2008 Third International Conference on Digital Information Management最新文献_第10页

Design and test of Application-Specific Integrated Circuits by use of mobile clients 利用移动客户端设计和测试专用集成电路

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746848

D. Zutin, M. Auer

引用次数: 1

An empirical study of e-Government formulation in Kingdom of Saudi Arabia & United Arab Emirates 沙特阿拉伯王国和阿联酋电子政务制定的实证研究

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746841

A. Al-Tameem

引用次数: 1

A ranking algorithm for user-generated video contents based on social activities 基于社交活动的用户生成视频内容排序算法

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746721

Lisa Wiyartanti, Yo-Sub Han, Laehyun Kim

引用次数: 6

A data model for versioned XML documents using XQuery 使用XQuery的版本化XML文档的数据模型

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746718

Luis Arévalo Rosado, Antonio Polo Márquez, Miryam Salas Sánchez

引用次数: 2

English sentence retrieval system based on dependency structure and its evaluation 基于依存结构的英语句子检索系统及其评价

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746762

Yoshihide Kato, Seiji Egawa, S. Matsubara, Y. Inagaki

引用次数: 7

Privacy in context: Privacy issues in Ubiquitous Computing applications 上下文中的隐私:普适计算应用中的隐私问题

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746842

C. O'Driscoll

引用次数: 13

Classification of personal names with application to DBLP 人名分类及其在DBLP中的应用

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746754

M. Biryukov, Yafang Wang

引用次数: 3

Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge 利用领域和语言知识从科学论文中提取无监督关键短语

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746749

M. Krapivin, M. Marchese, Andrei Yadrantsau, Yanchun Liang

{"title":"Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge","authors":"M. Krapivin, M. Marchese, Andrei Yadrantsau, Yanchun Liang","doi":"10.1109/ICDIM.2008.4746749","DOIUrl":"https://doi.org/10.1109/ICDIM.2008.4746749","url":null,"abstract":"The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves - on the same data-set - on state-of-the-art Bayesian learning system KEA from a minimum 27% to a maximum 77% depending on KEA parameters tuning and specific evaluation set. Our assessment is performed by comparison with key-phrases assigned by human experts in the specific domain and freely available through ACM portal.","PeriodicalId":415013,"journal":{"name":"2008 Third International Conference on Digital Information Management","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114422217","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: 17

Chain ontology based: A model for protecting personal information privacy 基于链本体的个人信息隐私保护模型

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746767

Esraa Omran, A. Bokma, S. Al-Maati

引用次数: 4

Anomaly detection and visualization using Fisher Discriminant clustering of network entropy 基于Fisher网络熵判别聚类的异常检测与可视化

2008 Third International Conference on Digital Information Management Pub Date : 2008-11-01 DOI: 10.1109/ICDIM.2008.4746810

M. Celenk, T. Conley, John Willis, James Graham

{"title":"Anomaly detection and visualization using Fisher Discriminant clustering of network entropy","authors":"M. Celenk, T. Conley, John Willis, James Graham","doi":"10.1109/ICDIM.2008.4746810","DOIUrl":"https://doi.org/10.1109/ICDIM.2008.4746810","url":null,"abstract":"Entropy has been widely used to quantify information for display and examination in determining network status and in detecting anomalies. Although entropy-based methods are effective, they rely on long-term network statistics. Here, we propose an approach that deduces short term observations of network features and their respective time averaged entropies. Acute changes are detected in network feature space and depicted in a visually compact information graph. First, average entropy for each feature is calculated for every second of observation. Then, the resultant short-term information measurement is subjected to first- and second-order time averaging statistics. These time-varying statistics are used as the basis of a novel approach to anomaly estimation based on the well-known Fisher linear discriminant (FLD). This process then initiates stochastic clustering to identify the exact time of the security incident or attack on the network. The proposed method is tested on real-time network traffic data collected from Ohio Universitypsilas main Internet connection. Experimentation has shown that the presented FLD based method is accurate in identifying anomalies in network feature space. Furthermore, itpsilas performance is highly robust in the presence of bursty network traffic and it is able to detect network anomalies such as BotNet, worm outbreaks, and denial of service attacks.","PeriodicalId":415013,"journal":{"name":"2008 Third International Conference on Digital Information Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126878879","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