
| 17 | LMO4 inhibits p53-mediated proliferative inhibition of breast cancer cells through interacting p53显示文摘 | Xinliang Zhou Meixiang Sang Wei Liu Wei Gao Enhong Xing Weihua Lü Yingying Xu Xiaojie Fan Shaowu Jing Baoen Shan | 2012 | Life Sciences . 2012 (9-10)2012，，： | 1 |
| 18 | A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimiza- tion technique coupled with artificial neural network显示文摘 | Jiansheng Wu Enhong Chen | 2009 | Lecture Note Computer Science2009，5553，3： | 1 |
| 19 | A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimization technique coupled with artificial neural network显示文摘 | Wu Jiansheng Chen Enhong | 2009 | Lecture Note Computer Science2009，5553，3： | 1 |
| 20 | A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimization technique coupled with artificial neural network显示文摘 | WU Jiansheng CHEN Enhong | 2009 | Lecture Notes in Computer Science2009，5553，3： | 1 |
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