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Is there a good way to measure this impact, or is such measurement impossible or pointless?
For many applications like optimization of hearing instruments, it is desirable to measure this impact automatically using a simulation model.
In the analysis, we used a TD-LTE software radio platform developed at Aalto University [2] to measure this impact.
We are examining this aspect of scenario work in another, still ongoing, project with Syrian refugees in Sweden, where we try to measure this impact with the help of a questionnaire modelled after Aizen' theory of planned behavior [8].
To measure this impact, we assess the performance of SimAttack when the user profiles contains only a limited part of the user query history (from 0 to 100%%). Figure 14 depicts for AOL100 dataset, the percentage of identified queries according to the ratio of the query history considered in the user profile for varying number of fake queries generated.
To measure this impact, we depict on Fig. 6 the precision and the recall of SimAttack for AOL100 when the profile of users pre-built by the adversary only contains a sub part of their query history (from 0 to 100 %).
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15 Part of the problem may lie in the difficulty in measuring this impact.
We measured this impact in terms of endogenous target repression scores for about half a million miRNA-mRNA duplexes.
Although the ultimate goal of responding to community views is to improve health outcomes, measuring this impact is beyond the scope of many studies.
We measured this impact by analysing the affect of evidence-based therapies for secondary prevention after myocardial infarction on the incidence of a new ischaemic heart attack.
Measuring this impact is a relevant alternative, as target prediction is generally one of the first steps undertaken towards obtaining a clue on a given miRNA's function.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com