Disclosed are a text recognition method and apparatus, an electronic device, a storage medium, and a program product. The method includes performing a training iteration on a text recognition model based on a pre-constructed text sample set and a reference model, to obtain a trained text recognition model; and inputting text to be recognized into the trained text recognition model, and recognizing a named entity in the text to be recognized, to obtain text content corresponding to the named entity. Each training iteration comprises respectively inputting a text sample selected from the text sample set into the reference model and the text recognition model; obtaining an output difference and a prediction difference; and adjusting parameters of the text recognition model based on the output differences and the prediction difference.

Disclosed are a text recognition method and apparatus, an electronic device, a storage medium, and a program product. The method includes performing a training iteration on a text recognition model based on a pre-constructed text sample set and a reference model, to obtain a trained text recognition model; and inputting text to be recognized into the trained text recognition model, and recognizing a named entity in the text to be recognized, to obtain text content corresponding to the named entity. Each training iteration comprises respectively inputting a text sample selected from the text sample set into the reference model and the text recognition model; obtaining an output difference and a prediction difference; and adjusting parameters of the text recognition model based on the output differences and the prediction difference.

Captured samples of a physical structure or other scene are mapped to a predetermined multi-dimensional coordinate space, and spatially-adjacent samples are organized into array cells representing subspaces thereof. Each cell is classified according to predetermined target-identifying criteria for the samples of the cell. A cluster of spatially-contiguous cells of common classification, peripherally bounded by cells of different classification, is constructed, and a boundary demarcation is defined from the peripheral contour of the cluster. The boundary demarcation is overlaid upon a visual display of the physical scene, thereby visually demarcating the boundaries of a detected target of interest.

Captured samples of a physical structure or other scene are mapped to a predetermined multi-dimensional coordinate space, and spatially-adjacent samples are organized into array cells representing subspaces thereof. Each cell is classified according to predetermined target-identifying criteria for the samples of the cell. A cluster of spatially-contiguous cells of common classification, peripherally bounded by cells of different classification, is constructed, and a boundary demarcation is defined from the peripheral contour of the cluster. The boundary demarcation is overlaid upon a visual display of the physical scene, thereby visually demarcating the boundaries of a detected target of interest.

A fault detection apparatus and computer-implemented method for providing physically explainable fault information of a bearing built in a machine by a fault detection model is provided, including: obtaining sensor data measured at the bearing as input data relating to an input data domain and the fault detection model, mapping the measured sensor data from the input data domain to a selected data domain resulting in an augmented fault detection model which outputs augmented predicted failure value related to the selected data domain, wherein the selected data domain has a physical meaning to the fault of the bearing, performing a feature attribution on the augmented fault detection model quantifying an importance of at least one individual feature to the augmented failure value related to the selected data domain, and displaying the individual feature and the respective quantified importance in the selected data domain at a user interface.

The disclosure relates to a method for controlling a driver assistance system during operation of a, especially partially automated, fully automated or autonomous, vehicle, wherein the driver assistance system comprises: a, for example at least one, sensor for observing an environment of the vehicle and an electronic control unit using a, especially at least one, neural network for analyzing sensor data of the sensor and providing perception tasks based on the analyzed sensor data, the method comprising: providing a data set of the sensor data by the sensor) in a spatial domain; transforming the data set of the sensor data by the electronic control unit using frequency analysis into a frequency spectrum in a frequency domain; and analyzing the frequency spectrum of the data set in order to detect an adversarially attacked data set, for example before analyzing the sensor data for providing perception tasks.

The disclosure relates to a method for controlling a driver assistance system during operation of a, especially partially automated, fully automated or autonomous, vehicle, wherein the driver assistance system comprises: a, for example at least one, sensor for observing an environment of the vehicle and an electronic control unit using a, especially at least one, neural network for analyzing sensor data of the sensor and providing perception tasks based on the analyzed sensor data, the method comprising: providing a data set of the sensor data by the sensor) in a spatial domain; transforming the data set of the sensor data by the electronic control unit using frequency analysis into a frequency spectrum in a frequency domain; and analyzing the frequency spectrum of the data set in order to detect an adversarially attacked data set, for example before analyzing the sensor data for providing perception tasks.

Provided are a floor material identification method, system and device, and a storage medium. The floor material identification method the floor material identification method includes: acquiring a vibration signal generated by a cleaning robot when operating on a floor of a material of a plurality of materials; determining at least one identification parameter for floor material identification based on the vibration signal; and identifying a floor material type based on the at least one identification parameter. Floor materials are distinguished by characteristics of different vibration signals from the floor of different materials, and thus problems such as high cost, low reliability and inaccurate identification result of the existing floor material identification method can be solved.

A device identification method, apparatus, and system are provided. A management device or a collection device first determines a network traffic feature of a to-be-identified device based on a first dataset. The first dataset includes a plurality of pieces of first data, and each piece of first data includes a data amount of a data packet that is of the to-be-identified device and that is collected within one first periodicity. Then, the management device or the collection device determines a device type of the to-be-identified device based on a device identification model and the network traffic feature of the to-be-identified device.

In an example implementation, a method includes populating a training dataset for training a machine-learning model to provide estimations associated with at least one object by obtaining a predetermined input sample comprising one or more sets of time-resolved values for one or more observables of the at least one object, generating a further input sample based on the predetermined input sample by applying a transformation over a time interval of at least one of the one or more sets of the time-resolved values of the predetermined input sample, and adding the further input sample to the training dataset to provide an augmented training dataset.