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	1. Deep Reinforcement Learning for Robotic Arm Control: A Survey
	LI Yanjiang,WANG Chensheng,YANG Guang,JING Xueliang,LI Yangguang,QIAN Zhixuan
	Computer Science and Technology 19 November 2019
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	Abstract:Deep reinforcement learning (DRL) shows great performance in solving decision-making problems, in which an agent interacts with the environment and learns how to behavior. On the other side, complex robotics control missions offer DRL new challenging applications. This paper surveys the application of deep reinforcement learning in robotic arm control. It starts with the background of deep reinforcement learning and robotics then reviews different algorithms of DRL, introduces some typical deep reinforcement learning models for continuous control and then discusses the applications of DRL in robotic arms; finally, we will conclude the paper.
	TO cite this article:LI Yanjiang,WANG Chensheng,YANG Guang, et al. Deep Reinforcement Learning for Robotic Arm Control: A Survey[OL].[19 November 2019] http://en.paper.edu.cn/en_releasepaper/content/4749929


	2. Motion-enhanced Semi-3D: A novel feature learning framework for Surveillance Video Classification
	Pan Yulin,Li Yong
	Computer Science and Technology 14 March 2019
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	Abstract:This paper introduced a new framework, named Motion-enhanced Semi-3D (MeS3D), for surveillance video classification. Considering there was no public surveillance video classification benchmark for us to evaluate the performance of models, we made SV Dataset that is a small dataset consisting of 1979 trimmed surveillance videos. With two branches extracting motion information separately from videos, our MeS3D achieved higher accuracy on SV Dataset than state-of-the-art, demonstrating that the proposed MeS3D is a novel feature learning framework for surveillance video classification.
	TO cite this article:Pan Yulin,Li Yong. Motion-enhanced Semi-3D: A novel feature learning framework for Surveillance Video Classification[OL].[14 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747867

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	3. FA-XGBoost model and precision medical prediction
	GONG Yicheng,YU Li,ZHANG Yanna
	Computer Science and Technology 18 February 2019
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	Abstract:To predict scientifically and effectively on the increasing scale and dimension data with lack of some feature values, this paper proposes an XGBoost coupled with factor analysis model (FA-XGBoost), where factor analysis (FA) is used to reduce dimension of feature variables and then an XGBoost model is trained by using the data after FA. To test the model\'s effect, this paper analyzes some medical data, which are provided by the Tianchi Precision Medical Contest. The mean-square error (MSE) and the running time (t) are respectively 1.3800 and 1.3771 seconds for FA-XGBoost. Finally, we compared the FA-XGBoost model with four models based on decision trees. In general, GBDT and FA-XGBoost performed best on MSE, while FA-XGBoost worked best on running time.
	TO cite this article:GONG Yicheng,YU Li,ZHANG Yanna. FA-XGBoost model and precision medical prediction[OL].[18 February 2019] http://en.paper.edu.cn/en_releasepaper/content/4747304


	4. Multilevel Feature Network for Visual Sentiment Classification
	Li Minghao,Juan Yang
	Computer Science and Technology 22 January 2019
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	Abstract:Visual sentiment classification is one of the popular research area in affective image content analysis (AICA). At present, most of the recent methods focus on utilizing only the last layer output of the convolutional neural network, which is suboptimal in this area. Various research work on psychology theory, art theory and hand-crafted feature design denote that low-level and mid-level features are also essential to visual sentiment classification. In this work, we focus instead on the low-level and mid-level features and propose a novel architectural unit, which we term the "Multilevel Feature Extracting Structure" (MFES) block, that adaptively utilizing the multilevel feature outputted by the base convolutional neural network. Based on the proposed module, our model achieves the accuracy of 67.39%, surpassing the original accuracy of 66.16% by 1.23%.
	TO cite this article:Li Minghao,Juan Yang. Multilevel Feature Network for Visual Sentiment Classification[OL].[22 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747135


	5. Predicting the Habitability of Exoplanets based on GBRT Algorithm
	ZHU Weijun,WANG Xin
	Computer Science and Technology 02 June 2018
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	Abstract:The habitability of exoplanets is a hot research topic in the field of the exploration of the universe in recent years. The Machine Learning (ML) technique provides a viable means for classifying exoplanets according to their habitability. However, the existing ML-based approaches of habitability classification have some serious shortcomings and limitations. To this end, we provide a novel method for predicting the habitability of exoplanet based on Gradient Boosted Regression Trees (GBRT). First, the physical and astronomical data on the potentially habitable exoplanets and the inhabitable ones are employed to train by algorithm GBRT. Then, the trained model is used to predict the habitability of the exoplanets in our test set. The simulated experimental results show that the predictive accuracy of the new method is as high as 100 %.
	TO cite this article:ZHU Weijun,WANG Xin. Predicting the Habitability of Exoplanets based on GBRT Algorithm[OL].[ 2 June 2018] http://en.paper.edu.cn/en_releasepaper/content/4745364


