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  Partner: UNT College of Engineering
 Decade: 2010-2019
Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
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.
Contributing Partner: UNT College of Engineering
Anchor Nodes Placement for Effective Passive Localization

Anchor Nodes Placement for Effective Passive Localization

Date: 2011
Creator: Akl, Robert G.; Pasupathy, Karthikeyan & Haidar, Mohamad
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.
Contributing Partner: UNT College of Engineering
Virtualization Based Secure Execution And Testing Framework

Virtualization Based Secure Execution And Testing Framework

Date: December 2011
Creator: Kotikela, Srujan Das; Nimgaonkar, Satyajeet & Gomathisankaran, Mahadevan
Description: This article discusses virtualization based secure execution and testing.
Contributing Partner: UNT College of Engineering
Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

Date: May 28, 2010
Creator: Amthauer, Heather A. & Tsatsoulis, C. (Costas), 1962-
Description: This article discusses classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning. This provides insight into a gene's functionality in the eukaryotic genome.
Contributing Partner: UNT College of Engineering
Hybrid Approach for Energy-Aware Synchronization

Hybrid Approach for Energy-Aware Synchronization

Date: December 2010
Creator: Akl, Robert G.; Saravanos, Yanos & Haidar, Mohamad
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. It focuses on aspects of wireless sensor networks. These include designing a hybrid method between reference broadcast synchronization (RBS) and timing-sync protocol for sensor networks (TPSN) to reduce the number of transmissions required to synchronize an entire network, extending single-hop synchronization methods to operate in large multi-hop networks, verifying that the hybrid methods operate as desired by simulating against RBS and TPSN, and maintaining network connectivity and coverage.
Contributing Partner: UNT College of Engineering
Evaluation Results of an E and ET Education Forum

Evaluation Results of an E and ET Education Forum

Date: 2011
Creator: Ramos, Miguel; Chapman, Lauren; Cannady, Mac & Barbieri, Enrique
Description: This article discusses evaluation results of an Engineering (E) and Engineering Technology (ET) education forum at the University of Houston. A central focus to these discussions revolved around whether Engineering and Engineering Technology exist as separate fields or whether there was value in thinking about them as part of a continuum.
Contributing Partner: UNT College of Engineering
Computational Models for Incongruity Detection in Humour

Computational Models for Incongruity Detection in Humour

Date: March 2010
Creator: Mihalcea, Rada, 1974-; Strapparava, Carlo, 1962- & Pulman, Stephen
Description: This paper discusses computational models for incongruity resolution. Abstract: Incongruity resolution is one of the most widely accepted theories of humor, suggesting that humor is due to the mixing of two disparate interpretation frames in one statement. In this paper, the authors explore several computational models for incongruity resolution. The authors introduce a new data set, consisting of a series of 'set-ups' (preparations for a punch line), each of them followed by four possible coherent continuations out of which only one has a comic effect. Using this data set, the authors redefine the task as the automatic identification of the humorous punch line among all the plausible endings. The authors explore several measures of semantic relatedness, along with a number of joke-specific features, and try to understand their appropriateness as computational models for incongruity detection.
Contributing Partner: UNT College of Engineering
Quantifying the Limits and Success of Extractive Summarization Systems Across Domains

Quantifying the Limits and Success of Extractive Summarization Systems Across Domains

Date: June 2010
Creator: Ceylan, Hakan; Mihalcea, Rada, 1974-; Ozertem, Umut; Lloret, Elena & Palomar, Manuel
Description: This paper analyzes the topic identification stage of single-document automatic text summarization across four different domains, consisting of newswire, literary, scientific and legal documents. The authors present a study that explores the summary space of each domain via an exhaustive search strategy, and finds the probability density function (pdf) of the ROUGE score distributions for each domain. The authors then use this pdf to calculate the percentile rank of extractive summarization systems. Their results introduce a new way to judge the success of automatic summarization systems and bring quantified explanations to questions such as why it was so hard for the systems to date to have a statistically significant improvement over the lead baseline in the news domain.
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
Specialized Research Datasets in the CiteSeer˟ Digital Library

Specialized Research Datasets in the CiteSeer˟ Digital Library

Date: 2012
Creator: Bhatia, Sumit; Caragea, Cornelia; Chen, Hung-Hsuan; Wu, Jian; Treeratpituk, Pucktada; Wu, Zhaohui et al.
Description: This article discusses specialized research datasets in the CiteSeer˟ digital library. Abstract: We provide an overview of some of the specialized datasets that were created for various projects related to the CiteSeer˟ digital library. These datasets are not those usually available from CiteSeer˟ and awareness of these datasets may further advance state-of-the-art research in academic digital library data management and analysis.
Contributing Partner: UNT College of Engineering
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