Attention-Based Dense Point Cloud Reconstruction From a Single Image (open access)

Attention-Based Dense Point Cloud Reconstruction From a Single Image

Article proposes a two-stage training dense point cloud generation network.
Date: September 23, 2019
Creator: Lu, Qiang; Xiao, Mingjie; Lu, Yiyang; Yuan, Xiaohui & Yu, Ye
Object Type: Article
System: The UNT Digital Library
Multi-Objective Response to Co-Resident Attacks in Cloud Environment (open access)

Multi-Objective Response to Co-Resident Attacks in Cloud Environment

This article introduces a novel multi-objective attack response system.
Date: September 17, 2017
Creator: Abazari, Farzaneh; Analoui, Morteza & Takabi, Hassan
Object Type: Article
System: The UNT Digital Library
Modelling HIV Intervention among Most-at-Risk/Key Population: Case Study of FWSS in Nigeria (open access)

Modelling HIV Intervention among Most-at-Risk/Key Population: Case Study of FWSS in Nigeria

This article discusses a novel risk equation for estimating new infections among Females who Sell Sex (FWSS), their clients, and communities.
Date: September 28, 2017
Creator: Akwafuo, Sampson; Shattock, Andrew & Mikler, Armin R.
Object Type: Article
System: The UNT Digital Library
Infusing NLU into Automatic Question Generation (open access)

Infusing NLU into Automatic Question Generation

This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.
Date: September 2016
Creator: Mazidi, Karen & Tarau, Paul
Object Type: Paper
System: The UNT Digital Library
Co-Training for Topic Classification of Scholarly Data (open access)

Co-Training for Topic Classification of Scholarly Data

This paper describes a co-training approach that uses the text and citation information of a research article as two different views to predict the topic of an article.
Date: September 2015
Creator: Caragea, Cornelia; Bulgarov, Florin & Mihalcea, Rada, 1974-
Object Type: Paper
System: The UNT Digital Library
Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data (open access)

Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data

This paper presents an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications.
Date: September 2017
Creator: Nielsen, Rodney D. & Parde, Natalie
Object Type: Paper
System: The UNT Digital Library