Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five combined preprocessing and post-processing methods for time-series binary classification problems that simultaneously increase the accuracy and reliability of DNN outputs applied in a 5G UAV security dataset. These techniques use DNN outputs as input parameters and process them in different ways. Two methods use a well-known Machine Learning (ML) algorithm as a complement, and the other three use only confidence values that the DNN estimates. We compare seven different metrics, such as the Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Mean Confidence (MC), Mean Accuracy (MA), Normalized Negative Log Likelihood (NLL), Brier Score Loss (BSL), and Reliability Score (RS) and the tradeoffs between them to evaluate the proposed hybrid algorithms. First, we show that the eXtreme Gradient Boosting (XGB) classifier might not be reliable for binary classification under the conditions this work presents. Second, we demonstrate that at least one of the potential methods can achieve better results than the classification in the DNN softmax layer. Finally, we show that the prospective methods may improve accuracy and reliability with better uncertainty calibration based on the assumption that the RS determines the difference between MC and MA metrics, and this difference should be zero to increase reliability. For example, Method 3 presents the best RS of 0.65 even when compared to the XGB classifier, which achieves RS of 7.22.

读取文本读取序列的确定是对记录理解的基础。在文本组织成一系列行和垂直对准的页面中，可以轻松解决此问题，并运行页面的高度（生成可以从左到右读取的多列）。我们展示了一种情况 - 目录页面解析问题 - 以不规则，视觉组织的二维格式在页面上呈现信息。目录页面在金融招股说明书中相当常见，并携带有关组织，其地址和关系的信息，这是客户在车内客户端的关键。有趣的是，目录页有时有分层结构，激励需要将读取序列概括为读取树。我们向识别目录页面和构建读取树的问题提供解决方案，使用（学习）文本段和自下而上的（向左，左上，顶部顶部）遍历的段的横向。该解决方案是支持从客户端船上文件自动提取组织，地址和关系信息的生产服务的关键部分。

大脑的结构网络或结构连接组可以由多种拖拉机方法产生的纤维束表示。尽管这种方法给出了对大脑结构的定性见解，但对于它们是否可以提供定量信息，尤其是在人口水平上，却存在争议。为了实现结构连接组的人口级统计分析，我们提出代表连接组为Riemannian度量，这是无限二维流形的点。我们将这种歧管配备了Ebin Metric，这是该空间的自然度量结构，以获得Riemannian歧管及其相关的几何特性。然后，我们使用这个Riemannian框架应用面向对象的统计分析来将地图集定义为Riemannian指标人群的fr \'echet平均值。这种表述与现有的图像图像构造构造的现有框架相关联，使我们能够通过同时整合DWMRI的互补白质结构细节和T1加权MRI的皮质细节来构建多模式地图集。我们用Connectome注册和地图集组的2D数据示例说明了我们的框架。最后，我们使用从人类Connectome Project的主体子集估计的扩散张量衍生的T1图像和连接组构建了一个示例3D多模式地图集。