Disclosed are a sample generation method, a model training method, a trajectory recognition method, a device, and a medium. The method is: determining a code result of a training Chinese character according to a preset code library, where the preset code library is generated based on code characters in a five-stroke code corpus; taking the code result as a training label of the training Chinese character; and generating a training sample according to both a writing trajectory and the training label of the training Chinese character. The amount of information carried in the training sample is enriched.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A web-based application constructor can be used to construct a web display. A specification, for constructing a web display to contain page components that display data from heterogeneous data sources, may be created. The page components may be associated with uniform resource locators. Data from heterogeneous data sources may be retrieved to produce the web display. Display and update of the page components may be controlled using the uniform resource locators. User-selectable options may allow annotation of the page components for a page with comments.

A web-based application constructor can be used to construct a web display. A specification, for constructing a web display to contain page components that display data from heterogeneous data sources, may be created. The page components may be associated with uniform resource locators. Data from heterogeneous data sources may be retrieved to produce the web display. Display and update of the page components may be controlled using the uniform resource locators. User-selectable options may allow annotation of the page components for a page with comments.

Even in the case of a document file including an annotation and text within a page, the file size is reduced while retaining annotation and text information. An image processing apparatus that extracts an annotation object and a text object from a page of an input document file, converts other objects included within the page except for the extracted annotation object and text object into one image object, compresses the converted one image object, and generates page data based on the compressed image object and the extracted annotation object and text object.

Computer implemented methods and systems are provided for providing customized web pages that are customized for each user. A database can store first entries each being configured to store user information for each user, and second entries each being configured to store an identifier and customized content for each user (or alternatively a way to retrieve such customized content). A tree-like data structure is stored in cache and includes nodes that each store an identifier that refers to one of the second entries. After log in, the server system retrieves user information associated with the particular user and uses it to retrieve a particular identifier stored by a particular node. Using a corresponding one of the second entries that includes the particular identifier, particular customized content for the particular user can be retrieved and sent to a user system where it is used to display a particular customized web page for that particular user.

A method for execution by a computing entity includes obtaining first and second learning objects regarding a topic. The method further includes deriving a first set of knowledge test-points for the first learning object regarding the topic based on a first set of knowledge bullet-points. The method further includes deriving a second set of knowledge test-points for the second learning object regarding the topic based on the second set of knowledge bullet-points. The method further includes generating a first knowledge assessment asset for the first learning object regarding the topic based on the first set of knowledge test-points, an illustrative asset, and a first descriptive asset of the first learning object. The method further includes generating a second knowledge assessment asset for the second learning object regarding the topic based on the second set of knowledge test-points, the illustrative asset, and a second descriptive asset of the second learning object.

An example method is provided for encoding text for language processing. The method may be executed by a processing system, and the method includes receiving text comprising a plurality of alphanumeric characters or symbols and converting the text into a numerical vector comprising a plurality of numerical values, by mapping each alphanumeric character or symbol of the text to a vertex coordinate of one of a plurality of vertices of a hypercube, wherein a number of the plurality of vertices is equal to or greater than a number of the plurality of alphanumeric characters or symbols, wherein the numerical vector consumes less space in memory than the text. An amount of time consumed by language processing of the numerical vector may be less than an amount of time consumed by language processing of the text.

An example method is provided for encoding text for language processing. The method may be executed by a processing system, and the method includes receiving text comprising a plurality of alphanumeric characters or symbols and converting the text into a numerical vector comprising a plurality of numerical values, by mapping each alphanumeric character or symbol of the text to a vertex coordinate of one of a plurality of vertices of a hypercube, wherein a number of the plurality of vertices is equal to or greater than a number of the plurality of alphanumeric characters or symbols, wherein the numerical vector consumes less space in memory than the text. An amount of time consumed by language processing of the numerical vector may be less than an amount of time consumed by language processing of the text.

Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.