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6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014

作者： Zhou, Zhihui Liu, Guixia Su, Lingtao Han, Liang Yan, Lun College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9781450328104

The rapid developments of chip-based technology have greatly improved human genetics and made routine the access of thousands of single nucleotide polymorphisms (SNPs) contributing to an informatics challenge. The characterization and interpretation of genes and gene-gene interactions that affect the susceptibility of common, complex multifactorial diseases is a computational and statistical challenge in genome-wide association studies (GWAS). Various methods have been proposed, but they have dificulty to be directly applied to GWAS caused by excessive search space and intensive computational burden. In this paper, we propose an ant colony optimization (ACO) based algorithm by combining the pheromone updating rule with the heuristic information. We tested power performance of our algorithm by conducting suficient experiments including a wide range of simulated datasets experiments and a real genome-wide dataset experiment. Experimental results demonstrate that our algorithm is time efficient and gain good performance in the term of the power of prediction accuracy. Copyright 2014 ACM.

关键词： Ant colony optimization

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Learning the structure of dynamic bayesian network with domain knowledge

Learning the structure of dynamic bayesian network with doma...

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Chen, Juan Jia, Haiyang Huang, Yuxiao Liu, Dayou College of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9781467314855

Dynamic Bayesian Network (DBN) is a graphical model for representing temporal stochastic processes. Learning the structure of DBN is a fundamental step for parameter learning, inference and application. For large scale problem, the structure learning is intractable. In some domains the training data is very limited and noisy, so learning the DBN structure only with training data is impractical. Domain knowledge may improve both the efficiency and the accuracy of the learning algorithm. But usually, the domain knowledge is uncertainty, unclear and even with conflict. This paper presents a novel algorithm for learning the structure of DBN, which consider the data and domain knowledge simultaneously, empirical experiment shows that the proposed algorithm improved the efficiency and the accuracy of the DBN structure learning. © 2012 IEEE.

关键词： Bayesian networks

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Research and implementation of improved real-time crawler modeling

Research and implementation of improved real-time crawler mo...

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International Conference on Electrical Information and Mechatronics, ICEIM 2012

作者： Zuo, Xianglin Wang, Wenbo Wang, Ying Zuo, Wanli College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9783037856901

The past decade has witnessed the rapid development of search engines, which has become an indispensable part of everyday life. However, people are no longer satisfied with accessing to ordinary information, and they may instead pay more attention to fresh information. This demand poses challenges to traditional search engines, which concern more about relevance and importance of web pages. A search engine compresses three modules: crawler, indexer and searcher. Changes are needed for all these three parts to improve search engine's freshness. This paper investigates the first part of search engine - crawler, we analyze the requirements for real-time crawler, and propose a novel real-time crawler based on more accurate estimation of refresh time. Experimental results demonstrate that the proposed real-time crawler can help search engine improve its freshness. © (2013) Trans Tech Publications, Switzerland.

关键词： Search engines

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Detecting epistasis by LASSO-penalized-model search algorithm in human Genome-Wide Association Studies

Detecting epistasis by LASSO-penalized-model search algorith...

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4th International Conference on Materials Science and Information Technology, MSIT 2014

作者： Zhou, Zhi Hui Liu, Gui Xia Su, Ling Tao Han, Liang Yan, Lun College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9783038351733

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy. © (2014) Trans Tech Publications, Switzerland.

关键词： Genes

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A PKI-based lightweight workflow model

A PKI-based lightweight workflow model

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5th International Conference on Control and Automation, ICCA'05

作者： Zhang, Jian Sun, Jigui Li, Niya Hu, Chengquan Li, Zehai College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education China

ISBN: (纸本)0780391381

The workflow model is the abstract expression of the workflow or the business process. Following the WfMC reference model, a PKI-based lightweight workflow model named as PBLW is put forward in this paper. The framework of PBLW is proposed. Based on the time constraint directed graph, the workflow theory model in formalization is described in detail, and the time constraint problems are discussed in order to enhance the time management ability of the workflow model. Furthermore, in order to express the complicated secure access control constraint of the workflow, a conditioned RBAC model based on PKI technology is presented, according to the traditional RBAC security access control model. Finally, the workflow engine driven algorithms for the workflow instance are given. By the actual inspection and users' feedback, it proves that this model can express the basic requirements of a majority of the process applications. © 2005 IEEE.

