This study seeks to develop and test a theory-driven model of technology addiction within the context of smartphone use. Drawing on incentive-sensitisation theory, we proposed a nomological network that centres on a second-order factor of smartphone addiction from a psychological perspective. We empirically evaluated the proposed model against longitudinal data collected from 441 smartphone users. Our analysis from structural equation modelling indicates that our two-factor measurement of smartphone addiction (heightened desire and mobile social interaction) is a parsimonious alternative to the four-factor model. In addition, our findings suggest that addiction and its negative consequences differ in their effects on subsequent perceptions, such as perceived usefulness and perceived enjoyment. We also found that personal innovativeness in information technology and a perception of loneliness explain a large amount of variation in smartphone addiction. Overall, the present study significantly contributes to the information systems literature by offering a refined model of smartphone addiction that effectively describes pathological smartphone use while correcting critical biases in existing models.

The goal of the IEEE 2018 FEMH Voice Data Challenge was to develop an effective algorithmic approach to classifying voice samples as normal or pathological, and further subdivide the pathological samples into three types. We adopted a multi-representation ensemble approach to the task. We designed a pipeline with three classification stages, where each stage used a combination of supervised, semi-supervised and multiple-instance learners. This approach was able to achieve a sensitivity of 89% and specificity of 76% in classifying normal from pathological samples and an unweighted average recall (UAR) of 60.67% in subclassifying pathological samples into three types.

An interesting observation about the well-known AdaBoost algorithm is that, though theory suggests it should overfit when applied to noisy data, experiments indicate it often does not do so in practice. In this paper, we study the behavior of AdaBoost on datasets with one-sided uniform class noise using linear classifiers as the base learner. We show analytically that, under some ideal conditions, this approach will not overfit, and can in fact recover a zero-error concept with respect to the true, uncorrupted instance labels. We also analytically show that AdaBoost increases the margins of predictions over boosting iterations, as has been previously suggested in the literature. We then compare the empirical behavior of AdaBoost using real world datasets with one-sided noise derived from multiple-instance data. Although our assumptions may not hold in a practical setting, our experiments show that standard AdaBoost still performs well, as suggested by our analysis, and often outperforms baseline variations in the literature that explicitly try to account for noise.