Usability Construct for Mobile Applications: A Clustering based Approach

The growth of mobile applications that run on cell phones and other handheld devices has introduced a broad range of usability challenges that were not faced by the web and standalone PC environments. The current usability models for mobile applications are mostly based on the experience of the usability experts and users that were collected through surveys and field studies. Many usability researchers and practitioners have developed conceptual usability frameworks that utilize either different or overlapping usability attributes. Moreover, the usability frameworks in existence they are limited in scope and do not consider all the usability dimensions. There is no consensus among usability researchers and standard organizations regarding what constitutes a usability model or framework. This research attempts to utilize a novel, computational, linguistic approach in order to identify the semantic relatedness between different usability attributes. We use text-mining and information-extraction techniques to mine for usability attributes in a large collection of published literature about mobile usability. A hierarchical clustering analysis is performed to cluster semantically related usability attributes. The results are utilized to develop a usability taxonomy and a unified usability construct for mobile applications

Correlated lip motion and voice audio data

This data set is comprised of correlated audio and lip movement data in multiple videos of multiple subjects reading the same text. It was collected to facilitate the development and validation of algorithms used to train and test a compound biometric system that consists of lip-motion and voice recognition. The data set is a collection of videos of volunteers reciting a fixed script that is intended to be used to train software to recognize voice and lip-motion patterns. A second video is included of the individual reciting a shorter phrase, which is designed to be used to test the recognition functionality of the system. The recordings were collected in a controlled, indoor setting with a 4K professional-grade camcorder and adjustable, LED lights

P-tree Classification of Yeast Gene Deletion Data

Genomics data has many properties that make it different from "typical" relational data. The presence of multi-valued attributes as well as the large number of null values led us to a P-tree-based bit-vector representation in which matching 1-values were counted to evaluate similarity between genes. Quantitative information such as the number of interactions was also included in the classifier. Interaction information allowed us to extend the known properties of one protein with information on its interacting neighbors. Different feature attributes were weighted independently. Relevance of different attributes was systematically evaluated through optimization of weights using a genetic algorithm. The AROC value for the classified list was used as the fitness function for the genetic algorithm