Kyeong-An Kwon

Learn More
In affective user studies, visual interfacing of data has received little attention. Such interfaces can support qualitative understanding, conveying insight about static and temporally evolving information; static information is exemplified by demographic data, while temporally evolving information is exemplified by physiological signals. In this paper we(More)
Analyzing affective studies is challenging because they feature multimodal data, such as psychometric scores, imaging sequences, and signals from wearable sensors, with the latter streaming continuously for hours on end. Meaningful visual representations of such data can greatly facilitate insights and qualitative analysis. Various tools that were proposed(More)
A typical affective study generates a great amount of data including physiological, performance, and demographic data. Visual representation of such data in compact form is challenging. Moreover, no visualization guidelines are available specific to this domain. Here we introduce a set of design principles for visualizing results of affective studies.(More)
In this paper, we present a novel approach of using the integrated GPU to accelerate conventional operations that are normally performed by the CPUs, the bulk memory operations, such as memcpy or memset. Offloading the bulk memory operations to the GPU has many advantages, i) the throughput driven GPU outperforms the CPU on the bulk memory operations; ii)(More)
  • 1