An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions.

In recent years among data hiding technologies, Reversible Data Hiding(RDH) technology has attracted widespread interest and application, which is to hide the secret information in a carrier image and recover the original carrier image losslessly to extract the secret information. Current research on RDH algorithms mainly involving frequency domain, spatial domain, and encryption domain. Based on the Prediction-Error Expansion(PEE) methods, as spatial domain approaches, achieved great progress in the past decade. However, there is a defect in the state-of-the-art methods that with the embedded payload increased, the distortion rate of the cover image increased simultaneously. To solve the problem, we proposed a refined reversible data hiding algorithm based on the PEE method with simple implementation. We improved an effective predictor that all the remaining pixels can be predicted in the embedding process, except for those in the first row, the first column, the last row, and the last column in the original image. The extraction process is the reverse of the embedding process that the embedded information and the original carrier is restored without damage. Our work utilized the correlation between image pixels better to solve the inherent contradiction between payload and distortion rate in the state-of-the-art data hiding algorithms. Proven through experiments, our method achieved a large embedding capacity while keeping the image distortion rate and computing complexity low.

We introduce Federated Medical Data Augmentation with Differential Privacy for Medical Assistance (FMDADP-MA), addressing the challenge of limited medical data sharing due to privacy regulations and data isolations. Unlike traditional Generative adversarial networks assuming Independent and Identically Distributed (IID) data, FMDADP-MA facilitates data augmentation in non-IID environments using federated learning. This framework enables medical institutions collaboration across different locations to enrich datasets without centralizing data, overcoming collection and computational constraints. By organizing edge nodes and selecting groups for global training, we minimize data transmission to a central server. Each local model uses two convolutional neural networks to generate and label data, incorporating local differential privacy to safeguard against gradient-based privacy breaches. Our experiments show that increasing participant institutions enhances the global model’s accuracy, boosts local model performance, and diversifies data generation, tackling real-world issues of medical data privacy, imbalance, and under-labeling.

Self-supervised auto-encoders have emerged as a successful framework for representation learning in computer vision and natural language processing in recent years, However, their application to graph data has been met with limited performance due to the non-Euclidean and complex structure of graphs in comparison to images or text, as well as the limitations of conventional auto-encoder architectures. In this paper, we investigate factors impacting the performance of auto-encoders on graph data and propose a novel auto-encoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training in order to mimic the process of human cognitive learning, and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.