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Description
Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are

Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic to source trustworthiness and result importance, which, given the autonomous and uncurated nature of deep-web, have become indispensible for searching deep-web. SourceRank provides a global measure for assessing source quality based on source trustworthiness and result importance. SourceRank's effectiveness has been evaluated in single-topic deep-web environments. The goal of the thesis is to extend sourcerank to a multi-topic deep-web environment. Topic-sensitive sourcerank is introduced as an effective way of extending sourcerank to a deep-web environment containing a set of representative topics. In topic-sensitive sourcerank, multiple sourcerank vectors are created, each biased towards a representative topic. At query time, using the topic of query keywords, a query-topic sensitive, composite sourcerank vector is computed as a linear combination of these pre-computed biased sourcerank vectors. Extensive experiments on more than a thousand sources in multiple domains show 18-85% improvements in result quality over Google Product Search and other existing methods.
ContributorsJha, Manishkumar (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
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Description
The present research expands on prior research that demonstrated a prototypical facial expression in response to cute, baby-like Kindchenschema targets. This expression, referred to as the tenderness expression, is recognizable to onlookers as a response to such stimuli. Across two studies, the current research examined if there were differences in

The present research expands on prior research that demonstrated a prototypical facial expression in response to cute, baby-like Kindchenschema targets. This expression, referred to as the tenderness expression, is recognizable to onlookers as a response to such stimuli. Across two studies, the current research examined if there were differences in perceptions of trustworthiness (Studies 1 and 2) and willingness to trust (Study 2) toward individuals displaying the tenderness expression as compared to a Duchenne smile or a neutral expression. Results indicate the tenderness expression is associated with lower ratings of trustworthiness relative to a smile, but no differences among the expressions on willingness to trust. Exploratory analyses demonstrate a replicated pattern of differences on the Big Five Personality Inventory among these three expressions. While these findings were not consistent with a priori hypotheses, this research provides further insight into the social implications associated with this tenderness expression.
ContributorsO'Neil, Makenzie J (Author) / Shiota, Michelle N. (Thesis advisor) / Kenrick, Douglas T. (Committee member) / Wynne, Clive D.L. (Committee member) / Bradley, Robert H. (Committee member) / Arizona State University (Publisher)
Created2019