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.
Sheng V.S.,University of Central Arkansas
Proceedings - IEEE International Conference on Data Mining, ICDM | Year: 2011
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.
Voss D.,University of Central Arkansas
International Journal of Logistics Management | Year: 2013
Purpose - The purpose of this paper is to explore the differences in preferred supplier choice criteria between food purchasing agents who focus on supplier security and those that do not. Specifically, this research determines the relationship between purchasing agents' supplier security preferences and their preferences for product quality, delivery reliability, price, and supplier location. The influence of international sourcing on demand for increased supplier security is also explored. Design/methodology/approach - Choice-based conjoint analysis with hierarchical Bayes (HB) estimation and
Gu B.,Nanjing University of Information Science and Technology |
Sheng V.S.,University of Central Arkansas
IEEE Transactions on Neural Networks and Learning Systems | Year: 2013
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.
Entrekin S.,University of Central Arkansas |
Evans-White M.,University of Arkansas |
Johnson B.,U.S. Environmental Protection Agency |
Frontiers in Ecology and the Environment | Year: 2011
Extraction of natural gas from hard-to-reach reservoirs has expanded around the world and poses multiple environmental threats to surface waters. Improved drilling and extraction technology used to access low permeability natural gas requires millions of liters of water and a suite of chemicals that may be toxic to aquatic biota. There is growing concern among the scientific community and the general public that rapid and extensive natural gas development in the US could lead to degradation of natural resources. Gas wells are often close to surface waters that could be impacted by elevated sediment runoff from pipelines and roads, alteration of streamflow as a result of water extraction, and contamination from introduced chemicals or the resulting wastewater. However, the data required to fully understand these potential threats are currently lacking. Scientists therefore need to study the changes in ecosystem structure and function caused by natural gas extraction and to use such data to inform sound environmental policy. © The Ecological Society of America.
New N.,University of Central Arkansas
Journal of the American Academy of Nurse Practitioners | Year: 2010
Purpose: To describe the development and outcomes of a co-created diabetes self-management education (DSME) intervention that resulted in statistically significant (. p = .02) improvements in diabetes self-care activities when compared to outcomes of a typical DSME program.Data sources: In this pilot study, an experimental self-management intervention with a self-selected group of adults with type 2 diabetes mellitus was held in the southern United States. Focus group results were used to develop the intervention and DSME participants co-created the sessions. Pre- and postintervention outcomes were compared to those of participants in DSME programs at two area, certified diabetes education centers.Conclusions: No significant differences were found between the experimental and control groups with regard to knowledge, adaptation, and program satisfaction. However, diabetes self-care activities significantly improved (. p = .02) for the experimental group.Implications for practice: A co-created teaching approach better meets the learning needs of adults with type 2 diabetes mellitus and results in enhanced ability to perform the self-care activities required for successful diabetes control. Better diabetes control reduces visits to monitor and treat complications and the need for repetitive educational sessions that exceed third-party pay limits and extend the time needed for patient encounters. © 2010 The Author Journal compilation © 2010 American Academy of Nurse Practitioners.