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EDAHT: An expertise degree analysis model for mass comments in the e-commerce system 1

12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Zhong, Jiang Y., Xiong W., Guo J., Xie Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

In order to help consumers to retrieve the most valuable information from amount of comments quickly, we present a method of evaluating the expertise degree of comments. Firstly, we propose an algorithm to construct automatically an attribute-word hierarchy tree from the massive comments data. Secondly, we develop an expertise degree analysis based on attribute-word hierarchy tree (EDAHT) to estimate the expertise degree of comments. The experiments results on 8,000 manual scoring data show that EDAHT model is high consistent with the manual scoring data, and this novel model is effective. © Springer International Publishing AG 2016.

关键词： Forestry

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Discovering trip hot routes using large scale taxi trajectory data 1

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12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Zheng, Linjiang Feng, Qisen Liu, Weining Zhao, Xin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

Discovering trip hot routes is very meaningful for drivers to pick up a passenger, as well as for managers to plan urban public transport. Riding by taxis is one of the important means of transportation. Large scale taxi trajectory data from taxi GPS device implicates residents’ trip behavior. In this paper, we present a method to discover trip hot routes using large scale taxi trajectory data. Firstly, we measure taxi trajectory similarity with longest common subsequence (LCS). LCS-based DBSCAN trajectory clustering algorithm was proposed. Then hot routes were extracted using large scale taxi trajectory data. Our experiment shows that the trajectory clustering algorithm and hot route extraction method are effective. © Springer International Publishing AG 2016.

关键词： Trajectories

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Improving temporal recommendation accuracy and diversity via long and short-term preference transfer and fusion models

Improving temporal recommendation accuracy and diversity via...

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Lecture Notes in Computer Science

作者： Zhang, Bei Feng, Yong College of Computer Science Chongqing University Chongqing400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China

ISBN: (纸本)9783319458168

Temporal factor plays an important role in products and services recommended process. It is necessary to combine temporal factors with effective methods to improve recommendation performance. In this paper, we present a novel approach to improve personalized recommendation performance with changing user preferences based on temporal dataset. In the approach, we take consideration of different influence of long and short-term user preferences and construct a preference transfer model based on our enhanced Hidden Markov Model. Then we accomplish preference fusion by adopting our Long and Short Term Graph, a graph model modified from Session-based Temporal Graph, to recommend unknown items. Finally, the experimental results show that our approach achieves important improvements compared to some existing approaches in performance. © Springer International Publishing Switzerland 2016.

关键词： Hidden Markov models

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Distributed lazy association classification algorithm based on spark 1

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12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Li, Xueming Zhang, Chaoyang Chen, Guangwei Sun, Xiaoteng Zhang, Qi Yang, Haomin Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

The lazy association classification algorithms are inefficient when classifying multiple unclassified samples at the same time. The existing lazy association classification algorithms are sequential which can’t deal with the big data problems. To solve these problems, we propose a distributed lazy association classification algorithm based on Spark, named as SDLAC. Firstly, it clusters the unclassified samples by K-Means algorithm. Secondly, it executes distributed projections according to clustered results, and mines classification association rules by a distributed mining algorithm based on spark. Then it constructs classifier to classify unclassified samples. The experiments are conducted on the 5 UCI datasets and a big dataset from the first national college competition on cloud computing(China). The results show that SDLAC algorithm is more accurate than the CBA algorithm. Besides, its efficiency is far more than the typical distributed lazy association classification algorithm. In other words, the SDLAC algorithm can adapt big data environment. © Springer International Publishing AG 2016.

关键词： Big data

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Dropout Prediction in MOOCs Using Behavior Features and Multi-view Semi-supervised Learning

Dropout Prediction in MOOCs Using Behavior Features and Mult...

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International Joint Conference on Neural Networks

作者： Wentao Li Min Gao Hua Li Qingyu Xiong Junhao Wen Zhongfu Wu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University)

ISBN: (纸本)9781509006212

While massive open online courses (MOOCs) have gained increasing popularity in recent years, dropout prediction has been an important task to solve due to the high rates of dropout students found in MOOCs. Current methods normally apply supervised learning methods to dropout prediction, using general features extracted from behavior records without the concern of behavior types. However, student learning behavior is diverse and there are no sufficient labeled data to train a model because it is a time-costing task to label enormous data in practice. To solve these problems, in this work, we proposed a novel multi-view semi-supervised learning model based on behavior features for the dropout prediction task. Specifically, we derived features from each type of learning behavior to form multi-view behavior features. In addition, based on these features, we proposed a new multi-view semi-supervised learning method to make use of a large number of unlabeled data to assist insufficient labeled data for improving prediction performance. We conducted experiments on KDD Cup 2015 dataset, and the results show that our proposed method achieves better prediction of student dropouts as compared to state-of-the-art approaches.

关键词： Semi-supervised Learning Student Dropouts Learning behavior Online Courses Popularity

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Duplication Detection for Software Bug Reports based on Topic Model 9

Duplication Detection for Software Bug Reports based on Topi...

