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To generate execution patterns for the migrating tasks, we use Eq. 2 first and if no pattern is found we follow an enumeration approach.
For example: is the set of syntactically correct SQL injection requests that are constructed to generate execution pages in the presence of vulnerabilities, but they might as well generate rejection pages in the absence of vulnerabilities.
A tiny grammar for the set Rvi (i.e., the set of SQL injection requests that are constructed in order to generate execution pages) can be expressed using BNFb notation as follows: This grammar generates different variations of SQL injection attacks.
This approach relies on three assumptions: (1) execution and rejection pages are different, (2) it is easy to build requests that generate rejection pages (by generating random or syntactically invalid requests for instance), and (3) it is difficult to build requests including injection attacks that actually generate execution pages (i.e., requests that successfully exploit a vulnerability).
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Alternatively, WINGS can generate execution-ready workflows that can be submitted to either Apache OODT [ 39] or the Pegasus/Condor execution engine [ 40], which are designed for large-scale distributed data processing in a variety of environments, such as local clusters, shared infrastructure, or cloud resources.
At runtime, the generated execution rule reads data from all sensors and other sources and invokes the step function to compute the commands.
These semantics offer a clear distinction between the concerns of the numerical analyst (solving differential equations) and those of the computer scientist (generating execution schemes).
The H/BZR program was also used to generate an execution rule template that was instantiated for each room.
The H/BZR program and the objects of the PUTUTU framework were used to generate an execution rule template.
We plan to use this information to generate an execution plan that matches the required boundary conditions (e.g. costs, time) as good as possible.
The former, which is the simplest one, does not take into account the "downstream" dependent components to generate the execution path of a tuple.
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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