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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction">
  <title><![CDATA[Clinical-Genomic Analysis for Disease Prediction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction</link>
  <description><![CDATA[Recent advances in genomic research have generated vast amounts of information that can help identify individuals who differ in their susceptibility to a particular disease or response to a specific treatment. This information may offer solutions for the treatment of complex chronic diseases that are influenced by a wide array of factors. This vast amount of information brings critical challenges in applying advanced technology to synthesize clinical-genomic patient data. Synthesizing this in...]]></description>
  <dc:date>2011-07-06</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach">
  <title><![CDATA[Clustering short status messages: A topic model based approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach</link>
  <description><![CDATA[Recently, there has been an exponential rise in the use of online social media systems like Twitter and Facebook. Even more usage has been observed during events related to natural disasters, political turmoil or other such crises. Tweets or status messages are short and may not carry enough contextual clues. Hence, applying traditional natural language processing algorithms on such data is challenging. Topic model is a popular method for modeling term frequency occurrences for documents in a...]]></description>
  <dc:date>2010-07-17</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies">
  <title><![CDATA[Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies</link>
  <description><![CDATA[We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our met...]]></description>
  <dc:date>2008-08-24</dc:date>
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  <title><![CDATA[StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANETs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/354/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANETs</link>
  <description><![CDATA[Automobile traffic is a major problem in developed societies. We collectively waste huge amounts of time and resources traveling through traffic congestion. Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip. Significant savings of fuel and time could be achieved if traffic congestion patterns could be effectively discovered and disseminated to drivers. We propose a system that uses a standard GPS driving ...]]></description>
  <dc:date>2007-04-22</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/350/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANET-s">
  <title><![CDATA[StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANET's]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/350/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANET-s</link>
  <description><![CDATA[Automobile traffic is a major problem in developed societies.  We collectively waste huge amounts of time and resources traveling through traffic congestion.  Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip.  Drivers that know the location of areas of slow traffic can choose other, more efficient routes.  We could save significant amounts of time if traffic congestion patterns could be effectively disco...]]></description>
  <dc:date>2006-08-01</dc:date>
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