2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)最新文献_第4页

Efficiently integrating MapReduce-based computing into a Hurricane Loss Projection model 有效地将基于mapreduce的计算集成到飓风损失预测模型中

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642499

Fausto Fleites, S. Cocke, Shu‐Ching Chen, S. Hamid

引用次数: 5

A knowledge-based semantic tool for standard part management in aerospace industry 航空航天工业标准件管理的基于知识的语义工具

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642528

Hasan Mert Taymaz, Tansel Özyer, C. Cangelir

{"title":"A knowledge-based semantic tool for standard part management in aerospace industry","authors":"Hasan Mert Taymaz, Tansel Özyer, C. Cangelir","doi":"10.1109/IRI.2013.6642528","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642528","url":null,"abstract":"Manufacture-based high-tech industries such as automotive and aerospace essentially pay attention to reducing costs and improving the quality of their products. In this context, assembly of products is one prominent part of finalizing the product. During the assembly, finding the most suitable standard parts1 that combines one or more bodies of assembly to form the final product is the common task for design engineers. Quite a few relational standard part databases can be found on the market, however none of them properly address the design needs. While querying relational databases, results are mostly vague or irrelevant, hence it requires domain experience about the features of parts. On the other hand semantic search [1] by the help of graphic model, it is possible to navigate and access all standard parts on graph; it returns relevant results as usual. In this paper, we propose semantic based search engine for inquiring standard parts from the repository. In our approach, we model and store Standard Part data on a commercial RDF (Resource Description Framework) Database [2]. We used OWLIM [3] to store data on database and we created an intelligent search engine, which discover link relations on RDF data. Our prototype system is still being developed in collaboration with the TOBB University. Final product will be used in Turkish Aerospace Industries (TAI)2 to resolve their critical problems.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045143","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: 5

An approach to develop frameworks from feature models 从特征模型开发框架的方法

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642523

M. C. Viana, R. Penteado, A. F. Prado, R. Durelli

引用次数: 1

Patient response datasets: Challenges and opportunities 患者反应数据集:挑战与机遇

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642480

Randall Wald, T. Khoshgoftaar

{"title":"Patient response datasets: Challenges and opportunities","authors":"Randall Wald, T. Khoshgoftaar","doi":"10.1109/IRI.2013.6642480","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642480","url":null,"abstract":"As the field of bioinformatics has grown in importance, more and more studies have investigated the use of gene microarray datasets to understand cancer. Although much of this research has focused on which genes are differently-expressed between cancerous and non-cancerous tissues, an equally important question is which genes are most useful for predicting the success of cancer treatment. How well a patient will respond to a given treatment depends on the specifics of their cancer, and biopsies alone cannot detect genetic markers; thus, gene chips are an increasingly valuable research tool in this field. The problem of identifying which gene markers are predictive of successful response to treatment differs from more general cancer-identification and cancer-classification problems due to the expected similarities among all patients (since they share a cancer type), and thus it is important to understand this collection of datasets as a group separate from other cancer-related microarray datasets. The present work surveys research using gene microarray datasets for the task of patient response prediction, in particular research which uses this data to discover the best genes for predicting patient response. We discuss the methods and procedures of the surveyed papers and which approaches were used for gene selection, and present ideas and strategies for future work which further explores how to best identify gene signatures which can be used clinically to help select the best treatment for a given cancer.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114612294","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

Chinese textual entailment with Wordnet semantic and dependency syntactic analysis 基于Wordnet的汉语文本蕴涵语义和依存句法分析

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642455

Chun-yung Tu, Min-Yuh Day

引用次数: 1

Detecting data exfiltration by integrating information across layers 通过跨层集成信息来检测数据泄露

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642487

Puneet Sharma, A. Joshi, Timothy W. Finin

引用次数: 4

Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots 基于过滤器和包装器的特征选择，用于预测用户与Twitter机器人的交互

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642501

Randall Wald, T. Khoshgoftaar, Amri Napolitano

{"title":"Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots","authors":"Randall Wald, T. Khoshgoftaar, Amri Napolitano","doi":"10.1109/IRI.2013.6642501","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642501","url":null,"abstract":"High dimensionality (the presence of too many features) is a problem which plagues many datasets, including mining from personality profiles. Feature selection can be used to reduce the number of features, and many strategies have been proposed to help select the most important features from a larger group. Feature rankers will produce a metric for each feature and return the best for a given subset size, while filter-based subset evaluation will perform statistical analysis on whole subsets and wrapper-based subset selection will use classification models with chosen features to decide which are most important for model-building. While all three approaches have been discussed in the literature, relatively little work compares all three with one another directly. In the present study, we do precisely this, considering feature ranking, filter-based subset evaluation, and wrapper-based subset selection (along with no feature ranking) on two datasets based on predicting interaction with bots on Twitter. For the two subset-based techniques, we consider two search techniques (Best First and Greedy Stepwise) to build the subsets, while we use one feature ranker (ROC) chosen for its excellent performance in previous works. Six learners are used to build models with the selected features. We find that feature ranking consistently performs well, giving the best results for four of the six learners on both datasets. In addition, all of the techniques other than feature ranking perform worse than no feature selection for four of six learners. This leads us to recommend the use of feature ranking over more complex subset evaluation techniques.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127757222","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: 5

Federated ensemble Kalman filter in no reset mode design 联邦集成卡尔曼滤波器无复位模式设计

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642539

M. Kazerooni, F. Shabaninia, M. Vaziri, S. Vadhva

引用次数: 6

Road segment partitioning towards anomalous trajectory detection for surveillance applications 面向监控应用的异常轨迹检测的路段划分

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642525

M. Saleem, Waqas Nawaz, Young-Koo Lee, Sungyoung Lee

{"title":"Road segment partitioning towards anomalous trajectory detection for surveillance applications","authors":"M. Saleem, Waqas Nawaz, Young-Koo Lee, Sungyoung Lee","doi":"10.1109/IRI.2013.6642525","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642525","url":null,"abstract":"Recently, the low cost and high availability of location acquisition technologies has significantly increased the demands for online anomalous trajectory detection. It is being used in social as well as commercial areas to provide human life care applications like healthcare, theft protection and taxi fraud detection. However, anomalous trajectory detection is still a challenging problem. The main complications involved in it, are inaccuracy in obtaining trajectory traces and evaluation of partial anomalous trajectories. In this study we contribute towards resolving these complications by proposing a novel method of Road segment Partitioning towards Anomalous Trajectory Detection (RPat). Our proposed method partitions the trajectory on the basis of road segments. Then, these sub-trajectories are evaluated, independently based on contemporary behavior of moving objects to accurately analyze the trajectories that possess abnormal behavior at any intermediate parts. The evaluation score of each sub-trajectory is aggregated to reflect the attitude of an overall itinerary as anomalous or regular. Further, the accuracy in the reconstruction of trajectories is achieved by plotting the itinerary traces on real world road maps. Experimental studies are conducted on real datasets and an accuracy of more than 81% is achieved.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132442856","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: 9

Natural image segmentation using morphological mathematics and fuzzy logic 利用形态学数学和模糊逻辑进行自然图像分割

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642542

Victoria L. Fox, M. Milanova

引用次数: 0