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Figure 2 shows the extracted interest points on a few sequences from the KTH dataset [37].
The extracted interest points from each sequence are then matched pairwise using Hungarian method with shape context features incorporated.
The Harris corner detector [45] is first applied to extract interest points in each image; using the coordinates of the extracted interest points, bounding boxes are created around actors, and are vertically partitioned in three regions approximately corresponding to the head, torso, and legs (see Figure 4).
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Table 12 Evaluation of extracted interests SCALE EXTRACTED INTERESTS Fully agree 0,47 Partially agree 0,22 I do not agree, nor disagree 0,10 Partially disagree 0,04 Fully disagree 0,18.
Thus, the same metric is used to evaluate the extraction of profile features, the extracted interests and those discovered by the enrichment step.
Third column shows the results for specific educational interests related to the extracted interests.
The 14 participants evaluated 255 extracted interests, repeating them only for different users.
He/she also declared neutral in relation to 6% of the extracted interests, as can be seen in Fig. 18.
Whereas the extracted interests were enriched and new interests have been discovered, a second proof of concept was conducted specifically to analyze these discovered interests.
Regarding extracted interests there was also good acceptance reaching the rate of 0.69 against 0.22 of rejection, as Precision metric results close to 1 indicates better recommendation system.
Combining the evaluations where Researcher A answered fully agree 29% and partly agree 46% for the extracted interests, we have a total of 75% of interest consistent with his/her actual educational interests.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com