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PSO with reverse edge for multi-objective software module clustering

International Journal of Performability Engineering 2018年第10期14卷 2423-2431页

作者： Sun, Jiaze Xu, Yang Wang, Shuyan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China

The multi-objective software module clustering problem (MOSMCP) divides the complex software system into subsystems to obtain a perfect structure, which is based on the relations between modules to meet the conflicting software refactor objectives as much as possible. The modularization quality (MQ) and reverse edges number between clusters are considered as evaluation objectives, and a novel particle swarm optimization (PSO) with reverse edge, called REPSO, is proposed. First, the module dependency graph (MDG) in software system under clustering is constructed, and then the multi-objective particle swarm optimization (MOPSO) is improved to cluster the MDG. The exploring approach is used to update the particle locations. Four typical open source projects for module clustering are selected to verify the effectiveness of the REPSO. The laboratorial results prove that the REPSO algorithm is very effective and stable, and the diversity of the optimal solution is good. The REPSO algorithm provides an efficient engineering method for MOSMCP, which enhances the software structure and maintainability. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Modular construction

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

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A Model robustness optimization method based on adversarial sample detection 5

A Model robustness optimization method based on adversarial ...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Sun, Jiaze Long, Siyuan Ma, Xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. The detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments. © 2022 ACM.

关键词： Deep neural networks

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Research on Image Description Generation Method Based on G-AoANet 5

Research on Image Description Generation Method Based on G-A...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Shen, Ruixue Li, Yuan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively. © 2022 ACM.

关键词： Semantics

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Blockchain Fragment Expansion Scheme Based on Community Detection and MSR Code 5

Blockchain Fragment Expansion Scheme Based on Community Dete...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Zhai, Sheping Tong, Tong Bai, Xifang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Aiming at the current performance of blockchain system can not meet the actual needs of high-frequency data interaction in the era of information explosion, a blockchain fragment expansion scheme based on community detection and MSR code is proposed. Firstly, based on the random walk community detection algorithm and the blockchain node network, a node grouping method is proposed. Each group of nodes maintains a copy of the blockchain ledger to reduce the storage redundancy of blockchain. Secondly, the block data is divided by minimum storage regenerating codes (MSR), which further reduces the storage pressure of nodes, reduces the amount of data required for block requests, and reduces network pressure. Finally, a blockchain scalable fragment storage scheme is proposed. The simulation experiment and result analysis show that the blockchain fragment expansion scheme based on community detection and MSR has improved in terms of storage scalability, response efficiency and fault tolerance. © 2022 ACM.

关键词： Blockchain

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An Anti-Noise Hybrid Clustering Oversampling Technique for Imbalanced data Classification 5

An Anti-Noise Hybrid Clustering Oversampling Technique for I...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Pan, Xiaoying Feng, Congcong Liu, Chen Mu, Yuanzhen Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Oversampling techniques have always been favored in the field of imbalanced data classification. Noise processing plays an important role in the field of unbalanced data classification because the noise data directly affects the distribution of newly synthesized samples. We propose a new anti-noise hybrid clustering oversampling technique for imbalanced data classification to synthesize high-quality samples (ANCO). The algorithm is the first to propose a combination of noise filtering and optimization in an oversampling method. Firstly, based on the sample location information, the processing strategy of noise filtering and optimization is designed. Then, sample nearest neighbor interpolation is used to create a new sample. To compare the performance of our approach with representative oversampling, five datasets with variable imbalance rates in KEEL are chosen for testing. The results show that the ANCO algorithm improves the classifier's overall performance. © 2022 ACM.

关键词： Classification (of information)

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An Efficient Attribute Reduction Approach Based on Partitioned Neighborhood Rough Sets

SSRN

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SSRN 2022年

作者： Lu, Zhengcai He, Jialin Tian, Zhengwei School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College Luzhou646000 China Luzhou Key Laboratory of Intelligent Data Analysis and Processing Luzhou646000 China

Neighborhood rough set based attribute reduction, as a powerful tool to deal with numerical data, is widely used in areas such as machine learning, pattern recognition, and decision support. A neighborhood actually contains rich potential knowledge, which deserves in-depth study for more optimized reduction and better classification quality. However, few literatures care for this work. This study introduces the neighborhood partition technique for original exploration and obtain a remarkable achievement supporting a novel attribute reduction. We firstly construct the partitioned neighborhood rough set model (PNRS), which brings a significant revolution in the rules for determining which region an object belongs to. Then, the neighborhood positive region certainty (NPRC) is proposed for attribute *** NPRC expands the contribution of a single object to the positive region from {0,1} to [0,1] so as to describe classification abilities of attributes more concretely and profoundly. Finally, a new attribute reduction algorithm is designed. It employs the NPRC to evaluate attributes and guide greedy search to find an optimal subset of features. Experimental results show that the proposed algorithm is capable of finding a smaller number of attributes and has higher classification performance than other available algorithms. © 2022, The Authors. All rights reserved.

关键词： Pattern recognition

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Text Matching application for integration between industry and education 14

Text Matching application for integration between industry a...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Luo, Yuxuan Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

A new talent training mode of collaboration between industry and education was proposed, which aims to reduce the gaps of talent definition between enterprise demand and college education. We formulates scenario as a text matching task of similarity between courses details and job position information, developed a ERNIE+DPCNN based model for the task. The model has been benchmarked on LCQMC dataset, the results show that it achieves the art-of state performance of Acc 0.8809, Recall 0.8745, and FX score 0.8739, We conducted many experiments about the text similarity calculation between domains of industry and educations. The experiment results show our approach has much potential to optimize the curriculum system while applying in this field. © 2022 IEEE

关键词： Industries Training Mechatronics Automation Collaboration Benchmark testing Task analysis

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Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang Transform and Feature Fusion Dimensionality Reduction 5

Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang T...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Fan, Lin Yao, Ruiling Wang, Zhongmin School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'An Key Laboratory of Big Data and Intelligent Computing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time. © 2022 ACM.

关键词： Hilbert-Huang transform

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The Research of Retinopathy Image Recognition Method Based on Vit 5

The Research of Retinopathy Image Recognition Method Based o...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Xu, Zongyu Xu, Xuebin Huang, Zihao School of Computer Science and Technology Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China

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