This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: probes

Endpoint Name Data Format Nature
comparison_ids system/array_1d_text/1 Input
keystroke tutorial/atvs_keystroke/1 Input
probe_client_id system/text/1 Input
scores elie_khoury/string_probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/text/1 Input
id system/text/1 Input
features tutorial/atvs_keystroke/1 Input

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
field Data field used to generate the feature template string given_name given_name, family_name, email, nationality, id_number, all_five
distance Distance to obtain the matching score string Modified Scaled Manhattan Scaled Manhattan, Modified Scaled Manhattan, Combined Manhattan-Mahalanobis, Mahalanobis + Nearest Neighbor
This algorithm is only usable to you. Its code was not shared.

This algorithm is designed to be used as a simple enrollment strategy of keystroke data. It enrolls a model from several features by computing the average and standard deviation of the enrollment features.


All features must have the same length.


Updated Name Databases/Protocols Analyzers
robertodaza/robertodaza/example2/2/article-block-all1 atvskeystroke/1@A robertodaza/analyzerahora/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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