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First International Multi- Symposiums on Computer and computational Sciences, IMSCCS'06

作者： Da, Wei Yanheng, Liu Xuegang, Yu Xiaodong, Li Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education China

ISBN: (纸本)0769525814

The Diameter protocol is recommended by IETF as AAA (Authentication, Authorization and Accounting) protocol criterion for the next generation network, Because the IPv6 protocol will be widely applied in the intending all-IP network, mobile IPv6 application based on Diameter protocol will play more important role in authentication, authorization and accounting. In this paper, the implementation of mobile node's authentication and authorization is presented with PANA (Protocol for carrying Authentication for Network Access) protocol. It is based on Diameter protocol for the application expansion of mobile IPv6, which provides the supports to the basic AAA process of mobile IPv6 nodes and dynamic Home Agent distribution in the visited network and the secret key distribution. Finally, the correctness of this application expansion is testified with developing the design of protocol based on OpenDiameter. © 2006 IEEE.

关键词： Mobile telecommunication systems

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Hyponymy Graph Model for Word Semantic Similarity Measurement

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Chinese Journal of Electronics 2015年第1期24卷 96-101页

作者： WANG Junhua ZUO Wanli PENG Tao College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering attached to the Ministry of Education

Measuring word semantic similarity is a generic problem with a broad range of applications such as ontology mapping, computational linguistics and artificial intelligence. Previous approaches to computing word semantic similarity did not consider concept occurrence frequency and word’s sense number. This paper introduced Hyponymy graph, and based on which proposed a novel word semantic similarity model. For two words to be compared, we first retrieve their related concepts; then produce lowest common ancestor matrix and distance matrix between concepts; finally calculate distance-based similarity and information-based similarity, which are integrated to get final semantic similarity. The main contribution of our method is that both concept occurrence frequency and word’s sense number are taken into account. This similarity measurement more closely fits with human rating and effectively simulates human thinking process. Our experimental results on benchmark dataset M&C and R&G with Word Net2.1 as platform demonstrate roughly 0.9%–1.2%improvements over existing best approaches.

关键词： Word semantic similarity Concept semantic similarity Semantic similarity Hyponymy graph model Word Net.

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Dilated Convolutional Pixels Affinity Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2021年第6期30卷 1120-1130页

作者： ZHANG Zhe WANG Bilin YU Zhezhou LI Zhiyuan College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

This paper studies semantic segmentation primarily under image-level weak-supervision. Most stateof-the-art technologies have recently used deep classification networks to create small and sparse discriminatory seed regions of each interest target as pseudo-labels for training segmentation networks, which achieve inferior performance compared with the fully supervised *** propose a Dilated convolutional pixels affinity network(DCPAN) to localize and expand the seed regions of objects to bridge this gap. Although introduced dilated convolutional units enable capture of additional location information of objects, it falsely highlighted true negative regions as dilated rate enlarge. To address this problem,we properly integrate dilated convolutional units with different dilated rates and self-attention mechanisms to obtain pixel affinity measure matrix for promoting classification network to generate high-quality object seed regions as pseudo-labels; thus, the performance of the segmentation network is boosted. Furthermore,although our approach seems simple, our method obtains a competitive performance, and experiments show that the performance of DCPAN outperforms other state-ofart approaches in weakly-supervised settings, which only use image-level labels on the Pascal VOC 2012 dataset.

关键词： weakly supervised semantic segmentation pseudolabels deep classification networks high-quality object seed regions pattern classification image segmentation weakly-supervised settings dilated convolutional pixels affinity network image-level weak-supervision image classification DCPAN self-attention mechanism object recognition feature extraction object detection sparse discriminatory seed regions pixel affinity measure matrix Pascal VOC 2012 dataset

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A Novel Iterative Velocity Control Algorithm and Its FPGA Implementation Based on Trigonometric Function

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Chinese Journal of Electronics 2019年第2期28卷 237-245页

作者： WEN Quangang LIANG Yanchun Adriano Tavares Ricardo Teixeira WU Chunguo Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Department of Industrial Electronics University of Minho Campus de Azurm

