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  Partner: UNT College of Engineering
 Department: Computer Science and Engineering
Strategies for Retention and Recruitment of Women and Minorities in Computer Science and Engineering

Strategies for Retention and Recruitment of Women and Minorities in Computer Science and Engineering

Date: 2007
Creator: Akl, Robert G.; Keathly, David & Garlick, Ryan
Description: Article discussing strategies for the retention and recruitment of women and minorities in computer science and engineering.
Contributing Partner: UNT College of Engineering
Struct-NB: Predicting Protein-RNA Binding Sites Using Structural Features

Struct-NB: Predicting Protein-RNA Binding Sites Using Structural Features

Date: 2008
Creator: Towfic, Fadi; Caragea, Cornelia; Gemperline, David; Dobbs, Drena & Honavar, Vasant
Description: Article discussing predicting protein-RNA binding sites using structural features.
Contributing Partner: UNT College of Engineering
The Structure and Performance of an Open-Domain Question Answering System

The Structure and Performance of an Open-Domain Question Answering System

Date: October 2000
Creator: Moldovan, Dan I.; Harabagiu, Sanda M.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Gîrju, Corina R.; Goodrum, Richard A. et al.
Description: This article discusses the structure and performance of an open-domain question answering system.
Contributing Partner: UNT College of Engineering
Subjectivity Word Sense Disambiguation

Subjectivity Word Sense Disambiguation

Date: August 2009
Creator: Akkaya, Cem; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Description: This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses.
Contributing Partner: UNT College of Engineering
Subscriber Maximization in CDMA Cellular Networks

Subscriber Maximization in CDMA Cellular Networks

Date: August 2004
Creator: Akl, Robert G.
Description: This presentation gives an overview of code division multiple access (CDMA), traffic and mobility models, subscriber optimization formulation, and numerical results.
Contributing Partner: UNT College of Engineering
Subscriber Maximization in CDMA Cellular Networks

Subscriber Maximization in CDMA Cellular Networks

Date: August 2004
Creator: Akl, Robert G.
Description: This paper discusses subscriber maximization in CDMA cellular networks.
Contributing Partner: UNT College of Engineering
Tantra: A fast PRNG algorithm and its implementation

Tantra: A fast PRNG algorithm and its implementation

Date: June 2009
Creator: Gomathisankaran, Mahadevan & Lee, Ruby Bei-Loh
Description: This paper discusses Tantra. Tantra is a novel Pseudorandom number generator (PRNG) design that provides a long sequence high quality pseudorandom numbers at very high rate both in software and hardware implementations.
Contributing Partner: UNT College of Engineering
Technologies That Make You Smile: Adding Humor to Text-Based Applications

Technologies That Make You Smile: Adding Humor to Text-Based Applications

Date: 2006
Creator: Mihalcea, Rada, 1974- & Strapparava, Carlo, 1962-
Description: Article discussing technologies that make people smile and adding humor to text-based applications.
Contributing Partner: UNT College of Engineering
Text and Structural Data Mining of Influenza Mentions in Web and Social Media

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

Date: February 22, 2010
Creator: Corley, Courtney; Cook, Diane J., 1963-; Mikler, Armin R. & Singh, Karan P.
Description: This article discusses text and structural data mining of influenza mentions in web and social media.
Contributing Partner: UNT College of Engineering
Text Mining for Automatic Image Tagging

Text Mining for Automatic Image Tagging

Date: August 2010
Creator: Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer
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.
Contributing Partner: UNT College of Engineering