Edge computing (EC) is a distributed computing paradigm that brings computation and data storage closer to the data sources. With the rapid development of EC, offloading scientific computing and data processing tasks from end devices to edge nodes (ENs) can satisfy these tasks’ requirements. However, a single EN with limited computing capacity is usually insufficient to handle these tasks. Hence, using a coalition structure (CS) of many ENs to handle multiple concurrent tasks in an EC environment becomes a feasible scheme. Still, many existing methods cannot be used directly in this scenario because of enormous CS solution space, low search speed, long-running time, poor optimal solution quality, etc. In response, we propose an arbitrary discrete political optimizer (ADPO) algorithm with a discrete recent-past position updating strategy to explore the potential CS space. Unlike other heuristic algorithms, ADPO further improves the better solutions by interacting with each other in the parliamentary affairs phase. After that, we formulate an integer programming (IP) optimization model to alleviate the low search speed or long time consuming. Finally, extensive experiments demonstrate that ADPO is superior to the existing heuristic algorithm. In addition, the IP model excels in exiting algorithms at the running time (milliseconds level) and resource occupied ratio.

In remote sensing change detection (RSCD) tasks, high-resolution remote sensing images can provide fine image details and complex texture features. Deep learning based RSCD methods have achieved state-of-the-art (SOTA) performance. However, most of these methods only consider vertical multiscale features when extracting features, while ignoring the problem of loss of contextual features due to scale changes. Hence, in order to overcome the above issues, a deeper multiscale encoding-decoding feature fusion network (DMEDNet) is proposed in this letter. The encoding stage is considered in both horizontal (Features of the same scale in the same layer) and vertical (Different scale features in different layers) dimensions, allowing the model to fully extract deeper multiscale features. The decoding phase further overcomes the problem of contextual information loss by reusing the obtained same scale features. In addition, a parallel convolutional block attention module (PCBAM) is applied to better focus on the most representative features, thereby improving the detection effect of the model. The proposed method is compared with several other SOTA change detection methods on the season-varying change detection dataset (CDD). The experimental results show that the proposed method obtains the highest F1 score of 97.4% and exhibits stronger robustness in dealing with complex scenes and small change targets.

More and more research has been focused on improving system expansion and data sharing abilities. of the original system. For China railway information systems, in order to follow its development, how to construct the newly effective information system is its important problem, the existing information service must be used in the newly system. In this paper, aiming at those problems mentioned above, a new system service integration method based on Autonomous Decentralized System is proposed. Based on Autonomous Decentralized System and its correlative technologies, this paper proposes a new integrated method based on Knowledge Data Base via Information Flexibility Descending Field (IFDF) Firstly, according to the information service feature, the definition and its logical functional structure of Autonomous Integration Node (AIN)is described, AIN is the key node to implement service integration. Secondly, this paper describes the flexible and rigid information formats, these information formats can be used to implements logical nodes data exchanging autonomously. Lastly, based on the paper's research, China railway autonomous Service Integration System (CSIS) model is given out. Based on ADS and KDB, its on-line ability can be assured.