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Complex knowledge system modeling based on Hierarchical Fuzzy Petri Net

Complex knowledge system modeling based on Hierarchical Fuzz...

2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007

作者： Hongjun, Pan Jigui, Sun Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)0769530281

The difficulties of modeling complex knowledge system lie in a large quantity of knowledge rules and the difficulty in organizing rules and grasping their mutual logical relationships. This article proposed a concept of hierarchical fuzzy Petri nets (HFPN), which is more suitable for modeling complex knowledge system than other fuzzy Petri Net models. In addition, the concepts of abstract place and abstract transition in HFPN are allowed to describe and analyze the knowledge system at diverse abstract levels. Therefore, using HFPN, the iterative and incremental methods can be applied to modeling complex knowledge system. In addition, structured approach can be naturally applied to the process of modeling knowledge system. © 2007 IEEE.

关键词： Petri nets

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computing minimal diagnosis with binary decision diagrams algorithm

Computing minimal diagnosis with binary decision diagrams al...

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6th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2009

作者： Wang, Kun Li, Zhan-Shan Ai, Yang Zhang, Yong-Gang Key Laboratory of Symbolic Computation Knowledge Engineering for Ministry of Education Jilin University Jilin Changchun China

ISBN: (纸本)9780769537351

In this paper we propose an algorithm of computing minimal diagnosis based on BDD (Binary Decision Diagram). First we give the concept of disjunction equations, and map the collection of conflict sets into disjunction equations, then we compile the system into a BDD, further we compute the minimal hitting sets by solving the BDD and minimizing hitting sets. The reliability and completeness of the algorithm are proved. Variable ordering and construct controlling of BDD are used to further reduce the size of final BDD and the number of non-minimal hitting sets to be generated. The efficiency of computing minimal hitting sets is highly improved. Experimental results show that the algorithm isn't restricted by the structure of system and conflict sets, compared with three algorithms in the references, it can generate all the minimal hitting sets, and it works well in random circumstances. © 2009 IEEE.

关键词： Binary decision diagrams

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Efficient Distributed Skyline Queries for Mobile Applications

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计算机科学技术学报（英文版） 2010年第3期25卷 523-536页

作者： Ying-Yuan Xiao Yue-Guo Chen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191China Key Laboratory of Computer Vision and System Tianjin University of TechnologyTianjin 300191China School of Computing National University of Singapore117417Singapore Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

关键词： skyline distributed query processing mobile database system

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Spatial query preprocessing in distributed GIS

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3rd International Conference on Grid and Cooperative computing, GCC2004

作者： Wang, Sheng-Sheng Liu, Da-You College of Computer Science and Technology Key Laboratory of Symbolic Computing Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)3540235647

This paper focuses on spatial query optimization in distributed GIS. A new qualitative spatial relation model and its consistency problem solution which compose topology, direction, distance and size are proposed. Research integrating the four aspects has not appeared before. A new method to deduce the constraints of spatial query is given, thus saves the query process time in distributed GIS. Finally, The methods and theories are applied to a distributed GIS project, the experiment result is satisfactory. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Topology

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COVID-19 knowledge Graph for Drug and Vaccine Development

COVID-19 Knowledge Graph for Drug and Vaccine Development

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Huang, Lan Guan, Hongrui Liang, Yanchun Guan, Renchu Feng, Xiaoyue Jilin University The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Sub Laboratory Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Zhuhai China

ISBN: (纸本)9781665401265

The worldwide spread of COVID-19 has made a severe impact on human health and life. It has shown rapid propagation, long in vitro survival, and a long incubation period. More seriously, COVID-19 is more susceptible to variation, as it is an RNA virus. Mutations of COVID-19 have been reported in multiple countries worldwide, which makes drug and vaccine development a significant challenge. To search for potential drugs and vaccines and reveal the atlas of COVID-19 evolution, we extract information from massive unstructured data and construct a COVID-19 knowledge graph using the COVID-19 data. Based on machine learning approaches, we infer and predict novel coronavirus pneumonia-related diseases, drug action targets, etc. to speculate on new and more effective treatment methods. In addition, to study transcriptome of SARS-CoV-2, new ideas can be provided to biomedical experts with flexible responses to viral variation. An in-depth analysis of the COVID-19 pathomechanism at the pharmaceutical, genetic, and protein levels provides effective means and tools for novel coronavirus pneumonia vaccines, drug development, and therapeutic program design. © 2021 IEEE.

关键词： COVID-19

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L-priorities Bloom Filter: A New Member of the Bloom Filter Family

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International Journal of Automation and computing 2012年第2期9卷 171-176页

作者： Huang-Shui Hu Hong-Wei Zhao Fei Mi College of Computer Science and Technology Jilin University Changchun 130012 PRC Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 PRC

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.

关键词： Bloom filter bit space matrix false positive L-priorities time and space complexity.

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Fine-grained sentimental tendency analysis based on Chinese online commentary of mobile phone

Fine-grained sentimental tendency analysis based on Chinese ...

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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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