Abstract: A method is provided.

Abstract: The present disclosure discloses a method and apparatus for displaying map points of interest, and an electronic device, relates to the field of artificial intelligence, and in particular to intelligent transportation. A specific implementation solution includes: acquiring features corresponding to multiple candidate points of interest; determining predicted popularity of the multiple candidate points of interest according to a mapping relation between each feature and each popularity and the features of the multiple candidate points of interest, and the mapping relation is determined based on the frequency of operations performed by a user for each sample point of interest in a historical time period; and displaying the candidate points of interest of which predicted popularity meets a preset popularity condition in a map. Therefore, the accuracy of the displayed points of interest may be enhanced.

Abstract: A technical solution relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process. The encoder performs a coding operation using region features of the sample regions to obtains a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region.

Abstract: A query method is provided and includes: acquiring association records, in which the association record is configured to indicate an execution area, execution time and user attribute data of an execution user, of a behavior; splitting the association record into behavior records based on attribute items included in the user attribute data of the association record, in which the behavior record is configured to indicate a mapping relationship between at least one of the attribute items and the execution area-the execution time; grouping the behavior records to determine behavior statistics information of each group; in which behavior records having the same attribute item, the same execution area and the same execution time belong to the same group; and displaying behavior statistics information of a target group in response to a query operation.

Abstract: A method for transfer learning includes: obtaining a pre-trained model, and generating a model to be transferred based on the pre-trained model, in which the model to be transferred includes N Transformer layers, and N is a positive integer; obtaining a mini-batch by performing random sampling on a target training set; and training the model to be transferred based on the mini-batch, in which a loss value for each Transformer layer is generated based on an empirical loss value and a noise stability loss value.

Abstract: The disclosure provides a multi-task deployment method, and an electronic device. The method includes: obtaining N first tasks and K network models, in which N and K are positive integers greater than or equal to 1; allocating the N first tasks to the K network models differently for operation, to obtain at least one candidate combination of tasks and network models, in which each candidate combination includes a mapping relation between the N first tasks and the K network models; selecting a target combination with a maximum combination operation accuracy from the at least one candidate combination; and deploying a target mapping relation comprised in the target combination and the K network models on a prediction machine.

Abstract: A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.

Abstract: The present disclosure provides a method of federated learning. A specific implementation solution includes: determining, for a current learning period, a target device for each task of at least one learning task to be performed, from a plurality of candidate devices according to a plurality of resource information of the plurality of candidate devices; transmitting a global model for the each task to the target device for the each task, so that the target device for the each task trains the global model for the each task; and updating, in response to receiving trained models from all target devices for the each task, the global model for the each task according to the trained models, so as to complete the current learning period. The present disclosure further provides an electronic device, and a storage medium.

Abstract: A traffic classification method and apparatus, a training method and apparatus, a device and a medium are provided. An implementation is: performing a preprocessing operation on each characteristic of one or more characteristics of an object to be classified; and inputting the one or more characteristics of the object to be classified into a traffic classifier to determine a traffic type of the object to be classified. The preprocessing operation includes at least one of: setting, in response to determining that a characteristic value of the characteristic is invalid data, the characteristic value to a null value; converting, in response to determining that the characteristic is a non-numeric characteristic, the characteristic value of the characteristic to an integer value, and normalizing, in response to determining that the characteristic is a non-port characteristic, the characteristic value of the characteristic.

Abstract: The present disclosure discloses an information processing method and device, and relates to the field of artificial intelligence, in particular, to a graph neural network in the field of deep learning. A specific implementation solution according to an embodiment includes determining initial representation of edges connected between a plurality of atoms in a molecule based on three-dimensional structure information of the molecule; determining first representation of a neighbor edge of each of the atoms based on the initial representation of the edges, the neighbor edge of each of the atoms indicating at least one edge connected with each of the atoms; determining first representation of each of the atoms based on the first representation of the neighbor edge of each of the atoms; determining feature representation for characterizing the molecule based on the first representation of each of the atoms.

