Abstract: A method for training a machine learning model. The method comprises receiving a training dataset that includes a plurality of images. The method also includes identifying, by a machine learning model, at least one portion of at least one image of the plurality of images in the training dataset associated with a first object type. The method further includes identifying other images having at least one portion that includes the first object type. The method also includes grouping the identified other images into a first image group. The method also includes generating for display a first user interface, that at least includes a rank matrix, wherein a first row of the rank matrix represents the images of the first image object. The user may provide feedback for the visualization using the first interface. The method may also include training the machine learning model based on the user feedback.

Abstract: Disclosed is an electronic apparatus, including: a display device, a base, a detachable connector, an auxiliary display module, and a placement portion for placing the auxiliary display module, the display device being disposed on the base through the detachable connector.

Abstract: Disclosed are a continuous rolling device and method with interfacial temperature-force controllability for a seamless metal cladding pipe. The device comprises a multi-roller skew rolling mill set, an interfacial temperature-force controlled rolling mill set and a sizing and finishing mill set sequentially distributed along a rolling axis; and the interfacial temperature-force controlled rolling mill set comprises an interface temperature control module and an interface pressure control module sequentially distributed along the rolling axis, the interface pressure control module comprises two racks and a plurality of controlled rolling rollers circumferentially and evenly distributed along the rolling axis, two ends of the controlled rolling roller are respectively mounted on the two racks through corresponding screwdown apparatuses, and the interface pressure control module realizes passive driving through a spiral pushing force or a traction force.

Abstract: Methods and apparatuses are described for enhancing chatbot recognition of user intent through graph analysis. A computing device classifies user messages associated with a chat session as either comprehensible user messages that are mapped to a user intent or incomprehensible utterances that are not mapped to a user intent. The computing device arranges the comprehensible messages into first clusters and arranges the incomprehensible messages into second clusters. The computing device generates a first sub-graph for each first cluster and generates a second sub-graph for each second cluster. The computing device calculates a graph kernel matrix using the sub-graphs and trains a classification model using the graph kernel matrix. The computing device executes the trained classification model using the graph kernel matrix as input to generate a predicted intent identifier for the incomprehensible messages.

Abstract: A fully-automatic photography method for a fundus image includes moving a lens of the fundus camera to align with the pupil, controlling the lens to approach the eyeball and capturing an image, the image being formed by an illuminating beam reflected by the cornea, determining a working distance by using the image, adjusting the focal length, capturing a fundus image, and determining a photography focal length by using the fundus image, and photographing a fundus image by using the photography focal length on the working distance.

Abstract: Embodiments of the present disclosure provide an AI capability research and development platform and a data processing method. The AI capability research and development platform includes: a data management module, a tool management module, a process management module and a model management module, where the data management module is configured to perform data processing on received data, including at least one of the following: analyzing data type of the data, converting the data according to preset data format and storing the data; the tool management module is configured to store at least one tool, each tool being used to execute a preset processing flow; the process management module is configured to perform model training according to the tool provided by the tool management module and the data provided by the data management module; the model management module is configured to store a model obtained by the model training.

Abstract: Methods and apparatuses are described for generating a domain-specific knowledge graph from unstructured computer text. A computing device extracts unstructured computer text associated with pairs of entities from domain-independent documents, and trains an entity relationship classification model using a domain-independent knowledge graph and the extracted text. The computing device extracts other unstructured text associated with a first domain from domain-specific documents. The computing device identifies pairs of entities contained within the text for the first domain, and executes the trained model to determine a relationship between the entities in each pair of entities identified from the text for the first domain. The computing device generates a domain-specific knowledge graph using (i) the pairs of entities identified from the text for the first domain and (ii) the relationships between the entities in each pair of entities identified from the text for the first domain.

Abstract: Embodiments of the present disclosure provide an AI capability research and development platform and a data processing method. The AI capability research and development platform includes: a data management module, a tool management module, a process management module and a model management module, where the data management module is configured to perform data processing on received data, including at least one of the following: analyzing data type of the data, converting the data according to preset data format and storing the data; the tool management module is configured to store at least one tool, each tool being used to execute a preset processing flow; the process management module is configured to perform model training according to the tool provided by the tool management module and the data provided by the data management module; the model management module is configured to store a model obtained by the model training.