UNT College of Engineering - 16 Matching Results

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Abstraction Augmented Markov Models

Description: Article discussing the abstraction augmented Markov models.
Date: December 2010
Creator: Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina & Honavar, Vasant

Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Description: In this paper, the authors discuss research on whether they can use Mechanical Turk (MTurk) to acquire goo annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task.
Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce M. & Mihalcea, Rada, 1974-

Anchor Nodes Placement for Effective Passive Localization

Description: This paper discusses anchor nodes placement for effective passive localization. The authors show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity.
Date: 2011
Creator: Akl, Robert G.; Pasupathy, Karthikeyan & Haidar, Mohamad

Automated measurement of quality of mucosa inspection for colonscopy

Description: This paper from the International Conference on Computational Science conference proceedings presents new methods that derive a new quality metric for automated scoring of quality of mucosa inspection performed by the endoscopist.
Date: May 31, 2010
Creator: Liu, Xuemin; Tavanapong, Wallapak; Wong, Johnny; Oh, JungHwan & de Groen, Piet C.

Gleipnir: A Memory Analysis Tool

Description: This paper from the International Conference on Computational Science, ICCS 2011 conference proceedings describes a program profiling and analysis tool called Gleipnir.
Date: May 14, 2011
Creator: Janjusic, Tomislav; Kavi, Krishna M. & Potter, Brandon

Text Mining for Automatic Image Tagging

Description: This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
Date: August 2010
Creator: Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer