For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.

回声状态网络（ESN）是一类复发性神经网络，具有大量隐藏的隐藏权重（在所谓的储层中）。典型的ESN及其变化最近由于在非线性动力学系统的建模方面取得了显着的成功而受到了极大的关注。储层随机连接到没有改变学习过程的固定权重。仅训练从储层到输出的权重。由于储层在训练过程中是固定的，因此我们可能会想知道是否完全利用了复发结构的计算能力。在本文中，我们提出了一种新的ESN类型计算模型，该模型代表傅立叶空间中的储层权重，并对这些权重进行微调，该权重应用了频域中的遗传算法。主要兴趣是，与经典ESN相比，该过程将在小得多的空间中起作用，从而提供了初始方法的降低性变换。提出的技术使我们能够利用大型复发结构的好处，以避免基于梯度的方法的训练问题。我们提供了一项详细的实验研究，该研究证明了我们使用众所周知的混沌系统和现实数据的良好表现。

薄文件借款人是由于缺乏信用历史而不确定的信誉评估的客户;许多研究人员使用借款人的关系和交互网络以图形的形式作为替代数据源来解决这个问题。包含网络数据传统上由手工制作的特征工程制作，并且最近，图形神经网络已成为替代方案，但它仍然没有改善传统方法的性能。在这里，我们介绍一个框架来通过混合几个图形表示学习方法来改进信用评分模型：功能工程，图形嵌入和图形神经网络。我们堆叠了他们的产出以在这种方法中产生单一分数。我们使用独特的多源数据集进行了验证了此框架，该数据集具有与拉丁美洲国家的整个人口的关系和信用历史，将其应用于信用风险模型，应用和行为，针对个人和公司。我们的结果表明，图表表示学习方法应用作补充，并且这些方法不应被视为自给自足的方法，就像目前所做的那样。在AUC和KS方面，我们提升了统计表现，优于传统方法。在公司贷款中，在收益要高得多的情况下，它证实，评估了一个不道实的公司，不能仅考虑其特征。这些公司与业主，供应商，客户和其他公司互动的商业生态系统提供了新颖的知识，使金融机构能够提高信誉评估。我们的结果让我们知道何时以及哪些组使用图表数据以及对性能的影响。它们还展示了图形数据的巨大价值，主要是为了帮助公司的银行业务。

当没有足够的数据来证实客户的身份时，身份盗窃是信贷贷方的主要问题。在超级应用程序中，包含许多不同服务的大型数字平台，此问题更为相关;在一个分支中丢失客户通常意味着在其他服务中丢失它们。在本文中，我们审查了超级应用程序信息，手机线数据和传统信用风险变量的特征级融合的有效性，以便早日检测身份盗窃信用卡欺诈。通过提出的框架，我们在使用投入是替代数据和传统信贷局数据融合的模型时实现了更好的性能，从而实现了0.81的ROC AUC评分。我们从信用贷方的数字平台数据库中评估我们的方法超过大约90,000个用户。评估是使用传统的ML指标进行的，但金融成本也是如此。

最近关于Covid-19的研究表明，CT成像提供了评估疾病进展和协助诊断的有用信息，以及帮助理解疾病。有越来越多的研究，建议使用深度学习来使用胸部CT扫描提供快速准确地定量Covid-19。兴趣的主要任务是胸部CT扫描的肺和肺病变的自动分割，确认或疑似Covid-19患者。在这项研究中，我们使用多中心数据集比较12个深度学习算法，包括开源和内部开发的算法。结果表明，合并不同的方法可以提高肺部分割，二元病变分割和多种子病变分割的总体测试集性能，从而分别为0.982,0.724和0.469的平均骰子分别。将得到的二元病变分段为91.3ml的平均绝对体积误差。通常，区分不同病变类型的任务更加困难，分别具有152mL的平均绝对体积差，分别为整合和磨碎玻璃不透明度为0.369和0.523的平均骰子分数。所有方法都以平均体积误差进行二元病变分割，该分段优于人类评估者的视觉评估，表明这些方法足以用于临床实践中使用的大规模评估。