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.

With the rapid development of information technologies such as cloud computing, big data, artificial intelligence, and edge computing, data centers have become essential infrastructure supporting the modern information society. When constructing data center networks, as the network scale increases, both latency and cost also grow. Therefore, it is essential not only to consider network scalability but also to focus on link overhead and communication latency. The hypercube is an excellent base topology for constructing data center networks. The Möbius cube, a version of the hypercube, not only retains the hypercube’s favorable properties, but also outperforms it in terms of link overhead and network diameter. In this paper, we propose a new server-centric data center network architecture, called MBDC, which is based on the Möbius cube. For networks of the same scale, MBDC achieves a smaller diameter than most existing server-centric networks. Additionally, we present an adaptive fault-tolerant routing scheme for MBDC, which is based on an improved local security information model. Extensive evaluations demonstrate that MBDC is an attractive data center network for constructing low-latency and cost-effective data centers.

Attention Deficit Hyperactivity Disorder (ADHD) is a common psychiatric disorder in childhood, and its pathogenesis may be related to abnormalities in brain network connectivity.In this study, we employ minimum spanning tree and hierarchical clustering algorithms to analyze the differences in brain functional network structures between ADHD patients and normal individuals.Initially, we compute Pearson correlation coefficients to construct functional connectivity matrices, delving into the mean matrices for both groups to pinpoint regions marked by significant discrepancies.Subsequently, we construct the minimum spanning trees for each group, assessing their average leaf scores and examining the variations in network topology.In the final phase, we apply hierarchical clustering to the minimum spanning trees of both cohorts, with an analysis of the community structure conducted through homogeneous and heterogeneous slicing.The results show significant differences in functional connectivity networks in the ADHD group, revealing unique features in the neural mechanisms.

Topology design is vital to the high performance data center networks. Due to the limited scalability, many traditional server-centric data center networks are confronting the updating and upgrading hurdles. To address the issue, this paper proposes a highly scalable DCell-based server-centric data center network topology, called HS-DCell, which can use inexpensive and typical switches and servers with only three network ports to achieve excellent network performance HS-DCell can accommodate a large number of servers, and its diameter increases linearly with the growth of network levels, which is better than that of most existing server-centric networks. Furthermore, a fault-free routing algorithm and a fault-tolerant routing algorithm are developed based on HS-DCell. Compared with other mainstream server-centric network topologies, the experimental results show that HS-DCell has obvious advantages in many key performance indicators including scalability, fault tolerance, and server port utilization.

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.

Sensing human activity via WiFi Channel State Information (CSI) has considerable application prospects in future intelligent interaction scenarios such as virtual reality, intelligent games, metaverse, etc. Recently, many Deep Learning-based WiFi sensing schemes have been proposed in the literature, which gained high accuracy for a wide range of simple activities such as standing, squatting, and bending. However, the performance will be suffered when existing approaches are used to recognize drastic activities, such as actions in vigorous sports. This is mainly due to the reason that the spatiotemporal information of these actions is not well utilized. To overcome this drawback, we propose a novel DL-based WiFi sensing method for drastic activity recognition by combining the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) network. The designed CNN-BiLSTM architecture is in parallel with feature extraction, which can simultaneously extract sufficient spatiotemporal features of action data and establish the mapping relationship between actions and CSI streams, thereby improving the accuracy of activity recognition. The CNN is used to extract information on the spatial dimension, while the BiLSTM extracts information on the time dimension. To verify the performance of the proposed scheme, we build a hardware experiment platform and constrain a dataset with 1400 pieces of records for 7 classes of basketball actions. After training over the dataset, the proposed CNN-BiLSTM scheme achieves 96% experimental accuracy on the test set, which is better than the benchmark methods.