Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding.

Text generation has always been limited by the lack of corpus data required for language model (LM) training and the low quality of the generated text. Researchers have proposed some solutions, but these solutions are often complex and will greatly increase the consumption of computing resources. Referring to the current main solutions, this paper proposes a lightweight language model (EDA-BoB) based on text augmentation technology and knowledge understanding mechanism. Experiments show that the EDA-BoB model cannot only expand the scale of the training data set but also ensure the data quality at the cost of consuming little computing resources. Moreover, our model is shown to combine the contextual semantics of sentences to generate rich and accurate texts.

A fusion data security protection scheme for sensitive E-documents in the open network environment.

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment.