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In this paper, we analyse the process of designing a Content-
Based 3D shape Retrieval (CB3DR) adapted for non-experts.
Our CB3DR solution aims at scanning an object on the fly
with a low-cost 3D sensor and retrieve similar shapes from
a database using the 3D point cloud acquired. Our system
should meet the requirements of archaeologists who would
like to be able to acquire artefacts without prior expertise in
scanning, then query easily from the field knowledge bases
for Cultural Heritage, and thus retrieve artefacts (i.e. objects


When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning.


In this paper, distributed learning is studied using the approach of coreset. In the context of classification, an algorithm of coreset construction is proposed to reduce the redundancy of data and thus the communication requirement, similarly to source coding in traditional data communications. It is shown that the coreset based boosting has a high convergence rate and small sample complexity. Moreover, it is robust to adversary distribution, thus leading to potential applications in distributed learning systems.