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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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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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Reasoning and predicting POMDP planning complexity via covering numbers

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Frontiers of Computer Science 2016年第4期10卷 726-740页

作者： Zongzhang ZHANG Qiming FU Xiaofang ZHANG Quan LIU School of Computer Science and Technology Soochow University Suzhou 215000 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou 215000 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Partially observable Markov decision processes （POMDPs） provide a rich mathematical framework for planning tasks in partially observable stochastic environments. The notion of the covering number, a metric of capturing the search space size of a POMDP planning problem, has been proposed as a complexity measure of approximate POMDP planning. Existing theoretical results are based on POMDPs with finite and discrete state spaces and measured in the l1- metric space. When considering heuristics, they are assumed to be always admissible. This paper extends the theoretical results on the covering numbers of different search spaces, including the newly defined space reachable under inadmissible heuristics, to the ln-metric spaces. We provide a simple but scalable algorithm for estimating covering numbers. Experimentally, we provide estimated covering numbers of the search spaces reachable by following different policies on several benchmark problems, and analyze their abilities to predict the runtime of POMDP planning algorithms.

关键词： POMDP planning complexity theory covering number search space

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A modified evidence theory model for data fusion

A modified evidence theory model for data fusion

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Jie Liu Dayou Liu Xinhua Wang Ni Ni Yungang Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Changchun China State Key Laboratory of Applied Optics Chinese Academy of Sciences Changchun China Bejing Entry-Exit Inspection and Quarantine Bureau Beijing China

ISBN: (纸本)9781509045006

To overcome the drawbacks of traditional convex evidence, in this paper we proposed a modified convex evidence theory model, we presented the modified combination function and use it to combine mass function of ordered propositions, we present the calculation of the parameters of the proposed combination function, and proposed a more accurate method to find the proposition which is most likely true. The theoretical analysis and experimental results demonstrate that the proposed method has higher accuracy than traditional convex evidence.

关键词： inference mechanisms sensor fusion uncertainty handling Soft computing Inference mechanisms Sensor fusion Data fusion Evidence Theory Convexity mass function Proposition data integration

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Integrity constraint validation in dl-liter based ontology using rewriting 2nd

Integrity constraint validation in dl-liter based ontology u...

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2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Cui, Xianji Ouyang, Dantong He, Jialiang College of Information and Communication Engineering Dalian Minzu University Dalian116600 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789811020971

With the rapid development of information technology, semantic web data present features of massiveness and complexity. As the data-centric science, social computing have great influence in collecting and analyzing semantic data. In our contribution, we propose an integrity constraint validation for DL-LiteR based ontology in view of data correctness issue in the progress of social computing applications. Firstly, at the basis of translations from integrity constraint axioms into a set of conjunctive queries, integrity constraint validation is converted into the conjunctive query answering over knowledge bases. Moreover, rewriting rules are used for reformulating the integrity constraint axioms using standard axioms. On this account, the integrity constraint validation can be reduced to the query evaluation over the ABox, and use query mechanisms in database management systems to optimize integrity constraint validation. Finally, the experimental result shows that the rewritingbased method greatly improves the efficiency of integrity constraint validation and is more appropriate to scalable data in the semantic web. © Springer Science+Business Media Singapore 2016.

关键词： Ontology

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Integrating topic modeling with word embeddings by mixtures of vMFs 26

Integrating topic modeling with word embeddings by mixtures ...

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26th International Conference on computational Linguistics, COLING 2016

作者： Li, Ximing Chi, Jinjin Li, Changchun Ouyang, Jihong Fu, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Computer and Information Technology Liaoning Normal University China

ISBN: (纸本)9784879747020

Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of words into account. However, the Euclidean similarity used in Gaussian topics is not an optimal semantic measure for word embeddings. Acknowledgedly, the cosine similarity better describes the semantic relatedness between word embeddings. To employ the cosine measure and capture complex topic structure, we use von Mises-Fisher (vMF) mixture models to represent topics, and then develop a novel mix-vMF topic model (MvTM). Using public pre-trained word embeddings, we evaluate MvTM on three real-world data sets. Experimental results show that our model can discover more coherent topics than the state-of-the-art baseline models, and achieve competitive classification performance. © 1963-2018 ACL.

关键词： Embeddings

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Group sparse Bayesian learning for active surveillance on epidemic dynamics

arXiv

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arXiv 2017年

作者： Pei, Hongbin Yang, Bo Liu, Jiming Dong, Lei College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the γ value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data. Copyright © 2017, The Authors. All rights reserved.

关键词： Monitoring

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Author Correction: Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

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Scientific reports 2018年第1期8卷 8995页

作者： Renchu Guan Xu Wang Mary Qu Yang Yu Zhang Fengfeng Zhou Chen Yang Yanchun Liang Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and Univ. of Arkansas Medical Sciences Little Rock AR 72204 USA. Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 China. College of Earth Sciences Jilin University Changchun 130061 China. yangc616@***. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. ycliang@***. Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China. ycliang@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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A local gradient based peak search algorithm for three-dimensional rotation electron diffraction image processing

A local gradient based peak search algorithm for three-dimen...

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International Conference on Systems and Informatics (ICSAI)

作者： Xinzheng Dong Yixuan Wang He Yin Zhuhai College of Jilin University Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai China

ISBN: (纸本)9781509055227

Peak search for three-dimensional rotation electron diffraction image is almost the most important step in crystal structure determination. The difference of Gaussian method is the traditional approach for this task with the disadvantage that values of three tunable parameters need to be determined by users. To address this drawback, this paper presents a local gradient based peak search algorithm that needs only one tunable parameter. Experiments show that our proposed method is as effective as the difference of Gaussian method in peak detection task, but has obvious advantages in speed and convenience.

关键词： Diffraction Dogs Kernel Standards Software algorithms Shape

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