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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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Pattern recognition of UAV flight data based on semi-supervised clustering

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Journal of Physics: Conference Series 2019年第1期1195卷 012001-012001页

作者： Wang, N Liu, Y Z SH Xu Sun, SH W School of Management Science and Information Engineering Jilin University of Finance and EconomicsChina Laboratory of Logistics Industry Economy and Intelligent Logistics Jilin University of Finance and Economics China Jilin Big Data Research Center for Business Jilin University of Finance and EconomicsChina Jilin Province Key Laboratory of Fintech Jilin University of Finance and EconomicsChina Shenyang Woozoom Technology Co. LtdChina School of Management Science and Information Engineering Jilin University of Finance and EconomicsChina Laboratory of Logistics Industry Economy and Intelligent Logistics Jilin University of Finance and Economics China

UAV is an unmanned aerial vehicle controlled by a remote radio signal or a trajectory planning software carried itself. It is widely used in military, civil and scientific research fields. However, due to the lack of real-time decision-making ability, the UAV has high fault rate. The flight quality assessment of UAV and the construction of fault prediction model can be used for debugging and fault-removing to customer’s aircraft, and also to increase the added value of the civilian UAV products. Before building a fault prediction model, a very important step is to identify the pattern of sampled data. For each group of flight data, the efficiency and accuracy rate of manual quality evaluation and fault identification are not acceptable. Based on the UAV flight data accumulated in the big data platform of an UAV production company in Shenyang, Liaoning Province of China, this paper proposes a semi-supervised clustering technique to do automatic pattern recognition for the sampling points. According to the characteristics of UAV flight data, two different methods are designed to choose initial centroids. Meanwhile, we use the existing normal flight data to train distance thresholds to combine some clusters to eliminate the resulting error clustering. Real flight data or flight test data with manually added labels are used to run the proposed algorithms to verify the recognition results. The experimental results show that the proposed methods greatly improve the efficiency and accuracy of adding precise labels to the historical flight data and play a role in assisting the manual recognition of sampling points while strengthening the management and statistics.

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Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network for Distributed Motion Planning of Multiple robot Arms

Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network ...

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International Joint Conference on Neural Networks

作者： Long Jin Shuai Li Xin Luo Ming-sheng Shang School of Information Science and Engineering Lanzhou University Department of Computing The Hong Kong Polytechnic University Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

ISBN: (纸本)9781509061839

This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

关键词： robotic arm motion planning Distribute Noise Neural network Automatic incident detection Simulation results

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Using Cost-Sensitive Ranking Loss to Improve Distant Supervised Relation Extraction

Using Cost-Sensitive Ranking Loss to Improve Distant Supervi...

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第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

作者： Daojian Zeng Junxin Zeng Yuan Dai Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004P.R.China

ISBN: (纸本)9783319690049

Recently,many researchers have concentrated on using neu-ral networks to learn features for Distant Supervised Relation Extraction(DSRE).However,these approaches generally employ a softmax classi-fier with cross-entropy loss,and bring the noise of artificial class NA into classification ***,the class imbalance problem is serious in the automatically labeled data,and results in poor classification rates on minor classes in traditional *** this work,we exploit cost-sensitive ranking loss to improve *** first uses a Piecewise Convolutional Neural Network(PCNN)to embed the semantics of *** the features are fed into a classifier which takes into account both the ranking loss and ***-periments show that our method is effective and performs better than state-of-the-art methods.

关键词： Classifiers Neural network Semantics Noise Balance impairment Taxonomy embedding

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Maximum Likelihood Estimation based on Random Subspace EDA: Application to extrasolar planet detection

arXiv

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arXiv 2017年

作者： Liu, Bin Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu210023 China

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear;2) the likelihood surface has a huge number of peaks;3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ML search a very challenging problem, as it lacks any analytical or gradient based searching solution to explore the parameter space. A population based searching method, called estimation of distribution algorithm (EDA), is adopted to explore the model parameter space starting from a batch of random locations. EDA is featured by its ability to reveal and utilize problem structures. This property is desirable for characterizing the detections. However, it is well recognized that EDAs can not scale well to large scale problems, as it consists of iterative random sampling and model fitting procedures, which results in the well-known dilemma curse of dimensionality. A novel mechanism to perform EDAs in interactive random subspaces spanned by correlated variables is proposed and the hope is to alleviate the curse of dimensionality for EDAs by performing the operations of sampling and model fitting in lower dimensional subspaces. The effectiveness of the proposed algorithm is verified via both benchmark numerical studies and real data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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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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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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A dynamic neural controller for adaptive optimal control of permanent magnet DC motors

A dynamic neural controller for adaptive optimal control of ...

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International Joint Conference on Neural Networks

作者： Yinyan Zhang Shuai Li Xin Luo Ming-sheng Shang Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China

ISBN: (纸本)9781509061839

The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters.

关键词： DC motors Velocity control Friction Optimal control Permanent magnet motors Biological neural networks

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Texture synthesis and design based on elements distribution creation 1

Texture synthesis and design based on elements distribution ...

引用

1st EAI International Conference on Computer Science and Engineering, COMPSE 2016

作者： Gui, Yan Liu, Yang Li, Feng Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China

ISBN: (纸本)9781631901362

The texture synthesis and design starts with an initial elements arrangement and expands it outward by using local and global growth, to obtain the new larger distribution of texture elements. There are two types of synthesized distributions, and their diversity consists in changing the layout of texture elements and decreasing or increasing the number of texture elements according to user's creation. Furthermore, we apply a set of deformation operations to locally change the shapes of texture elements when placing the extracted texture elements into the synthesized distribution, in order to guarantee structure consistency in final synthesized textures. Experimental results show that our method creates a large variety of textures from a given texture sample.

关键词： Computer graphics

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