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Microsoft Excel uses dedicated file formats that are not part of OOXML, and use the following extensions:.xlsb – Excel binary worksheet (BIFF12).xla – Excel add-in that can contain macros.xlam – Excel macro-enabled add-in.xll – Excel XLL add-in; a form of DLL-based add-in [1].xlw – Excel work space; previously known as "workbook"
Both free and paid versions are available. It can handle Microsoft Excel .xls and .xlsx files, and also produce other file formats such as .et, .txt, .csv, .pdf, and .dbf. It supports multiple tabs, VBA macro and PDF converting. [10] Lotus SmartSuite Lotus 123 – for MS Windows. In its MS-DOS (character cell) version, widely considered to be ...
Protocol Used by Defunct clients ActivityPub: Friendica, Libervia, Lemmy, Mastodon, Micro.blog, Nextcloud, PeerTube, Pixelfed, Pleroma: Advanced Peer-to-Peer ...
The authority is identified by a secure hash of an associated public key, or by a place-holder (the number zero) if the peer name is "unsecured". The qualifier is a string, allowing an authority to have different peer names for different services. [4] If a peer name is secure, the PNRP name records are signed by the publishing authority, and ...
The following is a list of notable report generator software. Reporting software is used to generate human-readable reports from various data sources.
This comparison of optical character recognition software includes: OCR engines, that do the actual character identification; Layout analysis software, that divide scanned documents into zones suitable for OCR; Graphical interfaces to one or more OCR engines
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Optical mark recognition (OMR) collects data from people by identifying markings on a paper. OMR enables the hourly processing of hundreds or even thousands of documents. A common application of this technology is used in exams, where students mark cells as their answers. This allows for very fast automated grading of exam sheets.