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2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献

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A Microservices Identification Approach based on Problem Frames 基于问题框架的微服务识别方法
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832106
Zhumei Yang, Sijie Wu, Cheng Zhang
{"title":"A Microservices Identification Approach based on Problem Frames","authors":"Zhumei Yang, Sijie Wu, Cheng Zhang","doi":"10.1109/seai55746.2022.9832106","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832106","url":null,"abstract":"As software grows in size continuously, traditional monolithic architecture is difficult to upgrade and update. As a more flexible architecture, microservices consist of smaller pieces or services that are loosely coupled and can be deployed independently. More and more IT companies are adopting microservices architecture. However, migrating to microservices is still a big challenge due to the lack of a more systematic methodology on microservices identification. Most existing studies on microservices identification are mainly based on refactoring the system module rather than considering the actual functional requirements. Therefore, we propose a microservices identification approach based on the problem frames to address this challenge, which considers real-world requirements. It relies on the merging process with the problem diagram, where the problem domain is processed, and the causal chain is obtained by searching the problem diagram. Subsequently, we calculate the correlation degree of the domains in the problem diagram. Then, based on the calculated correlation, we implement a clustering algorithm for the problem domains and get the categorization result of the problem domains which is the initial microservice candidate. The developers can consider each category of the problem domain as a microservice by converting the problem domains into the microservices. Finally, a case study verifies the validity of our proposed approach.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-objective Antenna Design Based on Improved Sparrow Search Algorithm to Optimize BP Neural Network Surrogate Model 基于改进麻雀搜索算法优化BP神经网络代理模型的多目标天线设计
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832222
Zhongxin Wang, Jian Qin, Zijiang Hu, Jian He
{"title":"Multi-objective Antenna Design Based on Improved Sparrow Search Algorithm to Optimize BP Neural Network Surrogate Model","authors":"Zhongxin Wang, Jian Qin, Zijiang Hu, Jian He","doi":"10.1109/seai55746.2022.9832222","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832222","url":null,"abstract":"In response to the problems of time-consuming and low potency of traditional antenna design methods, a multi-objective antenna design method based on MISSA-BP surrogate model is proposed. Meanwhile, for the problems of sparrow search algorithm, such as single population and difficulty in reaching global optimum, proposes a multi-strategy improved sparrow search algorithm (MISSA), three strategies of reverse learning, inertial weight and adaptive t-distribution are introduced for improvement to enrich the population diversity and strengthen the flexibility of the algorithm to reach the optimum result. The experimental results show that the improved sparrow search algorithm has higher improvement performance, and the proposed MISSA-BP surrogate model can be bound to the multi-objective optimization algorithm to achieve fast multi-objective antenna design.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130177907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Locally Linear Embedding Based on Faure Sequence Initialized Killer Whale Algorithm 基于故障序列初始化虎鲸算法的局部线性嵌入
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832134
Liying Wang, Lizi Yan, Yunyi Chen, Zan Yang, Wei Nai, Dan Li
{"title":"Locally Linear Embedding Based on Faure Sequence Initialized Killer Whale Algorithm","authors":"Liying Wang, Lizi Yan, Yunyi Chen, Zan Yang, Wei Nai, Dan Li","doi":"10.1109/seai55746.2022.9832134","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832134","url":null,"abstract":"In the field of artificial intelligence (AI), and to be specific, in the field of manifold learning, local linear embedding (LLE) algorithm, which is a nonlinear data dimensionality reduction method, has become an indispensable and important tool. The strength of LLE algorithm is that after dimensionality reduction, it can make the points in high-dimensional space isomorphic with the points in low-dimensional space after projection, and the algorithm has low complexity and fast operation speed. However, LLE also has its own drawback, it depends on gradient descent (GD) method or stochastic gradient descent (SGD) method in solving the optimization of its objective function, resulting in falling into the trap of local minimum. In the past, there was little discussion about the global optimum solution issue for LLE, therefore, in this paper, a Faure sequence initialized killer whale algorithm (FSKWA) has been proposed to effectively overcome the drawback in finding the global optimum solution of the objective function for LLE. And via numerical experiment, the superiority of the method proposed has been verified.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126717781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Data Association Approach for Angle-only Sensors based on Faster Clustering and Multidimensional Assignment 基于快速聚类和多维分配的角度传感器数据关联方法
