2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献

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Musical Pitch Alphabets Generator Using Haar-like Feature 使用哈尔特征的音高字母生成器
Kiratijuta Bhumichitr, Menh Keo, Aung Khant Oo
{"title":"Musical Pitch Alphabets Generator Using Haar-like Feature","authors":"Kiratijuta Bhumichitr, Menh Keo, Aung Khant Oo","doi":"10.1109/JCSSE53117.2021.9493818","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493818","url":null,"abstract":"Optical Music Recognition (OMR) has become a study trend with the increasing demand for digital sheet music. In this paper, we explore techniques and algorithms to implement optical music recognition. This paper aims to encourage people who just begin and enjoy learning object detection by using a simple and comprehensible framework called Haar-like Feature to detect the music notation. Furthermore, it also assists beginner musicians who have a difficult time in memorizing the music theory and rules by generating musical alphabets. The paper will include the process of how to generate the cascade classifier model and how to imply them to detect the target object.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"3 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122592255","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
Performance Measurement of Federated Learning on Imbalanced Data 非平衡数据下联邦学习的性能度量
Pramote Sittijuk, Kriengsuk Tamee
{"title":"Performance Measurement of Federated Learning on Imbalanced Data","authors":"Pramote Sittijuk, Kriengsuk Tamee","doi":"10.1109/JCSSE53117.2021.9493819","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493819","url":null,"abstract":"AI often suffers from getting imbalanced data distribution as unequal samples in classes which will increase the bias of machine learning algorithms. This research aimed to study effects of skew data distribution towards development of data rebalancing on Federated learning (FL) in the future. This research sets left skewed distribution, right skewed distribution and symmetric distribution on Modified National Institute of Standards and Technology database (MNIST) to operate on Convolutional neural network (CNN) in FL mechanism. Then, FL’s performance for working on these imbalanced distributions was tested. Results showed that in overview, the symmetric, left skewed, and right skewed distribution were not different in accuracy but theses imbalanced distributions were different in accuracy from the balanced distribution which has equal samples in all classes at significant level of.05. Standard deviation (SD) of data distribution was directly correlated with FL’s accuracy in high level.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122176506","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: 3
Identification of Discriminative Features from Light Curves for Automatic Classification of Variable Stars 基于光曲线判别特征的变星自动分类
P. Techa-angkoon, N. Tanakul, Jakramate Bootkrajang, Worawit Kaewplik, Douangpond Loongkum, C. Suwannajak
{"title":"Identification of Discriminative Features from Light Curves for Automatic Classification of Variable Stars","authors":"P. Techa-angkoon, N. Tanakul, Jakramate Bootkrajang, Worawit Kaewplik, Douangpond Loongkum, C. Suwannajak","doi":"10.1109/JCSSE53117.2021.9493847","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493847","url":null,"abstract":"Variable stars are stars whose brightness changes overtime. Due to their change of brightness, they are relatively easy to observe. Astronomers use variable stars as a tool to learn about the formation and evolution of the system that they are in. Different types of variable stars provide unique information about the host system. To classify the types of these stars, astronomers traditionally look at their light curve to see how their light changes over time. Recently, observational data of variable stars has increased exponentially, making machine learning based classification a viable alternative to manual classification. In this work, we tackle the task of extracting and selecting a good set of features from light curve profiles retrieved from ASAS-SN archive for variable star classification. We found that by combining several feature selection methods in an increasing order of their aggressiveness towards feature reduction, we obtained a set of highly discriminative features which is smaller in size as compared to that of Mutual Information, Gradient Boosted Tree, L1 Regularization, Elastic net, and manually chosen by the expert, while still maintaining comparable classification performance.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"52 4-5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359801","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
Non-Functional Requirement Extraction by using Conceptual Graphs 利用概念图提取非功能需求
Taweewat Luangwiriya, R. Kongkachandra
{"title":"Non-Functional Requirement Extraction by using Conceptual Graphs","authors":"Taweewat Luangwiriya, R. Kongkachandra","doi":"10.1109/JCSSE53117.2021.9493849","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493849","url":null,"abstract":"In software engineering process, the representation of non-functional requirements (NFR) is one of major problems during analysis and design phase. Since NFR is difficult to be understood and may be interfered, conflicted, or contradicted with other NFRs. In addition, many of NFRs relate to the same quality attribute which leads to be the requirement misunderstanding problem. This paper presents a knowledge representation method for extracting the NFRs using the conceptual graphs. The proposed method enhances the understanding of NFR. The case study of billing system in telecommunication company is discussed for a feasibility and an effectiveness of the proposed method.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127189644","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
Epsilon: A Microservices based distributed scheduler for Kubernetes Cluster Epsilon:用于Kubernetes集群的基于微服务的分布式调度器
Alex Neo Jing Hui, Bu-Sung Lee
{"title":"Epsilon: A Microservices based distributed scheduler for Kubernetes Cluster","authors":"Alex Neo Jing Hui, Bu-Sung Lee","doi":"10.1109/JCSSE53117.2021.9493827","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493827","url":null,"abstract":"Kubernetes is a popular container orchestration platform designed to simplify the deployment of containers in a compute cluster. Kubernetes provides a monolithic cluster scheduler, which is responsible for allocating resources in the cluster. We developed a Kubernetes cluster scheduler using microservices as its foundation called Epsilon. Epsilon is designed as a proof of concept to examine the viability of using microservices to develop a cluster scheduler. On top of the basic scheduler microservice, additional microservices are developed to support the scheduler operations. e.g. The Retry service handles unsuccessful deployment of pods and reschedules them after a period of time. In our experiments, we deployed Epsilon in a Kubernetes cluster using AWS Cloud services. The results shows that the scheduling throughput of Epsilon is comparable to the default Kubernetes scheduler. Epsilon also shows even distribution of pods among the nodes. Furthermore, the distributed nature of the Epsilon’s microservices decrease code complexity when compared with the Kubernetes default scheduler.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"28 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114041081","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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