Abstract
The moment immediately before the "add to cart" decision is very critical in online shopping. Drawing on theories of transfer, spreading activation and human-computer interaction, the superiority of markerless Augmented Reality (AR) and Marker-based augmented reality (M) over Conventional Interactive (CI) is hypothesized. Although those multimedia tools are not part of the product/brand motivating the consumer interest they interfere in the interactive performance of the ecommerce. 150 consumers in a lab experiment showed higher emotional response, interactive response and brand evaluation in M and AR than CI. Contrary to what was expected the usability results were the inverse. That is, usability of CI outperforms M and AR. Considering only AR and M interfaces their effect on psychological variables was not statistically significant. A sophisticated or a simple interface had no impact on intention to buy the target brand, but the brand recommendation improved from M to AR. The differing effect of those three interface systems was mediated by brand familiarity, perceived risk, opinion leadership and positive emotional traits.

Abstract
Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

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