Abstract: A method, applied to an apparatus for mammographic multi-view mass identification, includes receiving a main image, a first auxiliary image, and a second auxiliary image. The main image and the first auxiliary image are images of a breast of a person, and the second auxiliary image is an image of another breast of the person. The method further includes detecting the nipple location based on the main image and the first auxiliary image; generating a first probability map of the main image based on the main image, the first auxiliary image, and the nipple location; generating a second probability map of the main image based on the main image, the second auxiliary image, and the nipple location; and generating and outputting a fused probability map based on the first probability map and the second probability map.

Abstract: Embodiments of this application provide an electronic device and a picture processing method. The electronic device includes a rear cover, a front-facing camera, a rear-facing camera, and a light reflective apparatus. The light reflective apparatus may be switched between a usage state and an idle state.

Abstract: A cable modem in communication with a cable modem termination system, configured to perform at least the following: during an operation phase of the cable modem, acquiring and storing damaged channel information indicating a damaged channel among a plurality of channels between the cable modem and the cable modem termination system which is detected during the operation phase; during an initialization phase of the cable modem, adjusting a scanning sequence of the plurality of channels according to the stored damaged channel information so as to cause the damaged channel to have the latest scanning order; and scanning the plurality of channels according to the adjusted scanning sequence to determine, among the plurality of channels, a channel for performing communication between the cable modem and the cable modem termination system.

Abstract: A method for topic early warning includes: acquiring a self-defined keyword; calculating similarity between the self-defined keyword and each word in a corpus, and acquiring extended keywords related to the self-defined keyword from the corpus according to the similarity; selecting a target keyword from the extended keywords according to a type of the extended keywords and similarity between the extended keywords and the self-defined keyword, and adding the target keyword to a target keyword list; performing real-time monitoring according to the target keyword in the target keyword list; and performing topic early warning when it is monitored that the number of topics corresponding to the target keyword reaches a preset threshold.

Abstract: The present solution provides a target customer identification method and a device, an electronic device and a medium, which is applicable to the field of information processing.

Abstract: A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

Abstract: A speaker acquires audio data and performing processing. Audio features of the audio data are extracted. The audio features are inputted into a preset neural network model for training to obtain vector features. A first similarity value between a first vector feature and a second vector feature of a first speaker and a second similarity value between the first vector feature and a third vector feature of a second speaker are calculated. A loss function value is calculated and when is less than or equal to a preset loss function threshold, a training process of the speaker separation model is ended and parameters are updated. A two-speaker separation method, a terminal, and a storage medium are disclosed. Feature extraction capabilities of the model are enhanced, and accuracy of separation between speakers is improved, especially in long meetings and conversations.

Abstract: This application relates to the field of video coding technologies, and discloses a video coder and a corresponding method, to help improve video coding performance. In this application, encoding and decoding are collectively referred to as coding. A video coding method includes: determining a block split policy of a current picture block based on a size relationship between the width and the height of the current picture block; applying the block split policy to the current picture block to obtain a coding block; and reconstructing the obtained coding block to reconstruct the current picture block.

Abstract: A voice user interface display method and a conference terminal are provided. The method includes: when voice information input by a user into the conference terminal is received, a voice of the user is collected. A user voice instruction may be obtained based on the voice information input by the user. Identity information of the user may be obtained in real time based on the voice of the user. Further, user interface information that matches the user may be displayed based on the identity information of the user, the user voice instruction, and a current conference status of the conference terminal. The method improves diversity of display of the user interface information and improves user experience in using the conference system.

Abstract: The present disclosure publishes a vulnerable plaque identification method. The method includes: receiving an angiocarpy image sent by a terminal device; transforming the angiocarpy image in an original Cartesian coordinate system into a polar coordinate system to form a polarization image; constructing a faster RCNN architecture and accomplishing a training; inputting the polarization image into the faster RCNN architecture accomplished the training to identify the polarization image, and outputting the image with the marked vulnerable plaques; feed backing the image with the marked vulnerable plaques to the terminal device. The present disclosure also publishes an application server and a computer readable medium. The vulnerable plaque identification method, the application server, and the computer readable medium can quickly and correctly conform the position of the vulnerable plaque.

Abstract: Disclosed are a speech recognition method, apparatus, computer device and storage medium. The method includes: performing a framing and an acoustic feature extraction of a speech-information-to-be-tested according to a default rule to obtain a frame-level speech feature sequence; dividing the frame-level speech feature sequence into n blocks sequentially; inputting all blocks into a preset bidirectional LSTM-RNN model parallelly to obtain an output result of the corresponding neuron in an output layer of the preset bidirectional LSTM-RNN model corresponding to the forward recognition result and backward recognition result of each block to obtain a speech recognition result of the speech-information-to-be-tested. The present application can improve the speech recognition effect significantly and reduce the time delay of the speech decoding effectively.

