Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)最新文献_第7页

Extracting control-flow from text 从文本中提取控制流

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051891

Pol Schumacher, Mirjam Minor

引用次数: 12

A study of clickjacking worm propagation in online social networks 在线社交网络中点击劫持蠕虫传播的研究

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051873

M. R. Faghani, U. T. Nguyen

引用次数: 10

Who wrote this paper? Learning for authorship de-identification using stylometric featuress 这篇论文是谁写的?使用文体学特征学习作者身份去识别

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051981

Jose L. Hurtado, Napat Taweewitchakreeya, Xingquan Zhu

引用次数: 8

Text classification for automatic detection of alcohol use-related tweets: A feasibility study 酒精使用相关推文自动检测的文本分类:可行性研究

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051877

Y. Aphinyanaphongs, Bisakha Ray, A. Statnikov, P. Krebs

{"title":"Text classification for automatic detection of alcohol use-related tweets: A feasibility study","authors":"Y. Aphinyanaphongs, Bisakha Ray, A. Statnikov, P. Krebs","doi":"10.1109/IRI.2014.7051877","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051877","url":null,"abstract":"We present a feasibility study using text classification to classify tweets about alcohol use. Alcohol use is the most widely used substance in the US and is the leading risk factor for premature morbidity and mortality globally. Understanding use patterns and locations is an important step toward prevention, moderation, and control of alcohol outlets. Social media may provide an alternate way to measure alcohol use in real time. This feasibility study explores text classification methodologies for identifying alcohol use tweets. We labeled 34,563 geo-located New York City tweets collected in a 24 hour period over New Year's Day 2012. We preprocessed the tweets into stem/ not stemmed and unigram/ bigram representations. We then applied multinomial naïve Bayes, a linear SVM, Bayesian logistic regression, and random forests to the classification task. Using 10 fold cross-validation, the algorithms performed with area under the receiver operating curve of 0.66, 0.91, 0.93, and 0.94 respectively. We also compare to a human constructed Boolean search for the same tweets and the text classification method is competitive with this hand crafted search. In conclusion, we show that the task of automatically identifying alcohol related tweets is highly feasible and paves the way for future research to improve these classifiers.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116337772","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

UFOM: Unified fuzzy ontology matching 统一模糊本体匹配

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051969

Yinuo Zhang, A. Panangadan, V. Prasanna

{"title":"UFOM: Unified fuzzy ontology matching","authors":"Yinuo Zhang, A. Panangadan, V. Prasanna","doi":"10.1109/IRI.2014.7051969","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051969","url":null,"abstract":"Unified Fuzzy Ontology Matching (UFOM) is an ontology matching system designed to discover semantic links between large real-world ontologies populated with entities from heterogeneous sources. In such ontologies, several entities in different ontologies are expected to be related to each other but not necessarily with one of the typical well-defined correspondence relationships (equivalent-to, subsumed-by). In particular, we define a new kind of correspondence relation called Relevance that reflects the relation between entities when they share a certain amount of mutual information. UFOM uses fuzzy set theory as a general framework for fuzzy ontology alignment. The framework enables representation of multiple types of correspondence relations and characterization of the uncertainty in the correspondence discovery process. UFOM computes multiple measures of similarity among ontology entities - syntactic, semantic, and structural. These measures are composed in a principled manner for ontology alignment. The system is evaluated using publicly available ontologies provided by Ontology Alignment Evaluation Initiative (OAEI). The performance of the proposed system is comparable to the top performing ontology matchers in OAEI. We also evaluate the UFOM system on a dataset from an enterprise application domain.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127125653","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

A cluster-based key management framework for resource constraint networks 基于集群的资源约束网络密钥管理框架

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051919

Mohammad Rezaeirad, Sahar Mazloom, Mahdi Orooji, D. Perkins, M. Bayoumi

引用次数: 0

Detecting unintentional information leakage in social media news comments 检测社交媒体新闻评论中的无意信息泄露

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051874

I. Yahav, D. Schwartz, Gahl Silverman

引用次数: 8

A CEGAR approach for the reachability analysis of PLC-controlled chemical plants plc控制化工厂可达性分析的CEGAR方法

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051930

J. Nellen, E. Ábrahám

引用次数: 4

Correlation-based re-ranking for semantic concept detection 基于关联的语义概念重排序检测

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051966

Hsin-Yu Ha, Fausto Fleites, Shu‐Ching Chen, Min Chen

引用次数: 8

DiCoTraM: A distributed and coordinated DDoS flooding attack tailored traffic monitoring DiCoTraM:一种分布式和协调的DDoS洪水攻击，为流量监控量身定制

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051881

S. Zargar, J. Joshi, D. Tipper

{"title":"DiCoTraM: A distributed and coordinated DDoS flooding attack tailored traffic monitoring","authors":"S. Zargar, J. Joshi, D. Tipper","doi":"10.1109/IRI.2014.7051881","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051881","url":null,"abstract":"The success in detecting Distributed Denial of Service (DDoS) flooding attacks is highly dependent on the quality and quantity of the covered flows by the traffic monitoring mechanism that is employed in any DDoS defense mechanism. In this paper, we propose DiCoTraM, a DDoS flooding attack tailored distributed and coordinated traffic monitoring mechanism that centrally and periodically coordinates the monitoring responsibilities and distributes them among all the monitoring devices within each autonomous system (AS) while satisfying the monitoring devices' memory constraints. DiCoTraM monitors traffic flows in such a way that the flows intended for the same destination (possible network/transport level DDoS flooding attack flows) are analyzed together in the same monitoring device if there is enough memory to cover those flows on the monitoring device; hence, this can enable distributed detection mechanisms in place to analyze the monitored flows. The enabled distributed detection leads to reduced communication overhead that is a problem in centralized detection mechanisms as they need to collect centrally all the flows for analysis. Moreover, the centralized coordination structure of DiCoTraM eliminates the redundant flow monitoring among the routers. We simulate and compare DiCoTraM with other traffic monitoring mechanisms in terms of: the overall flow coverage, and the DDoS flooding attack flow coverage. The experimental results show that DiCoTraM, compared to other monitoring mechanisms, covers more DDoS flooding attack flows and it has reasonable overall flow coverage.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596170","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: 6