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A BRS-based approach for modeling RFID untraceability

International Journal of Advancements in Computing Technology 2011年第11期3卷 96-103页

作者： Jialiang, He Dantong, Ouyang Youjun, Xu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Information Technology Daqing Normal University China

Untraceability is an important aspect of RFID security. A BRS-based approach for modeling RFID untraceability is provided in this paper. Elements in a RFID protocol can be represented as bigraphs, communications between elements can be represented as reaction rules. RFID untraceability can be represented as behavioral congruences. We take a RFID air interface protocol as a case study and show the usability of this approach.

关键词： Radio frequency identification (RFID)

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Detecting epistasis by LASSO-penalized-model search algorithm in human Genome-Wide Association Studies

Detecting epistasis by LASSO-penalized-model search algorith...

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4th International Conference on Materials Science and Information Technology, MSIT 2014

作者： Zhou, Zhi Hui Liu, Gui Xia Su, Ling Tao Han, Liang Yan, Lun College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9783038351733

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy. © (2014) Trans Tech Publications, Switzerland.

关键词： Genes

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Learning essential graph with immune co-evolutionary algorithm

Learning essential graph with immune co-evolutionary algorit...

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International Conference on Information Science, Signal Processing and Their Applications

作者： Jia, Haiyang Chen, Juan Liu, Dayou College of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China

ISBN: (纸本)9780769541327

Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number of illegal structures is exponential, which infect the efficiency of structure learning;(2) comparing the structures in same equivalent class slow down the speed of convergence;(3) if the prior distribution for each structure is equal, the more structures contain in the equivalent class the higher prior probability of the class has. This paper employs two competitive bio-inspired algorithms, immune algorithm and co-evolutionary algorithm, for learning Essential graph. The algorithm combines dependency analysis and search-scoring approach together. Experiments show that the searching space was decreased, compare with prior works, the convergence speed and the efficiency was improved. © 2010 IEEE.

关键词： Bayesian networks

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Vertical Traffic Scheduling Control Method Based On Dual Fuzzy Neural Network

Vertical Traffic Scheduling Control Method Based On Dual Fuz...

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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Sun, Xinhao An, Siqi Gao, Xiaoting Cui, Enchang College of Light Industry Liaoning University Shenyang China Jilin University Key Laboratory of Symbolic Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350309003

With the complexity of the functions of modern buildings, the problem of vertical traffic in buildings is becoming more and more prominent. As the only vertical transportation, the elevator is a necessary prerequisite for the development of modern buildings to solve its group control distribution problem. In this paper, a vertical traffic scheduling control method is proposed based on dual fuzzy neural network, a recognition strategy is designed to identify the passenger flow models. The dual fuzzy neural network is used to first identify the traffic network model in which the elevator is located, and then combine the corresponding weights and the confidence of the group control allocation to complete the group control scheduling. Finally, the proposed method is verified through a semi-physical simulation platform, proving the correctness and effectivness for traffic pattern recognition and scheduling strategy optimization of group controllers. © 2023 IEEE.

关键词： Fuzzy neural networks

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Extracting a justification for OWL ontologies by critical axioms

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Frontiers of Computer Science 2020年第4期14卷 55-64页

作者： Yuxin YE Xianji CUI Dantong OUYANG College of Computer Science and Technology Jilin UniversityChangchun130012China Key Lab of Symbolic Computation and Knowledge Engineering(Jilin University) Ministry of EducationChangchun130012China College of Information and Communication Engineering Dalian Minzu UniversityDalian116600China

Extracting justifications for web ontology language(OWL)ontologies is an important mission in ontology *** this paper,we focus on black-box techniques which are based on ontology *** creating a recursive expansion procedure,all elements which are called critical axioms in the justification are explored one by *** this detection procedure,an axiom selection function is used to avoid testing irrelevant *** addition,an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests *** is implemented by employing a pseudo model to detect“obvious”satisfiability *** experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.

