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Institution

University of North Carolina at Charlotte

EducationCharlotte, North Carolina, United States
About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.


Papers
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Journal ArticleDOI
TL;DR: In response to the growing concern of the marginalization of social studies education, members of the North Carolina Professors of Social Studies Education (NCPSSE) organization began a long-itudinal study in 2003 to examine elementary social education education as mentioned in this paper.
Abstract: In response to the growing concern of the marginalization of social studies education, members of the North Carolina Professors of Social Studies Education (NCPSSE) organization began a longtitudinal study in 2003 to examine elementary social studies education. This study is part of a statewide initiative among six universities in the North Carolina University system. The participating universities include: UNC Charlotte, UNC Asheville, Appalachian State University, A & T State University, UNC Wilmington, and East Carolina University. The purpose of this research was to gather data from practicing elementary teachers in North Carolina to identify (a) how often social studies is being taught, (b) how decisions are made regarding how instructional time is used, (c) how satisfied teachers are with the amount of instructional time devoted to social studies instruction, and (d) what barriers exist that might inhibit the teaching of the social studies curriculum. This study documents the current status...

126 citations

Journal ArticleDOI
TL;DR: The present findings suggest the need for additional research as it relates to the development and fostering of self-compassion as well as the potential clinical implications of using acceptance-based interventions for college-aged women currently engaging in or who are at risk for disordered eating patterns.

126 citations

Journal ArticleDOI
TL;DR: This paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions.
Abstract: With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

126 citations

Journal ArticleDOI
TL;DR: Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap, and empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.
Abstract: In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap The contribution of this paper is twofold It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (ie the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network

126 citations

Proceedings ArticleDOI
10 Oct 2004
TL;DR: This paper proposes a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components (salient objects) and the relevant semantic concepts to achieve automatic image annotation at the content level.
Abstract: Automatic image annotation is a promising solution to enable semantic image retrieval via keywords. In this paper, we propose a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components (salient objects) and the relevant semantic concepts. To achieve automatic image annotation at the content level, we use salient objects as the dominant image components for image content representation and feature extraction. To support automatic image annotation at the concept level, a novel image classification technique is developed to map the images into the most relevant semantic image concepts. In addition, Support Vector Machine (SVM) classifiers are used to learn the detection functions for the pre-defined salient objects and finite mixture models are used for semantic concept interpretation and modeling. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. We have also demonstrated that our algorithms are very effective to enable multi-level annotation of natural scenes in a large-scale image dataset.

126 citations


Authors

Showing all 8936 results

NameH-indexPapersCitations
Chao Zhang127311984711
E. Magnus Ohman12462268976
Staffan Kjelleberg11442544414
Kenneth L. Davis11362261120
David Wilson10275749388
Michael Bauer100105256841
David A. B. Miller9670238717
Ashutosh Chilkoti9541432241
Chi-Wang Shu9352956205
Gang Li9348668181
Tiefu Zhao9059336856
Juan Carlos García-Pagán9034825573
Denise C. Park8826733158
Santosh Kumar80119629391
Chen Chen7685324974
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202361
2022231
20211,470
20201,561
20191,489
20181,318