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Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

Computers, Materials & Continua 2020年第7期64卷 207-216页

作者： Yu Jiang Dengwen Yu Mingzhao Zhao Hongtao Bai Chong Wang Lili He College of Computer Science and Technology Jilin UniversityChangchun130012China A Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun130012China Department of Engineering Mechanics State Marine Technical University of St.PetersburgSt.Petersburg190008Russia

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for *** paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text *** K-Means and Affinity Propagation(AP)are two classical clustering *** Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable ***-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster *** semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering *** unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.

关键词： Unsupervised learning semi-supervised learning text clustering

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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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Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach

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Neural computing and Applications 2024年第22期36卷 13731-13744页

作者： Ouyang, Jihong Wang, Teng Cao, Jingyue Wang, Yiming College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130015 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130015 China

In recent years, neural topic modeling has increasingly raised extensive attention due to its capacity on generating coherent topics and flexible deep neural structures. However, the widely used Dirichlet distribution in shallow topic models is difficult to reparameterize. Therefore, most existing neural topic models assume the Gaussian as the prior of topic proportions for reparameterization. Gaussian distribution does not have the sparsity like Dirichlet distribution, which limits the model’s topic extraction ability. To address this issue, we propose a novel neural topic model approximating the Dirichlet prior with the reparameterizable Kumaraswamy distribution, namely Kumaraswamy Neural Topic Model (KNTM). Specifically, we adopted the stick-breaking process for posterior inference with the Kumaraswamy distribution as the base distribution. Besides, to capture the dependencies among topics, we propose a Kumaraswamy Recurrent Neural Topic Model (KRNTM) based on the recurrent stick-breaking construction to ensure that the model can still generate coherent topical words in high-dimensional topic space. We examined our method on five prevalent benchmark datasets over six Dirichlet-approximating neural topic models, among which KNTM has the lowest perplexity and KRNTM performance best on topic coherence and topic uniqueness. Qualitative analysis of the top topical words verifies that our proposed models can extract more semantically coherent topics compared with state-of-the-art models, further demonstrating our method’s effectiveness. This work contributes to the broader application of VAEs with Dirichlet priors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Benchmarking

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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CBR and micro-architecture antipatterns based software design improvement

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IFIP TC12 1st International Conference on Artificial Intelligence Applications and Innovations, AIAI 2004

作者： Feng, Tie Zhang, Jiachen Wang, Hongyuan Wang, Xian Institute of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)1402081502

This paper presents a Case Based Reasoning (CBR) approach to identifying micro-architecture anti-patterns and replacing them with "good" patterns in order to improve the design of software system. The resulting system design benefits from better flexibility for adapting to future requirement change and expansion. In this approach, both problematic inflexible structures and their corresponding refactoring designs are formally defined and organized in a case base. The identification of anti-patterns is carried out through similarity measurement on class diagrams, sequence diagrams, OO quality metric and semantic constraints. A supporting system CBDIT was developed to aid this approach. © 2004 Springer Science + Business Media, Inc.

关键词： Case based reasoning

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Model for human dynamics based on habit

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Chinese Science Bulletin 2010年第24期55卷 2744-2749页

作者： JIAO Yu LIU YanHeng WANG Jian WANG Jing 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 Changchun 130012 China Archives of Jilin University Changchun 130012 China

The quantitative understanding of human behavior is a central question of modern science. Because of the complexity of human behavior, it is almost impossible to seek regularities in human dynamics. It is assumed that human actions are randomly distributed in time in current models for human dynamics. While the characteristics of human behavior combined with the queue model are considered as model for human dynamics based on habit to explain bursts and heavy tails in human dynamics more exactly. Normal distribution is used to simulate intervals of succession of events, and random parameters are set as unexpected events disturbing habit behaviors. Moreover, duration of events are proposed to imitate continual attention to some events in human behaviors.

关键词：人类行为基于模型动力学模型学习突发事件时间间隔现代科学随机分布

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Feature Recognition Based on Graph Decomposition and Neural Network

Feature Recognition Based on Graph Decomposition and Neural ...

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International Conference on Convergence Information Technology

作者： Yi RongQing Li WenHui Wang Duo Yuan Hua Key laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University Changchun China

A hybrid of graph-based and neural network recognition system is developed. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are first checked to see whether they match with the predefined feature library. If so, a feature vector is assigned to them. Otherwise, base faces are obtained as heuristic information and used to restore the missing faces and update the sub-graphs. The sub-graphs are transformed into vectors, and these vectors are presented to the neural network, which classifies them into feature classes. The scope of instances variations of predefined feature that can be recognized is very wide. A new BP algorithm based on the enlarging error is also presented.

关键词： Neural networks Libraries Machining Face recognition Pattern recognition Information technology Laboratories Computer networks knowledge engineering Systems engineering education

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A new SAT Encoding Scheme for Exactly-one Constraints 5

A new SAT Encoding Scheme for Exactly-one Constraints

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5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019

作者： Cai, Jiatong Su, Yating Yang, Xixi Min, Jionghao Lai, Yong College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Exactly-one constraints have comprehensive applications for the fields of artificial intelligence and operations research. For many encoded SAT problems generated by the existing encoding schemes of exactly-one constraints, the state-of-the-art knowledge compilers cannot complete compilation. In this paper, we propose a new encoding scheme of exactly-one constraints. We introduce two-dimensional auxiliary variables (represented as a matrix) to denote the constraint that exactly one of some variables can be assigned as true. The clauses generated by our scheme is significantly less than those generated by three other existing encoding schemes. The experimental results on the exact cover problems show that the encoded CNF formulas generated by our scheme requires less compilation time, compared with the other three coding schemes. © Published under licence by IOP Publishing Ltd.

关键词： Operations research

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Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

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ZTE Communications 2022年第4期20卷 32-40页

作者： WANG Pengfei SONG Wei SUN Geng WEI Zongzheng ZHANG Qiang School of Computer Science and Technology Dalian University of TechnologyDalian 116024China School of Computer Science and Technology Jilin UniversityChangchun 130015China Key Laboratory of Symbolic Computing and Knowledge Engineering Jilin UniversityChangchun 130015China

Federated learning(FL)is a distributed machine learning approach that could provide secure 6G communications to preserve user *** 6G communications,unmanned aerial vehicles(UAVs)are widely used as FL parameter servers to collect and broadcast related parameters due to the advantages of easy deployment and high ***,the challenge of limited energy restricts the populariza⁃tion of UAV-enabled FL *** airground integrated low-energy federated learning framework is proposed,which minimizes the overall energy consumption of application communication while maintaining the quality of the FL ***,a hierarchical FL framework is proposed,where base stations(BSs)aggregate model parameters updated from their surrounding users separately and send the aggregated model parameters to the server,thereby reducing the energy consumption of *** addition,we optimize the deploy⁃ment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement,thus improv⁃ing the energy efficiency of the airground integrated *** evaluation results show that our proposed method can reduce the system en⁃ergy consumption while maintaining the accuracy of the FL model.

关键词： federated learning 6G communications privacy preserving secure communication

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of Computer Science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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