We present a study examining the impact of physical and cognitive challenge on reported immersion for a mixed reality game called Beach Pong. Contrary to prior findings for desktop games, we find significantly higher reported immersion among players who engage physically, regardless of their actual game performance. Building a mental map of the real, virtual, and sensed world is a cognitive challenge for novices, and this appears to influence immersion: in our study, participants who actively attended to both physical and virtual game elements reported higher immersion levels than those who attended mainly or exclusively to virtual elements. Without an integrated mental map, in-game cognitive challenges were ignored or offloaded to motor response when possible in order to achieve the minimum required goals of the game. From our results we propose a model of immersion in mixed reality gaming that is useful for designers and researchers in this space.
ABSTARCTFeature selection is an important technique for identifying informative genes in microarray datasets. In order to select small subset of informative genes from the large datasets various evolutionary methods have been used. However, because of the small number of samples compared to the huge number of genes many of the computational methods face difficulties to select the small subset. This paper proposes a modified PSO algorithm, Minimized Feature Space (MFS) Particle Swarm Optimization to optimize feature selection. In the modified PSO approach we propose a new method which controls a particle's movement towards the best solution. The proposed approach is applied on leukemia, colon and lung cancer benchmark datasets and experimental analysis show good performance.
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