University of Central Florida

About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.

Task scheduling algorithms for heterogeneous processors

Abstract: Scheduling computation tasks on processors is the key issue for high-performance computing. Although a large number of scheduling heuristics have been presented in the literature, most of them target only homogeneous resources. The existing algorithms for heterogeneous domains are not generally efficient because of their high complexity and/or the quality of the results. We present two low-complexity efficient heuristics, the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm for scheduling directed acyclic weighted task graphs (DAGs) on a bounded number of heterogeneous processors. We compared the performances of these algorithms against three previously proposed heuristics. The comparison study showed that our algorithms outperform previous approaches in terms of performance (schedule length ratio and speedup) and cost (time-complexity).

Leadership training design, delivery, and implementation: A meta-analysis.

Abstract: Recent estimates suggest that although a majority of funds in organizational training budgets tend to be allocated to leadership training (Ho, 2016; O'Leonard, 2014), only a small minority of organizations believe their leadership training programs are highly effective (Schwartz, Bersin, & Pelster, 2014), calling into question the effectiveness of current leadership development initiatives. To help address this issue, this meta-analysis estimates the extent to which leadership training is effective and identifies the conditions under which these programs are most effective. In doing so, we estimate the effectiveness of leadership training across four criteria (reactions, learning, transfer, and results; Kirkpatrick, 1959) using only employee data and we examine 15 moderators of training design and delivery to determine which elements are associated with the most effective leadership training interventions. Data from 335 independent samples suggest that leadership training is substantially more effective than previously thought, leading to improvements in reactions (δ = .63), learning (δ = .73), transfer (δ = .82), and results (δ = .72), the strength of these effects differs based on various design, delivery, and implementation characteristics. Moderator analyses support the use of needs analysis, feedback, multiple delivery methods (especially practice), spaced training sessions, a location that is on-site, and face-to-face delivery that is not self-administered. Results also suggest that the content of training, attendance policy, and duration influence the effectiveness of the training program. Practical implications for training development and theoretical implications for leadership and training literatures are discussed. (PsycINFO Database Record

Intelligent Agent Transparency in Human-Agent Teaming for Multi-UxV Management.

Abstract: Objective:We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human–agent teaming for multirobot management.Background:Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans—a top recommendation and a secondary recommendation—for every mission.Method:A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander’s intent and intelligence). Operator performance, trust, workload, and usability data were collected.Results:Results indicate that operator performance, trust, and perce...

Molecular adsorption on graphene

Abstract: Current studies addressing the engineering of charge carrier concentration and the electronic band gap in epitaxial graphene using molecular adsorbates are reviewed. The focus here is on interactions between the graphene surface and the adsorbed molecules, including small gas molecules (H(2)O, H(2), O(2), CO, NO(2), NO, and NH(3)), aromatic, and non-aromatic molecules (F4-TCNQ, PTCDA, TPA, Na-NH(2), An-CH(3), An-Br, Poly (ethylene imine) (PEI), and diazonium salts), and various biomolecules such as peptides, DNA fragments, and other derivatives. This is followed by a discussion on graphene-based gas sensor concepts. In reviewing the studies of the effects of molecular adsorption on graphene, it is evident that the strong manipulation of graphene's electronic structure, including p- and n-doping, is not only possible with molecular adsorbates, but that this approach appears to be superior compared to these exploiting edge effects, local defects, or strain. However, graphene-based gas sensors, albeit feasible because huge adsorbate-induced variations in the relative conductivity are possible, generally suffer from the lack of chemical selectivity.