Nottingham Trent University

About: Nottingham Trent University is a education organization based out in Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 4702 authors who have published 12862 publications receiving 307430 citations. The organization is also known as: NTU & Trent Polytechnic.

Demographic factors and playing variables in online computer gaming

Abstract: Despite the growing popularity of online game playing, there has been no primary survey of its players. Therefore, an online questionnaire survey was used to examine basic demographic factors of online computer game players who played the popular online game Everquest (i.e., gender, age, marital status, nationality, education level, occupation). The survey also examined playing frequency (i.e., amount of time spent playing the game a week), playing history (i.e., how long they had been playing the game, who they played the game with, whether they had ever gender swapped their game character), the favorite and least favorite aspects of playing the game, and what they sacrifice (if anything) to play the game. Results showed that 81% of online game players were male, and that the mean age of players was 27.9 years of age. For many players, the social aspects of the game were the most important factor in playing. A small minority of players appear to play excessively (over 80 h a week), and results suggest th...

Granular prototyping in fuzzy clustering

Abstract: We introduce a logic-driven clustering in which prototypes are formed and evaluated in a sequential manner. The way of revealing a structure in data is realized by maximizing a certain performance index (objective function) that takes into consideration an overall level of matching (to be maximized) and a similarity level between the prototypes (the component to be minimized). The prototypes identified in the process come with the optimal weight vector that serves to indicate the significance of the individual features (coordinates) in the data grouping represented by the prototype. Since the topologies of these groupings are in general quite diverse the optimal weight vectors are reflecting the anisotropy of the feature space, i.e., they show some local ranking of features in the data space. Having found the prototypes we consider an inverse similarity problem and show how the relevance of the prototypes translates into their granularity.

Standardized or simple effect size: what should be reported?

Abstract: It is regarded as best practice for psychologists to report effect size when disseminating quantitative research findings. Reporting of effect size in the psychological literature is patchy – though this may be changing – and when reported it is far from clear that appropriate effect size statistics are employed. This paper considers the practice of reporting point estimates of standardized effect size and explores factors such as reliability, range restriction and differences in design that distort standardized effect size unless suitable corrections are employed. For most purposes simple (unstandardized) effect size is more robust and versatile than standardized effect size. Guidelines for deciding what effect size metric to use and how to report it are outlined. Foremost among these are: i) a preference for simple effect size over standardized effect size, and ii) the use of confidence intervals to indicate a plausible range of values the effect might take. Deciding on the appropriate effect size statistic to report always requires careful thought and should be influenced by the goals of the researcher, the context of the research and the potential needs of readers.

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Abstract: Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.