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2016 IEEE International Conference on Cloud computing and Big Data Analysis, ICCCBDA 2016

作者： Huang, Jiao Sun, Haobo Guo, Pengbo Zhao, Minghao Niu, Kai Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781509025930

In recent years, with the development of the Internet, it is more and more common for users to buy mobile phones on the Internet. On the one hand, sentiment analysis help customers to fully understand the performance of the phone, on the other hand, it provides improvement direction for producer to upgrade the goods. In this paper, we use the method of semantic analysis to analyze the sentiment of Chinese online comments about mobile phone. It is worth noting that we focus on the analysis of characteristics of different commentators for the product of the independent sentimental rather than for the product's overall feelings. On the basis of predecessors' work, we creatively put forward a solution to solve the problem of multiple-sentiment sentimental words, which remarkable improve the rate of recall. In the aspect of accuracy, we provide a more powerful method for the judgment of neutral words by analyzing the influence of the degree adverbs on neutral words. In the end of the article, several times with different experiments verify the effectiveness of the method in the aspect of accuracy rate and recall rate. © 2016 IEEE.

关键词： Cellular telephones

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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 734-754页

作者： Yong Zhang Ying Hu Xiaozhi Gao Dunwei Gong Yinan Guo Kaizhou Gao Wanqiu Zhang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina School of Computing University of Eastern FinlandKuopioFinland The Macao Institute of Systems Engineering Macao University of Science and TechnologyTaipaChina

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy *** case that different attributes/features of the same instance are stored in different institutions is called vertically distributed *** pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature *** solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first *** optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more *** the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'***,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each ***,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation ***-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

关键词： Evolutionary optimization feature selection privacy protection vertical‐federated learning

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Stochastic Variational Inference-Based Parallel and Online Supervised Topic Model for Large-Scale Text Processing

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Journal of Computer Science & Technology 2018年第5期33卷 1007-1022页

作者： Yang Li Wen-Zhuo Song Bo Yang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China Aviation University of Air Force Changchun 130062 China

Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.

关键词： topic modeling, large-scale text classification, stochastic variational inference, cloud computing online learning

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A Graph Attribute Aggregation Method based on Feature engineering

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Journal of The Institution of Engineers (India): Series B 2022年第3期103卷 711-719页

作者： Wang, Hao Dong, Li-Yan Ma, Xin-Tao Sun, Ming-Hui College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

In the fields of social network analysis and knowledge graph, many semi-supervised learning algorithms based on graph convolutional neural network (GCN) have been widely used. Most of these algorithms usually improve the structure of the neural network and the sampling method of each layer of the neural network. However, they don’t pay much attention to the data pre-processing of the algorithm. In the analysis of the input data, the words of different quality in these original data are unevenly distributed. This may obscure some useful data and highlight some irrelevant data. In order to verify the correctness of this hypothesis, the paper proposes a feature matrix compression algorithm (FMC algorithm) for data pre-processing of GCN-based algorithms. The algorithm analyzes and arranges the word columns of the input matrix (the feature of graph) according to the frequency of the word, then merges those words in which the word frequency is smaller, so as to emphasize the role of these words in the graph and optimize the data scale. The present work uses four mainstream datasets in the field and several representative and different algorithms to complete the experiment. The experimental results show that the FMC algorithm achieves better performance. © 2021, The Institution of Engineers (India).

关键词： Learning algorithms

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Painlevé Property and Integrability of Polynomial Dynamical Systems

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Communications in Mathematical Research 2014年第4期30卷 358-368页

作者： Li Wen-lei Shi Shao-yun School of Mathematics Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

The main purpose of this paper is to investigate the connection between the Painlev′e property and the integrability of polynomial dynamical systems. We show that if a polynomial dynamical system has Painlev′e property, then it admits certain class of first integrals. We also present some relationships between the Painlev′e property and the structure of the differential Galois group of the corresponding variational equations along some complex integral curve.

关键词： Painlev′e property integrability differential Galois group

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Community mining in dynamic social networks - Clustering based improved ant colony algorithm

Community mining in dynamic social networks - Clustering bas...

