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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Bai, Yuan Yang, Bo Eggo, Rosalind M. Du, Zhanwei College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China London School of Hygiene and Tropical Medicine LondonWC1E7HT United Kingdom Department of Integrative Biology University of Texas at Austin Austin78705 United States

ISBN: (纸本)9781577357865

The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events. Existing work relies on snapshot data and misses out some key functions of networks. Here, we study contact network evolution where no snapshot data are available. In spite of the challenge, this study demonstrates how contact networks can be used to predict and control infectious disease epidemics. We first model structural changes in contact networks during the 2009 influenza pandemic in Hong Kong, and then present a probabilistic framework to address it, aiming to answer when and how the underlying structure changes, utilizing multiple data sources including demographic data, and epidemic surveillance data. The efficacy and public health utility of the method are demonstrated using both synthetic and real data. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Disease control

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

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Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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Sparse hybrid variational-Gibbs algorithm for latent Dirichlet allocation 16

Sparse hybrid variational-Gibbs algorithm for latent Dirichl...

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16th SIAM International Conference on Data Mining 2016, SDM 2016

作者： Li, Ximing Ouyang, Jihong Zhou, Xiaotang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781510828117

Topic modeling algorithms such as the latent Dirichlet allocation (LDA) play an important role in machine learning research. Fitting LDA using Gibbs sampler-related algorithms involves a sampling process over K topics. We can use the sparsity in LDA to accelerate this expensive topic sampling process even for very large K values. However, LDA gradually loses sparsity as the number of documents increases. Motivated by the goal of fast LDA inference with large numbers of both topics and documents, in this paper we propose the novel sparse hybrid variational-Gibbs (SHVG) algorith-m. The SHVG algorithm divides the topic sampling probability into a sparse term that scales linearly with the number of per-document instantiated topics Kd, and a dense term that uses the Alias method to reduce the time cost to constant 0(1) time. This will lead to a significant improvement on efficiency. Using stochastic optimization techniques, we further develop an online version of SHVG for streaming documents. Experimental results on corpora with a wide range of sizes demonstrate the efficiency and effectiveness of the proposed SHVG algorithm. Copyright © by SIAM.

关键词： Efficiency

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Detecting computer generated images based on local ternary count

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Pattern Recognition and Image Analysis 2016年第4期26卷 720-725页

作者： Tan, D.Q. Shen, X.J. Qin, J. Chen, H.P. College of Computer Science and Technology Jilin University Changchun Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China

Local binary patterns was used to distinguish the Photorealistic Computer Graphics and Photographic Images, however the dimension of the extracted features is too high. Accordingly, the Local Ternary Count based on the Local Ternary Patterns and the Local Binary Count was developed in this study. Furthermore, a novel algorithm is presented based on the Local Ternary Count to classify photorealistic Computer Graphics and Photographic images. The experiment results show that the proposed algorithm effectively reduces the dimension of the classification features and maintains a good classification performance. © 2016, Pleiades Publishing, Ltd.

关键词： Local binary pattern

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Anti-Collision Warning Algorithm based on Visual Perception in Front of Vehicle

Anti-Collision Warning Algorithm based on Visual Perception ...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Pengcheng Ding Jindong Zhang Yanghao Zhou Hongliang Liu Xiao Wang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

The vehicles' driving safety plays an important role in the transportation safety's development, and it's a necessary requirement in intelligent transportation and intelligent vehicle. The point that is suggested in this paper is that vehicles' collision warning system which is based on computer vision, and the system is built on computer vision, pattern recognition, machine learning and some other artificial intelligent theory and techniques, discerning the vehicles which is in front of your vehicle and measure the security range, warning the possible danger timely to make sure your drive safe. We use the characteristic which is called haar of the samples to train in the classifier to get a cascaded classifier named Boosted, loading the classifier and image of the vehicles marked and calculating the distance and relative speed. In the last, we do a lot of system simulation experiments, verifying the accuracy and the effectiveness of the system from vehicle outline detection results and safe vehicle determination.

关键词： Computer Vision Pattern Recognition Rear-End Collision Warning

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Vehicle Automatic Vertical & Horizontal Parking Trajectory Prediction and Fuzzy Control

Vehicle Automatic Vertical & Horizontal Parking Trajectory P...

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2017 International Conference on Mechanical,Electronic,Control and Automation engineering(MECAE 2017)

作者： Jinqing Li Jindong Zhang Weikai Wang Chengliang Liu Hongyan Guo Kuo Zhang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

ISBN: (纸本)9781510838451

Automatic parking system is a non-accurate modeling system, and the fuzzy algorithm is an excellent choice for the non-accurate modeling. Based on the vehicle kinematics modeling and the experience of artificial parking, we establish the horizontal and vertical models of the vehicle automatic trajectory and the control system through fuzzy inference. By fuzzy controller, parking optimal trajectory can control the speed of the vehicle when reversing garage, and at the same time, the fuzzy controller can control the vehicle body posture reverse garage. These two controls work closely together to achieve accurate body trajectory. The accuracy of the controller is verified by the simulation experiment.

关键词： Vertical Parking Horizontal Parking Trajectory Prediction Fuzzy

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Author Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

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Scientific reports 2018年第1期8卷 838页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. fengfengzhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. fengfengzhou@***.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

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Publisher Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

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Scientific reports 2018年第1期8卷 4218页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. FengfengZhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. FengfengZhou@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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A FAST AND SCALABLE SUPERVISED TOPIC MODEL USING STOCHASTIC VARIATIONAL INFERENCE AND MAPREDUCE 5

A FAST AND SCALABLE SUPERVISED TOPIC MODEL USING STOCHASTIC ...

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Wenzhuo Song Bo Yang Xuehua Zhao Fei Li Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Digital Media Shenzhen Institute of Information Technology George Mason University

ISBN: (纸本)9781509012473

An important and widespread topic in cloud computing is text *** often use topic model which is a popular and effective technology to deal with related *** all the topic models,sLDA is acknowledged as a popular supervised topic model,which adds a response variable or category label with each document,so that the model can uncover the latent structure of a text dataset as well as retains the predictive power for supervised ***,sLDA needs to process all the documents at each iteration in the training *** the size of dataset increases to the volume that one node cannot deal with,sLDA will no longer be *** this paper we propose a novel model named *** which extends sLDA with stochastic variational inference(SVI) and *** can reduce the computational burden of sLDA and MapReduce extends the algorithm with *** makes the training become more efficient and the training method can be easily implemented in a large computer cluster or cloud *** results show that our approach has an efficient training process,and similar accuracy with sLDA.

关键词： Text classification MapReduce Variational inference Topic modeling

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Statistical inference for community detection in signed networks

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Physical Review E 2017年第4期95卷 042313-042313页

作者： Xuehua Zhao Bo Yang Xueyan Liu Huiling Chen School of Digital Media Shenzhen Institute of Information Technology Shenzhen 518172 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Physics and Electronic Information Wenzhou University Wenzhou 325035 China

The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community. In this study, we present a statistical inference method for community detection in signed networks, in which a probabilistic model is proposed to model signed networks and the expectation-maximization–based parameter estimation method is deduced to find communities in signed networks. In addition, to efficiently analyze signed networks without any a priori information, a model selection criterion is also proposed to automatically determine the number of communities. In our experiments, the proposed method is tested in the synthetic and real-word signed networks and compared with current methods. The experimental results show the proposed method can more efficiently and accurately find the communities in signed networks than current methods. Notably, the proposed method is a mathematically principled method.

关键词： Clustering Community structure Stochastic inference

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