The servo motor’s flexible acceleration/deceleration(acc/dec) control is an emerging research topic in the automation field. A velocity control algorithm based on trigonometric function is proposed in this *** required parameters, it transforms trigonometric calculations into elementary mathematical operations and calculates the velocity controlling values iteratively, which could avoid trigonometric calculation and reduce the computation time. Experimental results show that the proposed algorithm is suitable for implementation on field programmable gate arrays and achieves a flexible controlling, enhancing both the equipment’s stability and reliability. It is promising to significantly improve the high-speed computerized numerical control equipment’s controlling accuracy, without a huge hardware resource consumption.

关键词： Acceleration Velocity control Algorithm design and analysis Field programmable gate arrays Servomotors

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A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting XIA Chunxu ZHANG Yan ZHANG Xueyan LIU Bo YANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University College of Artificial Intelligence Jilin University College of Computer Science and Technology Jilin University College of Communication Engineering Jilin University

Graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named Graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification

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Deriving all minimal consistency-based diagnosis sets using SAT solvers

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Progress in Natural Science:Materials International 2009年第4期19卷 489-494页

作者： Xiangfu Zhao a,b, Liming Zhang a,b, Dantong Ouyang a,b, Yu Jiao a,b a key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University, Changchun 130012, China b School 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 School of Computer Science and Technology Jilin University Changchun 130012 China

In this paper, a novel method is proposed for judging whether a component set is a consistency-based diagnostic set, using SAT solv- ers. Firstly, the model of the system to be diagnosed and all the observations are described with conjunctive normal forms (CNF). Then, all the related clauses in the CNF files to the components other than the considered ones are extracted, to be used for satisfiability checking by SAT solvers. Next, all the minimal consistency-based diagnostic sets are derived by the CSSE-tree or by other similar algorithms. We have implemented four related algorithms, by calling the gold medal SAT solver in SAT07 competition – RSAT. Experimental results show that all the minimal consistency-based diagnostic sets can be quickly computed. Especially our CSSE-tree has the best effciency for the singleor double-fault diagnosis.

关键词： Conjunctive normal form Consistency-based diagnosis Model-based diagnosis SAT solver

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Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

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ACM Transactions on knowledge Discovery from Data 2024年第9期18卷 1-30页

作者： Liu, Yonghao Li, Mengyu Li, Ximing Huang, Lan Giunchiglia, Fausto Liang, Yanchun Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China University of Trento Trento Italy Zhuhai Lab. of the Key Lab. of Symbolic Computation and Knowledge Eng. of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-Training to extract valuable information from unlabeled nodes. Additionally, we adopt S (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS. Our code is available here. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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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 Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

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Computer Modeling in engineering & Sciences 2020年第7期124卷 303-313页

作者： Yan Li Dayou Liu Yungang Zhu Jie Liu College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but *** differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory *** a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between ***,in this way,the similarities between the pairwise GRNs are not taken into *** joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach *** this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential ***,a Bayesian inference method is used to make joint differential analysis by solving the integrated *** evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different *** performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet *** the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

关键词： Gene regulatory networks structural equation models joint

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Error Formulas for Lagrange Projectors Determined by Cartesian Sets

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Journal of Systems Science & Complexity 2018年第4期31卷 1090-1102页

作者： LI Zhe ZHANG Shugong DONG Tian GONG Yihe School of Science Changchun University of Science and Technology Changchun 130022 China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun 130012 China School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University Changchun 130012 China Northeast Electric Power University Jilin 132012 China School of Mathematics Jilin University Changchun 130012 China

This paper studies error formulas for Lagrange projectors determined by Cartesian sets. Cartesian sets are properly subgrids of tensor product grids. Given interpolated functions with all order continuous partial derivatives, the authors directly construct the good error formulas for Lagrange projectors determined by Cartesian sets. Owing to the special algebraic structure, such a good error formula is useful for error estimate.

关键词： Cartesian sets error formulas ideal interpolation multivariate polynomial interpolation

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