Sentence examples for different types of context from inspiring English sources

Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored.

In Multiverse Recommendation, different types of context are considered as addition dimensions in the representation of the data as tensor.

These include the availability of relevant contextual information, heterogeneity of information that is available, inter-linking different types of context from possibly different sources, aggregating independently sourced contextual information to infer higher-order context about citizens and environments.

Karatzoglou et al. [10] proposed Multiverse Recommendation model in which the different types of context are considered as additional dimensions in the representation of the data as a tensor.

In this section, we will compare in more detail the way that Hunston (2000), Thetela (1997) and Hood (2004) distinguish between different types of context, with reference to the texts they themselves have analyzed.

Below we review the evidence for the mechanisms underlying these different types of context effect, how they affect circuit and host function, and their implications for effective and reliable circuit design.

Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour.

To compare and contrast the different types of contexts discussed by Hunston (2000), Thetela (1997) and Hood (2004) in order to identify those aspects of the theory that require further development and clarification.

These data sets were chosen because of their heterogeneity – since they represent different types of contexts they increase the generalizability of the results.

In this and the following sections we discuss the curation of the structured RNA annotation utilizing the different types of contexts added to the annotation in the preceding sections.

By contrast, relying on the normal variation in the population to provide a range of dispositions that might match different types of contexts – e.g. people who are less averse to harming others might make be good law-enforcers or soldiers – is a far cruder way of dealing with this problem.