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  Partner: UNT College of Engineering
 Collection: UNT Scholarly Works
Secure execution environments through reconfigurable lightweight cryptographic components

Secure execution environments through reconfigurable lightweight cryptographic components

Date: 2006
Creator: Gomathisankaran, Mahadevan
Description: This doctoral dissertation discusses secure execution environments through reconfigurable lightweight cryptographic components. The author considers the four most important dimensions of software protection.
Contributing Partner: UNT College of Engineering
Self-Configuring Wireless MEMS Network

Self-Configuring Wireless MEMS Network

Date: January 2008
Creator: Akl, Robert G.; Kavi, Krishna M. & El Rewini, Hesham
Description: This presentation discusses miniature, lightweight, self-powered wireless sensors, and networking software needs.
Contributing Partner: UNT College of Engineering
Semantic Document Engineering with WordNet and PageRank

Semantic Document Engineering with WordNet and PageRank

Date: March 2005
Creator: Tarau, Paul; Mihalcea, Rada, 1974- & Figa, Elizabeth
Description: This article discusses semantic document engineering with WordNet and PageRank.
Contributing Partner: UNT College of Engineering
Semantic Indexing using WordNet Senses

Semantic Indexing using WordNet Senses

Date: October 2000
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This article discusses semantic indexing using WordNet senses.
Contributing Partner: UNT College of Engineering
The Semantic Wildcard

The Semantic Wildcard

Date: May 2002
Creator: Mihalcea, Rada, 1974-
Description: This paper introduces the semantic wildcard, one of the most powerful operators implemented in IRSLO, which allows for searches along general-specific lines.
Contributing Partner: UNT College of Engineering
SemEval-2007 Task 14: Affective Text

SemEval-2007 Task 14: Affective Text

Date: June 2007
Creator: Strapparava, Carlo, 1962- & Mihalcea, Rada, 1974-
Description: This paper discusses affective text. Abstract: The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.
Contributing Partner: UNT College of Engineering
SemEval-2010 Task 2: Cross-Lingual Lexical Substitution

SemEval-2010 Task 2: Cross-Lingual Lexical Substitution

Date: July 2010
Creator: Mihalcea, Rada, 1974-; Sinha, Ravi & McCarthy, Diana
Description: This article describes the SemEval-2010 Cross-Lingual Lexical Substitution task.
Contributing Partner: UNT College of Engineering
A Semi-Complete Disambiguation Algorithm for Open Text

A Semi-Complete Disambiguation Algorithm for Open Text

Date: 2000
Creator: Mihalcea, Rada, 1974-
Description: This paper discusses a semi-complete disambiguation algorithm for open text.
Contributing Partner: UNT College of Engineering
Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

Date: October 26, 2010
Creator: Caragea, Cornelia; Caragea, Doina; Silvescu, Adrian & Honavar, Vasant
Description: Paper discussing developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data.
Contributing Partner: UNT College of Engineering
SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

Date: 2004
Creator: Mihalcea, Rada, 1974- & Faruque, Ehsanul
Description: This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.
Contributing Partner: UNT College of Engineering