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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

Journal of Harbin Institute of Technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

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Application of deep learning in the hydraulic equipment fault diagnosis

Application of deep learning in the hydraulic equipment faul...

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2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2018

作者： Jing, Liu Yankai, Liu Shanshan, Chen Zhijie, Wang Haipeng, Ji Engineering School University of Iowa Iowa52246 United States School of Computer Science and Engineering Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China Research Institute for Energy Equipment Materials Hebei University of Technology Tianjin3004011 China

ISBN: (纸本)9781538631836

In this paper, combined with the advantages of deep learning and the characteristics of big data equipment to put forward the intelligent fault diagnosis of Diamond Model. In this model, firstly, the data is extracted by the Sparse Autoencoder network and completed the feature extraction;and then the extracted feature vector combined with Softmax classifier is used to classify the equipment fault condition;finally the whole model uses the back propagation algorithm to fine tune the whole network parameters to obtain the optimal parameters that minimize the value of the loss function;ultimately, realized the model of adaptive extraction of fault features and fault conditions of intelligent diagnosis. And through simulation analysis, the accuracy rate of the classification of the DM model is verified to reach 81%, which is significantly higher than the shallow classifier and has a good self-learning ability and the level of knowledge learning ability. © 2018 IEEE.

关键词： Failure analysis

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Relay Node Selection Strategy for Wireless Sensor Network 4th

Relay Node Selection Strategy for Wireless Sensor Network

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Kong, Lingping Pan, Jeng-Shyang Chu, Shu-Chuan Roddick, John F. Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

In the past few years, people have experienced advanced interest in the potential use of wireless sensor network in applications such as environment surveillance, military field protection and medical treatment. Usually hundreds even thousands of sensors are scattered randomly in remote environment. In generally, for the scalability of sensor network, cluster techniques are often used to group nodes into several sets. And in this paper, we proposed a new method called GF_CENTER to select the positions of centers, which is also a kind of k-center problem. And a new fitness function is presented for optimize the resolution. We compare our method with two other algorithms. GF_CENTER method minimizes the number of centers in the network. From the experiments, GF_CENTER find smaller number of centers than the other methods, which lower the construction fee of network. © 2018, Springer International Publishing AG.

关键词： Genetic algorithms

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User Heterogeneity and Individualized Recommender

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Chinese Physics Letters 2017年第6期34卷 135-138页

作者：王庆先张君杰史晓雨尚明生 School of Information and Software Engineering University of Electronic Science and Technology of China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences

Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at an individual level. In this study, we propose an individualized recommender that can satisfy every user with a customized parameter. Experimental results on four benchmark datasets demonstrate that the individualized recommender can significantly improve the accuracy of recommendation. The work highlights the importance of the user heterogeneity in recommender design.

关键词： User Heterogeneity and Individualized Recommender

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User Behavior Analysis of Location-Based Social Network

User Behavior Analysis of Location-Based Social Network

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Jun Zeng Xin He Yingbo Wu Sachio Hirokawa Key Laboratory of Dependable Service Computing in Cyber Physical Society School of Big Data and Software Engineering Chongqing China School of Big Data and Software Engineering Chongqing China Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9781538674482;9781538674475

User behavior changes over time under the influence of their activities. We contend that these activities are non-random behavior and have a desire to explore the underlying information behind these changes. In this paper, we analyze user behavior by using the check-in data in Location Based Social Networks (LBSNs), and examine whether they have the features of trend, periodicity and surprise or not. We explore some dynamics behaviors of people through their check-in times, and divide time into annually, monthly and even weekly analysis to find out the pattern of their behavior. Eventually, we found the check-in data do exhibit these three features by analyzing them deeply. The analytical work lays the foundation for the further recommendation research.

关键词： Market research Social networking (online) big data Software engineering Accidents Trajectory Motion pictures

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Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach 2

Privacy Preserving Scheme for Location Based Services Using ...

