<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Sagonas, Christos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Li, Shipeng</style></author><author><style face="normal" font="default" size="100%">El-Saddik, Abdulmotaleb</style></author><author><style face="normal" font="default" size="100%">Wang, Meng</style></author><author><style face="normal" font="default" size="100%">Mei, Tao</style></author><author><style face="normal" font="default" size="100%">Sebe, Nicu</style></author><author><style face="normal" font="default" size="100%">Yan, Shuicheng</style></author><author><style face="normal" font="default" size="100%">Hong, Richang</style></author><author><style face="normal" font="default" size="100%">Gurrin, Cathal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">MMM (1)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7732</style></volume><pages><style face="normal" font="default" size="100%">1-12</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-35725-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an approach for detecting concepts in images bya graph-based semi-supervised learning scheme. The proposed approach builds a similarity graph between both the labeled and unlabeled images of the collection and uses the Laplacian Eigemaps of the graph as features for training concept detectors. Therefore, it offers multiple options for fusing different image features. In addition, we present an incremental learning scheme that, given a set of new unlabeled images, efficiently performs the computation of the Laplacian Eigenmaps. We evaluate the performance of our approach both on synthetic datasets and on MIR Flickr, comparing it with high-performance state-of-the-art learning schemes with competitive and in some cases superior results.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stampouli, Anastasia</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Yoshikawa, Masatoshi</style></author><author><style face="normal" font="default" size="100%">Meng, Xiaofeng</style></author><author><style face="normal" font="default" size="100%">Yumoto, Takayuki</style></author><author><style face="normal" font="default" size="100%">Ma, Qiang</style></author><author><style face="normal" font="default" size="100%">Sun, Lifeng</style></author><author><style face="normal" font="default" size="100%">Watanabe, Chiemi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tag Disambiguation through Flickr and Wikipedia</style></title><secondary-title><style face="normal" font="default" size="100%">DASFAA Workshops</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DBpedia project</style></keyword><keyword><style  face="normal" font="default" size="100%">flick</style></keyword><keyword><style  face="normal" font="default" size="100%">mashup</style></keyword><keyword><style  face="normal" font="default" size="100%">term disambiguation</style></keyword><keyword><style  face="normal" font="default" size="100%">Wikipedia</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6193</style></volume><pages><style face="normal" font="default" size="100%">252-263</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-14588-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Given the popularity of social tagging systems and the limitationsthese systems have, due to lack of any structure, a common issue that arises involves the low retrieval quality in such systems due to ambiguities of certain terms. In this paper, an approach for improving the retrieval in these systems, in case of ambiguous terms, is presented that attempts to perform tag disambiguation and, at the same time, provide users with relevant content. The idea is based on a mashup that combines data and functionality of two major web 2.0 sites, namely Flickr and Wikipedia and aims at enhancing content retrieval for web users. A case study with the ambiguous notion â€śAppleâ€ť illustrates the value of the proposed approach.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Vossen, Gottfried</style></author><author><style face="normal" font="default" size="100%">Long, Darrell D. E.</style></author><author><style face="normal" font="default" size="100%">Yu, Jeffrey Xu</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering of Social Tagging System Users: A Topic and Time Based Approach</style></title><secondary-title><style face="normal" font="default" size="100%">WISE</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Social tagging systems</style></keyword><keyword><style  face="normal" font="default" size="100%">time</style></keyword><keyword><style  face="normal" font="default" size="100%">topic</style></keyword><keyword><style  face="normal" font="default" size="100%">user clustering</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5802</style></volume><pages><style face="normal" font="default" size="100%">75-86</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-04408-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Under Social Tagging Systems, a typical Web 2.0 application,users label digital data sources by using freely chosen textual descriptions(tags). Mining tag information reveals the topic-domain ofusers interests and significantly contributes in a profile construction process.In this paper we propose a clustering framework which groups usersaccording to their preferred topics and the time locality of their taggingactivity. Experimental results demonstrate the efficiency of the proposedapproach which results in more enriched time-aware users profiles.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoupa, Konstantina</style></author><author><style face="normal" font="default" size="100%">Simeoforidis, Zisis</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Levi, Albert</style></author><author><style face="normal" font="default" size="100%">Savas, Erkay</style></author><author><style face="normal" font="default" size="100%">Yenigün, Hüsnü</style></author><author><style face="normal" font="default" size="100%">Balcisoy, Selim</style></author><author><style face="normal" font="default" size="100%">Saygin, Yücel</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Credential-Based Policies Management in an Access Control Framework Protecting XML Resources</style></title><secondary-title><style face="normal" font="default" size="100%">ISCIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4263</style></volume><pages><style face="normal" font="default" size="100%">603-612</style></pages><isbn><style face="normal" font="default" size="100%">3-540-47242-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;XML has been widely adopted for Web data representation undervarious applications (such as DBMSs, Digital Libraries etc). Therefore, accessto XML data sources has become a crucial issue. In this paper we introduce acredential-based access control framework for protecting XML resources. Underthis framework, we propose the use of access policy files containing policiesconcerning a specific credentials type. Moreover, we propose the reorganizationof the policies in these files based on their frequency of use (the morefrequently it is used the higher in the file it is placed). Our main goal is to improverequest servicing times. Several experiments have been conducted whichare carried out either on single request or on multiple requests base. The proposedframework is proven quite beneficial for protecting XML-based frameworkssuch as digital libraries or any other data resources whose format is expressedin XML.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Yakhno, Tatyana M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary Prefetching and Caching in an Independent Storage Units Model</style></title><secondary-title><style face="normal" font="default" size="100%">ADVIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data prefetching and caching</style></keyword><keyword><style  face="normal" font="default" size="100%">object-based storage models</style></keyword><keyword><style  face="normal" font="default" size="100%">parallel storage units</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1909</style></volume><pages><style face="normal" font="default" size="100%">265-274</style></pages><isbn><style face="normal" font="default" size="100%">3-540-41184-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern applications demand support for a large number ofclients and require large scale storage subsystems. This paper presentsa theoretical model of prefetching and caching of storage objects undera parallel storage units architecture. The storage objects are definedas variable sized data blocks and a specific cache area is reserved fordata prefetching and caching. An evolutionary algorithm is proposed foridentifying the storage objects to be prefetched and cached. The storageobject prefetching approach is experimented under certain artificialworkloads of requests for a set of storage units and has shown significantperformance improvement with respect to request service times, as wellas cache and byte hit ratios.&lt;/p&gt;
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