Robots in classroom settings can help teachers with providing personalised attention to children's health and development. As part of this personalisation, robots should store and use (verbal or multi-modal) information about the children they interact with. One aspect that has been unexplored in existing literature is the detection of memorable moments during these child-robot interaction. Eye-gaze tracking is a low cost and non-invasive method applied widely to gain insight into human's inner processes. This study has found that several state-of-the-art time series machine learning models perform better-than-chance on the detection of memorable moments using gaze tracking.
In addition, a shapelet-based transform classifier also performed better-than-chance in distinguishing memories according to $3$ different levels of recall detail. Manual data analysis has identified significantly different gaze behaviour during memorable moments and not memorable moments as well as in the gaze behaviour for different levels of recall detail. The comparison of the results with related literature leads to the hypothesis that memorable moments are likely to be moments of both high levels of engagement and deep thinking. The data analyses also provided insight into children's gaze behaviour for different reasons for remembering a moment. The results show that these reasons, or `internal processes', are distinguishable by gaze patterns and thus provide insight into items or concept that draw the child's attention. This study shows that memorable moments detection for children is a developing and promising field that could potentially provide a lot of insight into children's situated thought processes.

Almende B.V., a technologically innovative and research-oriented company, has been working on a new algorithm that optimizes routes for parcel delivery trucks. The algorithm contains novel features, like including the possible use of autonomous vehicles, that are at this moment in time not taken into account in existing route optimization algorithms and thus visualization applications. To this end and to get a more tangible overview of the algorithm’s behavior and performance, they requested to have a customized visualization tool developed. This report describes the process and results of developing such a tool. The tool is presented as a single-page application and has been partly depicted on the cover of this document. The goal of the project is to have a more clear overview of the routing algorithm’s capabilities, by showing its unique features on a map and displaying statistics on the side. In addition, comparing the algorithm to existing ones should provide added insights into the (expected) benefits of the new algorithm. The main purpose of the tool developed in this project is to show insight into the workings of the algorithm and to help with enhancing and developing the algorithm. An added side-bonus is that the tool can also be used to show the performance to various groups of interested parties.