To alleviate this issue, elements with an assigned event-handler are tried first.
To detect each type, we use the following techniques: 1. Statically assigned event-handlers: to find this type of handlers, it is enough to traverse the DOM and check the existence of attributes related to event-handlers (e.g. onclick, onscroll,…).
Detection of statically assigned handlers can be done by just scanning the DOM, however the algorithm needs a more advanced mechanism to keep track of dynamically assigned events.
In the case of 100,000 assigned events, the performance of the algorithm depends on the number of observed events.
The final count of assigned events was equivalent to the observed number of insertions/deletions in the corresponding data set.
Performance goes down to about 75%and40%0% sensitivity in the case of 10,000 and 1,000 assigned events, whereas still maintaining more than 95% specificity.
Observed counts of assigned events were treated as real counts of insertions/deletions, where corresponding rate values as well as the expected distributions of event rates were calculated as described above.
GE tasks were evaluated by splitting the problem as follows -subtask 1—locating bio-event triggers, assigning event types and identifollows -subtaskcipants (i.e. Theme and Cause); subtask 2—identifying additional participants, including locative information; subtask 3—identifying negation and speculation.
The competing risks were implemented by deriving the distribution of occurrence times for each type of event and sampling from each, one at a time, in order to assign event times to each patient.
Despite their occasional obscurity, these early historians accomplished the considerable task of organizing the past into units of the same size (years) and assigning events to them.
After clustering, SWIFT therefore stochastically assigns events to each cluster, in proportion to membership probabilities.
