Kent State University

About: Kent State University is a education organization based out in Kent, Ohio, United States. It is known for research contribution in the topics: Liquid crystal & Population. The organization has 10897 authors who have published 24607 publications receiving 720309 citations. The organization is also known as: Kent State & KSU.

Distance-constraint reachability computation in uncertain graphs

Abstract: Driven by the emerging network applications, querying and mining uncertain graphs has become increasingly important. In this paper, we investigate a fundamental problem concerning uncertain graphs, which we call the distance-constraint reachability (DCR) problem: Given two vertices s and t, what is the probability that the distance from s to t is less than or equal to a user-defined threshold d in the uncertain graph? Since this problem is #P-Complete, we focus on efficiently and accurately approximating DCR online. Our main results include two new estimators for the probabilistic reachability. One is a Horvitz-Thomson type estimator based on the unequal probabilistic sampling scheme, and the other is a novel recursive sampling estimator, which effectively combines a deterministic recursive computational procedure with a sampling process to boost the estimation accuracy. Both estimators can produce much smaller variance than the direct sampling estimator, which considers each trial to be either 1 or 0. We also present methods to make these estimators more computationally efficient. The comprehensive experiment evaluation on both real and synthetic datasets demonstrates the efficiency and accuracy of our new estimators.

Uses and Effects of Mobile Computing Devices in K–8 Classrooms

Abstract: This preliminary study employed mixed methodologies to explore students’ use of mobile computing devices and its effects on their motivation to learn, engagement in learning activities, and support for learning processes. Data collected from students in four elementary and two seventh grade science classes in Northeast Ohio included usage logs, student work samples, student and teacher interviews, and classroom observations. Findings highlight the personalization of learning afforded by such devices both in terms of individuals and individual classroom cultures, as well as their usefulness in extending learning beyond the classroom. They also suggest that increased motivation due to mobile device use leads to increases in the quality and quantity of student work.

Old and new parameter choice rules for discrete ill-posed problems

Abstract: Linear discrete ill-posed problems are difficult to solve numerically because their solution is very sensitive to perturbations, which may stem from errors in the data and from round-off errors introduced during the solution process. The computation of a meaningful approximate solution requires that the given problem be replaced by a nearby problem that is less sensitive to disturbances. This replacement is known as regularization. A regularization parameter determines how much the regularized problem differs from the original one. The proper choice of this parameter is important for the quality of the computed solution. This paper studies the performance of known and new approaches to choosing a suitable value of the regularization parameter for the truncated singular value decomposition method and for the LSQR iterative Krylov subspace method in the situation when no accurate estimate of the norm of the error in the data is available. The regularization parameter choice rules considered include several L-curve methods, Reginska's method and a modification thereof, extrapolation methods, the quasi-optimality criterion, rules designed for use with LSQR, as well as hybrid methods.