Abstract: The present disclosure provides a method for asynchronous federated learning, including: in response to a request for participating in asynchronous federated learning sent by a target electronic device, determining, according to performance information of a server, a first number of electronic devices that the server supports to participate in the asynchronous federated learning, and acquiring a second number of other electronic devices that have participated in the asynchronous federated learning; if the first number is greater than the second number, sending a global model to be optimized to the target electronic device, and receiving target feedback information which is obtained by the target electronic device from training on the global model to be optimized; and optimizing, according to the target feedback information, the global model to be optimized to obtain an optimized global model.

Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.

Abstract: A method for multi-task scheduling, a device and a storage medium are provided. The method may include: initializing a list of candidate scheduling schemes, the candidate scheduling scheme being used to allocate a terminal device for training to each machine learning task in a plurality of machine learning tasks; perturbing, for each candidate scheduling scheme in the list of candidate scheduling schemes, the candidate scheduling scheme to generate a new scheduling scheme; determining whether to replace the candidate scheduling scheme with the new scheduling scheme based on a fitness value of the candidate scheduling scheme and a fitness value of the new scheduling scheme, to generate a new scheduling scheme list; and determining a target scheduling scheme, based on the fitness value of each new scheduling scheme in the new scheduling scheme list.

Abstract: A computer-implemented method is provided. The method includes: executing, for each task in a federated learning system, a first training process comprising: obtaining resource information of a plurality of terminal devices of the federated learning system; determining one or more target terminal devices corresponding to the task based on the resource information; and training a global model corresponding to the task by the target terminal devices until the global model meets a preset condition.

Abstract: A method for data processing is provided. The method includes obtaining first retrieving data associated with a first user and a first retrieving result selected by the first user from at least one retrieving result corresponding to the first retrieving data. The first retrieving data is labelled with an intention tag indicating a retrieving intention of the first user. The method further includes obtaining second retrieving data that is used by a second user to conduct retrieving and selecting the first retrieving result within a predetermined time period. The method further includes assigning the intention tag to the second retrieving data.

Abstract: A method of predicting traffic volume, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to big data and deep learning technologies The method includes: generating, for a plurality of traffic regions, a function relation graph and a volume relation graph; generating a volume feature of a target traffic region among the plurality of traffic regions, according to a historical volume information of the target traffic region; generating a volume and function relation feature for the target traffic region, based on the function relation graph and the volume relation graph; and predicting a volume of the target traffic region according to the volume feature and the volume and function relation feature.

Abstract: The present disclosure provides a method and apparatus for generating an electronic map, an electronic device and a storage medium, and relates to the field of data processing technology. A specific implementation comprises: establishing a plurality of groups of corresponding relationships between enterprise names and enterprise addresses using network data; determining respectively a fine-grained region where each enterprise address is located; and creating an enterprise electronic map of the fine-grained region based on the corresponding relationships between the enterprise names and the enterprise addresses and the fine-grained region where the each enterprise address is located. A corresponding relationship between an enterprise name and an enterprise address is established using existing network data.

Abstract: The present disclosure provides a method and apparatus for determining an encryption mask, a method and apparatus for recognizing an image, a method and apparatus for training a model, a device, a storage medium and a computer program product. A specific implementation comprises: acquiring a test image set and an encryption mask set; superimposing an image in the test image set with a mask in the encryption mask set to obtain an encrypted image set; recognizing an image in the encrypted image set using a pre-trained encrypted image recognition model and recognizing the image in the encrypted image set using a pre-trained original image recognition model to obtain a first recognition result; and determining a target encryption mask from the encryption mask set based on the first recognition result.

Abstract: A method and apparatus for generating a table description text, a device, and a storage medium are provided. An implementation of the method includes: acquiring a to-be-described table, and analyzing the to-be-described table to obtain a set of metalanguage of the to-be-described table, and finally generating a description text of the to-be-described table based on the metalanguage in the set of metalanguage.

Abstract: A computer-implemented method for text classification is provided. The method for text classification includes obtaining an entity category set and a part-of-speech tag set associated with a text. The method further includes constructing a first isomorphic graph for the entity category set and a second isomorphic graph for the part-of-speech tag set. A node of the first isomorphic graph corresponds to an entity category in the entity category set, and a node of the second isomorphic graph corresponds to a part-of-speech tag in the part-of-speech tag set. The method further includes obtaining, based on the first isomorphic graph and the second isomorphic graph, a first text feature and a second text feature of the text through a graph neural network. The method further includes classifying the text based on a fused feature of the first text feature and the second text feature.