Sentence examples for high level contexts from inspiring English sources

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Naïve Bayesian classifiers are used to recognize high level contexts in terms of their constituent atomic context elements involving uncertainty.

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Detail of each layer is described sufficiently to insight the process of context generation from low level contextual data up to high level context reporting scheme in a given pervasive environment.

If crime is not scale invariant, this implies that there are different processes at each level, or that there is some form of hierarchical arrangement where higher level contexts help shape the outcomes of lower level processes (e.g., street segments provide a context that moderates the address level dynamics of crime).

In one of the very few papers looking at internationalisation in the Irish higher level context Dunne [ 15] investigated the host student experience of international students.

Since high-level contexts such as a "user is currently at home/work" is not useful in news domain.

A mobile peer mainly captures low-level contexts (sensor data), while super peer is responsible for processing the low-level to high-level contexts.

As a way of describing profiles and the data held within them, we hypothesise that there are at least five high-level contexts to which profiles could be associated.

Once the topics have been identified, we would then move to group related topics into high-level contexts– these contexts would provide general insight into how the profile is actually being used by the individual.

In this sense, the machine learning process examines a set of atomic contexts which have been pre-assigned to categories, and makes inductive abstractions based on these data that assist in the process of classifying unknown and future atomic contexts into high-level contexts.

Application: To ease the further processing of the classified contexts for traffic awareness, certain high-level contexts can be interpreted based on the classified low-level traffic contexts and then integrated into the existing traffic sensing applications (e.g., traffic density, traffic jam/flow and vehicular location).

A number of generic context platforms have been developed previously to relieve application developers from the aggregation and abstraction of context information and the derivation of high-level contexts.

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