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6th International Conference on Computer Science and Education, ICCSE 2011

作者： Nan, Zhang Zhe, Wang Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781424497188

Community mining has been the focus of many recent researches on dynamic social networks. In this paper, we propose a clustering based improved ant colony algorithm (CIACA) for community mining in social networks. The CIACA combines the local pheromone update rule with the global update rule and utilizes heuristic function to adjust the clustering solution dynamically, assisted by decay coefficient of dynamic network model. In order to improve clustering accuracy and convergence rate in the process of ant migration, a structure tightness between nodes based clustering centers initializing method is proposed, which can provide us initial clustering centers with certain clustering precision and high diversity. In addition, random number and specific parameter are used in the ant transition probability, which strengthens the search stochastic properties of CIACA effectively. The proposed CIACA is tested on some benchmark social networks, and is compared with current representative algorithms in community mining. Experimental results show the feasibility and validity of CIACA. © 2011 IEEE.

关键词： Complex networks

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KAM Type-Theorem for Lower Dimensional Tori in Random Hamiltonian Systems

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Communications in Mathematical Research 2011年第1期27卷 81-96页

作者： LI YONG XU LU College of Mathematics Key Laboratory of Symbolic computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012

In this paper, we study the persistence of lower dimensional tori for random Hamiltonian systems, which shows that majority of the unperturbed tori persist as Cantor fragments of lower dimensional ones under small per... 详细信息

关键词： random Hamiltonian system KAM type theorem Cantor fragment of invariant tori

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Color Image Super-Resolution and Enhancement with Inter-Channel Details at Trivial Cost

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Journal of Computer Science & Technology 2020年第4期35卷 889-899页

作者： Chuang-Ye Zhang Yan Niu Tie-Ru Wu Xi-Ming Li College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computer and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China School of Mathematics Jilin UniversityChangchun 130012China

Image super-resolution is essential for a variety of applications such as medical imaging,surveillance imaging,and satellite imaging,among ***,the most popular color image super-resolution is performed in each color channel *** this paper,we show that the super-resolution quality can be further enhanced by exploiting the cross-channel *** by the High-Quality Linear Interpolation(HQLI)demosaicking algorithm by Malvar et al.,we design an image super-resolution scheme that integrates intra-channel interpolation with cross-channel details by isotropic linear *** its simplicity,our super-resolution method achieves the accuracy comparable with the existing fastest state-of-the-art super-resolution algorithm at 20 times faster *** is well applicable to applications that adopt traditional interpolations,for improved visual quality at trivial computation *** comparative study verifies the effectiveness and efficiency of the proposed super-resolution algorithm.

关键词： image super-resolution isotropic linear interpolation inter-channel detail enhancement low cost

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Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

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Journal of Bionic engineering 2011年第4期8卷 440-448页

作者： Haipeng Chen Xuanjing Shen Xiaofei Li Yushan Jin College of Computer Science and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China

To satisfy the requirements of real-time and high quality mosaics, a bionic compound eye visual system was designed by simulating the visual mechanism of a fly compound eye. Several CCD cameras were used in this system to imitate the small eyes of a compound eye. Based on the optical analysis of this system, a direct panoramic image mosaic algorithm was proposed. Several sub-images were collected by the bionic compound eye visual system, and then the system obtained the overlapping proportions of these sub-images and cut the overlap sections of the neighboring images. Thus, a panoramic image with a large field of view was directly mosaicked, which expanded the field and guaranteed the high resolution. The experimental results show that the time consumed by the direct mosaic algorithm is only 2.2% of that by the traditional image mosaic algorithm while guaranteeing mosaic quality. Furthermore, the proposed method effectively solved the problem of misalignment of the mosaic image and eliminated mosaic cracks as a result of the illumination factor and other factors. This method has better real-time properties compared to other methods.

关键词： bionic compound eye panoramic image image mosaic direct mosaic algorithm

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Reinforcement Learning Path Planning based on Step Batch Q-Learning Algorithm

Reinforcement Learning Path Planning based on Step Batch Q-L...

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2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022

作者： Yin, Zhiqian Cao, Wei Song, Tao Yang, Xu Zhang, Tianhao Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

With the rapid development of artificial intelligence, people have put forward higher requirements for robot path planning. As a more commonly used algorithm, reinforcement learning learns from experience by imitating the process of human learning skills and continuously iterates to do. In this paper, an improved Step Batch Q-Learning algorithm is proposed to solve the problem that the traditional Q-Learning algorithm has a slow convergence speed in discrete states. Prediction can detect obstacles and target points earlier, reduce the number of iterations, and significantly speed up the convergence time. Under the same experimental conditions, the method in this paper is compared with the traditional Q-Learning algorithm. The experimental results show that the improved Step Batch Q-Learning algorithm has significantly improved convergence speed and good adaptability. © 2022 IEEE.

关键词： Reinforcement learning

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