显示用于误差校正的小型神经网络（NNS）可改善经典通道代码并解决通道模型更改。我们通过多次使用相同的NN使用相同的NN扩展了任何此类结构的代码维度，这些NN与外部经典代码串行串联。我们设计具有相同网络参数的NN，其中每个REED - Solomon CodeWord符号都是对其他NN的输入。与小型神经代码相比，增加了加斯噪声通道的块误差概率的显着改善，以及通道模型变化的稳健性。

通过考虑一个嘈杂的测量值是用于安全源重建的相关随机变量的远程源，可以扩展使用多个终端的安全源编码的问题。该问题的主要添加包括1）所有终端非本质都观察到远程源的嘈杂测量； 2）所有合法终端都可以使用私钥； 3）编码器和解码器之间的公共通信链接是限制的； 4）根据编码器输入测量了窃听器的保密泄漏，而与远程源测量了隐私泄漏。在安全性，隐私，通信和失真约束下，使用私钥，远程源和解码器侧信息的有损源编码问题的确切速率区域的特征是。通过用可靠性约束替换失真约束，我们还可以获得无损案例的确切速率区域。此外，确定了标量离散时间高斯源和测量通道的损耗率区域。

Jensen Inequality是众多领域的广泛使用的工具，例如信息理论和机器学习。它还可以用于得出其他标准不等式，例如算术和几何手段的不等式或H \“较旧的不等式。在概率设置中，Jensen不等式描述了凸起函数和预期值之间的关系。在这项工作中，我们希望从不平等的反向方向看概率设置。我们表明在最小的限制和适当的缩放下，Jensen不等式可以逆转。我们相信由此产生的工具对许多应用有所帮助与当前估算器相比，相互信息的变分估计，反向不等式导致具有卓越训练行为的新估计。

新一代头戴式显示器，如VR和AR眼镜，正在进入市场，具有集成的眼踪，预计将能够在许多应用中启用人机交互的新方法。然而，由于眼睛运动属性包含生物信息，因此必须正确处理隐私问题。最近已经应用于从这种显示器获得的眼部移动数据等差分隐私机制等隐私保存技术。标准差异隐私机制;然而，由于眼睛运动观测之间的时间相关性而易受伤害。在这项工作中，我们提出了一种新颖的基于转换编码的差分隐私机制，以进一步调整它对眼球运动特征数据的统计数据并比较各种低复杂性方法。我们扩展了傅立叶扰动算法，这是一个差异隐私机制，并在证明中纠正了缩放错误。此外，除了查询敏感性之外，我们还说明了对样本相关性的显着还原，这提供了在眼睛跟踪文献中提供了最佳的效用隐私权衡。我们的结果提供了明显高的隐私，而在隐藏个人标识符的同时，在分类准确性损失的情况下提供了明显高的隐私。

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.

Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall $\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and $\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.

关于使用ML模型的一个基本问题涉及其对提高决策透明度的预测的解释。尽管已经出现了几种可解释性方法，但已经确定了有关其解释可靠性的一些差距。例如，大多数方法都是不稳定的（这意味着它们在数据中提供了截然不同的解释），并且不能很好地应对无关的功能（即与标签无关的功能）。本文介绍了两种新的可解释性方法，即Varimp和Supclus，它们通过使用局部回归拟合的加权距离来克服这些问题，以考虑可变重要性。 Varimp生成了每个实例的解释，可以应用于具有更复杂关系的数据集，而Supclus解释了具有类似说明的实例集群，并且可以应用于可以找到群集的较简单数据集。我们将我们的方法与最先进的方法进行了比较，并表明它可以根据几个指标产生更好的解释，尤其是在具有无关特征的高维问题中，以及特征与目标之间的关系是非线性的。

脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。

Advances in image processing and analysis as well as machine learning techniques have contributed to the use of biometric recognition systems in daily people tasks. These tasks range from simple access to mobile devices to tagging friends in photos shared on social networks and complex financial operations on self-service devices for banking transactions. In China, the use of these systems goes beyond personal use becoming a country's government policy with the objective of monitoring the behavior of its population. On July 05th 2021, the Brazilian government announced acquisition of a biometric recognition system to be used nationwide. In the opposite direction to China, Europe and some American cities have already started the discussion about the legality of using biometric systems in public places, even banning this practice in their territory. In order to open a deeper discussion about the risks and legality of using these systems, this work exposes the vulnerabilities of biometric recognition systems, focusing its efforts on the face modality. Furthermore, it shows how it is possible to fool a biometric system through a well-known presentation attack approach in the literature called morphing. Finally, a list of ten concerns was created to start the discussion about the security of citizen data and data privacy law in the Age of Artificial Intelligence (AI).