Note: some parts of the abstract have been censored to hide company secrets. This project was performed for Orchid N.V., an innovator in agriculture. The project focuses on their Scalar system. This is a system that consists out of multiple machines that automatically feed cows. While farmers are generally pleased with these systems, ---- -- --- ---- - --- -- ------- -- --- ----------- -- --- ------. --- ------ ---- ------- - --- -- --- ----- --- ----- -- -- -------- ---- ----- ---- ---- -- -- -------------- --- -- --- ------. ---- ------- ----- -- ----- ---- ----- -- ---------- - -------- -------- ----- ------- ------- -- ---- ------- ---- ----- -------- ---- ---- ---- --- ------ -------- --- -------- --- ----------. A daily report about the performance of the systems on the farm was the initial assignment for the project. The project started with a two week research phase in which the assignment was further investigated by visiting testfarms and conducting interviews with farmers and Orchid employees. --- ----- --------- --- -- -- ---- -- ---------- ------ -- ------- --- ------- -- -------- -- ---- ------- ------- ---- ----- -------. ----- ---- --- --------- farmers had to be informed about potential problems on their farm. This can be done by looking at patterns and outliers. ------ ---- ----- - -------- --- ---- ---- -- ------- -- ---.--- --- ------------ ---- ---- -- ----- ----- -- ---------- --- -------- -------- --------- -- ----. After the research phase, three development cycles were executed. At the end of each design cycle, a new version of the product was reviewed within Orchid and at a testfarm. The final product consists out of a cloud solution that processes logs to generate data and a web application dashboard, which visualizes this data. The web application dashboard shows an overview of the performance of the Scalar system on the farm and a list of points of attention, with interesting findings within the data. These could be potential problems. The reactions from Orchid and the farm were very positive. During the process there were not many problems and the project can be considered a success. -------------- --- --- ---- -- --- ----- -- --------- --- ------------ --------- --- ---- --- --------- ----------- --- --- -------- --------- ------- ----------- --- ------------ -- --------- --- -- --------. The project was finalized with a final presentation at Orchid and a final presentation at TUDelft.

This work analyses context effect in the evaluation of music similarity performed by human annotators to better understand the impact of context effects in the current annotation protocol of Music Information Retrieval Evaluation eXchange (MIREX). Human annotators are known to be subjective when giving similarity judgements. The Audio Music Similarity task in MIREX uses human annotators to collect similarity judgements. The annotator gives judgements to a list of candidate songs that are similar according to the participating system. The annotation protocol has no clear guidelines, and on top of that, literature shows psychological effects which can influence the similarity score. Studies show that disagreement exists between different annotators in the Audio Music Similarity task. It is argued that the disagreement is due to the natural subjectivity of human annotators, but how much of the subjectivity is natural? In this work, context effects are explored, which are the over- or underrating of candidate songs due to specific properties of the annotated list of candidates. The properties of the list of candidates are called factors and will be used as dependent variables. The exploration of context effects is split into two parts, 1) recognizing context effects and 2) measuring the impact of the context effect. New similarity judgements are collected through crowdsourcing, this data is checked on reliability before analysing the context effects. For recognizing context effects, the changes of previous judgements made by annotators are taken as a metric to see if the annotators are noticing potential context effects. The second part is measuring the magnitude of the over- or underrating by looking at the distance of the set of judgements to the ground truth. Hypotheses are made for the dependant variables change and distance, based on the factors Order, Trend, Location, Spread and Outlier. It seems that the collected data shows signs of context effects, with the Trend and Outlier hypotheses being in line with the data. The Order hypothesis seems to be the opposite of the data. When changes are made by an annotator, the final scores of the judgements are closer to the ground truth than before the changes. However, throughout the work, no significant results are found related to context effects.