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.

Robust quantification of polymerase chain reactions using global fitting.

Abstract: Background Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismissed. Numerous analytical methods have been employed that can extract the relative template abundance between samples. However, each method is sensitive to baseline assignment and to the unique shape profiles of individual reactions, which gives rise to increased variance stemming from the analytical procedure itself.

Spatiotemporal Deformable Part Models for Action Detection

Abstract: Deformable part models have achieved impressive performance for object detection, even on difficult image datasets. This paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video. Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples. For each action model, the most discriminative 3D sub volumes are automatically selected as parts and the spatiotemporal relations between their locations are learned. By focusing on the most distinctive parts of each action, our models adapt to intra-class variation and show robustness to clutter. Extensive experiments on several video datasets demonstrate the strength of spatiotemporal DPMs for classifying and localizing actions.

Toward a Taxonomy Linking Game Attributes to Learning: An Empirical Study

Abstract: The serious games community is moving toward research focusing on direct comparisons between learning outcomes of serious games and those of more traditional training methods. Such comparisons are difficult, however, due to the lack of a consistent taxonomy of game attributes for serious games. Without a clear understanding of what truly constitutes a game, scientific inquiry will continue to reveal inconsistent findings, making it hard to provide practitioners with guidance as to the most important attribute(s) for desired training outcomes. This article presents a game attribute taxonomy derived from a comprehensive literature review and subsequent card sorts performed by subject matter experts (SMEs). The categories of serious game attributes that emerged represent the shared mental models of game SMEs and serve to provide a comprehensive collection of game attributes. In order to guide future serious games research, the existing literature base is organized around the framework of this taxonomy.