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作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and TechnologySchool of Computer Science Department of Computer Science and Technology School of Computer ScienceZhuhai College of Jilin University

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval,a daily business report generator system based on web scraping with k nearest neighbor(kNN)classification algorithm is proposed in this *** covers the web crawler technology that is to access existing website system and extract business *** k NN algorithm is applied to identify the verification code on the login page,and the brief daily report generating in a spreadsheet style *** with some OCR engine for image recognition,the system in Python can automatically generate the brief daily business reports by the kNN algorithm,which is better than some library with default training set on validating the verification code.

关键词： image Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm Web KNN

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A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI

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Chinese Journal of Electronics 2017年第6期26卷 1168-1176页

作者： DING Weiping GUAN Zhijin WANG Jiehua TIAN Di School of Computer Science and Technology Nantong University State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology

As the conventional feature selection algorithms are prone to the poor running efficiency in largescale datasets with interacting features, this paper aims at proposing a novel rough feature selection algorithm whose innovation centers on the layered co-evolutionary strategy with neighborhood radius hierarchy. This hierarchy can adapt the rough feature scales among different layers as well as produce the reasonable decompositions through exploiting any correlation and interdependency among feature subsets. Both neighborhood interaction within layer and neighborhood cascade between layers are adopted to implement the interactive optimization of neighborhood radius matrix, so that both the optimal rough feature selection subsets and their global optimal set are obtained efficiently. Our experimental results substantiate the proposed algorithm can achieve better effectiveness, accuracy and applicability than some traditional feature selection algorithms.

关键词： Layered co-evolution Rough feature se lection Adaptive neighborhood radius hierarchy Longitudinal cortical surface labeling

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A cloud-based evaluation system for science-and-engineering students 4th

A cloud-based evaluation system for science-and-engineering ...

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Huang, Qian Ye, Feng Chen, Yong Xu, Peiling College of Computer and Information Hohai University Nanjing211100 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Postdoctoral Centre Nanjing Longyuan Micro-Electronic Company Nanjing211106 China Suzhou College of Information Technology Suzhou215200 China

ISBN: (纸本)9789811322051

With the burgeoning of IT industry, more and more companies and universities concentrate on the scientific evaluation of science-and-engineering students. Existing evaluation strategies typically lie on grades or scores of the courses taken by students, which have obvious drawbacks nowadays and cannot lead to a proper improvement of education management. This paper proposes an overall student evaluation system architecture that includes three levels, i.e. data collection infrastructure which comprises student data collection and student data transmission, data center which is composed of four sub-levels, and unified portal which defines unified applications and classes of visiting terminals. In the proposed architecture, the main contribution lie in the upper most sub-level of data center, i.e. evaluation services. Four categories of student evaluation services, i.e. moral trait, civic literacy, knowledge level and comprehensive ability, are defined. Furthermore, in order to have a satisfactory feedback in the practical teaching process, the most important comprehensive ability for science-and-engineering students is fractionized into four sub-categories and visualized from several different aspects for a good feedback in practical teaching process. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Students

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Approximate Model Counting by Partial knowledge Compilation

arXiv

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arXiv 2018年

作者： Lai, Yong College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Changchun China

Model counting is the problem of computing the number of satisfying assignments of a given propositional formula. Although exact model counters can be naturally furnished by most of the knowledge compilation (KC) methods, in practice, they fail to generate the compiled results for the exact counting of models for certain formulas due to the explosion in sizes. Decision-DNNF is an important KC language that captures most of the practical compilers. We propose a generalized Decision-DNNF (referred to as partial Decision-DNNF) via introducing a class of new leaf vertices (called unknown vertices), and then propose an algorithm called PARTIALKC to generate randomly partial Decision-DNNF formulas from the given formulas. An unbiased estimate of the model number can be computed via a randomly partial Decision-DNNF formula. Each calling of PARTIALKC consists of multiple callings of MICROKC, while each of the latter callings is a process of importance sampling equipped with KC technologies. The experimental results show that PARTIALKC is more accurate than both SampleSearch and SearchTreeSampler, PARTIALKC scales better than SearchTreeSampler, and the KC technologies can obviously accelerate sampling. Copyright © 2018, The Authors. All rights reserved.

关键词： Importance sampling

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Genetic Fuzzy System in Weapon Control Module of Unmanned Tanks

Genetic Fuzzy System in Weapon Control Module of Unmanned Ta...

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2018 International Conference on Computer Modeling, Simulation and Algorithm（CMSA2018）

作者： Xinzhu Liu Di Ding Zihuan Yu Zhongshuai Wang Yuhan Dai Jing Huang School of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education

The genetic fuzzy system is applied to weapon control module of unmanned tanks. In this paper, the simulation system is built to train the fuzzy rule base of genetic fuzzy system in order to get the optimal fuzzy rule base with training tasks. Testing tasks are used to test the success rate of different missions under the optimal rule base. The fuzzy inference system obtained by the algorithm achieves high success rate in various tasks.

关键词： unmanned tanks genetic fuzzy system fuzzy rule base weapon control module

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Image clustering segmentation based on SLIC superpixel and transfer learning

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Pattern Recognition and Image Analysis 2017年第4期27卷 838-845页

作者： Li, X.X. Shen, X.J. Chen, H.P. Feng, Y.C. College of Computer Science and Technology Jilin University ChangchunJilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China

Traditional fuzzy C-means clustering algorithm has poor noise immunity and clustering results in image segmentation. To overcome this problem, a novel image clustering algorithm based on SLIC superpixel and transfer learning is proposed in this paper. In the proposed algorithm, SLIC superpixel method is used to improve the edge matching degree of image segmentation and enhances the robustness to noise. Transfer learning is adopted to correct the image segmentation result and further improve the accuracy of image segmentation. In addition, the proposed algorithm improves the original SLIC superpixel algorithm and makes the edge of the superpixel more accurate. Experimental results show that the proposed algorithm can obtain better segmentation results. © 2017, Pleiades Publishing, Ltd.

关键词： Image segmentation

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An effective dimensionality reduction method for text classification based on TFP-tree

An effective dimensionality reduction method for text classi...

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作者： Liu, Lu College of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun China

Obtaining interesting and topic-relevant information is a very important task in Web mining. Text classification using a small proportion of labeled data and a large proportion of unlabeled data, also called semi-supervised learning, is a well-known problem. Despite plenty of research on text classification, however, how to effectively and efficiently apply valuable frequent patterns and deal with high-dimensional data in text classification is still an open issue. Due to the increasing data volumes and plenty of high-dimensional data, both distance measures and time complexity could be influenced by the noisy data. This paper targets on this problem and presents a novel method for text classification called CTFP (Classification based on TFP-tree), which uses TFP-tree (Text-Frequent-Pattern-tree) to generate frequent patterns in tremendous amount of texts and conduct text classification in a relatively low dimensional data space. It effectively reduces the data dimensionality during constructing the classifier. Substantial experiments on three datasets (RCV1, SRAA and Reuters-21578) show that our proposed method can achieve better performance than many existing state-of-the-art methods on precision, efficiency and many other evaluation metrics. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Support vector machines

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A hybrid feature selection algorithm for microarray data

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Concurrency and computation: Practice and Experience 2018年

作者： Zheng, Yuefeng Li, Ying Wang, Gang Chen, Yupeng Xu, Qian Fan, Jiahao Cui, Xueting College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China BODA College of Jilin Normal University SiPing China

For each microarray data set, only a small number of genes are beneficial. Due to the high-dimensional problem, gene selection research work remains a challenge. In order to solve the high-dimensional problem, we propose a dimensionality reduction algorithm named K Value Maximum Relevance Minimum Redundancy Improved Grey Wolf Optimizer (KMR2IGWO). First, in the processing of KMR2, the K genes are selected. Second, the K genes are initialized by two ways according to random selection feature and different proportions of selection feature. Finally, the IGWO algorithm selects the optimal classification accuracy and the optimal combination of gene by adjusting the parameters of fitness function. The algorithm has a significant dimensionality reduction effect and is suitable for high-dimensional data sets. Experimental results show that the proposing KMR2IGWO strategy significantly reduces the dimension of microarray data and removes the redundant features. On the 14 microarray datasets, compared with the four algorithms mRMR+PSO, mRMR + GA, mRMR + BA, mRMR + CS, the proposed algorithm has higher performance in classification accuracy and feature subset length. © 2018 John Wiley & Sons, Ltd.

关键词： Support vector machines

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Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

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The Computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

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A Cloud-Based Evaluation System for Science-and-engineering Students

A Cloud-Based Evaluation System for Science-and-Engineering ...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Qian Huang Feng Ye Yong Chen Peiling Xu College of Computer and Information Hohai University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministryof Education Jilin University Postdoctoral Centre Nanjing Longyuan Micro-Electronic Company Suzhou College of Information Technology

With the burgeoning of IT industry, more and more companies and universities concentrate on the scientific evaluation of science-and-engineering students. Existing evaluation strategies typically lie on grades or scores of the courses taken by students, which have obvious drawbacks nowadays and cannot lead to a proper improvement of education management. This paper proposes an overall student evaluation system architecture that includes three levels, i.e. data collection infrastructure which comprises student data collection and student data transmission, data center which is composed of four sub-levels, and unified portal which defines unified applications and classes of visiting terminals. In the proposed architecture, the main contribution lie in the upper most sub-level of data center, i.e. evaluation services. Four categories of student evaluation services, i.e. moral trait, civic literacy, knowledge level and comprehensive ability,are defined. Furthermore, in order to have a satisfactory feedback in the practical teaching process, the most important comprehensive ability for science-andengineering students is fractionized into four sub-categories and visualized from several different aspects for a good feedback in practical teaching process.

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