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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Luwen Huangfu Wenji Mao Daniel Zeng Lei Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

ISBN: (纸本)9781467362146

Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. To facilitate the automatic extraction of emotions from textual data, in this paper, we propose an emotion model based approach to emotion extraction from online reviews. Informed by the widely used OCC emotion model, we employ a statistical method to extract emotion words with their dimension values from texts, and implement OCC model to obtain emotions based on the emotion-dimension dictionary. We conduct an empirical study using security-related news reviews. The experimental results demonstrate the effectiveness of our proposed approach.

关键词： Dictionaries Data mining Computational modeling Syntactics Intelligent systems Statistical analysis Psychology

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An empirical study of information diffusion in micro-blogging systems during emergency events

An empirical study of information diffusion in micro-bloggin...

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14th International Conference on Web-Age Information management, WAIM 2013

作者： Cui, Kainan Zheng, Xiaolong Zeng, Daniel Dajun Zhang, Zhu Luo, Chuan He, Saike School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 China

ISBN: (纸本)9783642395260

Understanding the rapid information diffusion process in social media is critical for crisis management. Most of existing studies mainly focus on information diffusion patterns under the word-of-mouth spread mechanism. However, to date, the mass-media spread mechanism in social media is still not well studied. In this paper, we take the emergency event of Wenzhou train crash as a case and conduct an empirical analysis, utilizing geospatial correlation analysis and social network analysis, to explore the mass-meida spread mechanism in social media. By using the approach of agent-based modeling, we further make a quantativiely comparison with the information diffusion patterns under the word-of-mouth spread mechanism. Our exprimental results show that the mass-meida spread mechanism plays a more important role than that of the word-of-mouth in the information diffusion process during emergency events. The results of this paper can provide significant potential implications for crisis management. © 2013 Springer-Verlag.

关键词： Information management

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Boundary value problems for stochastic budget distribution in search advertisements

Boundary value problems for stochastic budget distribution i...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Rui Qin Yanwu Yang Feiyue Wang Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

ISBN: (纸本)9781479905287

In sponsored search auctions, advertisers have to distribute the budget to a series of temporal slots in order to maximize the expected revenue. There exists a budget demand for each temporal slot, which can not be known exactly by the advertiser due to some uncertainties in the search marketing environments. The estimation of the value range of budget demand in a temporal slot seriously affects the advertising performance. In this paper we study the effect of the value range on the revenue and conduct some experiments to validate our model and identified properties with the real-world data collected from practical advertising campaigns. Experimental results show that, under a certain condition, (a) the higher estimation of the upper bound and the lower bound might increase the expected revenue, and (b) the expected revenue is positively proportional to the mean value of the value range and is negatively proportional to the size.

关键词： Estimation Advertising Upper bound Stochastic processes Random variables Search problems Data models

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A novel sparse auto-encoder for deep unsupervised learning

A novel sparse auto-encoder for deep unsupervised learning

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Xiaojuan Jiang Yinghua Zhang Wensheng Zhang Xian Xiao State Key Laboratory of Intelligent Control and Management of Complex System at Institute of Automation Chinese Academy of Sciences Beijing

This paper proposes a novel sparse variant of auto-encoders as a building block to pre-train deep neural networks. Compared with sparse auto-encoders through KL-divergence, our method requires fewer hyper-parameters and the sparsity level of the hidden units can be learnt automatically. We have compared our method with several other unsupervised leaning algorithms on the benchmark databases. The satisfactory classification accuracy (97.92% on MNIST and 87.29% on NORB) can be achieved by a 2-hidden-layer neural network pre-trained using our algorithm, and the whole training procedure (including pre-training and fine-tuning) takes far less time than the state-of-art results.

关键词： Training Classification algorithms Data models Principal component analysis Lead

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Vehicle detection method based on active basis model and symmetry in ITS

Vehicle detection method based on active basis model and sym...

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2013 16th International IEEE Conference on Intelligent Transportation systems: Intelligent Transportation systems for All Modes, ITSC 2013

作者： Yao, Yanjie Gang, Xiong Wang, Kunfeng Zhu, Fenghua Wang, Fei-Yue State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Dongguan China

ISBN: (纸本)9781479929146

Vehicle detection is a foundational and significant task in video surveillance systems. In this paper, a vehicle detection method using a deformable model and symmetry is proposed. First, we learn the active basis model (ABM) from the target training sample set by using the shared sketch algorithm. Then, we utilize the edge information obtained by ABM and HSV color information to do symmetry analysis for vehicle objects. The ABM can detect vehicles in various poses, shapes, and sizes for its deformability. By doing edge and color symmetry analysis, subtle difference between two images and environment noises can be adapted. The results of experiments indicate that our approach is capable of detection different vehicles and localization vehicle in bad environment. What's important, the detection results support the capability of the proposed method to enable the introduction of novel intelligent transportation systems applications. © 2013 IEEE.

