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Conway, AR, United States

The University of Central Arkansas is a state-run institution located in the city of Conway, the seat of Faulkner County, north of Little Rock and is the fourth largest university by enrollment in the U.S. state of Arkansas, and the third largest college system in the state. The school is most respected for its programs in Education, Occupational Therapy, and Physical Therapy. It is also the home of the UCA Honors College as well as four Residential Colleges. Wikipedia.

McCullough G.H.,University of Central Arkansas | Kim Y.,Ohio University
Dysphagia

The Mendelsohn maneuver, voluntary prolongation of laryngeal elevation during the swallow, has been widely used as a compensatory strategy to improve upper esophageal sphincter (UES) opening and bolus flow. Recent research suggests that when used as a rehabilitative exercise, it significantly improves duration of hyoid movement and positively impacts duration of UES opening (DOUESO). The data presented here were derived from that same prospective crossover study of 18 participants with dysphagia post-stroke evaluated with videofluoroscopy after treatment using the Mendelsohn maneuver versus no treatment. Results demonstrate gains in the extent of hyoid movement and UES opening and improvements in coordination of structural movements with each other as well as with bolus flow. © 2013 Springer Science+Business Media New York. Source

Campbell B.,University of Central Arkansas
Journal of Sustainable Agriculture

In situ conservation refers to the perpetuation of genetic resources in their original cultural and biophysical habitats and to the diverse strategies employed to sustain crop genetic diversity and the cultural milieus that support and maintain it. This article reviews in situ agrobiodiversity conservation in the Arkansas Ozarks, in the United States over a four-year period (2006-2010) through applied ethnographic and agroecological research. I examine the (re)introduction of "seed swaps" in the Ozark region as an in situ conservation strategy to connect diverse Ozark inhabitants, (re)institute a dynamic flow of agroecological knowledge, and conserve agricultural biodiversity in the region. © 2012 Copyright Taylor and Francis Group, LLC. Source

Sheng V.S.,University of Central Arkansas
Proceedings - IEEE International Conference on Data Mining, ICDM

With the outsourcing of small tasks becoming easier, it is possible to obtain non-expert/imperfect labels at low cost. With low-cost imperfect labeling, it is straightforward to collect multiple labels for the same data items. This paper addresses the strategies of utilizing these multiple labels for improving the performance of supervised learning, based on two basic ideas: majority voting and pairwise solutions. We show several interesting results based on our experiments. The soft majority voting strategies can reduce the bias and roughness, and improve the performance of the directed hard majority voting strategy. Pairwise strategies can completely avoid the bias by having both sides (potential correct and incorrect/ noisy information) considered (for binary classification). They have very good performance whenever there are a few or many labels available. However, it could also keep the noise. The improved variation that reduces the impact of the noisy information is recommended. All five strategies investigated are labeling quality agnostic strategies, and can be applied to real world applications directly. The experimental results show some of them perform better than or at least very close to the gnostic strategies. © 2011 IEEE. Source

Voss D.,University of Central Arkansas
International Journal of Logistics Management

The ν-support vector machine (ν-SVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors and margin errors. Recently, an interesting accurate on-line algorithm accurate on-line ν -SVM algorithm (AONSVM) is proposed for training ν-SVM. AONSVM can be viewed as a special case of parametric quadratic programming techniques. It is demonstrated that AONSVM avoids the infeasible updating path as far as possible, and successfully converges to the optimal solution based on experimental analysis. However, because of the differences between AONSVM and classical parametric quadratic programming techniques, there is no theoretical justification for these conclusions. In this paper, we prove the feasibility and finite convergence of AONSVM under two assumptions. The main results of feasibility analysis include: 1) the inverses of the two key matrices in AONSVM always exist; 2) the rules for updating the two key inverse matrices are reliable; 3) the variable $\zeta$ can control the adjustment of the sum of all the weights efficiently; and 4) a sample cannot migrate back and forth in successive adjustment steps among the set of margin support vectors, the set of error support vectors, and the set of the remaining vectors. Moreover, the analyses of AONSVM also provide the proofs of the feasibility and finite convergence for accurate on-line $C$-SVM learning directly. © 2012 IEEE. Source