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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
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

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.
Item Type: Paper

Hybrid Approach for Energy-Aware Synchronization

Description: This book chapter discusses a time synchronization scheme for wireless sensor networks that aims to save sensor battery power while maintaining network connectivity for as long as possible.
Date: December 2010
Creator: Akl, Robert G.; Saravanos, Yanos & Haidar, Mohamad
Item Type: Book Chapter

On B.S.E and B.S.ET for the Engineering Profession

Description: Article discussing biological systems engineering (B.S.E.) and a proposed model for baccalaureate programs for engineering education.
Date: 2010
Creator: Barbieri, Enrique; Attarzadeh, Farrokh; Pascali, Raresh; Shireen, Wajiha & Fitzgibbon, William
Item Type: Article

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