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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 | 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 |
| 4 | 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 |
| 5 | 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 |
| 6 | 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 |
| 7 | 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 |
| 8 | 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 |
| 9 | 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 |
| 10 | 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 |
| 11 | 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 |
| 12 | 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 |
| 13 | 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 |
| 14 | 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 |
| 15 | 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 |
| 16 | Enhancing col- laborative filtering by user interest 显示文摘 | Liu Qi Chen Enhong Xiong Hui | 2012 | IEEE Transac- tions on Systems Man and Cybernetice-Part B: Cyber- netics2012，42，1： | 1 |
| 17 | Heterogeneous-attributes enhancement deep framework for network embedding显示文摘Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data. | Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN | 2021 | Frontiers of Computer Science2021，15，6： | 0 |
| 18 | Adam revisited:a weighted past gradients perspective显示文摘Adaptive learning rate methods have been successfully applied in many fields,especially in training deep neural networks.Recent results have shown that adaptive methods with exponential increasing weights on squared past gradients(i.e.,ADAM,RMSPROP)may fail to converge to the optimal solution.Though many algorithms,such as AMSGRAD and ADAMNC,have been proposed to fix the non-convergence issues,achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.In this paper,we propose a novel adaptive method weighted adaptive algorithm(WADA)to tackle the non-convergence issues.Unlike AMSGRAD and ADAMNC,we consider using a milder growing weighting strategy on squared past gradient,in which weights grow linearly.Based on this idea,we propose weighted adaptive gradient method framework(WAGMF)and implement WADA algorithm on this framework.Moreover,we prove that WADA can achieve a weighted data-dependent regret bound,which could be better than the original regret bound of ADAGRAD when the gradients decrease rapidly.This bound may partially explain the good performance of ADAM in practice.Finally,extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks. | Hui Zhong Zaiyi Chen Chuan Qin Zai Huang Vincent W.Zheng Tong Xu Enhong Chen | 2020 | Frontiers of Computer Science2020，14，5： | 0 |
| 19 | Accelerating local SGD for non-IID data using variance reduction显示文摘Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in parallel.Among them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and privacypreserving.Nevertheless,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence rate.In this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous data.Without extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence rate.Moreover,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite diverse.Benefiting from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical datasets.We conduct experiments on three machine learning tasks.The experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers. | Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN | 2023 | Frontiers of Computer Science2023，17，2： | 0 |
| 20 | Inhibition of Probimane on Lipoperoxidation of Human Red Cells in Vitro显示文摘铮? Human Red Cells in VitroTX1IntroductionProbimane,asanantineoplasticagentfirstdevel-opedinChina[1]wasaderivativeofrazoxane.T?.. | Lu Dayong Cao Jingyi Gong Lu (School of Life Sciences, Shanghai University) Chen Enhong Chen Weizhou Xu Bin (Shanghai Institute of Materia Medica, Chinese Academy of Sciences) | 1998 | Advances in Manufacturing1998，，4： | 0 |
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