3 results about "MultiDimensional eXpressions" patented technology

Described herein are systems and methods for use with a multidimensional database environment, for providing bottom-up multidimensional data analysis in complex formula types. Complex formulas expressions with conditional branches executed over very sparse regions can result in large computational overheads when the iteration is performed in a top-down mode without a knowledge of the target multidimensional cells that have real base data for evaluation. In accordance with an embodiment, the system employs a bottom-up approach that allows identification of those target cells having real data, and executes only those intersections for complex multidimensional expression evaluation. The bottom-up query path functionality can be activated in autonomous mode. The described approach minimizes the amount of redundant executions that have no base data, in some instances to zero level.

Described herein are systems and methods for use with a multidimensional database environment, for providing bottom-up multidimensional data analysis in complex formula types. Complex formulas expressions with conditional branches executed over very sparse regions can result in large computational overheads when the iteration is performed in a top-down mode without a knowledge of the target multidimensional cells that have real base data for evaluation. In accordance with an embodiment, the system employs a bottom-up approach that allows identification of those target cells having real data, and executes only those intersections for complex multidimensional expression evaluation. The bottom-up query path functionality can be activated in autonomous mode. The described approach minimizes the amount of redundant executions that have no base data, in some instances to zero level.

This invention discloses a semi-automatic annotation method and system for student facial expression data based on multimodal fusion, comprising: S1 generating multidimensional expressions based on a semantic conditional diffusion model, and simultaneously outputting discrete expression classifications and continuous VAD emotion dimension labels through text prompts in an educational setting; S2 obtaining synthetic expression images and their corresponding 68 facial key point coordinates based on S1, performing classroom scene-adaptive style transfer, and using a key point-constrained adversarial generative network to preserve expression features and adapt to classroom lighting and viewing angle; S3 identifying the classification probability of discrete expression categories and the three-dimensional predicted value of the continuous VAD emotion dimension, using hybrid uncertainty-driven active learning, and combining classification entropy and regression variance to select high-value samples; S4 performing identity decoupling and privacy anonymization on the selected high-value samples, reconstructing the face through a 3D deformation model and specifically blurring the eyebrow region to achieve anonymity protection. This invention can ensure privacy compliance, reduce annotation costs, and improve model accuracy and robustness.