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Computational Models for Incongruity Detection in Humour
In this paper, the authors explore several computational models for incongruity resolution.
Date:
March 2010
Creator:
Mihalcea, Rada, 1974-; Strapparava, Carlo, 1962- & Pulman, Stephen
Object Type:
Paper
System:
The UNT Digital Library
Text-to-text Semantic Similarity for Automatic Short Answer Grading
In this paper, the authors explore unsupervised techniques for the task of automatic short answer grading.
Date:
March 2009
Creator:
Mohler, Michael & Mihalcea, Rada, 1974-
Object Type:
Paper
System:
The UNT Digital Library
On the security of the Courtois-Finiasz-Sendrier signature
This article proves that a variant of the Courtois-Finiasz-Sendrier signature is strongly existentially unforgeable under chosen message attack in the random oracle model, assuming hardness of the Permuted Goppa Syndrome Decoding Problem (also known as the Niederreiter problem).
Date:
March 20, 2018
Creator:
Morozov, Kirill; Roy, Partha Sarathi; Steinwandt, Rainer & Xu, Rui
Object Type:
Article
System:
The UNT Digital Library
Impact of Mobility on Convergence Rate in a Wireless Sensor Network
This paper shows that introducing mobility increases the rate of convergence in partially connected and disconnected wireless sensor networks (WSN).
Date:
March 22, 2017
Creator:
Norouzi Kandalan, Roya; Singh, Ramanpreet; Namuduri, Kamesh; Varanasi, Murali R. & Buckles, Bill
Object Type:
Paper
System:
The UNT Digital Library
Linguistic Ethnography: Identifying Dominant Word Classes in Text
This paper discusses linguistic ethnography.
Date:
March 2009
Creator:
Pulman, Stephen & Mihalcea, Rada, 1974-
Object Type:
Paper
System:
The UNT Digital Library
Comparison of Machine Learning Algorithms for Identifying Cancer Types
Poster for the 2014 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Annual Conference. This poster discusses a comparison of machine learning algorithms for identifying cancer types.
Date:
March 2014
Creator:
Saxena, Garima; Helsing, Joseph; Reyes, Omar Costilla & Azad, Rajeev K.
Object Type:
Poster
System:
The UNT Digital Library
Learning to Identify Emotions in Text
This paper discusses learning to identify emotions in text.
Date:
March 2008
Creator:
Strapparava, Carlo, 1962- & Mihalcea, Rada, 1974-
Object Type:
Paper
System:
The UNT Digital Library
Detection of Parkinson's Disease Through Automated Pupil Tracking of the Post-illumination Pupillary Response
This article describes a system for pupil size estimation with a user interface to allow rapid adjustment of parameters and extraction of pupil parameters of interest in order to identify Parkinson's disease (PD) as early as possible.
Date:
March 25, 2021
Creator:
Tabashum, Thasina; Zaffer, Adnaan; Yousefzai, Raman; Colletta, Kalea; Jost, Mary Beth; Park, Youngsook et al.
Object Type:
Article
System:
The UNT Digital Library
Semantic Document Engineering with WordNet and PageRank
This article discusses semantic document engineering with WordNet and PageRank.
Date:
March 2005
Creator:
Tarau, Paul; Mihalcea, Rada, 1974- & Figa, Elizabeth
Object Type:
Paper
System:
The UNT Digital Library
Evaluation of the RDP Classifier Accuracy Using 16S rRNA Gene Variable Regions
This article uses full-length 16S rRNA gene alignments from the SILVA database to simulate the PCR products of the combined variable regions.
Date:
March 12, 2012
Creator:
Vilo, Claudia A. & Dong, Qunfeng
Object Type:
Article
System:
The UNT Digital Library
Research Experiences for Undergraduates Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach
Data management plan for the grant, "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach." This Research Experiences for Undergraduates (REU) Site Program at the University of North Texas will enhance the knowledge and research skills of a diverse cohort of undergraduate students through empowering, innovative, and interdisciplinary research experiences in developing Deep Learning applications and systems. The program aims to 1) expose undergraduate students to real-world and cutting-edge research focused on accelerated deep learning through combined hardware and software development; 2) encourage more undergraduate students to continue their academic careers and seek graduate degrees in computer science, computer engineering, and related disciplines; 3) develop research skills and improve communication and collaborative skills in undergraduate students.
Date:
2021-03-01/2024-02-29
Creator:
Zhao, Hui & Albert, Mark
Object Type:
Text
System:
The UNT Digital Library