Contribution to Quality of Life: A New Outcome Variable for Mobile Data Service

The rapid spread of technological innovations like mobile data services (MDS) has made mobile computing a fact of everyday life for many people. Therefore, we need to understand the contribution of mobile computing to overall quality of life (QoL). Employing the satisfaction hierarchy model and bottom-up spillover theory, this study proposes a theoretical model in the context of MDS that connects user satisfaction (a traditional outcome variable of IT) with contribution to QoL (a new outcome variable for mobile computing) in a range of life domains. The validity of the proposed model and outcome variable was tested through three empirical studies conducted in Korea. User satisfaction with MDS was found to affect the contribution of MDS to QoL in eleven life domains, and these contributions in turn influenced the overall contribution of MDS to QoL. The paper ends with a discussion of the study\u27s implications and limitations

Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement

Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth localization network that effectively disentangles each individual tooth based on a Gaussian disentanglement objective function. The proposed network first performs heatmap regression accompanied by box regression for all the anatomical teeth. A novel Gaussian disentanglement penalty is employed by minimizing the sum of the pixel-wise multiplication of the heatmaps for all adjacent teeth pairs. Subsequently, individual tooth segmentation is performed by converting a pixel-wise labeling task to a distance map regression task to minimize false positives in adjacent regions of the teeth. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%, which results in a high performance in terms of individual tooth segmentation. The primary significance of the proposed method is two-fold: 1) the introduction of a point-based tooth detection framework that does not require additional classification and 2) the design of a novel loss function that effectively separates Gaussian distributions based on heatmap responses in the point-based detection framework.Comment: 11 pages, 7 figure

Contributing to Quality of Life: A New Outcome Variable for Information Technology in Ubiquitous Computing Environments

The rapid spread of technological innovations like mobile data services (MDS) has made ubiquitous computing a fact of everyday life for many people. We need therefore to understand the contribution of ubiquitous computing to overall quality of life. This study proposes a theoretical model that connects user satisfaction (a traditional outcome variable of IT) with contributions to quality of life (a new outcome variable for ubiquitous computing) in the domain of MDS. The reliability of the outcome variables and the validity of the proposed model were tested through three empirical studies in Korea. Study results indicate that user satisfaction with MDS affected the contribution of MDS to quality of life in eleven subordinate domains, and these contributions in turn influenced the overall contribution of MDS to quality of life. The paper ends with a discussion of the implications and limitations of the study results