As an infrastructure of the ubiquitous sensor networks, the wireless sensor network plays an important role in generation of multinetworks integrated system. And currently the wireless sensor network is not only being used to monitor and analyze information about the environment but also being used for more dynamic systems. The security system is one of standards for measuring whether a wireless sensor networks is an outstanding wireless sensor network. RUASN proposes a robust user authentication framework for wireless sensor networks, based on a two-factor concept. This proposed scheme possessed many advantages against major existing attacks and performed well at efficiency and low consumption. However, we have identified that the resistance of collusion attacks is weak. After analyzing, we proved that we can obtain the session key via controlling a compromised sensor node and using the collusion attack when a user wants to establish a session with a legal node. Therefore, to enhance the resistance of collusion attacks, we present two ways to solve the security drawbacks of RUASN scheme. One is to add a slight improvement into the RUASN scheme to enhance this scheme. Another is using the Hardware Security Module. After a simple analysis, we have proved that the improved scheme can resist the collusion attack.

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. INDEX TERMS reversible data hiding (RDH), prediction-error expansion (PEE), watermarking.

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.