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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Han, Xiaosong Li, Xiaoran Liang, Yanchun Wang, Xinghao Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9781665401265

The Traditional Chinese Medicine's ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction. © 2021 IEEE.

关键词： Acupuncture

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Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

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Computers, Materials & Continua 2020年第7期64卷 207-216页

作者： Yu Jiang Dengwen Yu Mingzhao Zhao Hongtao Bai Chong Wang Lili He College of Computer Science and Technology Jilin UniversityChangchun130012China A Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun130012China Department of Engineering Mechanics State Marine Technical University of St.PetersburgSt.Petersburg190008Russia

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for *** paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text *** K-Means and Affinity Propagation(AP)are two classical clustering *** Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable ***-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster *** semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering *** unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.

关键词： Unsupervised learning semi-supervised learning text clustering

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A community mining algorithm for web texts based on Multi-agent system

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Journal of computational Information Systems 2010年第11期6卷 3509-3516页

作者： Yang, Hui Wang, Zhe Zhou, Xu Zhou, Tong Wang, Zuo College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

Most of the existing methods for community discovery only deal with social network with a fixed structure, so they can not effectively deal with dynamic social network. This paper proposes a Multi-agent system method which is applied to real-time dynamic web texts for community discovery. This method combines the multi-agent system control mechanisms with community discovery algorithm. The approach not only divides community for the published web text, but also changes the community division according to the newly updated contents of web texts. Meanwhile, the similarity of two web texts is evaluated through similarity algorithm which compares content similarity and semantic similarity. The effectiveness of this algorithm has been tested on real web texts networks and experiment 3 illustrated the scalability of our algorithm for dynamic community discovery of web texts. © 2010 Binary Information Press.

关键词： Semantics

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A Chinese online document clustering algorithm based on hidden sentiment vector

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Journal of computational Information Systems 2011年第6期7卷 1974-1981页

作者： Yang, Hui Wang, Zhe Zhou, Xu Wang, Zuo Zhou, Tong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

The core idea of clustering algorithm is the division of data into groups of similar objects. Some clustering algorithms are proven good performance on document clustering, such as k-means and UPGMA etc. However, few document clustering algorithms pay attention to the hidden sentiment which is a very important feature of the documents. This paper presents an improved k-means algorithm (HSK-Means) based on hidden sentiment vector for Chinese document clustering. Chinese dependency grammar rules are used to extract document feature and evaluate hidden sentiment. A new method for selecting initial cluster centroids based on rank mechanism of similarity is also proposed to improve accuracy of clustering algorithm. The experimental results on real online document sets illustrate that the performance of HSK-Means algorithm is better than classic document clustering algorithms. Copyright © 2011 Binary Information Press.

关键词： Information retrieval

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Building trust networks in the absence of trust relations

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Frontiers of Information Technology & Electronic engineering 2017年第10期18卷 1591-1600页

作者： Xin WANG Ying WANG Jian-hua GUO College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun 130012 China School of Mathematics and Statistics Northeast Normal University Changchun 130024 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media *** issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.

关键词： Trust network Sparse learning Homophily effect Interaction behaviors

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Speech separation in the vehicle environment based on fastICA algorithm

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Journal of Multimedia 2012年第1期7卷 33-40页

作者： Zhang, Jindong Qin, Guihe Liu, Ye College of computer science and technology Jilin University Changchun China Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun China

The speech interaction in-vehicle was mainly realized by the speech recognition. The human-machine interaction around was usually disturbed by the noise, and the speech received by the receiver was not the original pure speech, so compared to the pure environment, the accuracy of the speech recognition declined so sharply that it could not meet the demand of the practical application of human- machine interaction. So the speech recognition was required to have the strong adaptability and processing capacity to the speech with noise. In this paper, the FastICA algorithm in signal process and statistics was studied and used to separate the driver's speech in the vehicle environment, and realizes the pretreatment in recognizing driver's speech. The effectiveness of the method had been validated in actual vehicle experimental configuration. © 2012 Academy Publisher.

关键词： Speech processing

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Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach

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Neural Computing and Applications 2024年第22期36卷 13731-13744页

作者： Ouyang, Jihong Wang, Teng Cao, Jingyue Wang, Yiming College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130015 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130015 China

In recent years, neural topic modeling has increasingly raised extensive attention due to its capacity on generating coherent topics and flexible deep neural structures. However, the widely used Dirichlet distribution in shallow topic models is difficult to reparameterize. Therefore, most existing neural topic models assume the Gaussian as the prior of topic proportions for reparameterization. Gaussian distribution does not have the sparsity like Dirichlet distribution, which limits the model’s topic extraction ability. To address this issue, we propose a novel neural topic model approximating the Dirichlet prior with the reparameterizable Kumaraswamy distribution, namely Kumaraswamy Neural Topic Model (KNTM). Specifically, we adopted the stick-breaking process for posterior inference with the Kumaraswamy distribution as the base distribution. Besides, to capture the dependencies among topics, we propose a Kumaraswamy Recurrent Neural Topic Model (KRNTM) based on the recurrent stick-breaking construction to ensure that the model can still generate coherent topical words in high-dimensional topic space. We examined our method on five prevalent benchmark datasets over six Dirichlet-approximating neural topic models, among which KNTM has the lowest perplexity and KRNTM performance best on topic coherence and topic uniqueness. Qualitative analysis of the top topical words verifies that our proposed models can extract more semantically coherent topics compared with state-of-the-art models, further demonstrating our method’s effectiveness. This work contributes to the broader application of VAEs with Dirichlet priors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Benchmarking

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Model for human dynamics based on habit

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Chinese Science Bulletin 2010年第24期55卷 2744-2749页

作者： JIAO Yu LIU YanHeng WANG Jian WANG Jing College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Archives of Jilin University Changchun 130012 China

The quantitative understanding of human behavior is a central question of modern science. Because of the complexity of human behavior, it is almost impossible to seek regularities in human dynamics. It is assumed that human actions are randomly distributed in time in current models for human dynamics. While the characteristics of human behavior combined with the queue model are considered as model for human dynamics based on habit to explain bursts and heavy tails in human dynamics more exactly. Normal distribution is used to simulate intervals of succession of events, and random parameters are set as unexpected events disturbing habit behaviors. Moreover, duration of events are proposed to imitate continual attention to some events in human behaviors.

关键词：人类行为基于模型动力学模型学习突发事件时间间隔现代科学随机分布

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Fast kernel for calculating structural information similarities

Fast kernel for calculating structural information similarit...

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3rd IEEE International Conference on Intelligent Systems

作者： Wei, Jin-Mao Wang, Shu-Qin Wang, Jing You, Jun-Ping Institute of Computational Intelligence Northeast Normal University Open Symbol Computation and Knowledge Engineering Laboratory of State Education Jilin University Changchun Jilin 130024 China School of Mathematics and Statistics Northeast Normal University Changchun Jilin 130024 China

ISBN: (纸本)9781424401956

Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method..

关键词： structural information content structural similarity structural pattern analysis web mining

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Combining Greedy Method and Genetic Algorithm to Identify Transcription Factor Binding Sites

Combining Greedy Method and Genetic Algorithm to Identify Tr...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Wanyu Wang Xiaoyu Chang Chunguang Zhou College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA.

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