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In this paper, we first study the implementation process of the AdaBoost model, initialize the weight distribution of training samples so that each sample weight is equal, then generate weak classifiers and calculate the minimum weighted error rate corresponding to each weak classifier, and then linearly combine the basic classifiers into strong classifiers by using weighted summation. Then the basic classifier weighting parameter calculation formula is improved by combining the characteristics of the logistics distribution function. Finally, the algorithm is used to analyze the rural governance willingness of river country residents, as well as to analyze the rural governance needs of rural residents at three levels: living conditions, public health and service facilities, to identify the types of rural governance, and to explore the rural governance patterns of different types of farm households. At the level of living conditions, farmers who wish to improve cooking fuel in 54 households, heating conditions in 61 households, bathing conditions in 40 households and family toilets in 53 households account for 43%, 49%, 32% and 42%, respectively. A total of 80 farming households, accounting for 29.41% of the research farming households, were optimized and upgraded, including 66 households in the key development area and 14 households in the production and living security area, accounting for 82.5% and 17.5% of the optimized and upgraded type, respectively. The research method of this paper has important references and reference significance for the construction of rural governance.

Property prices have surged recently, mainly due to limited real estate supply amid high demand supported by a large monetary base. This has driven up rental prices, especially in major cities compared to their peripheries. This manuscript examines rental prices in Prague, Brno, Ostrava, Pilsen, and České Budějovice from March to October 2023. It uses distribution functions, correlation coefficients, contour heat maps, and parameterized regression to define a probability price interval for office properties, identify a positive correlation between area and rental prices, and develop an algorithm for setting appropriate rental prices. The findings are useful for practitioners, real estate agents, and government institutions, though they are closely linked to macroeconomic variables. Future potential options are discussed. [ABSTRACT FROM AUTHOR]

3D local descriptors are essential for many computer vision-related tasks: point cloud registration, object recognition, etc. The first descriptors appeared decades ago, but new methods are still emerging today. To solve the above mentioned problems efficiently, we need robust and distinctive descriptors that are invariant to translation, rotation and normal orientation. In this paper, we revisit and analyze the well-known hand-crafted Fast Point Feature Histogram (FPFH) and the three geometric features underlying the FPFH. Modifications to the geometric features are proposed to increase the descriptiveness and improve robustness against inconsistent normal orientation. Thus, normal orientation methods with high computational complexity can be omitted and the performance of the descriptor is nearly doubled. We also propose a novel approach to representing descriptors: using cumulative distribution functions (CDF) instead of histograms. We mathematically analyze the point-pair features and their CDFs, focusing on a special case where two planes intersect. We use this knowledge to approximate functions on real point cloud data sets in the general case. CDFs can be represented by fitted parameters. By storing these parameters, which can be understood as a form of compression, the storage requirements can be reduced while maintaining the descriptiveness.

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