A method, an apparatus, an electronic device, a computer-readable storage a medium and a computer program product for labeling data are provided. The method may include: obtaining a labeling accuracy requirement for to-be-labeled data; determining a process monitoring parameter matching the to-be-labeled data; weighting the process monitoring parameter with a coefficient having a corresponding size to obtain a comprehensive accuracy according to dependent and causal relationships between contents of different to-be-labeled data; and outputting, in response to the comprehensive accuracy satisfying the labeling accuracy requirement, labeled data.

A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

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.

An electronic apparatus which acquires input data to be input into a TTS module for outputting a voice through the TTS module, acquires a voice signal corresponding to the input data through the TTS module, detects an error in the acquired voice signal based on the input data, corrects the input data based on the detection result, and acquires a corrected voice signal corresponding to the corrected input data through the TTS module.

Embodiments provide a system and method for short form and long form detection. Given candidate short forms, the system can generate one or more n-gram combinations, resulting in one or more candidate short form and n-gram combination pairs. For each candidate short form and n-gram combination pair, the system can calculate an approximate string matching distance, calculate a best possible alignment score, calculate a confidence score, calculate a topic similarity score, and calculate a semantic similarity score. The system can determine the validity, through a meta learner, of the one or more valid candidate short form and n-gram combination pairs based upon each short form and n-gram combination pair's confidence score, topic similarity score, and semantic similarity score, and store the valid short form and n-gram combination pairs in a repository. The system has no language specific constraints and can extract short form and long form pairs from documents written in various languages. The system is also not limited to whether the language of the given corpus is case sensitive or not.

A computer-implemented system and method for predicting and flagging an anomaly entered in a digital form. A server computing device classifies a plurality of data fields of the digital form to identify a set of non-zero value data fields; and obtains an anomaly detection model comprising a statistical tree structure associated with the data field of the digital form. The server computing device receives datasets including a target value of a data field and values of a set of cohorting data features; traverses a statistical tree structure of the anomaly detection model with the target dataset to form a set of target cohorts to determine a target statistic value for the data field; flags the data field value of the target dataset as an anomaly item; and generates one or more confidence scores for a runtime prediction based on one or more variance changes for the data field.

Embodiments of the present invention provide a method and a system for managing a storage system. Specifically, in one embodiment of the present invention there is provided a method for managing a storage system, the method comprising: in response to receiving a write request for writing target data to the storage system, writing the target data to an intermediate address range in an intermediate storage area of the storage system; parsing, based on an address mapping of the storage system, a target address range associated with the write request so as to obtain an actual address range; and moving the target data from the intermediate address range to the actual address range. In one embodiment of the present invention there is further provided a corresponding system and apparatus.

A computer-implemented method of obtaining form data can include selecting a control from within an electronic form, determining a textual representation of the control, and sending the textual representation of the control as part of a message to a client messaging system through a real-time communication system. A response message from the client messaging system can be received through the real-time communication system. Form data specified by the response message can be stored within memory when the form data is valid, as specified according to the electronic form.

A document that includes a plurality of textual portions is accessed. At least some of the textual portions include an input command that is configured to be directed to a process. For each respective textual portion that includes an input command the respective textual portion is input to a machine-learning model (MLM) that has been trained with a plurality of textual phrases and process identifiers that identify corresponding processes, a process identifier that identifies a process to which the input command included in the respective textual portion is to be directed is received from the MLM, and a command record that identifies the input command is stored in a data structure.