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This is far away from curiosity in the "anomaly approach".
The "anomaly approach" is placed in the final phase of the theory testing, as well.
In addition, in contrast to that, the "anomaly" approach is the only design that receives data from dialogue between observer and participants and participant observation.
In the "anomaly" approach, the data analysis is aggregation of data, but these aggregated data are related to its external field and their pressures and influences by structuration to reconstruct the theory.
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The size of the reconstructed anomaly approaches the true shape but its actual location is off.
There is a distinct difference from theoretical sampling in the "anomalies" approach.
Considering that the proportion of patients with tooth agenesis and structural dental anomalies approaches 60%, we found that approximately 160 individuals are needed to ensure that a 99% confidence interval estimate of the proportion is within 10% of the true proportion [19].
This paper suggests an anomaly detection approach that summarizes the raw transactional SQL queries into a compact data structure called hexplet, which can model normal database access behavior (abstract the user's profile) and recognize impostors specifically tailored for role-based access control (RBAC) database system.
Then in Section 3, the assumed scenario as well as details of the proposed anomaly detection approach is outlined.
Our anomaly detection approach and the associated theoretical analysis are based on a "distance preservation" property of A, which is stated formally in (18).
To the best of our knowledge, this is the first time that the anomaly detection approach is applied for PUE attacks on CWSNs.
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