Abstract: A driving method of an electronic paper display apparatus, including: inputting, by a controller, a first driving signal to a pixel electrode of a pixel driving circuit corresponding to a pixel which is required to display black, according to an image to be displayed; and inputting, by the controller, a second driving signal to a pixel electrode of a pixel driving circuit corresponding to a pixel which is required display white, according to the image to be displayed. A driving stage of the electronic paper display apparatus comprises a first homogenization stage which comprises a plurality of homogenization sub-stages. At a last homogenization sub-stage, the first and second driving signals comprise first and second driving sub-signals, respectively. Voltages of the first driving sub-signal and the second driving sub-signal have polarities opposite to polarities of the black particles and the white particles, respectively.

Abstract: Provided are a cross-tables search method, an electronic device and a storage medium, relating to a field of artificial intelligence, and in particular, to natural language processing, big data, knowledge graph technology, which may be applied in scenarios of smart cloud, smart city, and smart government affairs. The cross-tables search method includes: parsing a query to obtain an entity, attribute and relationship required to be searched; determining a cross-tables search strategy according to the entity, the attribute and the relationship; and performing a cross-tables search operation according to the cross-tables search strategy.

Abstract: The disclosure provides an image clustering method and an image clustering apparatus. The method includes: obtaining new images, and clustering the new images to obtain a first cluster; determining a historical cluster similar to the first cluster as a second cluster from existing historical clusters; obtaining a distance between the first cluster and the second cluster; and generating a target cluster by fusing the first cluster and the second cluster based on the distance. In the image clustering method, with the image clustering apparatus of the disclosure, secondary clustering processing performed on the existing historical clusters based on newly added images is not required, new and old clusters are directly fused.

Abstract: A method includes: sending, by one or more computers, in response to the number of data providers for federated learning being greater than a first threshold, a data field required for the federated learning to a coordinator, the coordinator comprising a computer; receiving, by one or more computers, from the coordinator, information about one or more data providers comprising the required data field, for determining the data providers comprising the required data field as the remaining data providers, wherein the coordinator stores a data field of each data provider; and performing, by one or more computers, federated learning-based modeling with each of the remaining data providers.

Abstract: The disclosure provides an image clustering method and an image clustering apparatus. The method includes: obtaining new images, and clustering the new images to obtain a first cluster; determining a historical cluster similar to the first cluster as a second cluster from existing historical clusters; obtaining a distance between the first cluster and the second cluster; and generating a target cluster by fusing the first cluster and the second cluster based on the distance. In the image clustering method, with the image clustering apparatus of the disclosure, secondary clustering processing performed on the existing historical clusters based on newly added images is not required, new and old clusters are directly fused.

Abstract: A method includes training, in collaboration with a plurality of collaborators, a plurality of tree models based on data of user samples shared with the plurality of collaborators; performing feature importance evaluation on the trained tree models for assigning weights to feature columns generated by respective ones of the tree models; in response to a determination that a linear model is to be trained in collaboration with a first collaborator of the plurality of collaborators, inputting data of a first user sample shared with the first collaborator into a first tree model of the plurality of tree models and one or more second tree models of the plurality of tree models to obtain a plurality of one-hot encoded feature columns; and screening the obtained feature columns based on the respective weights and training the linear model according to the screened feature columns and the data of the first user sample.

Abstract: A method, electronic device and storage medium for item recommendation and for model training, which relates to the field of artificial intelligence, are disclosed. According to some embodiments: an item feature expression database is created using a pre-trained user-clicking-item task model and at least two pieces of feature information of items in the repository of items to be recommended; a feature expression of a user is obtained using the pre-trained user-clicking-item task model and at least two pieces of feature information of the user; identifiers of N items to be recommended are obtained according to the feature expression of the user and the item feature expression database; relevant information of the N items is recommended to the user based on the identifiers of the N items.

Abstract: A method, an apparatus, a device and a storage medium for embedding user app interest are provided. The method includes: acquiring a user existing app installation list and a user app installation list within a predetermined time window, where the app includes app ID information and app category information; inputting the existing app installation list and the app installation list within the predetermined time window into a pre-trained user app interest embedding model to obtain a user app interest embedding vector. By combining the user existing app installation list information and the user recent app installation list information, the user app interest embedding vector may simultaneously reflect the user long-term interest and the user short-term interest.