University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

A unified model of knowledge sharing behaviours: theoretical development and empirical test

Abstract: Research and practice on knowledge management KM have shown that information technology alone cannot guarantee that employees will volunteer and share knowledge. While previous studies have linked motivational factors to knowledge sharing KS, we took a further step to thoroughly examine this theoretically and empirically. We developed a unified model that is comprehensive and yet parsimonious, based on the decomposed theory of planned behaviour DTPB with three sets of critical antecedents: psychological, organisational and technological that are theorised to influence KS behaviours. Results of a field survey of knowledge workers support the majority of hypothesised relationships, and explained 41.3% of the variance in the actual KS behaviours and 60.8% of the variance in the intention to share knowledge. These results far exceed the predictive powers achieved by previous studies. Among our significant findings include a strong positive influence of perceived enjoyment in helping others PEH and a strong negative influence of perceived loss of knowledge power PLK. Based on the findings, we discussed the study's implications for research and practice.

Sensitivity and stability of efficiency classifications in data envelopment analysis

Abstract: A new technique for assessing the sensitivity and stability of efficiency classifications in Data Envelopment Analysis (DEA) is presented. Here developed for the ratio (CCR) model, this technique extends easily to other DEA variants. An organization's input-outut vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. For the l 1, l ∞ and generalized l ∞ norms, the radius of the maximal cell can be computed using linear programming formulations. This radius can be interpreted as a measure of the classification's stability, especially with respect to errors in the data.

Upending the uncanny valley

Abstract: Although robotics researchers commonly contend that robots should not look too humanlike, many artforms have successfully depicted people and have come to be accepted as great and important works, with examples such as Rodin's Thinker, Mary Cassat's infants, and Disney's Abe Lincoln simulacrum. Extending this tradition to intelligent robotics, the authors have depicted late sci-fi writer Philip K Dick with an autonomous, intelligent android. In doing so, the authors aspire to bring robotic systems up to the level of great art, while using the technology as a mirror for examining human nature in social AI development and cognitive science experiments.

Exercise-Enhanced Sequential Modeling for Student Performance Prediction

Abstract: In online education systems, for offering proactive services to students (e.g., personalized exercise recommendation), a crucial demand is to predict student performance (e.g., scores) on future exercising activities. Existing prediction methods mainly exploit the historical exercising records of students, where each exercise is usually represented as the manually labeled knowledge concepts, and the richer information contained in the text description of exercises is still underexplored. In this paper, we propose a novel Exercise-Enhanced Recurrent Neural Network (EERNN) framework for student performance prediction by taking full advantage of both student exercising records and the text of each exercise. Specifically, for modeling the student exercising process, we first design a bidirectional LSTM to learn each exercise representation from its text description without any expertise and information loss. Then, we propose a new LSTM architecture to trace student states (i.e., knowledge states) in their sequential exercising process with the combination of exercise representations. For making final predictions, we design two strategies under EERNN, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Extensive experiments on large-scale real-world data clearly demonstrate the effectiveness of EERNN framework. Moreover, by incorporating the exercise correlations, EERNN can well deal with the cold start problems from both student and exercise perspectives.

Measurement of the transverse momentum and phi(eta)*. distributions of Drell-Yan lepton pairs in proton-proton collisions at root s=8 TeV with the ATLAS detector

Abstract: Distributions of transverse momentum [Formula: see text] and the related angular variable [Formula: see text] of DrellΓCoYan lepton pairs are measured in 20.3┬afb[Formula: see text] of protonΓCoproton collisions at [Formula: see text]┬aTeV with the ATLAS detector at the LHC. Measurements in electron-pair and muon-pair final states are corrected for detector effects and combined. Compared to previous measurements in protonΓCoproton collisions at [Formula: see text]┬aTeV, these new measurements benefit from a larger data sample and improved control of systematic uncertainties. Measurements are performed in bins of lepton-pair mass above, around and below the Z-boson mass peak. The data are compared to predictions from perturbative and resummed QCD calculations. For values of [Formula: see text] the predictions from the Monte Carlo generator ResBos are generally consistent with the data within the theoretical uncertainties. However, at larger values of [Formula: see text] this is not the case. Monte Carlo generators based on the parton-shower approach are unable to describe the data over the full range of [Formula: see text] while the fixed-order prediction of Dynnlo falls below the data at high values of [Formula: see text]. ResBos and the parton-shower Monte Carlo generators provide a much better description of the evolution of the [Formula: see text] and [Formula: see text] distributions as a function of lepton-pair mass and rapidity than the basic shape of the data.