<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Baja</style></author><author><style face="normal" font="default" size="100%">William Romine</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Amir Hassan Monadjemi</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">John M. Meddar</style></author><author><style face="normal" font="default" size="100%">Annie Myers</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multimodal mental health analysis in social media</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypothesis Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">National Language Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&amp;type=printable</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Depression is a major public health concern in the U.S. and globally. While successful early&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;identification and treatment can lead to many positive health and behavioral outcomes,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;depression, remains undiagnosed, untreated or undertreated due to several reasons,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;including denial of the illness as well as cultural and social stigma. With the ubiquity of social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media platforms, millions of people are now sharing their online persona by expressing their&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;thoughts, moods, emotions, and even their daily struggles with mental health on social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media. Unlike traditional observational cohort studies conducted through questionnaires&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;and self-reported surveys, we explore the reliable detection of depressive symptoms from&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;data to discern depressive behaviors using a wide variety of features including individuallevel&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;demographics. By developing a multimodal framework and employing statistical techniques&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;to fuse heterogeneous sets of features obtained through the processing of visual,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;textual, and user interaction data, we significantly enhance the current state-of-the-art&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;approaches for identifying depressed individuals on Twitter (improving the average F1-&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Score by 5 percent) as well as facilitate demographic inferences from social media. Besides&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;providing insights into the relationship between demographics and mental health, our&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;research assists in the design of a new breed of demographic-aware health interventions.&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%">Satya S. Sahoo</style></author><author><style face="normal" font="default" size="100%">Olivier Bodenreider</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Michael Gertz</style></author><author><style face="normal" font="default" size="100%">Bertram Ludäscher</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biomedical knowledge repository</style></keyword><keyword><style  face="normal" font="default" size="100%">Context theory</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenance context entity</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenance Management Framework.</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenir ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">RDF reification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-13818-8_32</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">6187</style></volume><pages><style face="normal" font="default" size="100%">461–470</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.&amp;nbsp;&lt;/p&gt;
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