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Abstract: Accurate identification of cotton top bud is important for cotton topping operation to detect cotton terminal bud accurately in field, a recognition method using Faster R-CNN (Faster Region Convolutional Neural Networks, Faster R-CNN) by integrating dynamic mechanism was proposed to solve the recognition difficulties caused by the small size of cotton terminal bud when it is topped in densely planted fields. The RegNetX-6.4GF model was used as the backbone network to improve its image feature extractor capabilities. Due to number of proposals under a higher IOU(Intersection Over Union, IOU) as well as the matching degree between anchor and the target shape affect the performance of the detector, the method proposed in this paper changed the original anchor generation mechanism by combining FPN (Feature Pyramid Network, FPN) and Guided Anchoring in RPN (Region Proposal Network, RPN), which will cause the distribution of the proposals generated by the RPN of the algorithm under different IOUs dynamically change during the training process. To adapt the dynamic change of proposals distribution, we integrated Dynamic Region Convolutional Neural Networks ( Dynamic R-CNN) in Faster R-CNN, which can dynamically adjust the value of IOU to obtain high quality proposals. And the GROIE (Generic ROI Extractor, GROIE) mechanism was inducted to extract ROI (Region of Interest, ROI) to improve the feature fusion capability. In this paper, 4 819 images of gossypium hirsutum population which contain seven leaf types were taken from the top of cotton plant at distance of 30-50 cm (medium distance) and 50-100 cm (long distance) under uniform light, oblique strong light, direct strong light, and shadows. Those images were processed as MS COCO 2017 format dataset and assigned to the training set, validation set, and test set, which contained 2 815, 704, and 1 300 pictures respectively. The experimental results demonstrated that FPS (Frames Per Second, FPS) of proposed model was up to 10.3 frames/s and the Mean Average Precision (MAP) of bud identification reached to 98.1% which was 3.2 percentage points higher than original Faster R-CNN model. The validation set were used to compare performance of mainstream recognition algorithm and proposed method. Results showed that the improved Faster R-CNN's MAP was 7.3% higher than original Faster R-CNN, which was also higher than RetinaNet, Cascade R-CNN (Cascade Region Convolutional Neural Networks, Cascade R-CNN) and RepPoints by 78.9%, 10.1% and 8.3% when IOU was set to 0.5. The improved Faster R-CNN proposed in this paper meets the accuracy and real-time requirements of cotton topping operation. [ABSTRACT FROM AUTHOR]

In comparison with the former ZUC, this paper adopted a different way of design in initialization and the generation of MAC in ZUC-256 algorithm, and significantly promoted the cryptographic performance. In order to implement the algorithm by software in optimized and efficient way, this paper introduced the general structure and workflow of the algorithm in the first, and then implemented the basic functions of the algorithm by programming referring to the original algorithm. Furthermore, taking into consideration the transfer execution process of the array parameter in programming function together with the computational characteristics of the algorithm itself, this paper analyzed the factors affecting the execution efficiency of the program, and then promoted basic function program previously completed. After the actual programming test, the effectiveness of the two optimization methods is verified. In general, in two different software running reference machine environments, the optimized code execution efficiency combined with the two optimization schemes is improved 8% ~ 9% and 18% ~ 36% higher than that of the basic function implementation code respectively. It has practical significance for the future application of ZUC-256 cryptography in software engineering. [ABSTRACT FROM AUTHOR]

The paper took different regions and regional industries as initial water right allocation objects in the basin; the compound system for basin initial water right allocation was given; the connotation and objective of the compound system was analyzed; and the bi-level optimization model of the compound system optimization was established to allocate water right of different regions and regional industries, and eliminate the water conflict and contradiction among regions, embody the fairness and efficiency of different regions' water right, prevent different industries from the vicious water right competition, optimize comprehensive benefit of society-economy-ecology in the basin. Meanwhile, according to the interactive decision-making theory, the model algorithm was put forward. The case analysis verified the effectiveness of the model. [ABSTRACT FROM AUTHOR]

In the actual process of locating facility, the decision-maker always faces the following constraints: they must determine where to locate the initial facility (or facilities) when the number of edges to be served is uncertain, and, the constructed facilities can not be removed when the new facility is built. The existed models and algorithms are always pointed to a static facility location process, but here we need a dynamic facility location model with above constraints. Considered the online vertex covering problem in this paper, we present a structural lower bound which does not need any complexity assumption, and prove that the analysis is tight by giving a competitive algorithm as well as competitive ratio proof for a restricted version of the online vertex covering problem, and we also obtain that this algorithm is optimal if we are permitted to use non-polynomial time. [ABSTRACT FROM AUTHOR]

Discovery of influential people is a very important research topic in microblog. Betweenness centrality is an efficient method to find important nodes in networks. However, traditional betweenness centrality algorithms are only fit in small size of networks, and not efficient in microblog of massive messages. This paper proposed a random walk based betweenness centrality algorithm, and this algorithm could deal with massive microblog messages, and was more efficient than related work. [ABSTRACT FROM AUTHOR]