Abstract: Disclosed are a process parasitism-based branch prediction method and device for serverless computing, an electronic device, and a readable storage medium. The method includes: receiving a calling request of a user for a target function; when capacity expansion is required, scheduling a container executing the target function to a new server that has not executed the target function in a preset period of time, wherein a parasitic process is pre-added to a base image of the container; triggering the parasitic process when the container is initialized on the new server, the parasitic process being used for initiating a system call, and triggering a system kernel to select a target template function according to the type of the target function and copying the target template function N times; using execution data of the copied N target template functions as training data to train a branch predictor on the new server.

Abstract: Disclosed are a process parasitism-based branch prediction method and device for serverless computing, an electronic device, and a readable storage medium. The method includes: receiving a calling request of a user for a target function; when capacity expansion is required, scheduling a container executing the target function to a new server that has not executed the target function in a preset period of time, wherein a parasitic process is pre-added to a base image of the container; triggering the parasitic process when the container is initialized on the new server, the parasitic process being used for initiating a system call, and triggering a system kernel to select a target template function according to the type of the target function and copying the target template function N times; using execution data of the copied N target template functions as training data to train a branch predictor on the new server.

Abstract: A method for processing synthetic features is provided, and includes: the synthetic features to be evaluated and original features corresponding to the synthetic features are obtained. A feature extraction is performed on the synthetic features to be evaluated based on a number S of pre-trained samples, to obtain meta features with S samples. S is a positive integer. The meta features are input into the pre-trained meta feature evaluation model for a binary classification prediction, to obtain a probability of binary classification. Quality screening is performed on the synthetic features to be evaluated according to the probability of the binary classification, to obtain second synthetic features to be evaluated. The second synthetic features are classified in a good category. The second synthetic features and original features are input into a first classifier for evaluation. classified in a poor category.

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: This application relates to an indoor positioning method and system and an electronic device. The method includes: calculating distances between an unknown node and at least three known nodes; obtaining at least three square regions around the at least three known nodes by using the distances as radiuses, and obtaining a minimum overlapping region based on overlapping parts of the at least three square regions; reducing the minimum overlapping region in an equal proportion by using a geometric center of the minimum overlapping region as a center to obtain a new square region; calculating an optimal vertex location of the new square region through iteration; and forming a new smaller region around the optimal vertex location by using the optimal vertex location as a new central point, and using an optimal vertex location of the smaller region as an estimated location of the unknown node.