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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
Item Type: Paper

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-
Item Type: Paper

Reducing Our Carbon Footprint through Ridesharing

Description: This report discusses research on reducing our carbon footprint through ridesharing. The focus of this research project was to analyze and collect travel trajectories to calculate carbon footprints under different travel modes and identify ways to reduce it.
Date: 2012
Creator: Garrett, Kim; Bell, Jesse; Huang, Yan & Powell, James
Item Type: Report

Shared Ride: Transportation, Carbon Footprint and Ridesharing

Description: This poster discusses research on transportation, carbon footprinting and ridesharing. The focus of this research project was to analyze and collect travel trajectories to calculate carbon footprints under different travel modes and identify ways to reduce it.
Date: 2012
Creator: Garrett, Kim; Bell, Jesse; Huang, Yan & Powell, James
Item Type: Poster

Abstraction Augmented Markov Models

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

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
Item Type: Paper