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International Conference on Intelligent Computing, ICIC 2005

作者： Guo, Xiaoxin Xu, Zhiwen Lu, Yinan Liu, Zhanhui Pang, Yunjie College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Qianjin Street 2699 Changchun 130012 China

ISBN: (纸本)9783540282266

This paper proposes a novel registration algorithm based on Pseudo-Polar Fast Fourier Transform (FFT) and Analytical Fourier-Mellin Transform (AFMT) for the alignment of images differing in translation, rotation angle, and uniform scale factor. The proposed algorithm employs the AFMT of the Fourier magnitude to determine all the geometric transformation parameters with its property of the invariance to translation and rotation. Besides, the proposed algorithm adopt a fast high accuracy conversion from Cartesian to polar coordinates based on the pseudo-polar FFT and the conversion from the pseudo-polar to the polar grid, which involves only ID interpolations, and obtain a more significant improvement in accuracy than the conventional method using cross-correlation. Experiments show that the algorithm is accurate and robust regardless of white noise. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image processing

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An efficient method for very large scale out-of-core terrain visualization

An efficient method for very large scale out-of-core terrain...

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16th International Conference on Artificial Reality and Telexistence - Workshops, ICAT 2006

作者： Zhang, Huijie Sun, Jigui Yu, Haihong Qi, Changsong College of Computer Science and Technology Jilin University College of Computer Science Northeast Normal University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education

ISBN: (纸本)076952754X

Large scale terrain visualization with high-resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio. © 2006 IEEE.

关键词： Efficiency

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A hybrid algorithm for combining forecasting based on AFTER-PSO

A hybrid algorithm for combining forecasting based on AFTER-...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

作者： Feng, Xiaoyue Liang, Yanchun Sun, Yanfeng Lee, Heow Pueh Zhou, Chunguang Wang, Yan Key Laboratory of Symbol Computation and Knowledge Engineering Juin University Ministry of Education Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore

ISBN: (纸本)3540228179

A novel hybrid algorithm based on the AFTER (Aggregated forecast through exponential re-weighting) and the modified particle swarm optimization (PSO) is proposed. The combining weights in the hybrid algorithm are trained by the modified PSO. The linear constraints are added in the PSO to ensure that the sum of the combining weights is equal to one. Simulated results on the prediction of the stocks data show the effectiveness of the hybrid algorithm. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Algorithms

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Community discovery algorithm and its technical improvement based on link structure–taking web community algorithm as an example

Community discovery algorithm and its technical improvement ...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Gao, Rui Yang, Wenzhe Shi, Xiaohu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China

ISBN: (纸本)9783030539795

Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algorithm based on HITS algorithm for edge capacity allocation. The traditional maximum flow problem, but there are still shortcomings. Based on the analysis of existing link similarity definitions, this paper proposes a new definition of link similarity and topic dissimilarity to better describe the relationship between linked pages, and to measure based on link similarity and topic dissimilarity. The similarity of the pages gives a more reasonable and efficient maximum flow-side capacity allocation scheme. The community discovery of 6 topics shows that the maximum flow algorithm proposed in this paper using the link similarity and topic dissimilarity model to allocate edge capacity can better solve the problems of existing algorithms and significantly improve the quality of the Web community. This paper proposes new methods and ideas for page similarity measurement, and provides new strategies for improving the Web community discovery algorithm based on link analysis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Computer programming

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Deep Feature-Based Text Clustering and Its Explanation 39

Deep Feature-Based Text Clustering and Its Explanation

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Guan, Renchu Zhang, Hao Liang, Yanchun Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China University of Trento Disi Trento Italy

ISBN: (纸本)9798350322279

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering. © 2023 IEEE.

关键词： Cluster analysis

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Optimizing simple tabular reduction with a bitwise representation 25

Optimizing simple tabular reduction with a bitwise represent...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Wang, Ruiwei Xia, Wei Yap, Roland H.C. Li, Zhanshan School of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Education Ministry China School of Computing National University of Singapore Singapore

Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency algorithms. We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure. STRbit is extended to deal with compression of the underlying constraint with c-tuples. Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff.

关键词： Data structures

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Hilbert-huang transform-based local regions descriptors

Hilbert-huang transform-based local regions descriptors

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2007 18th British Machine Vision Conference, BMVC 2007

作者： Han, Dongfeng Li, Wenhui Guo, Wu Li, Zongcheng Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China School of Engineering Technology Shandong University of Technology Zibo China

ISBN: (纸本)1901725340

This paper presents a new interest local regions descriptors method based on Hilbert-Huang Transform. The neighborhood of the interest local region is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs). Then the Hilbert transform is applied to each component and get the phase and amplitude information. The proposed descriptors samples the phase angles information and amalgamates them into 10 overlap squares with 8-bin orientation histograms. The experiments show that the proposed descriptors are better than SIFT and other standard descriptors. Essentially, the Hilbert-Huang Transform based descriptors can belong to the class of phase-based descriptors. So it can provides a better way to overcome the illumination changes. Additionally, the Hilbert-Huang transform is a new tool for analyzing signals and the proposed descriptors is a new attempt to the Hilbert-Huang transform.

关键词： Hilbert-Huang transform

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Traffic optimization for distributed network model base on Petri Nets

Traffic optimization for distributed network model base on P...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Chang, Xin Pang, Huanli Hu, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China Collage of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

ISBN: (纸本)9780769541396

Petri Nets is a powerful mathematical modeling tool for system description and analysis, with which we can describe the relationship among entities efficiently. The present thesis puts forward the modeling of the distributed computer network DNMPN and DNMPNTO based on Petri Nets. DNMPN implements the basic function of distributed computer network, and DNMPNTO modifies DNMPN's repetitive forward problem. Thus, it is efficiently helpful to optimize the unit traffic of the network, save the net work resource and improve the efficiency of the network system. © 2010 IEEE.

关键词： Petri nets

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PSO based partitional clustering algorithm

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第6期38卷 1371-1377页

作者： Zhang, Chang-Sheng Sun, Ji-Gui Cui, Yan Yang, Feng-Qin Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.

关键词： Clustering algorithms

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The BBC news hunter: A novel crawler for BBC news 2nd

The BBC news hunter: A novel crawler for BBC news

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2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Wang, Mingxin Wang, Ning Wang, Boran Tian, Can Liang, Yanchun Zhao, Guozhong Han, Xiaosong College of Software Jilin University Changchun130012 China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory for Symbol Computation Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai519041 China Daqing Oilfield Personnel Development Institute CNPC Daqing163000 China

ISBN: (纸本)9789811020971

In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole system consists of 6 subsystems, respectively named: UI, Control, Download, Analysis, Storage and Log. Numerical experiments show that satisfactory results can be obtained from the BBC news website, whose average accuracy as well as efficiency are acceptable. © Springer Science+Business Media Singapore 2016.

关键词： Data mining

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