Abstract: A data noise reduction method based on data resource.

Abstract: A method, a device, a terminal and a storage medium for acquiring slant value of slant image are provided. The method for acquiring slant value of a slant image comprises: analyzing the slant image and acquiring coordinate information of a plurality of boundary lines of the slant image; acquiring first slant values of the boundary lines by analysing and calculating the coordinate information; acquiring a correction value; calculating difference values between the first slant values and the correction value respectively; determining the first slant value corresponding to the minimum difference value as the slant value of the slant image. The technical solution of the present disclosure can uniquely determine a slant value of slant image.

Abstract: The present solution provides a method and apparatus for predicting a customer purchase intention, an electronic device and a medium, which is applicable to the field of information processing. The method includes: obtaining personal characteristics data of a customer; inputting the personal characteristic data into a pre-established random forest model, to output an objective purchase tendency value of the customer; obtaining a subjective purchase tendency value of the customer according to an emotional tendency of the customer in a historical telemarketing process; weighting the objective purchase tendency value and the subjective purchase tendency value, and outputting the weighted result as an actual purchase tendency degree of the customer; and determining the customer whose actual purchase tendency degree is greater than a preset threshold as a potential customer, so that a telephone sales person makes a telephone call back to the potential customer and market a telemarketed product.

Abstract: A method for topic early warning includes: acquiring a self-defined keyword; calculating similarity between the self-defined keyword and each word in a corpus, and acquiring extended keywords related to the self-defined keyword from the corpus according to the similarity; selecting a target keyword from the extended keywords according to a type of the extended keywords and similarity between the extended keywords and the self-defined keyword, and adding the target keyword to a target keyword list; performing real-time monitoring according to the target keyword in the target keyword list; and performing topic early warning when it is monitored that the number of topics corresponding to the target keyword reaches a preset threshold.

Abstract: A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

Abstract: An electronic device for identity verification includes a memory and a processor; the system of identity verification is stored in the memory, and executed by the processor to implement: after receiving current voice data of a target user, carrying out framing processing on the current voice data according to preset framing parameters to obtain multiple voice frames; extracting preset types of acoustic features in all the voice frames by using a predetermined filter, and generating multiple observed feature units corresponding to the current voice data according to the extracted acoustic features; pairwise coupling all the observed feature units with pre-stored observed feature units respectively to obtain multiple groups of coupled observed feature units; inputting the multiple groups of coupled observed feature units into a preset type of identity verification model generated by pre-training to carry out the identity verification on the target user.

Abstract: This application provides a dynamic vision sensor. The sensor converts an optical signal into an electrical signal by using a photoelectric conversion unit, to generate a photovoltage; performs second-order difference on the photovoltage by using a second-order differential circuit; and generates a second-order event signal based on a result of second-order difference. A camera including the sensor can generate an image based on the second-order event signal, where the image represents a change of a light change speed.

Abstract: The application discloses a method for knowledge extraction based on TextCNN, comprising: S10, collecting first training data, and constructing a character vector dictionary and a word vector dictionary; S20, constructing a first convolutional neural network, and training the first convolutional neural network based on a first optimization algorithm, the first convolutional neural network comprises a first embedding layer, a first multilayer convolution, and a first softmax function connected in turn; S30, constructing a second convolutional neural network, and training the second convolutional neural network based on a second optimization algorithm, the second convolutional neural network comprises a second embedding layer, a second multilayer convolution, a pooling layer, two fully-connected layers and a second softmax function, the second embedding layer connected in turn; S40, extracting a knowledge graph triple of the to-be-predicted data according to an entity tagging prediction output by the first trai

Abstract: A speech recognition method, comprising: acquiring speech data to be recognized; extracting a Filter Bank feature and a Mel-Frequency Cepstral Coefficient (MFCC) feature in the speech data; using the MFCC feature as input data of a trained Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) model, acquiring a first likelihood probability matrix outputted by the trained GMM-HMM model; using the Filter Bank feature as an input feature of a trained long short-term memory (LSTM) model which has a connection unit, acquiring a posterior probability matrix outputted by the LSTM model; using the posterior probability matrix and the first likelihood probability matrix as input data of a trained HMM model, acquiring an second likelihood probability matrix; and acquiring a target word sequence corresponding to the speech data to be recognized from a phoneme decoding network according to the second likelihood probability matrix.