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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

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Recommendation system based on trusted relation transmission

Recommendation system based on trusted relation transmission

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yixiong Bian Huakang Li Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.

关键词： Collaboration Reliability Social network services Heuristic algorithms Optimization System performance Convergence

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ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

arXiv

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arXiv 2017年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of outliers incrementally during the process of particle filtering. The learned range of outliers is then used to improve subsequent filtering of the future state. Convergence of the outlier range estimation procedure is indicated by extensive empirical simulations using a set of differing outlier distribution models. The validity of the ILAPF algorithm is evaluated by illustrative simulations, and the result shows that ILAPF is more accurate and faster than a recently published state-of-the-art robust particle filter. It also shows that the incremental learning property of the ILAPF algorithm provides an efficient way to implement transfer learning among related state filtering tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Using deep learning to recognize biomedical entities

Using deep learning to recognize biomedical entities

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xuemin Yang Zhifei Zhang Ronggen Yang Daoyu Huang Geng Yang Lejun Gong Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.

关键词： Machine learning Neural networks Text mining Dictionaries Distributed databases Biological system modeling Telecommunications

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UB-CQA: A user attribute based community question answering system

UB-CQA: A user attribute based community question answering ...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Ziye Zhu Xiaoqian Liu Huakang Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

intelligent Community Question Answering (CQA) system is a popular research topic in Natural Language processing (NLP) area. Recently, the development of distributed data mining has significantly improved the efficiency of the searching and sorting operations in CQA database. However, problems are left unanswered such as how to choose the best answer among multiple candidates for a single question. To solve this issue, we design a CQA system called UB-CQA. First, in database refinement part, we construct an optimized and structured database by the best answer choosing method leveraging user attribute provided by the response provider. Second, in human-computer part, the UB-CQA is able to search and provide a more satisfying answer to users by similarity calculation and re-ranking method leveraging text categorization information. Empirical evaluations show that the best answer choosing and the candidate question re-ranking methods bring great improvements in accuracy and reliability.

关键词： databases Knowledge based systems Mathematical model Knowledge discovery Internet Computer science

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Named entity recognition for Chinese telecommunications field based on Char2Vec and Bi-LSTMs

Named entity recognition for Chinese telecommunications fiel...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yu Wang Bin Xia Zheng Liu Yun Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Named Entity Recognition (NER) is a basic task in Natural Language processing (NLP), which extracts the meaningful named entities from the text. Compared with the English NER, the Chinese NER is more challenge, since there is no tense in the Chinese language. Moreover, the omissions and the Internet catchwords in the Chinese corpus make the NER task more difficult. Traditional machine learning methods (e.g., CRFs) cannot address the Chinese NER effectively because they are hard to learn the complicated context in the Chinese language. To overcome the aforementioned problem, we propose a deep learning model Char2Vec+Bi-LSTMs for Chinese NER. We use the Chinese character instead of the Chinese word as the embedding unit, and the Bi-LSTMs is used to learn the complicated semantic dependency. To evaluate our proposed model, we construct the corpus from the China TELECOM FAQs. Experimental results show that our model achieves better performance than other baseline methods and the character embedding is more appropriate than the word embedding in the Chinese language.

关键词： Hidden Markov models Logic gates Computer architecture Machine learning Semantics Telecommunications

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Incentivizing the Biased Requesters: Truthful Task Assignment Mechanisms in Crowdsourcing

Incentivizing the Biased Requesters: Truthful Task Assignmen...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Jia Xu Hui Li Yanxu Li Dejun Yang Tao Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Department of Computer Science Colorado School of Mines Golden CO USA

ISBN: (纸本)9781509066001

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.

关键词： Crowdsourcing Computational modeling Cost accounting Mobile communication Aggregates Mechanical factors Knowledge engineering

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

Deep User Modeling for Content-based Event Recommendation in...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

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Image representation based on multi-features

Image representation based on multi-features

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xianzhong Long Lei Chen Qun Li College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China

As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NMF), Graph regularized Non-negative Matrix Factorization (GNMF) and Locally Consistent Concept Factorization (LCCF). These methods possess respective strength and weakness. The common problem existing in these clustering algorithms is that they only use one kind of feature. However, different kinds of features complement each other and can be used to improve performance results. In this paper, in order to make use of the complementarity between different features, we propose an image representation method based on multi-features. Clustering results on several benchmark image data sets show that the proposed scheme outperforms some classical methods.

关键词： Clustering algorithms Image representation Feature extraction Optimization Symmetric matrices Machine learning algorithms Signal processing algorithms

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