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16th SIAM International Conference on Data Mining 2016, SDM 2016

作者： Li, Ximing Ouyang, Jihong Zhou, Xiaotang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781510828117

Topic modeling algorithms such as the latent Dirichlet allocation (LDA) play an important role in machine learning research. Fitting LDA using Gibbs sampler-related algorithms involves a sampling process over K topics. We can use the sparsity in LDA to accelerate this expensive topic sampling process even for very large K values. However, LDA gradually loses sparsity as the number of documents increases. Motivated by the goal of fast LDA inference with large numbers of both topics and documents, in this paper we propose the novel sparse hybrid variational-Gibbs (SHVG) algorith-m. The SHVG algorithm divides the topic sampling probability into a sparse term that scales linearly with the number of per-document instantiated topics Kd, and a dense term that uses the Alias method to reduce the time cost to constant 0(1) time. This will lead to a significant improvement on efficiency. Using stochastic optimization techniques, we further develop an online version of SHVG for streaming documents. Experimental results on corpora with a wide range of sizes demonstrate the efficiency and effectiveness of the proposed SHVG algorithm. Copyright © by SIAM.

关键词： Efficiency

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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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Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

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Science China(Information Sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

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Detecting computer generated images based on local ternary count

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Pattern Recognition and Image Analysis 2016年第4期26卷 720-725页

作者： Tan, D.Q. Shen, X.J. Qin, J. Chen, H.P. College of Computer Science and Technology Jilin University Changchun Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China

Local binary patterns was used to distinguish the Photorealistic Computer Graphics and Photographic Images, however the dimension of the extracted features is too high. Accordingly, the Local Ternary Count based on the Local Ternary Patterns and the Local Binary Count was developed in this study. Furthermore, a novel algorithm is presented based on the Local Ternary Count to classify photorealistic Computer Graphics and Photographic images. The experiment results show that the proposed algorithm effectively reduces the dimension of the classification features and maintains a good classification performance. © 2016, Pleiades Publishing, Ltd.

关键词： Local binary pattern

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A parallel scheduling algorithm for reinforcement learning in large state space

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Frontiers of Computer Science 2012年第6期6卷 631-646页

作者： Quan LIU Xudong YANG Ling JING Jin LI Jiao LI Institute of Computer Science and Technology Soochow UniversitySuzhou 215006China Department of Computer Science and Technology Nanjing UniversityNanjing 210093China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, is proposed on the basis of divide-and-conquer strategy, and its convergence is proved. In this method, the learning problem in large state space or continuous state space is decomposed into multiple smaller subproblems. Given a specific learning algorithm, each subproblem can be solved independently with limited available resources. In the end, component solutions can be recombined to obtain the desired result. To ad- dress the question of prioritizing subproblems in the scheduler, a weighted priority scheduling algorithm is proposed. This scheduling algorithm ensures that computation is focused on regions of the problem space which are expected to be maximally productive. To expedite the learning process, a new parallel method, called DCS-SPRL, is derived from combining DCS-SRL with a parallel scheduling architecture. In the DCS-SPRL method, the subproblems will be distributed among processors that have the capacity to work in parallel. The experimental results show that learning based on DCS-SPRL has fast convergence speed and good scalability.

关键词： divide-and-conquer strategy parallel schedule scalability large state space continuous state space

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Traffic jam prediction based on analysis of residents spatial activities

Traffic jam prediction based on analysis of residents spatia...

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2020 International Conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Lv, Zhijin Fu, Hao Tang, Wei Chen, Xiaoxu College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China

ISBN: (纸本)9781728198378

The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value. © 2020 IEEE.

关键词： Traffic congestion

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BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

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Chinese Journal of Electronics 2019年第4期28卷 797-804页

作者： BAI Tian GE Yan YANG Changqing LIU Xiaohua GONG Leiguang WANG Ye HUANG Lan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Yantai Intelligent Information Technologies Ltd. Department of Computer Science and Technology Zhuhai College of Jilin University

To facilitate the search of rapidly growing biomedical knowledge in literature, we developed a Biomedical entity-relationship search tool(BERST). It is also a biomedical knowledge integration framework, which presently contains six popular databases represented in terms of a network of concepts and relations extracted from these knowledge sources. Users search the integrated knowledge network by entering keywords, and BERST returns a sub-network matching and representing the keywords and their relationships. The resulting graph can be navigated interactively allowing users to explore specific paths between any two nodes representing potentially interesting relationships between them. A graphical UI was developed to provide a more intuitive and overall view of the information being searched and studied. BERST framework can be naturally expanded to integrate other biomedical knowledge sources. BERST is implemented as a Java web application.

关键词： Biomedical ontology Relationship mining Relationship searching tool

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The overview of feature selection algorithms based swarm intelligence and rough set

The overview of feature selection algorithms based swarm int...

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2011 7th International Conference on computational Intelligence and Security, CIS 2011

作者： Sun, Chengmin Liu, Dayou Department of Computer Science Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

ISBN: (纸本)9780769545844

Compared with traditional algorithms of rough set feature selection, the stochastic algorithms for feature selection based on rough set and swarm intelligence are popular. This paper gives the overview of rough set algorithms for feature selection based on ant colony optimization, and algorithms based on particle swarm optimization. © 2011 IEEE.

关键词： Ant colony optimization

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Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network

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Journal of Bionic engineering 2022年第6期19卷 1816-1829页

作者： Yuzhao Wang Tian Bai Tong Li Lan Huang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Department of Orthopedics The Second Hospital of Jilin UniversityChangchun130012China

Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关键词： Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network Computer-aided diagnosis

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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

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Journal of Bionic engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

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