University of North Carolina at Charlotte

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.

Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers

Abstract: The performance of image classifiers largely depends on two inter-related issues:(1)suitable frameworks for image content representation and automatic feature extraction;(2) effective algorithms for image classifier training and feature subset selection. To address the first issue, a multiresolution grid-based framework is proposed for image content representation and feature extraction to bypass the time-consuming and erroneous process for image segmentation. To address the second issue, a hierarchical boosting algorithm is proposed by incorporating feature hierarchy and boosting to scale up SVM image classifier training in high-dimensional feature space. The high-dimensional multi-modal heterogeneous visual features are partitioned into multiple low-dimensional single-modal homogeneous feature subsets and each of them characterizes certain visual property of images. For each homogeneous feature subset, principal component analysis (PCA)is performed to exploit the feature correlations and a weak classifier is learned simultaneously. After the weak classifiers for different feature subsets and grid sizes are available, they are combined to boost an optimal classifier for the given object class or image concept, and the most representative feature subsets and grid sizes are selected. Our experiments on a specific domain of natural images have obtained very positive results.

Using Randomized Response for Differential Privacy Preserving Data Collection

Abstract: This paper studies how to enforce differential privacy by using the randomized response in the data collection scenario. Given a client’s value, the randomized algorithm executed by the client reports to the untrusted server a perturbed value. The use of randomized response in surveys enables easy estimations of accurate population statistics while preserving the privacy of the individual respondents. We compare the randomized response with the standard Laplace mechanism which is based on query-output independent adding of Laplace noise. Our research starts from the simple case with one single binary attribute and extends to the general case with multiple polychotomous attributes. We measure utility preservation in terms of the mean squared error of the estimate for various calculations including individual value estimate, proportion estimate, and various derived statistics. We theoretically derive the explicit formula of the mean squared error of various derived statistics based on the randomized response theory and prove the randomized response outperforms the Laplace mechanism. We evaluate our algorithms on YesiWell database including sensitive biomarker data and social network relationships of patients. Empirical evaluation results show effectiveness of our proposed techniques. Especially the use of the randomized response for collecting data incurs fewer utility loss than the output perturbation when the sensitivity of functions is high.

Right-Lateralized Pain Processing in the Human Cortex: An fMRI Study

Abstract: Neuroimaging studies of human pain have revealed a widespread “pain matrix” distributed across both hemispheres of the brain. It is not resolved whether the pain matrix is biased toward one hemisph...

Conservation opportunities across the world's anthromes

Abstract: Aim: Biologists increasingly recognize the roles of humans in ecosystems. Subsequently, many have argued that biodiversity conservation must be extended to environments that humans have shaped directly. Yet popular biogeographical frameworks such as biomes do not incorporate human land use, limiting their relevance to future conservation planning. 'Anthromes' map global ecological patterns created by sustained direct human interactions with ecosystems. In this paper, we set to understand how current conservation efforts are distributed across anthromes. Location: Global. Methods: We analysed the global distribution of IUCN protected areas and biodiversity hotspots by anthrome. We related this information to density of native plant species and density of previous ecological studies. Potential conservation opportunities in anthromes were then identified through global analysis and two case studies. Results: Protected areas and biodiversity hotspots are not distributed equally across anthromes. Less populated anthromes contain a greater proportion of protected areas. The fewest hotspots are found within densely settled anthromes and wildlands, which occur at the two extremes of human population density. Opportunities for representative protection, prioritization, study and inclusion of native species were not congruent. Main conclusions: Researchers and practitioners can use the anthromes framework to analyse the distribution of conservation practices at the global and regional scale. Like biomes, anthromes could also be used to set future conservation priorities. Conservation goals in areas directly shaped by humans need not be less ambitious than those in 'natural areas'.

A Description and Demonstration of the Polytomous DFIT Framework.

Abstract: Raju, van der Linden, & Fleer (1995) proposed an item response theory based, parametric differential item functioning (DIF) and differential test functioning (DTF) procedure known as differential functioning of items and tests (DFIT). According to Raju et al., the DFIT framework can be used with unidimensional and multidimensional data that are scored dichotomously and/or polytomously. This study examined the polytomous-DFIT framework. Factors manipulated in the simulation were: (1) length of test (20 and 40 items), (2) focal group distribution, (3) number of DIF items, (4) direction of DIF, and (5) type of DIF. The findings provided promising results and indicated directions for future research. The polytomous DFIT framework was effective in identifying DTF and DIF for the simulated conditions. The DTF index did not perform as consistently as the DIF index. The findings are similar to those of unidimensional and multidimensional DFIT studies.