关键词： Vehicles

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Self-generation Voting Based Method for Handwritten Chinese Character Recognition

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Acta Automatica Sinica 2013年第4期39卷 450-454页

作者： Yun-Xue SHAO Chun-Heng WANG Bai-Hua XIAO Lin-Bo ZHANG State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China China Academy of Transportation Sciences Beijing 100029 China

Voting strategy is very useful in pattern recognition. Many methods, like Boosting and Bagging, are proposed and are successfully used in some applications using this strategy. However, these methods are infeasible or unsuitable for handwritten Chinese character recognition because of the problem's characteristics. In this paper, a self-generation voting method is proposed for further improving the recognition rate in handwritten Chinese character recognition. This method learns a set of parameters first for generating a set of samples from the test sample, and then classify these generated samples using a base-line classifier. At last, it gives the final recognition result by voting. Experimental results on two databases show that the proposed method is effective and useful in handwritten Chinese character recognition systems.

关键词： Handwritten Chinese character recognition self-generation voting modified quadratic discriminant function line density equalization

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Facility layouts based on differential evolution algorithm

Facility layouts based on differential evolution algorithm

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IEEE International Conference on Robotics and Biomimetics

作者： Jing Tao Peng Wang Hong Qiao Zhipeng Tang Research Center of Precision Sensing and Control Institute of Automation Beijing China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479927456

Facility layout problem (FLP) which deals with the placement of facilities in the plant area is one of the most critical problems in manufacturing systems design. This paper presents a novel facility layout approach based on differential evolution (DE) algorithm. Firstly, mathematical model with constraints for the FLP is established. Then, a new facility layout algorithm based on DE is proposed, which can improve the convergence rate by using the interference checking method. Finally, a simulation platform with three-dimensional visualization is developed. The comparison experiments of the proposed method and genetic algorithm (GA) based on robot work cell layout problem show that the proposed method converges faster and can obtain better optimal layout.

关键词： Layout Optimization Mathematical model Algorithm design and analysis Interference Manipulators

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A multi-scale model integrating multiple features for vehicle detection

A multi-scale model integrating multiple features for vehicl...

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International Conference on Intelligent Transportation

作者： Ye Li Fei-Yue Wang Bo Li Bin Tian Fenghua Zhu Gang Xiong Kunfeng Wang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Beijing China Chinese Academy of Sciences Cloud Computing Center Dongguan China

In traffic video surveillance systems, vehicles with various distances from the camera have different sizes, resolutions, and angles in traffic images. The common multi-scale method, which scales one vehicle template or the input image for detecting vehicles with different sizes, may fail to detect vehicles with various distances from the camera due to the change of the resolution and angle. To deal with this problem, we have proposed a multi-scale model including multiple templates with different scales and features. Our method includes two steps: constructing the multi-scale model and its probability model, and detecting vehicles from traffic images. In the first step, the multi-scale model is constructed by using three templates T 1 , T 2 , T 3 which represent vehicles with the short, medium, and long distance from the camera respectively. Each template contains one or some combination of sketch, texture, flatness, and color. In the second step, the three templates are applied for vehicle detection by using the template matching with local maximization operations. The main innovation of this paper is that the combination of multi-template and multi-scale method is applied to detect vehicles with various distances from the camera. To test our method, we have done several experiments on various traffic conditions. The experimental results show that our method effectively copes with vehicles with various distances from the camera and provides the detailed vehicle information after vehicle detection. Moreover, our method adapts to various weather conditions, slight pose variance, and slight occlusion.

关键词： Vehicles Vehicle detection Image color analysis Cameras Image edge detection Training Image resolution

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Portrayal of electronic cigarettes on YouTube

Portrayal of electronic cigarettes on YouTube

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2013 International Conference for Smart Health, ICSH 2013

作者： Luo, Chuan Zheng, Xiaolong Zeng, Daniel Dajun Leischow, Scott Cui, Kainan Zhang, Zhu He, Saike State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Dongguan China Department of Management Information Systems University of Arizona Tucson AZ United States Mayo Clinic in Arizona Phoenix AZ United States School of Electronic and Information Engisneering Xi'an Jiaotong University China

ISBN: (纸本)9783642398438

Despite the growing number of videos featuring electronic cigarettes, there has been no investigation of the portrayal of these videos. This paper presents the first surveillance data of electronic cigarette videos on YouTube. Our results suggest that viewers are primarily being exposed to content promoting the use of these emerging tobacco products and the viewership is global. This study shows that it is critical to develop appropriate health campaigns to inform potential consumers of harms associated with electronic cigarette use. © 2013 Springer-Verlag.

关键词： Data mining

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Forecasting complex group behavior via multiple plan recognition

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Frontiers of Computer Science 2012年第1期6卷 102-110页

作者： Xiaochen LI Wenji MAO Daniel ZENG State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Group behavior forecasting is an emergent re- search and application field in social computing. Most of the existing group behavior forecasting methods have heavily re- lied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we pro- pose a computational approach based on the recognition of multiple plans/intentions underlying group behavior. We fur- ther conduct human experiment to empirically evaluate the effectiveness of our proposed approach.

关键词： group behavior forecasting multiple planrecognition graph search

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