Exact(11)
Basic security requirements in VCC have been addressed such as entity authentication, authorization, and privacy [2, 3].
Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration.
The source area has standard models such as entity relationship diagram, and the destination area has standard models such as star schema, but the mapping area has not a standard model till now.
Theoretical analysis and experimental results show that the proposed algorithm is not only robust against incidental global operations such as rotation, translation and scaling, but can also detect and locate malicious attacks such as entity modification and entity addition/deletion.
This new header is used in the different messages to convey some basic information related to different processes that are associated to our proposal, such as entity registration, withdrawal of e-coins, payments, etc., (see overview in Section 4.2).
Although executed in a different technological context (.NET), the studies of Gruca et al. [38] and Cvetkovic et al. [39] seem to indicate that there is less overhead associated to translating abstraction query languages (such as Entity SQL, LINQ or Hibernate HQL) to SQL in the context of relational databases, when compared to our results.
Similar(49)
TM facilitates the extraction from documents of semantic information such as entities (proteins, genes, etc).
We thus interpreted the scope of mentions marked with a specific type to include not only the corresponding (canonical) types defined in ontologies but also variants such as entities or processes influenced by mutation, including also pathological variants.
But many traditional ontological disputes are over the existence of necessarily existing kinds of entity, such as mathematical entities or intensional entities.
Finding such information from text automatically requires IE technologies/applications, such as Named Entity Recognition (NER) and Relation Extraction (RE).
One is to manually analyze the results with some known meta-information (such as the entity each node represents), which is not scalable to large networks.
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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