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9th International Conference on service Science, ICSS 2016

作者： Zou, Jie Xu, Ling Yang, Mengning Yan, Meng Yang, Dan Zhang, Xiaohong School of Software Engineering Chongqing University Chongqing 401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 China

ISBN: (纸本)9781509027286

The traditional duplicate bug reports detection approaches are usually based on vector space model. However, the experimental result is rarely satisfying since this method cannot distinguish semantic correlation among bug reports which written by natural languages. Topic model, as a method to model underlying topics of texts, can solve the problem of document similarity calculation methods used in the information retrieving. It can find the semantic topics among the texts through massive training data, and obtain semantic relatedness among documents. Therefore, this paper proposes a novel duplication detection method based on topic model. Through selecting bug reports with execution information and combing with classified information of bugs, not only does this new method overcome the problem of high dimension, sparse data and loud noise, but also avoid the problem of synonymy and ambiguity in the natural languages. Comparing to the traditional SVM method, the recall rate and precision rate of our proposed approach have obviously increased, which indicates the effectiveness of this new method. © 2016 IEEE.

关键词： duplicate bug reports detection execution information topic model vector space model

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Global influenza surveillance with Laplacian multidimensional scaling

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Frontiers of Information Technology & Electronic Engineering 2016年第5期17卷 413-421页

作者： Xi-chuan ZHOU Fang TANG Qin LI Sheng-dong HU Guo-jun LI Yun-jian JIA Xin-ke LI Yu-jie FENG College of Communications Engineering Chongqing University Chongqing 400044 China MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing 400044 China Chongqing Engineering Laboratory of High Performance Integrated Circuits Chongqing University Chongqing 400044 China Centers for Disease Control and Prevention of Chongqing Chongqing 400042 China Chongqing Communication Institute Chongqing 400030 China Third Military Medical University Southwest Hospital Chongqing 400030 China

The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the resource-limited countries, making these surveillance gaps a more imminent *** present a spatial-transmission model to estimate epidemic risks in the countries where only partial or even no surveillance data are available. Motivated by the observation that countries in the same influenza transmission zone divided by the World Health Organization had similar transmission patterns, we propose to estimate the influenza epidemic risk of an unmonitored country by incorporating the surveillance data reported by countries of the same transmission zone. Experiments show that the risk estimates are highly correlated with the actual influenza morbidity trends for African and Asian countries. The proposed method may provide the much-needed capability to detect, assess, and notify potential influenza epidemics to the developing world.

关键词： Surveillance gap Influenza Spatial-transmission model

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An interval-based Bayesian generative model for human complex activity recognition

arXiv

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arXiv 2017年

作者： Liu, Li Yang, Yongzhong Govindarajan, Lakshmi N. Wang, Shu Hu, Bin Cheng, Li Rosenblum, David S. Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing400044 China School of Software Engineering Chongqing University Chongqing400044 China School of Computing National University of Singapore 117417 Singapore Bioinformatics Institute A*STAR 138671 Singapore College of Material Science and Engineering Lanzhou University of Technology Lanzhou730050 China School of Information Science and Engineering Lanzhou University 730000 China

Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations. We also show that local temporal dependencies can be retained and are globally consistent in the resulting interval network. Moreover, network structure can be learned from empirical data. A new dataset of complex hand activities has been constructed and made publicly available, which is much larger in size than any existing datasets. Empirical evaluations on benchmark datasets as well as our in-house dataset demonstrate the competitiveness of our approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Bayesian networks

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Glucose Oxidase Sensing: 3D Macroporous Zinc Compound/Silicone Hybrid Foams for Amperometric Sensing of Glucose Oxidase (Global Challenges 2/2019)

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Global Challenges 2019年第2期3卷 1970021-1970021页

作者： Ye Wu Hao Fu Weiwei Xie Yingcheng Lin Orhan Kizilkaya Jian Xu Division of Electrical and Computer Engineering Louisiana State University Baton Rouge LA 70803 USA Department of Mechanical Engineering Mcgill University 817 Sherbrooke St. West Montreal Quebec H3A 0C3 Canada Department of Chemistry Louisiana State University Baton Rouge LA 70803 USA Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) of Ministry of Education Chongqing 400044 China College of Communication Engineering Chongqing University Chongqing 400044 China Center for Advanced Microstructures and Devices Louisiana State University 6980 Jefferson Hwy. Baton Rouge LA 70806 USA

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Research Fronts of Robust Social Recommendation 9

Research Fronts of Robust Social Recommendation

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9th International Conference on service Science, ICSS 2016

作者： Jiang, Feng Gao, Min Li, Wentao Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400044 China College of Construction Engineering Chongqing Radio and TV University Chongqing400052 China School of Software Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9781509027279

Social recommendation plays an important role in solving the cold start problem in traditional recommender systems and improving the accuracy of recommendation results. However, it still faces serious challenges and problems due to bogus ratings or relationships injected by fake users. This practice seriously affects the authenticity of recommendation results as well as normal users' trustiness on recommender systems. Moreover, simply treating social relationships as a single type affects the recommendation accuracy. To solve these problems, in this paper, after summarizing the research of current social recommendation algorithms and detecting technologies of multiple relationships, we propose a framework for the research fronts of the robust social recommendation. This framework first models and extracts multidimensional relationships, then summarizes social recommendation shilling attack models based on the analysis of the relationships in social networks as well as the roles of relationships on recommendation. Finally, it forms robust social recommendation approaches that takes multidimensional relationships into consideration. © 2016 IEEE.

关键词： Recommender systems

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