关键词： description logics automated reasoning ontology engineering justification

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Prior-guided dual-stage diabetic retinopathy grading model based on feature collaboration of lesion and vascular structure

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Expert Systems with Applications 2025年 285卷

作者： Guo, Xiaoxin Yang, Guangqi Xu, Chenfangqian Dong, Hongliang Hu, Xiaoying Che, Songtian Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University 2699 Qianjin Street Changchun130012 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China Ophthalmology Department Bethune First Hospital of Jilin University Changchun Jilin130021 China Ophthalmology Department Bethune Second Hospital of Jilin University Changchun Jilin130041 China

In order to fully utilize lesion features and vascular structure and solve the problem of class imbalance, diabetes retinopathy (DR) grading is modeled as a dual-stage task, and the prior-guided dual-stage diabetes retinopathy grading model is proposed by designing an end-to-end hierarchical framework, consisting of XE-Net and MFC-Net. The former uses vascular structure from color fundus images as prior knowledge to guide the classification of proliferative diabetic retinopathy (PDR) and non-PDR;the latter further classifies non-PDR images using color fundus images with four types of segmented lesions. The core of MFC-Net is the feature collaboration block (FCB) for multi-classification tasks, which is proposed to facilitate effective collaboration between global and local features. In addition, a class-balanced (CB) loss weight is also introduced to address the common class imbalance problem in DR datasets. The experimental results on two publicly available datasets, DDR and IDRiD show that the proposed model has significant advantages. © 2025 Elsevier Ltd

关键词： Image segmentation

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Research and analysis of psychological data based on machine learning methods

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International Journal of Wireless and Mobile Computing 2022年第1期22卷 1-8页

作者： Chen, Guangshun Lv, Wei Ma, Junwei Liang, Yanchun Zhuhai Lab. of Key Lab. of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Jilin Zhuhai China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun China

The integration of psychology and computer science has become the mainstream contemporary research method on psychological data. Weibo, China's largest open platform for communication and information sharing between users, has many emotional contents hidden in its data. According to the current trend, the Weibo data are segmented by machine learning to obtain a psychological portrait of Weibo users. This design uses Long and Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to perform sentiment classification on Weibo data. The classification results are analysed using word frequency analysis and the Latent Dirichlet Allocation (LDA) model to obtain portraits of Weibo users' sentiment and an analysis of the results. The results are displayed in the form of word clouds. According to the clustering results of the word clouds, the main factors affecting different polar emotions can be analysed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Statistics

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Grid Partition and Agglomeration for Bidirectional Hierarchical Clustering 2nd

Grid Partition and Agglomeration for Bidirectional Hierarchi...

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2nd EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2019

作者： Wu, Lei Chen, Hechang Yu, Xiangchun Chao, Sun Yu, Zhezhou Dou, RuiTing College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9783030213725

Clustering is an important data processing tool, which can be used to reveal the distribution structure of unfamiliar domain data, or as preprocess methods to magnify data object to accelerate subsequent processing or simplify models. However, the distribution of many real-world data in feature space is very complex or uneven. Besides, the similarity/distance is not easy to be properly defined in feature space with different dimensional quantity. Therefore, many existing clustering algorithms are not stable in real datasets, and better performance of different datasets relies on artificial special design, such as scale normalization. In this paper, we propose a bidirectional hierarchical clustering (BHC) algorithm with two phases. In the first phase (Top-down), based on the probability density function of data in different dimensions, the feature space is divided into over-segmented grids to adapt to the complex distribution of data. In the second phase (Bottom-up), based on statistical information, a robust distance instead of geometrical distance is defined to agglomerate the grids into a dendrogram. Compared with the individual data points, grids created in the first phase can carry more statistical information, and the magnified processing objects can accelerate the clustering process. The second phase enhances the algorithm’s ability by the ability of recognize arbitrary shape data clusters. The effectiveness of BHC is compared with 20 popular or recent clustering algorithms on 8 artificial datasets and 6 real-world datasets. And the results show that our algorithm can achieve good results on most datasets. In particular, BHC surpasses all the comparison algorithms involved in the experiment on all real-world datasets. In addition, in order to test the efficiency of the algorithm, we design an experiment which can test the influence of dimension and data size on the operation time. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommuni

关键词： Clustering algorithms

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FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

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Journal of Computer Science & Technology 2022年第5期37卷 1200-1220页

作者： Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders the deployment of deep learning *** low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence ***,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive *** this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast *** evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end *** experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.

关键词： deep learning accelerator programming framework domain-specific language

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Learning dynamic bayesian network with immune evolutionary algorithm

Learning dynamic bayesian network with immune evolutionary a...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Jia, Hai-Yang Liu, Da-You Yu, Peng Key Laboratory of Symbolic Computing and Knowledge Engineering Juin University Ministry of Education Changchun China

ISBN: (纸本)078039092X

Dynamic Bayesian Networks (DBNs) are directed graphical models of stochastic processes, How to learn the structure of DBNs from data is a hot problem of research. In this paper the author presents an Immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms. © 2005 IEEE.

关键词： Learning systems

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