Jiangnan University

Hengyang Lu

Jun Sun

Wei Fang

Xiaojun Wu

Xin Zhang

Runsu Zhu

Chenyou Fan

The present invention provides a text classification backdoor attack method, system, device and a computer storage medium. The method includes: training a pretraining model by using a clean training set to obtain a clean model; generating a pseudo label data set by using a positioning label generator; performing multi-task training on a Sequence-to-Sequence model by using the pseudo label data set to obtain a locator model; generating a backdoor data set by using the locator model; and training the clean model by using the backdoor data set to obtain a dirty model. A pseudo label data set is generated by using a pretrained clean model without manual annotation. A backdoor attack location in a text sequence may be dynamically predicted by using a locator model based on a Sequence-to-Sequence and multi-task learning architecture without manual intervention, and a performance indicator obtained by dynamically selecting an attack location is better.

Text classification backdoor attack prediction method, system, and device

The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration. The present disclosure effectively combines the temporal change of air pollution with the spatial diffusion relationship, and namely proposes a more efficient way to select more highly relevant data to predict air quality so that a prediction result is more accurate.

Deep spatial-temporal similarity method for air quality prediction

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Wuxi, China

Hengyang Lu