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2nd IEEE Symposium on Privacy-Aware Computing, PAC 2018

作者： Pu, Yuwen Luo, Jin Wang, Ying Hu, Chunqiang Huo, Yan Zhang, Jiong School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Ningbo Branch of Agricultural Bank of China Ningbo China

ISBN: (纸本)9781538684429

Location based services pose strong security and privacy requirements but achieving privacy protection at the client side is a non-trial problem. In this paper, we propose an efficient and reliable location privacy preserving scheme using cryptographic approach. It can preserve users' privacy data by making their identity be anonymity for LBS provider and fog server. For communication, both AES with One-Time-Pad keys and IBE are employed to guarantee confidentiality and integrity of request and response data. Besides, we also provide security analysis to demonstrate that our scheme can resist internal attack, external attack and colluding attack. © 2018 IEEE.

关键词： Location based services

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A Novel Texture Generation Super Resolution Model

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Procedia Computer Science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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Recommendation with the cold-start problem in evolving user-movie network

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Journal of Computers (Taiwan) 2019年第5期30卷 18-30页

作者： Zhang, Shu-Juan Zhang, Juan Jin, Zhen School of Mechatronic Engineering North University of China Shanxi Taiyuan030051 China School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Complex Systems Research Center Shanxi University Shanxi Taiyuan030006 China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi University Shanxi Taiyuan030006 China

Faced with the problem of information overload on the Internet, recommender systems have been paid more and more attention. At present, many recommendation algorithms have been proposed. However, there are two problems in these recommendation methods which must be solved. Most of them are studied in a single round of recommendation, but lots of complex systems in reality are evolving with time. Besides, the cold-start problem has always been intractable in the recommender system. Therefore, in this paper, we propose two recommendation methods (i.e., Method I and Method II) to solve the movie cold-start problem for the evolving user-movie network which is comprised of users and movies, and then examine the recommendation performance and long-term effects of these two methods in evolving networks, and evaluate influence factors through the numerical simulation. The experiment results show that the network structure, the movie attributes and the length of the recommendation list affect the recommendation accuracy and its novelty. The effects of Method I and Method II are better than those of other recommendation methods in previous works. Moreover, in Method Iwe should lay more emphasis on the relation between nodes, while in Method II we should focus on node attributes. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Recommender systems

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Machine learning dynamical phase transitions in complex networks

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Physical Review E 2019年第5期100卷 052312-052312页

作者： Qi Ni Ming Tang Ying Liu Ying-Cheng Lai School of Information Science and Technology East China Normal University Shanghai 200241 China School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China School of Computer Science Southwest Petroleum University Chengdu 610500 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

Recent years have witnessed a growing interest in using machine learning to predict and identify critical dynamical phase transitions in physical systems (e.g., many-body quantum systems). The underlying lattice structures in these applications are generally regular. While machine learning has been adopted to complex networks, most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition points associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general comprehensive machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection

关键词： Collective behavior in networks Complex systems Network phase transitions Spreading Artificial neural networks Homogeneous networks Real world networks Scale free & inhomogeneous networks Centrality Monte Carlo methods Network Models SIS model

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ANALYSIS OF THE PREDICTION CAPABILITY OF WEB SEARCH data BASED ON THE HE-TDC METHOD - PREDICTION OF THE VOLUME OF DAILY TOURISM VISITORS

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Journal of Systems Science and Systems engineering 2017年第2期26卷 163-182页

作者： Geng Peng Ying Liu Jiyuan Wang Jifa Gu School of Economic and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce dimensionality is critical. This paper presents a new integrative method that combines Hurst Exponent （HE） and Time Difference Correlation （TDC） analysis to select keywords with powerful predictive ability. The method is called the HE-TDC screening method and requires keywords with predictive ability to satisfy two characteristics, namely, high correlation and fluctuation memorability similar to the predicting target series. An empirical study is employed to predict the volume of tourism visitors in the Jiuzhai Valley scenic area. The study shows that keywords selected using HE-TDC method produce a model with better robustness and predictive ability.

关键词： Tourism visitor volume prediction web-search data HE-TDC method Jiuzhai Valley,time series Hurst exponent

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