In the 6G era, Internet of Things (IoT) devices can form a blockchain network, which also faces the problems of data sharing. The data transmitted and stored through the network have the risk of privacy leaking. Encrypting the shared data can satisfy the need of the privacy, and retrieving the encrypted data can make the data used efficiently. However, to enable users to retrieve encrypted data and perform fine-grained authorization on their encrypted files is still a great challenge. Although attribute-based keyword search (ABKS) is a well-received solution to the challenge, there are still privacy and efficiency issues if the traditional ABKS schemes are directly used in blockchain data sharing. In order to solve the problems, this article proposes privacy protection data retrieval scheme with an inverted index, which is an application of attribute-based encryption. First, our scheme is proved secure against the outside keyword guessing attack (KGA) and chosen keyword attack (CKA) under the semitrusted model. Second, the scheme returns a multikeywords ranked result. Third, we analyze the efficiency of our scheme and verify it by simulation. The results show that our scheme has improvement in efficiency and can meet the data sharing needs of the blockchain network composed of IoT devices.

Artificial Intelligence has been a growth catalyst to our society and is cosidered across all idustries as a fundamental technology. However, its development has been limited to the signal processing domain that relies on the generated and collected data from other sensors. In recent research, concepts of Digital Artificial Intelligence and Physicial Artifical Intelligence have emerged and this can be considered a big step in the theoretical development of Artifical Intelligence. In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence. The paper will also examine the trend and governance of Physicial Artifical Intelligence.

In long-text classification tasks, the main issue we face is that traditional text classification methods lack the capability to analyze complex contextual information and implicit semantics, resulting in poor classification performance. To address the shortcomings of existing long-text classification models—such as poor accuracy, low efficiency, inadequate dynamic adjustment, and poor threshold adaptability—we propose a Transformer-based sliding window threshold optimization long-text classification model. We investigate an automatic classification and dynamic partitioning method for long-text semantic analysis. The approach utilizes context-aware fusion of semantic information from long texts to improve the accuracy of automated long-text classification. Finally, we employ an error feedback mechanism to dynamically adjust the classification threshold, achieving optimized threshold settings for long-text content classification. The experiment shows that this model surpasses the baseline model in accuracy and F1 performance on the dataset and exhibits good convergence speed, validating its effectiveness.

With the rapid development of the world today, the metaverse is increasingly affecting society. At the same time, NFT has continuously become a hot word in technology in recent years. This paper studies the economic cycle and its application in the virtual and real society under the background of the metaverse, the development status of digital works NFT, its role in the virtual and real society, and sorts out various problems about risks in finance, content, copyright, etc. Besides, it puts forward countermeasures from the aspects of legal supervision, technical means, management system, etc., which are of great significance to the healthy and orderly development of NFT, social governance, and digital economy planning and decision-making in the metaverse.