关键词： Electronic document identification systems

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An efficient and effective similarity measure to enable data mining of fabric image of Xinjiang folk art

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Journal of Information and computational Science 2015年第5期12卷 1695-1702页

作者： Te, Rigen Li, Xiongfei Zhang, Yueying Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China College of Materials Science and Engineering Jilin University Changchun China

Fabric pattern is a folk art of artificial fabrics pattern design which includes apparels (known as clothes and hats), and crafts (known as carpets and tapestries). Although image processing, information retrieval and data mining have made a large impact on many human endeavors, they never had an impact on the study of the fabric image. In this paper we will present the reasons for this, and introduce a novel similarity measure and algorithm (Pattern Similarity Measuring Algorithm (PSMA)) which allows efficient data mining for large collections of fabric image. PSMA, the effectiveness of which is proved be experiment test, is an image similarity measurement algorithm that achieves the machine-learning based theory via the gradient descent algorithm with multidimensional linear regression. Copyright © 2015 Binary Information Press.

关键词： Data mining

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The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT key-points for Iris Recognition

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Journal of Bionic engineering 2015年第3期12卷 504-517页

作者： Yuanning Liu Fei He Xiaodong Zhu Zhen Liu Ying Chen Ye Han Lijiao Yu 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 Chanschun 130012 China Graduate School of Engineering Nagasaki Institute of Applied Science Japan College of Software Nanchang Hangkong University Nanchang 330063 China

Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation （SIFT）, which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods.

关键词： iris recognition bionic Gabor features scale invariant feature transformation support vector regression score level fusion

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A BRS-based approach for modeling RFID untraceability

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International Journal of Advancements in Computing Technology 2011年第11期3卷 96-103页

作者： Jialiang, He Dantong, Ouyang Youjun, Xu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Information Technology Daqing Normal University China

Untraceability is an important aspect of RFID security. A BRS-based approach for modeling RFID untraceability is provided in this paper. Elements in a RFID protocol can be represented as bigraphs, communications between elements can be represented as reaction rules. RFID untraceability can be represented as behavioral congruences. We take a RFID air interface protocol as a case study and show the usability of this approach.

关键词： Radio frequency identification (RFID)

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A dynamic time delay neural network for ultrasonic motor identification and control

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Liang, Yanchun Zhang, Jie Xu, Xu Yang, Xiaowei Hao, Zhifeng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics Jilin University Changchun 130012 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)3540344373

A novel dynamic time delay neural network is proposed for ultrasonic motors identification and control in this paper. By introducing time delay neurons, the neural network identifier and controller of ultrasonic motors are constructed. Both of them are trained online by using an improved back-propagation algorithm. The usefulness and validity of the presented algorithm is examined by the experiments. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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An AutoEncoder-Based Matrix Factorization Approach to Estimating Cell Proportion from Bulk Tumor RNA-seq Data

An AutoEncoder-Based Matrix Factorization Approach to Estima...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingze Xu Yan Wang Xuping Xie Feilong Wang Qiong Chen Huiyan Sun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781665429825

The deconvolution of infiltrating immune cells and stromal cells from complex tumor tissues is significant for studying the impact of these cells on tumor development, as well as assisting cancer therapies. Integrating cell-specific marker genes from several references, we put forward an AutoEncoder-based matrix factorization method to estimate the cell proportion in heterogeneous samples. The proportion predicted by our method achieved over 0.95 Pearson correlation coefficient (PCC) with the ground truth. Moreover, through analyzing association between cell proportion of tumor tissues and clinical information of the tumor patients, we found that the proportion of cancer-associated fibroblast (CAF) gradually increased with the progress of tumor, and the proportion of B cell in tissues was significantly related to the five-year survival rate of tumor patients.

关键词： Head Deconvolution Medical treatment Melanoma Fibroblasts Neck Reliability

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