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Even when the prototypical doctor interpreter patient sequence is followed, there remains considerable scope for misunderstanding, due to ambiguity over the interpreter's role and how the interpreting task is actually performed in practice.
Examining steps children take to overcome difficulties in processing the relevant features of tasks and in correctly interpreting task instructions promise to illuminate mechanisms of conceptual development.
And, as different skills are generally involved in summarization and other integrated tasks, interpreting task performance raises questions about the role each skill plays as well as the interaction among them (Plakans 2009).
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Ways to handle verbatim responses, interpret task success and efficiency metrics, understand click path behavior, identifying the most salient usability issues, and presenting usability data in a compelling way are reviewed.
Using the management services presented in the previous section, the developer is able to ask any known Thing to create a processor that executes either (i) a compiled task by providing its identifier, or (ii) an interpreted task by providing the DiSPL source code of the task.
Second, we aim to analyze the overhead due to the code interpretation mechanism, by comparing the consumption of resources by compiled and interpreted tasks, respectively.
Technical details about DiSPL to describe interpreted tasks are provided in the next Section.
This makes it possible to build logical and physical mashup graphs that use both compiled and interpreted tasks.
Our second experiment assesses the capability of Dioptase to support dynamic deployment of tasks, by evaluating the resource consumption of processors for compiled and interpreted tasks.
As shown in Figure 4, interpreted tasks rely on a dedicated parser, which converts the source code into an abstract syntax tree (AST).
As already mentioned, non-blocking operations are executed by processors, which are components dedicated to the execution of (i) compiled tasks that are linked to the middleware during the customization phase, and (ii) interpreted tasks that are described using the DiSPL DSL and deployed during the execution.
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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