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

Towards a minimum description length based stopping criterion for semi-supervised time series classification 基于最小描述长度的半监督时间序列分类停止准则研究

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642490

Nurjahan Begum, Bing Hu, T. Rakthanmanon, Eamonn J. Keogh

{"title":"Towards a minimum description length based stopping criterion for semi-supervised time series classification","authors":"Nurjahan Begum, Bing Hu, T. Rakthanmanon, Eamonn J. Keogh","doi":"10.1109/IRI.2013.6642490","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642490","url":null,"abstract":"In the last decade the plunging costs of sensors/storage have made it possible to obtain vast amounts of medical telemetry. However for this data to be useful, it must be annotated. This annotation, requiring the attention of medical experts is very expensive and time consuming, and remains the critical bottleneck in medical analysis. Semi-supervised learning is an obvious way to mitigate the need for human labor, however, most such algorithms are designed for intrinsically discrete objects, and do not work well in this domain, which requires the ability to deal with real-valued objects arriving in a streaming fashion. In this work we make two contributions. First, we demonstrate that in many cases just a handful of human annotated examples are sufficient to perform accurate classification. Second, we devise a novel parameter-free stopping criterion for semi-supervised learning. We evaluate our work with a comprehensive set of experiments on diverse medical data sources including electrocardiograms. Our experimental results show that our approach can construct accurate classifiers even if given only a single annotated instance.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"17 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":"122203554","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: 21

Cloud-based tasking, collection, processing, exploitation, and dissemination 基于云的任务、收集、处理、开发和传播

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1007/978-3-319-04717-1_1

S. Rubin, Gordon K. Lee

引用次数: 0

Feature-to-code traceability in a collection of software variants: Combining formal concept analysis and information retrieval 软件变体集合中的功能到代码的可追溯性:结合形式概念分析和信息检索

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642474

H. E. Salman, A. Seriai, C. Dony

{"title":"Feature-to-code traceability in a collection of software variants: Combining formal concept analysis and information retrieval","authors":"H. E. Salman, A. Seriai, C. Dony","doi":"10.1109/IRI.2013.6642474","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642474","url":null,"abstract":"Today, developing new software variant to meet new demands of customers by ad-hoc copying of already existing variants of a software system is a frequent phenomenon in the software industry. Typically, maintaining such variants becomes difficult and expensive over the time. To re-engineer such software variants into a software product line (SPL) for systematic reuse, it is important to identify source code elements that implement a specific feature in order to understand product variants code. Information Retrieval(IR) methods have been used widely to support this purpose in a single software. This paper proposes a new approach to improve the performance of IR methods in a collection of similar software variants. Our proposal produces following two improvements. First, increasing the accuracy of IR results by exploiting commonality and variability across software variants. Secondly, increasing the number of retrieved links that are relevant by reducing the abstraction gap between feature and source code levels. We have validated our approach with a set of variants of two different systems. The experimental results showed that the proposed approach outperforms the conventional application of IR as well as the most relevant work on the subject.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"32 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":"121381948","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: 41

Creating GUI-based DSL formal tools 创建基于gui的DSL形式化工具

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642514

Robson Silva, A. Mota, R. R. Starr

引用次数: 1

Author attribution on streaming data 流数据的作者归属

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642511

Sadi Evren Seker, K. Al-Naami, L. Khan

{"title":"Author attribution on streaming data","authors":"Sadi Evren Seker, K. Al-Naami, L. Khan","doi":"10.1109/IRI.2013.6642511","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642511","url":null,"abstract":"The concept of novel authors occurring in streaming data source, such as evolving social media, is an unaddressed problem up until now. Existing author attribution techniques deals with the datasets, where the total number of authors do not change in the training or the testing time of the classifiers. This study focuses on the question, “what happens if new authors are added into the system by time?”. Moreover in this study we are also dealing with the problems that some of the authors may not stay and may disappear by time or may reappear after a while. In this study stream mining approaches are proposed to solve the problem. The test scenarios are created over the existing IMDB62 data set, which is widely used by author attribution algorithms already. We used our own shuffling algorithms to create the effect of novel authors. Also before the stream mining, POS tagging approaches and the TF-IDF methods are applied for the feature extraction. And we have applied bi-tag approach where two consecutive tags are considered as a new feature in our approach. By the help of novel techniques, first time proposed in this paper, the success rate has been increased from 35% to 61% for the authorship attribution on streaming text data.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"24 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":"117130517","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: 12

