New technologies such as Internet, cloud computing, artificial intelligence (AI) and blockchain have greatly promoted the innovation of financial industry structure and paradigm, improved the efficiency of financial services and brought the spillover of the financial technology (FinTech) risk. The existing financial regulation methods cannot meet the needs of the development of FinTech, therefore, there is an urgent need to improve the trust mechanism. Blockchain can effectively solve the problems of security and trust in FinTech. This paper will explore the expression method of trust index in blockchain, and build based on blockchain a multi-dimensional trust index system and evaluation mechanism (MDTEM) for FinTech. Firstly, a four-level blockchain structure has been built, including cloud level blockchain, Internet level blockchain, contract level blockchain and application level blockchain in the FinTech ecological environment to ensure the security, reliability and trustworthiness of financial services. Secondly, according to the trust structure of blockchain on FinTech payment behavior, the index system and evaluation mechanism of direct trust, indirect trust, recommendation trust and feedback trust of FinTech based on blockchain are designed. Finally, the trust simulation experiment of FinTech mechanism is carried out from three aspects: data sending, data transmission, data reception and delivery success rate. Simulation results show that the proposed MDTEM trust mechanism can better improve the safe and reliable application of FinTech trust mechanism.

Higher-order interactions exist widely in mobile populations and are extremely important in spreading epidemics, such as influenza. However, research on high-order interaction modeling of mobile crowds and the propagation dynamics above is still insufficient. Therefore, this study attempts to model and simulate higher-order interactions among mobile populations and explore their impact on epidemic transmission. This study simulated the spread of the epidemic in a spatial high-order network based on agent-based model modeling. It explored its propagation dynamics and the impact of spatial characteristics on it. Meanwhile, we construct state-specific rate equations based on the uniform mixing assumption for further analysis. We found that hysteresis loops are an inherent feature of high-order networks in this space under specific scenarios. The evolution curve roughly presents three different states with the initial value change, showing different levels of the endemic balance of low, medium, and high, respectively. Similarly, network snapshots and parameter diagrams also indicate these three types of equilibrium states. Populations in space naturally form components of different sizes and isolations, and higher initial seeds generate higher-order interactions in this spatial network, leading to higher infection densities. This phenomenon emphasizes the impact of high-order interactions and high-order infection rates in propagation. In addition, crowd density and movement speed act as protective and inhibitory factors for epidemic transmission, respectively, and depending on the degree of movement weaken or enhance the effect of hysteresis loops.

Arbitrary-oriented object detection (AOOD) plays a significant role in image understanding in remote-sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multigrained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the angle prediction with much lower costs than narrowing the granularity of DAE. Furthermore, an Intersection over Union (IoU)-aware FAR-Loss (IFL) is designed to improve the accuracy of angle prediction using an adaptive reweighting mechanism guided by IoU. Extensive experiments are performed on several public remote-sensing datasets, which demonstrate the effectiveness of the proposed MGAR. Moreover, experiments on embedded devices demonstrate that the proposed MGAR is also friendly for lightweight deployments.

The mobile ad hoc network (MANET) is a kind of dynamic, easy to construct and universal network, which has been widely concerned by a large number of researchers. Graph theory provides an effective theoretical tool for MANETs modeling and analysis. Clustering is one of the most effective methods to measure network performance with different attributes. This paper gives the basic concept of graph kernel and discusses the principle of optimizing graph kernel and multi-graph kernel. In this paper, we propose a Graph Kernel based Clustering Algorithm in MANETs (GKCA). The GKCA algorithm gives the basic concept of graph kernel, discusses the principle of optimizing graph kernel and multi-graph kernel, and proposes the basic principle based on $d$ -hop graph kernel. GKCA algorithm uses shortest path (SP) to connect different cluster head nodes for packet transmission. The performance of GKCA algorithm, such as the control packets ratio, packets loss ratio, and average end-to-end delay are experimentally evaluated using network simulation (NS2) software. Experimental analysis shows that the proposed approach is efficient, and its performance advantage in dynamic mobile networks is promising.