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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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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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Analysis the uncertainties of the Ant Colony Algorithm

Analysis the uncertainties of the Ant Colony Algorithm

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International Conference on computational Aspects of Social Networks, CASON

作者： Zhang, Yi Zhang, Meng Cao, Xin-Yan Department of Information Engineer Jilin Business and Technology College Changchun 130062 China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China College of Electronic Information Engineering University of Changchun Changchun Jilin Province 130000 China

ISBN: (纸本)9780769542027

In this paper, the subject of discussion is the uncertainties of Ant Colony Algorithm(ACA). In order to find application and popularize the ACA, we try to find some disciplinarians which can eliminate the impact of uncertainties in our algorithm and find the best assembled of the parameters by a series of experiments. Results show that our works had great effects in practicality. © 2010 IEEE.

关键词： Traveling salesman problem

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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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Fuzzy kernel clustering based on particle swarm optimization

Fuzzy kernel clustering based on particle swarm optimization

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IEEE International Conference on Granular Computing (GRC)

作者： Libiao Zhang Chunguang Zhou Ming Ma Xiaohua Liu Chunxia Li Caitang Sun Miao Liu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China Jilin University Changchun China

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Intelligent Sub-Object Image Retrieval System

Intelligent Sub-Object Image Retrieval System

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World Congress on Intelligent Control and Automation (WCICA)

作者： Wenhui Li Dongfeng Han Yi Wang Xiaosuo Lu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science&Technology Jilin University Changchun Jilin China

This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we use a segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Experimental and comparison results, which are performed using a general purpose database containing 20,000 images, are encouraging

关键词： Image retrieval Image segmentation Image databases Vehicle dynamics Indexing Information retrieval Content based retrieval Spatial databases Humans Laboratories

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Generating the semantic containers for the query interfaces of deep web

Generating the semantic containers for the query interfaces ...

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International Conference on Management and Service Science, MASS 2009

作者： Hao, Liang Wanli, Zuo Jian, Chen Yunge, Mao College of Computer Science and Technology Jilin University Changchun 130012 China Department of Information Changchun Taxation College Changchun 130117 China Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun 130012 China Network Center of Changchun Taxation College Changchun 130117 China

ISBN: (纸本)9781424446391

With the myriad emergence of the online Web Database, the Web is divided into a two layers information platform which is composed of Surface Web and Deep Web. The huge information hidden in the Deep Web is higher in the quality than the Surface Web. This interests the researchers of the traditional Database community. In order to adapt the Web community, the idea of information fusion is further transformed. However, the nature of the Web is great different from the Database in structure, principle and function. Query reformulation is one of the technical challenges. The only accessible method to get the information in the Deep Web is through the query interfaces provided by the Website of Deep Web. There are many semantic restrictions between the attributes in the query interface. Some attributes are co-occurred and the others are exclusive sometimes. To generate a valid query, we have to reconcile the key attributes and their semantic relations. To address the problem, a method based on the HTML codes is presented in this paper. Different kinds of semantic containers can be got through analyzing the codes of the query interface. The experiment is carried out on the famous Dataset of Deep Web to show the feasibility of the method. ©2009 IEEE.

关键词： Semantics

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Low cost edge sensing for high quality demosaicking

arXiv

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arXiv 2018年

作者： Niu, Yan State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China

Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Based on a close look at HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy;Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster. Copyright © 2018, The Authors. All rights reserved.

关键词： Costs

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Aspect Based Fine-Grained Sentiment Analysis for Public Policy Opinion Mining 21st

Aspect Based Fine-Grained Sentiment Analysis for Public Poli...

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21st International Symposium on knowledge and Systems Sciences, KSS 2022

作者： Zhao, Yueming Li, Ying Liu, Yijun Li, Qianqian College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institutes of Science and Development Chinese Academy of Sciences Beijing100190 China School of Public Policy and Management University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811936098

Fine-grained mining has substantial practical significance for understanding public opinion toward public policies and optimizing relevant decision-making processes. In this study, an aspect-based sentiment analysis model integrated with both a sentiment lexicon for the policy domain and mutual information (MI) is established from the perspective of attitudinal orientations toward several elements of public policies, including subjects, objects, and tools to analyze comments. First, terms related to the aspects of comments on different policy elements are extracted using the association rules in conjunction with the word2vec algorithm. In addition, the contextual neighbor principle is employed to extract terms that express evaluative opinions and to extricate aspect-based two-tuples from comments. Subsequently, the sentiment lexicon for the policy domain is expanded using the sentiment orientation pointwise mutual information (SO-PMI) algorithm. Four machine learning models (i.e., the support vector machine, logistic regression, naive Bayes, and k-nearest neighbors models) and two deep learning models (i.e., the convolutional neural network (CNN) and long short-term memory models) are compared in terms of their classification performance based on 87,304 comments on public policies crawled from the Weibo platform. The results show that the CNN model integrated with the domain-specific sentiment lexicon and mutual information is effective at facilitating policy element-oriented fine-grained sentiment analysis of public opinion toward policies and therefore has practical decision-making value for the government to better understand the policy demands of the public. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Public policy

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