监视自动实时流处理系统的行为已成为现实世界应用中最相关的问题之一。这种系统的复杂性已在很大程度上依赖于高维输入数据和数据饥饿的机器学习（ML）算法。我们提出了一个灵活的系统，功能监视（FM），该系统在此类数据集中检测数据漂移，并具有较小且恒定的内存足迹和流应用程序中的小计算成本。该方法基于多变量统计测试，并且是由设计驱动的数据（从数据中估算了完整的参考分布）。它监视系统使用的所有功能，同时每当发生警报时提供可解释的功能排名（以帮助根本原因分析）。系统的计算和记忆轻度是由于使用指数移动直方图而导致的。在我们的实验研究中，我们用其参数分析了系统的行为，更重要的是显示了它检测到与单个特征无直接相关的问题的示例。这说明了FM如何消除添加自定义信号以检测特定类型问题的需求，并且监视功能可用空间通常足够。

洗钱是一个全球性问题，涉及严重重罪（每年1.7-4万亿欧元的收益，如毒品处理，人口贩运或腐败。金融机构部署的反洗钱系统通常包括与监管框架一致的规则。人类调查人员审查警报和报告可疑案件。这种系统患有高​​假阳性率，破坏其有效性并导致高运营成本。我们提出了一种机器学习分类模型，它补充了基于规则的系统，并学会准确地预测警报的风险。我们的模型使用实体的设计功能和属性以基于图形的特征​​的形式表征实体间关系。我们利用时间窗口来构建动态图形，优化时间和空间效率。我们在真实的银行数据集上验证我们的模型，并展示分流模型如何将误报的数量减少80％，同时检测到90％的真实阳性。通过这种方式，我们的模型可以显着改善反洗钱操作。

在足球（或协会足球）中，球员迅速从英雄转变为零，反之亦然。性能不是静态度量，而是一种易变的措施。将绩效分析为时间序列而不是静止的时间点对于做出更好的决策至关重要。本文介绍并探讨了I-VAEP和O-VAEP模型，以评估行动和评估玩家的意图和执行。然后，我们随着时间的推移分析这些评级，并提出用例，以基本将我们将玩家评分视为连续问题的选择。结果，我们出席了谁是最好的球员以及他们的表现如何发展，定义波动率指标以衡量球员的一致性，并建立玩家发展曲线以帮助决策。

计算方法开始用于设计数据和生成过程所推动的动态视觉身份。在这项工作中，我们探索了这些计算方法，以生成创建定制效率和图像的视觉标识。我们实现了开发的生成设计系统，该设计系统会自动组装黑白视觉模块。该系统生成设计执行两种主要方法的设计：（i）辅助生成；（ii）自动生成。辅助生成方法产生输出，其中模块的放置由以前定义的配置文件确定。另一方面，自动生成方法会产生输出，其中组装模块以描绘输入图像。该系统加快了一个视觉标识设计的设计和部署的过程，并在它们之间生成了视觉连贯性。在本文中，我们可以压制地描述该系统及其成就。

能够分析和量化人体或行为特征的系统（称为生物识别系统）正在使用和应用变异性增长。由于其从手工制作的功能和传统的机器学习转变为深度学习和自动特征提取，因此生物识别系统的性能增加到了出色的价值。尽管如此，这种快速进步的成本仍然尚不清楚。由于其不透明度，深层神经网络很难理解和分析，因此，由错误动机动机动机的隐藏能力或决定是潜在的风险。研究人员已经开始将注意力集中在理解深度神经网络及其预测的解释上。在本文中，我们根据47篇论文的研究提供了可解释生物识别技术的当前状态，并全面讨论了该领域的发展方向。

本文介绍了基于2022年国际生物识别技术联合会议（IJCB 2022）举行的基于隐私感知合成训练数据（SYN-MAD）的面部变形攻击检测的摘要。该竞赛吸引了来自学术界和行业的12个参与团队，并在11个不同的国家 /地区举行。最后，参与团队提交了七个有效的意见书，并由组织者进行评估。竞争是为了介绍和吸引解决方案的解决方案，这些解决方案涉及检测面部变形攻击的同时，同时出于道德和法律原因保护人们的隐私。为了确保这一点，培训数据仅限于组织者提供的合成数据。提交的解决方案提出了创新，导致在许多实验环境中表现优于所考虑的基线。评估基准现在可在以下网址获得：https：//github.com/marcohuber/syn-mad-2022。

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

The field of Automatic Music Generation has seen significant progress thanks to the advent of Deep Learning. However, most of these results have been produced by unconditional models, which lack the ability to interact with their users, not allowing them to guide the generative process in meaningful and practical ways. Moreover, synthesizing music that remains coherent across longer timescales while still capturing the local aspects that make it sound ``realistic'' or ``human-like'' is still challenging. This is due to the large computational requirements needed to work with long sequences of data, and also to limitations imposed by the training schemes that are often employed. In this paper, we propose a generative model of symbolic music conditioned by data retrieved from human sentiment. The model is a Transformer-GAN trained with labels that correspond to different configurations of the valence and arousal dimensions that quantitatively represent human affective states. We try to tackle both of the problems above by employing an efficient linear version of Attention and using a Discriminator both as a tool to improve the overall quality of the generated music and its ability to follow the conditioning signals.

Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.

Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings. In most cases, the estimated distribution sums to 1 over all finite strings. However, in some pathological cases, probability mass can ``leak'' onto the set of infinite sequences. In order to characterize the notion of leakage more precisely, this paper offers a measure-theoretic treatment of language modeling. We prove that many popular language model families are in fact tight, meaning that they will not leak in this sense. We also generalize characterizations of tightness proposed in previous works.