Spatial-temporal motion information integration for action detection and recognition in non-static background 基于时空运动信息集成的非静态背景下动作检测与识别

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642527

Dianting Liu, M. Shyu, Guiru Zhao

{"title":"Spatial-temporal motion information integration for action detection and recognition in non-static background","authors":"Dianting Liu, M. Shyu, Guiru Zhao","doi":"10.1109/IRI.2013.6642527","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642527","url":null,"abstract":"Various motion detection methods have been proposed in the past decade, but there are seldom attempts to investigate the advantages and disadvantages of different detection mechanisms so that they can complement each other to achieve a better performance. Toward such a demand, this paper proposes a human action detection and recognition framework to bridge the semantic gap between low-level pixel intensity change and the high-level understanding of the meaning of an action. To achieve a robust estimation of the region of action with the complexities of an uncontrolled background, we propose the combination of the optical flow field and Harris3D corner detector to obtain a new spatial-temporal estimation in the video sequences. The action detection method, considering the integrated motion information, works well with the dynamic background and camera motion, and demonstrates the advantage of the proposed method of integrating multiple spatial-temporal cues. Then the local features (SIFT and STIP) extracted from the estimated region of action are used to learn the Universal Background Model (UBM) for the action recognition task. The experimental results on KTH and UCF YouTube Action (UCF11) data sets show that the proposed action detection and recognition framework can not only better estimate the region of action but also achieve better recognition accuracy comparing with the peer work.","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":"116361262","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: 25

Behavioral sequence prediction for evolving data stream 演化数据流的行为序列预测

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642509

Sheikh M. Qumruzzaman, L. Khan, B. Thuraisingham

引用次数: 1

Optimized retrieval algorithms for personalized content aggregation 个性化内容聚合的优化检索算法

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642482

Dan He, D. S. Parker

引用次数: 1

Predicting susceptibility to social bots on Twitter 预测Twitter上社交机器人的易感性

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642447

Randall Wald, T. Khoshgoftaar, Amri Napolitano, Chris Sumner

{"title":"Predicting susceptibility to social bots on Twitter","authors":"Randall Wald, T. Khoshgoftaar, Amri Napolitano, Chris Sumner","doi":"10.1109/IRI.2013.6642447","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642447","url":null,"abstract":"The popularity of the Twitter social networking site has made it a target for social bots, which use increasingly-complex algorithms to engage users and pretend to be humans. While much research has studied how to identify such bots in the process of spam detection, little research has looked at the other side of the question - detecting users likely to be fooled by bots. In this paper, we examine a dataset consisting of 610 users who were messaged by Twitter bots, and determine which features describing these users were most helpful in predicting whether or not they would interact with the bots (through replies or following the bot). We then use six classifiers to build models for predicting whether a given user will interact with the bot, both using the selected features and using all features. We find that a users' Klout score, friends count, and followers count are most predictive of whether a user will interact with a bot, and that the Random Forest algorithm produces the best classifier, when used in conjunction with one of the better feature ranking algorithms (although poor feature ranking can actually make performance worse than no feature ranking). Overall, these results show promise for helping understand which users are most vulnerable to social bots.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"471 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":"127813069","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: 61

A moving target defense approach for protecting resource-constrained distributed devices 一种保护资源受限的分布式设备的移动目标防御方法

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-10-24 DOI: 10.1109/IRI.2013.6642449

V. Casola, Alessandra De Benedictis, Massimiliano Albanese

引用次数: 51