Microservices is a flexible architectural style that has many advantages over the alternative monolithic style. These include better performance and scalability. It is particularly suitable, and widely adopted, for cloud-based applications, because in this architecture a software system consisting of a large suite of services of fine granularity, each running its own process and communicating with the others. However, programming such systems is more complex. In this paper we report on CIDE, an integrated software development environment that helps with this. CIDE supports programming in a novel agent-oriented language called CAOPLE and tests their execution in a cluster environment. We present the architecture of CIDE, discuss its design based on the principles of the DevOps software development methodology, and describe facilities that support continuous testing and seamless integration, two other advantages of Microservices.
The graph neural network (GNN) is a type of powerful deep learning model used to process graph data consisting of nodes and edges. Many studies of GNNs have modeled the relationships between the edges and labels of nodes only by homophily/heterophily, where most/few nodes with the same label tend to have an edge between each other. However, this modeling method cannot describe the multiconnection mode on graphs where homophily can coexist with heterophily. In this work, we propose a transition matrix to describe the relationships between edges and labels at the class level. Through this transition matrix, we constructed a more interpretable GNN in a neighbor-predicting manner, measured the information that the edges can provide for the node classification task, and proposed a method to test whether the labels match the edges. The results show the improvement of the proposed method against state-of-the-art (SOTA) GNNs. We also obtain the following two results: (1) the poor performance of GNNs is highly relevant to the information of edges instead of heterophily, which is always considered the main factor resulting in the decline in performance; and (2) most benchmark heterophilic datasets exhibit the label-edge mismatch problem, leading them to become intractable.
Remote sensing satellite mission planning is one of the hot issues in the space engineering research field, and a large number of mission planning approaches have been proposed in related research work. Numerous mission planning schemes were constructed for different mission requirements. How to evaluate the merits of the schemes is of great significance to improve the quality and effectiveness of remote sensing satellite missions. Based on the analysis of the basic problems of remote sensing satellite mission planning, a technology framework of mission scheme evaluation is proposed, and an evaluation index system for remote sensing mission planning schemes is constructed, including mission completion rate, planning timeliness and resources occupancy. A TOPSIS-based evaluation model is proposed to calculate the valuation of mission scheme according to the index system. The case study shows that the mission planning scheme evaluation approach proposed in this paper is feasible and effective.
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