	6. A Gamma-Poisson Block Model for Community Detection in Directed Network
	Gao Siyuan,Liu Ruifang
	Computer Science and Technology 19 December 2017
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	Abstract:Community detection in networks is to find groups of nodes with similar characteristics, which is commonly defined as finding dense connection groups in undirected networks. However, communities in directed networks usually represent group action patterns because of asymmetric relations, which is difficult to capture using traditional algorithms. In this paper, a Gamma-Poisson blockmodel is proposed for community detection in directed networks, which can model not only assortative communities but also communities with various connectivity patterns due to a block matrix. The model can also be extended to undirected networks if we set the block matrix symmetric, and for assortative community detection task if we set the block matrix diagonal. We develop an efficient Gibbs sampling algorithm for the inference work, which can scale to large sparse networks since only links are considered during each iteration. We compare our model with several previous methods and results demonstrate our advantages on a variety of real-world networks.
	TO cite this article:Gao Siyuan,Liu Ruifang. A Gamma-Poisson Block Model for Community Detection in Directed Network[OL].[19 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742668


	7. Nearest Neighbors based Density Peaks Approach to Intrusion Detection
	Zhang Hao,Li Lixiang
	Computer Science and Technology 14 December 2017
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	Abstract:Intrusion detection systems are very important for network security. However, traditional intrusion detection systems can not identify new type of network intrusion for example zero-day attack. Many machine learning techniques were used in intrusion detection system and they showed better detection performance than other methods. A novel clustering algorithm called Density peaks clustering (DPC) which does not need many parameters and its iterative process is based on density. Because of its simple steps and parameters, it may have many application fields. So we are going to use it in intrusion detection to find a more accurate and efficient classifier. On the basis of some good ideas of DPC, this paper proposes a hybrid learning model based on k-nearest neighbors (kNN) in order to detect attacks more effectively and introduce the density in kNN. In density peaks nearest neighbors (DPNN), KDD-CUP 99 which is the standard dataset in intrusion detection is used to the experiment. Experiment results suggest that the DPNN performs better than support vector machine (SVM), k-nearest neighbors (kNN) and many other machine learning methods, and it can effectively detect intrusion attacks and has a good performance in accuracy.
	TO cite this article:Zhang Hao,Li Lixiang. Nearest Neighbors based Density Peaks Approach to Intrusion Detection[OL].[14 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742698


	8. A Hybrid Ant Colony Optimization for Continuous Domains
	XIAO Jing,LI LiangPing
	Computer Science and Technology 11 July 2011
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	Abstract:Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.
	TO cite this article:XIAO Jing,LI LiangPing. A Hybrid Ant Colony Optimization for Continuous Domains[J].


	9. EmotionChat: A Web Chatroom with Emotion Regulation For E-Learners
	Zheng Deli,Tian Feng,Liu Jun,Zheng Qinghua,Qin Jiwei
	Computer Science and Technology 08 March 2011
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	Abstract:In order to compensate for lack of emotion communication between teachers and students in e-learning systems, we have designed and implemented the EmotionChat -- a web chatroom with emotion regulation. EmotionChat perceives e-learners' emotional states based on interactive text. And it recommends resources such as music, cartoons, and mottos to an e-learner when it detects negative emotional states. Meanwhile, it recommends emotion regulation cases to the e-learner's listeners and teachers. The result of our initial experiment shows that EmotionChat can recommend valuable emotion regulation policies for e-learners.
	TO cite this article:Zheng Deli,Tian Feng,Liu Jun, et al. EmotionChat: A Web Chatroom with Emotion Regulation For E-Learners[OL].[ 8 March 2011] http://en.paper.edu.cn/en_releasepaper/content/4415081


	10. Agent-based Interopration Model（AIM）For Agent-Web Services Combination （AWSC）In Bioinformatics Web Application
	Li Wei,Lv Zhongdogn
	Computer Science and Technology 11 January 2010
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	Abstract:As a new developing technology model，Agent attracts more attention in Internet，which benefits more the distributed application and integration, especially in the area of Web Service Collaboration(WSC) and Web Service Interoperation (WSI).An Agent-based Interoperation Model (AIM) proposed in this paper achieves the Agent-Web Services combination（AWSC）. The AIM mainly depending on ACL specifications to complete the Agent communication for Web Service composition and excution. Then a bioinformatics Web Service application has been introduced as Agent-Web Services combination case. It demonstrates how the proposed Agent-Web Services combination（AWSC）framework can be used to establish a collaborative environment that provides dynamic Web services integration and interopration.
	TO cite this article:Li Wei,Lv Zhongdogn . Agent-based Interopration Model（AIM）For Agent-Web Services Combination （AWSC）In Bioinformatics Web Application[OL].[11 January 2010] http://en.paper.edu.cn/en_releasepaper/content/38715

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