2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)最新文献_第2页

An Improved Artificial Fish Swarm Algorithm and its Application 一种改进的人工鱼群算法及其应用

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466458

Sili Gao, Yujun Wen

引用次数: 13

Analysis of Hot News Based on Big Data 基于大数据的热点新闻分析

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466427

Chengcheng Hu, Y. Li, Yongbin Wang, Lin Wu

引用次数: 1

Game Based Optical Experiment Simulation 基于游戏的光学实验仿真

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466521

Wenfeng Hu, Jiangchun Guo, Xiaolin Li

引用次数: 0

Concurrent Software Testing Method Based on CSP and PAT 基于CSP和PAT的并行软件测试方法

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466422

Yizhen Cao, Yongbin Wang

引用次数: 1

Real-Time Tracing Of A Weld Line Using Artificial Neural Networks 基于人工神经网络的焊缝实时跟踪

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466525

S. Rao, V. Kalaichelvi, R. Karthikeyan

引用次数: 3

A Learning-Based Bug Predicition Method for Object-Oriented Systems 面向对象系统的基于学习的Bug预测方法

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466535

Fikret Aktas, F. Buzluca

{"title":"A Learning-Based Bug Predicition Method for Object-Oriented Systems","authors":"Fikret Aktas, F. Buzluca","doi":"10.1109/ICIS.2018.8466535","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466535","url":null,"abstract":"Because of the increase in size and complexity of todays advanced software systems; the number of structural defective software classes in projects also increases, when necessary precautions are not taken. In this study, we purpose a machine-learning-based approach to detect defective classes, which generate most of the errors in the tests. Our objective is helping software developers and testers to predict error-prone classes, eliminate design defects and reduce testing costs. In learning-based methods, the dataset that is used for training the model, strongly affects the accuracy of the detection system. Therefore, we focus on steps of constructing the proper dataset using different metrics collected from existing software projects. First, we consider the rate of errors generated by a class to label it as \"Clean\" or \"Buggy\". Secondly, we use CFS (Correlation-based Feature Selection) and the PCA (Principal Component Analysis) methods to obtain the most appropriate subset of metrics. This feature selection process increases the understandability and the detection performance of the model. Lastly, we apply the Random Forest classification method to determine error-prone classes. We evaluated our approach using five different datasets that include data collected from various open-source Eclipse subprojects. The results show that our approach is successful in building learning-based models for detecting error-prone classes. However, we also observed that different models should be created for different software systems, because each project has its own character.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128967189","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: 7

Applying Feedback Information for Random Partition Testing 应用反馈信息进行随机分区测试

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466515

A. Sinaga

引用次数: 3

A Multi-label Image Classification Algorithm Based on Attention Model 一种基于注意力模型的多标签图像分类算法

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466472

Yugang Li, Yongbin Wang

引用次数: 8

Segmentation of Heterogeneous Documents into Homogeneous Components using Morphological Operations 利用形态学操作将异质文档分割成同质成分

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466395

Nasid Habib Barna, Tisa Islam Erana, Shabbir Ahmed, Hasnain Heickal

{"title":"Segmentation of Heterogeneous Documents into Homogeneous Components using Morphological Operations","authors":"Nasid Habib Barna, Tisa Islam Erana, Shabbir Ahmed, Hasnain Heickal","doi":"10.1109/ICIS.2018.8466395","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466395","url":null,"abstract":"The research on document layout analysis has been widespread over a large arena recently and is craving for more efficiency day by day. Document segmentation is an important preprocessing step before analyzing the layouts. This paper presents a language-independent document segmentation system that segments a heterogeneous printed document into homogeneous components like halftones and graphics, texts and tables including its individual cells. From an input document page homogeneous components are segmented in three steps with three separate modules, which are- extraction of halftone images, extraction of tables and segmentation of text blocks. These modules altogether build the whole page segmentation system which takes an input image of heterogeneous document page and produces an output with explicitly indicated homogeneous segments with colored bounding boxes. The modules use morphological operations to detect the components. To improve the performance of image segmentation Residual Image Fragments Retrieval (RIFR) is proposed. The paper also proposes Text Extraction from Table Cells (TETC). Combining RIFR and TETC together we get an overall accuracy of 93%. Table and cell detection have a higher accuracy of 96% whereas image and texts have around 90% accuracy.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164453","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: 4

Refined Fluent Builder in Java 在Java中改进的流畅生成器

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466532

Mikhail Chalabine

引用次数: 0