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.

Climate Change Denial Books and Conservative Think Tanks: Exploring the Connection

Abstract: The conservative movement and especially its think tanks play a critical role in denying the reality and significance of anthropogenic global warming (AGW), especially by manufacturing uncertainty over climate science. Books denying AGW are a crucial means of attacking climate science and scientists, and we examine the links between conservative think tanks (CTTs) and 108 climate change denial books published through 2010. We find a strong link, albeit noticeably weaker for the growing number of self-published denial books. We also examine the national origins of the books and the academic backgrounds of their authors or editors, finding that with the help of American CTTs climate change denial has spread to several other nations and that an increasing portion of denial books are produced by individuals with no scientific training. It appears that at least 90% of denial books do not undergo peer review, allowing authors or editors to recycle scientifically unfounded claims that are then amplified by the conservative movement, media, and political elites.

Motivational engagement and video gaming: a mixed methods study

Abstract: A mixed methods design was used to identify factors associated with motivational engagement in video gaming Self-report instruments were administered to 189 video game players to assess goal orientations, affect, need for cognition, and perceptions of engagement and flow Simultaneously, a sub-set of 25 participants were interviewed and results analyzed to identify patterns that influenced their propensity for gaming Regression results revealed motivational engagement for gaming was related to gender, hours of play, task orientation, and socialization Players indicated that gaming was socially captivating, fun, challenging but relaxing, and precipitated positive affect and cognition even when unsuccessful results were achieved The negative consequences normally associated with task failure were not reported by participants to take place during gaming We concluded transfer of motivational engagement in gaming for entertainment to educational contexts was unlikely to occur

Measuring Financial Condition: A Study of U.S. States

Abstract: This study tests a measure of financial condition using government-wide information as required under the new financial reporting model set forth in GASB Statement No. 34. The measure consists of four financial condition dimensions in cash, budget, long-run and service-level solvencies, and 11 financial condition indicators. Results show that the measure is relatively reliable and valid and that government-wide information reported under the requirements of GASB Statement No. 34 provides a useful reporting framework to evaluate financial condition of a government. Additionally, financial condition among states varies greatly and there is much room for improvement.

Acceleration of the EM Algorithm by using Quasi‐Newton Methods

Abstract: The EM algorithm is a popular method for maximum likelihood estimation. Its simplicity in many applications and desirable convergence properties make it very attractive. Its sometimes slow convergence, however, has prompted researchers to propose methods to accelerate it. We review these methods, classifying them into three groups: pure, hybrid and EM-type accelerators. We propose a new pure and a new hybrid accelerator both based on quasi-Newton methods and numerically compare these and two other quasi-Newton accelerators. For this we use examples in each of three areas: Poisson mixtures, the estimation of covariance from incomplete data and multivariate normal mixtures. In these comparisons, the new hybrid accelerator was fastest on most of the examples and often dramatically so. In some cases it accelerated the EM algorithm by factors of over 100. The new pure accelerator is very simple to implement and competed well with the other accelerators. It accelerated the EM algorithm in some cases by factors of over 50. To obtain standard errors, we propose to approximate the inverse of the observed information matrix by using auxiliary output from the new hybrid accelerator. A numerical evaluation of these approximations indicates that they may be useful at least for exploratory purposes.

Action Recognition in Realistic Sports Videos

Abstract: The ability to analyze the actions which occur in a video is essential for automatic understanding of sports. Action localization and recognition in videos are two main research topics in this context. In this chapter, we provide a detailed study of the prominent methods devised for these two tasks which yield superior results for sports videos. We adopt UCF Sports, which is a dataset of realistic sports videos collected from broadcast television channels, as our evaluation benchmark. First, we present an overview of UCF Sports along with comprehensive statistics of the techniques tested on this dataset as well as the evolution of their performance over time. To provide further details about the existing action recognition methods in this area, we decompose the action recognition framework into three main steps of feature extraction, dictionary learning to represent a video, and classification; we overview several successful techniques for each of these steps. We also overview the problem of spatio-temporal localization of actions and argue that, in general, it manifests a more challenging problem compared to action recognition. We study several recent methods for action localization which have shown promising results on sports videos. Finally, we discuss a number of forward-thinking insights drawn from overviewing the action recognition and localization methods. In particular, we argue that performing the recognition on temporally untrimmed videos and attempting to describe an action, instead of conducting a forced-choice classification, are essential for analyzing the human actions in a realistic environment.