The vigorous development of the Industrial Internet of Things (IIoT) has brought massive amounts of data. In order to serve users more extensively and fully utilize the potential of data, it is particularly important to establish a fair and open IIoT data market. In order to enhance trust between data owners and consumers and facilitate transactions between the two parties, this article proposes a blockchain-based IIoT data market framework. To address another important issue in the IIoT data market: data pricing, we formulate the problem of maximizing the interests of the data platform, data providers, and consumers as a Stackelberg game pricing model. In this model, the data platform charges the data provider for data transmission, the data provider sells data to the consumer, and the consumer can determine the amount of data to purchase. The existence of Stackelberg equilibrium is proved by backward induction. Finally, the performance of the model was evaluated through numerical simulations.

Named Entity Recognition (NER) for diabetes in China is the foundation for processing Chinese diabetic medical data. However, the structure of Chinese diabetes medical data is complex, with issues such as confusion between entity types and ambiguous entity boundaries, which pose difficulties and challenges for named entity recognition in Chinese diabetes data. Current mainstream named entity recognition models typically use single-feature representations of text, which do not fully use the features of the text, resulting in poor entity recognition performance. Therefore, this paper proposes a Chinese diabetes named entity recognition method that uses multi-feature fusion and a multi-head attention mechanism. It uses the RoBERTa-wwm pre-trained model to obtain character vectors from Chinese diabetes medical texts and enhances their semantic features by incorporating Chinese radical-level structural feature vectors. The obtained vectors are processed through a BiGRU module to extract global context features and a CNN module to capture multi-scale local features. After fusing these features, the most relevant feature information for entities is obtained by integrating the multi-head attention mechanism. Finally, the conditional random field is used for entity recognition to complete extraction of the entities. Experimental results show that the proposed method exhibits superior performance and achieves better results on the Chinese diabetes annotation dataset.

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure.In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest.To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas.This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas.

In recent years, the graph feature mining method of brain connection data based on graph theory has been regarded as a popular and universal technology in the field of neuroscience. How to mine valuable information from brain connection data has become a research hotspot. Current research shows that the pathogenic factors of attention deficit and hyperactivity disorder (ADHD) may be caused by the abnormal connection between brain network structures. In order to find out the pathogenic factors of ADHD patients, we also carried out frequent sub-graph mining on the connectivity graph data of brain functional network. By constantly adjusting the sup-port threshold, all the subgraphs of ADHD patients and healthy control group were mined, and the differences in brain region connectivity were successfully found out. By combining the recently introduced neural document embedding model with traditional pattern mining techniques, we regard the brain network connection structure graph as the document and frequent subgraph as the atomic unit of the embedding process. By learning the mapping, each graph can be mapped to a D-dimensional continuous vector. The mapping needs to capture the similarity between the graphs. Feature vectors can be used as the direct input of graph classification in many traditional machine learning methods. Finally, support vector machine in machine learning is used to verify the accuracy of classification, and the results show that the accuracy is high.

With the development of big data, people focus on data security and privacy, blockchain has been paid more and more attention. Compared to pre-2013, the number of blockchain literature publications has increased. Scholars from different research fields have deeply explored blockchain technology. In this paper, we collected blockchain literature data from 2013 to 2020, and then we conducted a quantitative analysis of publications, author groups, research content and future research content. For the current research status, we also discussed the problems facing blockchain. We started by mining representative authors, and revealed current status of blockchain research, author’s team cooperation, and blockchain’s development content. We can provide research objectives and inspiration for new researchers, and give research routes for the future development of blockchain.

Expression plays an important role in language inheritance, interpersonal communication, and social stability. With the rapid development of the Internet, people are becoming frequently interested in expressing themselves on social media. Meanwhile, massive anomic expressions are generated, which pollute network environments and even hinder social development. Hence, the purpose of this article is detecting anomic expressions in social media automatically, so as to reveal fine-grained status of online expressional anomie. Specifically, the authors used machine learning to detect anomic expressions and identify anomic types. Then, impacts of different factors (e.g. gender, region, time) on expressional anomie were analyzed. Finally, distributions and characteristics of expressional anomie about online contents were obtained. Empirical results indicate that the current situation of expressional anomie is severe, and scientific and effective treatments for anomic expression are necessary and urgently. Meanwhile, gender, region, and time should be taken into consideration in the formulation of treatments.