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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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An Ant Colony Optimization Method for Prize-collecting Traveling Salesman Problem with Time Windows

An Ant Colony Optimization Method for Prize-collecting Trave...

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International Conference on Natural computation (ICNC)

作者： Xiaohu Shi Liupu Wang You Zhou Yanchun Liang Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Focused on a variation of the Euclidean traveling salesman problem (TSP), namely the prize-collecting traveling salesman problem with time windows (PCTSPTW), this paper presents a novel ant colony optimization solving method. The time window constraints are considered in the computation for the probability of selection of the next city. The parameters of the algorithm are analyzed by experiments. Numerical results also show that the proposed method is effective for the PCTSPTW problem.

关键词： Ant colony optimization Traveling salesman problems Cities and towns Algorithm design and analysis Time factors Performance analysis Educational institutions Computer science Educational technology Laboratories

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Study on optimization of agent initial positions in land combat simulation

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自然科学进展(英文版) 2004年第3期14卷 257-261页

作者： WU Chunguo LIANG Yanchun LEE Heow Pueh LU Chun Yang Xiaowei College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore Department of Applied Mathematics South China University of Technology Guangzhou 510640 China Centre for ACES Department of Mechanical Engineering National University of Singapore 119260 Singapore

The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that using the traditional SVM with the same precision. We also summarize and present some experiences and trends in the study on the optimization problem in land combat simulation.

关键词： multi-agent support vector machine radial basis function genetic algorithm regression

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Robust estimation of the fundamental matrix based on an error model

Robust estimation of the fundamental matrix based on an erro...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hui-Xiang Zhong Yue-Ping Feng Yun-Jie Pang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique - the random sample consensus (RANSAC) to discard outliers in an initial set of matched points. It adopts the sampling strategy to generate inliers from the initial set. The second part of the method is an algorithm for computing fundamental matrix, using the output of the RANSAC. This algorithm is based on the consistent fundamental matrix estimation in a quadratic measurement error model. An extended system for determining the estimator is proposed, and an efficient implementation for solving the system - a continuation method is developed. The proposed algorithm avoids solving total eigenvalue problems. Results for both synthetic and real images show the effectiveness of the proposed method.

关键词： Robustness Measurement errors Computer errors Eigenvalues and eigenfunctions Computer vision Layout Educational institutions Computer science Educational technology Laboratories

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Explanation Chains Model Based on the Fine-Grained Data 8th

Explanation Chains Model Based on the Fine-Grained Data

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Ma, Fu-Yuan Chen, Wen-Qi Xiao, Min-Hao Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China Changchun Institute of Technology Changchun130012 China College of Software Jilin University Changchun130012 China

ISBN: (纸本)9783030322359

With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems. © 2019, Springer Nature Switzerland AG.

关键词： Recommender systems

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A Method of Gene-Function Annotation Based on Variable Precision Rough Sets

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Journal of Bionic Engineering 2007年第3期4卷 177-184页

作者： Zhi-li Pei Xiao-hu Shi Meng Niu Xu-ning Tang Li-sha Liu Ying Kong Yan-chun Liang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 P. R. China College of Mathematics and Computer Science Inner Mongolia University for NationalitiesTongliao 028043 P. R. China College of Software Jilin University Changchun 130012 P. R. China Department of Biochemistry and Molecular Biology Institution of Glycobiology Dalian Medical University Dalian 116027 P. R. China

It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.

关键词： gene function annotation variable precision rough set GO BLAST

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Translating query for deep web using ontology

Translating query for deep web using ontology

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International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Hao, Hang Wanli, Zuo Fei, Ren Junhua, Wang College of Computer Science and Technology Jilin University Department of Information Science and Technology Changchun Taxation College Changchun 130117 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)9780769533360

The E-commerce information on the Surface Web is supported by the Deep Web, which can not be accessed directly by the search engines or the web crawlers. The only way to access the backend database is through query interface. Extracting valid attributes from the query forms and automatic translating the source query into a target query is a solvable way for addressing the current limitations in accessing Deep Web data sources. To generate a valid query, we have to reconcile the key attributes and their semantic relation. We present our framework to solve the problem. To enrich the set of attributes contained in the semantic form, we use the WordNet as kinds of ontology technique and we try to find the semantic relation of the attributes in the same query from and different forms. Extensive experiments over real-word domains show the utility of our query translation framework. © 2008 IEEE.

关键词： Ontology

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AN IMPROVED BICLUSTERING ALGORITHM AND ITS APPLICATION TO GENE EXPRESSION SPECTRUM ANALYSIS

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Genomics, Proteomics & Bioinformatics 2005年第3期3卷 189-193页

作者： Hua Qu Liu-Pu Wang Yan-Chun Liang Chun-Guo Wu College of Software Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Guangzhou Institute of China Telecom Guangzhou 510630 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Key Laboratory of Information Science and Engineering of the Ministry of Railway/Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing 100044 China.

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the data possess a strong consistency with fluctuation on the condition while the computational time does not increase significantly.

关键词： biclustering algorithm gene expression pedigree analysis Cheng and Church algorithm

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A Chinese Topic Crawler Focused on Customer Development

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Procedia CIRP 2016年 56卷 476-480页

作者： Tong Wu Yanchun Liang Chunguo Wu Shifeng Piao Deyin Ma Guozhong Zhao Xiaosong Han College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China School of Business Jilin University Changchun 130012 China Daqing Oilfield Personnel Development Institute CNPC Daqing 163000 China

This paper presents a Chinese topic crawler focused on customer development, in order to meet the needs of users for more accurate and particular Internet information. The concept of meta-search engine is introduced, and the keywords are expanded by the ontology of HowNet. Through the web crawler, preprocessing and classification, the information on customer relations can be divided into three categories: company, platform and meaningless. Numerical experiments show that satisfactory results can be obtained in some particular information-seeking areas. The average accuracy for classification is more than 80%, which can meet customer needs in most cases.

关键词： Topic crawler Customer development Meta-search engine Semantic similarity

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