Abstract: The present disclosure provides a student performance evaluation method and system based on artificial intelligence (AI) identification data, and relates to the field of intelligent education. A lightweight network model suitable for student performance evaluation takes the AI identification data as an input and evaluation results as an output. A training data generation algorithm is provided, and multidimensional AI identification data and labels are uniformly processed into training data suitable for the network model through the above algorithm, which can solve the problems that dimensions between any AI identification data and various labels are not uniform, and original data cannot meet training of a multidimensional and cross-time prediction model. A simulated data generation algorithm and a simulated label generation algorithm are provided, and simulated training data is generated using these algorithms in conjunction with the training data generation algorithm.

Abstract: Embodiments of the present disclosure provide a quantitative method and system for attention based on a line-of-sight estimation neural network, which improves the stability and training efficiency of the line-of-sight estimation neural network. A few-sample learning method is applied to training of the line-of-sight estimation neural network, which improves generalization performance of the line-of-sight estimation neural network. A nonlinear division method for small intervals of angles of the line of sight is provided, which reduces an estimation error of the line-of-sight estimation neural network. Eye opening and closing detection is added to avoid the line-of-sight estimation error caused by an eye closing state. A method for solving a landing point of the line of sight is provided, which has high environmental adaptability and can be quickly used in actual deployment.

Abstract: The present disclosure discloses an adaptive high-precision compression method and system based on a convolutional neural network model, and belongs to the fields of artificial intelligence, computer vision, and image processing. According to the method of the present disclosure, coarse-grained pruning is performed on a neural network model by using a differential evolution algorithm first, and the coarse-grained space is quickly searched through an entropy importance criterion and an objective function with good guidance to obtain a near-optimal neural network structure. Then fine-grained search space is built on the basis of an optimal individual obtained from the coarse-grained search, and fine-grained pruning is performed on the neural network model by a differential evolution algorithm to obtain a network model with an optimal structure. Finally, the performance of the optimal model is restored by using a multi-teacher multi-step knowledge distillation network to reach the precision of an original model.

Abstract: A method for creating a group is disclosed, the method includes: presenting, by a terminal, at least one object on a display user interface; and when the terminal detects a first operation for a first object, reserving at least one to-be-selected object on the display user interface according to a preset group attribute of the first object, and triggering the at least one to-be-selected object to enter a to-be-grouped editing state. The method further incudes when the terminal detects a curved slide operation, and a track of the curved slide operation passes through multiple to-be-selected objects an odd number of times, using the multiple to-be-selected objects as one group, where the track of the curved slide operation is a non-closed curve. Therefore, a group can be quickly created, operations can be reduced, and operation efficiency can be improved.