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832161
Feng Ma, Huanzhang Lu, Luping Zhang, Xinglin Shen
{"title":"A Data Association Approach for Angle-only Sensors based on Faster Clustering and Multidimensional Assignment","authors":"Feng Ma, Huanzhang Lu, Luping Zhang, Xinglin Shen","doi":"10.1109/seai55746.2022.9832161","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832161","url":null,"abstract":"Combining clustering and assignment techniques to solve data association problem can not only improve the computational efficiency, but also obtain the global optimal association result. For angle measurements, the existing method uses pairwise clustering approach, and the computational complexity is quadratic with both the number of sensors and measurements. This paper proposes a faster clustering approach, which benefits from two operations. First, to avoid missing potential associations, the patching operation is added, thus the sequential clustering approach can be used. Second, to prevent the exponential growth in the number of clusters, the pruning operation is added, thus the clustering efficiency is improved. Thanks to these two kinds of operations, the computational complexity of the proposed approach reduces to linear with the number of sensors. Simulation results show that, while ensuring the association accuracy, the proposed approach improve the association efficiency significantly.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122673039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning Short-Term and Long-Term Facial Behaviors for Personality Traits Recognition 学习短期和长期面部行为对人格特征识别的影响
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832040
Dong Mingrui, Zhong Dong
{"title":"Learning Short-Term and Long-Term Facial Behaviors for Personality Traits Recognition","authors":"Dong Mingrui, Zhong Dong","doi":"10.1109/seai55746.2022.9832040","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832040","url":null,"abstract":"Personality is the integration of human psychological characteristics that influence a wide range of human behaviors. Previous automatic personality recognition methods either model personality only using frame-level or short segment-level facial behaviors, which ignores crucial long-term information, or extract long-term behaviors with a large number of frames being removed. In this paper, we first extract frame-wise facial attributes from a video. Then, we use statistic, spectral heatmaps and spectral vectors to encode video-level behaviors from the extracted frame-level facial attributes. Finally, the obtained video-level facial behavior representations are fed to Multilayer Perceptron (MLP) or Temporal Convolution Neural Network (TCN) for personality traits recognition. The experimental results on the ChaLearn First Impressions dataset show that the proposed approach which only used the facial attributes, has clear advantages over the static face image-based personality recognition approaches.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127869172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning for Single-object Tracking: A Survey 单对象跟踪的深度学习:综述
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832135
Jiahang Zhou, Yuanzhe Yao, Rong Yang
{"title":"Deep Learning for Single-object Tracking: A Survey","authors":"Jiahang Zhou, Yuanzhe Yao, Rong Yang","doi":"10.1109/seai55746.2022.9832135","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832135","url":null,"abstract":"With the rapid advancement of deep learning, this technique has also shown promise in single-object tracking, considerably enhancing the algorithm's performance. This paper will summarize and analyze the evaluation metrics, public datasets, state-of-the-art tracking methods, etc. It will classify the research status of the key technologies involved and reveal the mystery of single object tracking technology step by step. In addition, the significant challenges and problems of single object tracking technology are summarized.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132617300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Exploring Defect Data with Network Visualization 利用网络可视化探索缺陷数据
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832052
Pantipa Bunmapob, Y. Limpiyakorn
