Recapitulation a. Show the difference between the value of the entire property and the value of the remainder by deducting the property’s after value from it’s before value.
Recapitulation. 1. Legal name of each initial party to this Agreement: CenterPoint Properties Trust; and Berkeley County, South Carolina.
Recapitulation. 1. Legal name of each initial party to this Agreement: Project Orange, a South Carolina corporation
Recapitulation. The main workflow implemented by the framework is the analysis of media information units, in this context called (Content) Items. (Content) Items are representations of media resources together with their subsequent analysis results, called (Content Parts). (Content) Parts are results of analysis com- ponents with different media types that are directly related to the same (Content) Item. In other words, a (Content) Item is a semantic grouping of information objects (Content Parts) considering a specific multimedia asset. As extraction and analysis processes in general produce a manifold of different re- sults and output formats, unified ways of storing and querying the content and its context are required. To enable comprehensive cross-media querying, the underlying data model must be capable to reflect the performed workflow chain including the interaction and mutual dependencies between all analysis steps. Metadata as well as provenance can be persisted in close relationship to the multimedia content. Our existing model basically builds upon the Web Annotation Data Model (WADM, former Open An- notation Data Model, see D3.1.1) and several existing ontologies like Dublin Core, Media Fragments PROV-O, and FOAF. The concept of (web) annotations is adapted and extended to the cross-media environment. The baseline consists of three concepts, namely annotation, body, and target (see figure 16). Figure 16 Basic annotation in the MICO data model annotation rdf:type oa:hasBody oa:hasTarget body target oa:Annotation 35xxxxx://xxxxxx.xxx/anno4j/anno4j The body contains ways of describing and classifying the results or outcomes of the extractors. In general, every extractor has its own output type (e.g. mico:FaceRecognitionBody or mico:NERBody). The target describes what the body is about and usually refers to the input multimedia asset or previous (Content) Part and allows the creation of a traceable workflow chain. The target can be extended with more precise selections to only refer to a temporal or spatial fragment of a multimedia asset (e.g. a frame of a video). The actual annotation object connects the body and the target and also can be enriched with context and provenance information about the extraction workflow (e.g. timestamps, creator and configuration). As an overall example, figure 17 shows the results of an extractor that runs a face recognition algo- rithm. Therefore, its body is typed as an instance of the class mico:FaceRecognitionBody. The cont...
Recapitulation. 1. Legal name of each initial party to this Agreement: Riverbend Charleston Properties II, LLC; Xxxxxx Xxxxx LLC; and Berkeley County, South Carolina.
Recapitulation. Debentures. Compost and Lionhart hereby agree that, pursuant to the Debenture Purchase Agreement and the Debentures:
Recapitulation. 1. Legal name of each initial party to this Agreement: Volvo Car USA LLC (d/b/a Volvo Car US Operations) and Berkeley County, South Carolina.
Recapitulation. 1. Legal name of each initial party to this Agreement: Redwood Materials East, LLC, Camp Hall Campus 3, LLC, and Berkeley County, South Carolina.
Recapitulation. 1. Legal name of each initial party to this Agreement: ScanSource, Inc., a South Carolina corporation
Recapitulation. 1. Legal name of each initial party to this Agreement: DRS Naval Power Systems, Inc. and Berkeley County, South Carolina.