{"title":"Exploring Defect Data with Network Visualization","authors":"Pantipa Bunmapob, Y. Limpiyakorn","doi":"10.1109/seai55746.2022.9832052","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832052","url":null,"abstract":"Jira software is agile project management solutions, originally designed as a bug and issue tracker. Searching for issues or defect information can be performed by Jira Query Language (JQL). However, querying a data repository simply returns the specified data and information. In this work, we present an approach of network visualization to uncover relationships and communications among actors such as software features, defects, and staff. The technique of social network analysis is applied for analyzing the defects collected from a banking software project. And Gephi is used as a tool for generating a network of specified actors as nodes and their linkages. The approach of visual network analysis is practical and provides insights in defect analysis essential for the proactive software development process.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Modeling and Analysis of Biological Experimental Data Based on BP Neural Network 基于BP神经网络的生物实验数据建模与分析
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832365
J. Lu
{"title":"Modeling and Analysis of Biological Experimental Data Based on BP Neural Network","authors":"J. Lu","doi":"10.1109/seai55746.2022.9832365","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832365","url":null,"abstract":"According to the research on the optimization of fermentation process parameters, this paper analyzes that the accuracy rate of data verification of regression analysis prediction model in biological experiment is 75%. In order to improve the accuracy of the prediction model, the BP network prediction model is established by using the experimental data of three factors and three levels. The accuracy of the data verification in this biological experiment of the model reaches 100%, which fully meets the data analysis needs of this experiment. So BP neural network model can be well applied to the research and practice of bio industry production. This study puts forward the problem of multiple outputs of unified order of magnitude for the training of multi-output BP network model, and adjusts the unit of some outputs so that each output order of magnitude is the same, so as to reduce the times of determining weights and thresholds in network training and improve the accuracy of training.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134073495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Early Diagnosis of Parkinson's Disease with Speech Pronunciation Features Based on XGBoost Model 基于XGBoost模型的语音特征早期诊断帕金森病
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832191
Xiumei Wang, Xinghong Chen, Qing Wang, Guannan Chen
{"title":"Early Diagnosis of Parkinson's Disease with Speech Pronunciation Features Based on XGBoost Model","authors":"Xiumei Wang, Xinghong Chen, Qing Wang, Guannan Chen","doi":"10.1109/seai55746.2022.9832191","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832191","url":null,"abstract":"Speech disorder is one of the early symptoms of Parkinson's disease. In order to accurately diagnose the Parkinson's disease in early time, we propose a Parkinson's disease diagnosis method based on machine learning. XGBoost algorithm was applied to detect and classify the speech signals of Parkinson's disease patients. The performance of this method has been evaluated on several metrics based on the UCI Parkinson database, and in experiments, support vector machines, random forests, and neural networks have also been used to detect and classify the speech signal collected from Parkinson's patients. Experimental results show that the performance of XGBoost algorithm is better than other algorithms, the accuracy of Parkinson's disease diagnosis is 96%, precision is 100%, AUC is 0.97, F1_ Score is 0.97.It thus contributes to the better understanding of the Parkinson disease and possibly the further analyze of the speech feature.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Co-simulation of Power Grid, Information Network and Transportation Network Simulation System 电网、信息网络和交通网络仿真系统的联合仿真
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832036
Yongmin Shuai, Yu Zhang, Fuhao Liu, Xiaobin Qiao, Yunfei Xiong, Yong Zeng
{"title":"Co-simulation of Power Grid, Information Network and Transportation Network Simulation System","authors":"Yongmin Shuai, Yu Zhang, Fuhao Liu, Xiaobin Qiao, Yunfei Xiong, Yong Zeng","doi":"10.1109/seai55746.2022.9832036","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832036","url":null,"abstract":"The three-network integration of power grid, information network and transportation network has become a global issue and trend. However, the current research on triple play is still in its infancy. Most of the researches define the concept of triple play, and lack of simulation research on the power grid-information network-transportation network coupling system. Therefore, this paper studies the key technologies of power grid-information network-transportation network co-simulation. The key technologies of simulation are data interaction method and time synchronization method. By building a simulation prototype system, it provides simulation support for the theoretical study of the power grid-information network-transportation network coupling system.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132533097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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