对于自动导航和机器人应用，正确感知环境至关重要。存在许多用于此目的的感应方式。近年来，一种使用的方式是空中成像声纳。它在具有灰尘或雾之类的粗糙条件的复杂环境中是理想的选择。但是，就像大多数传感方式一样，要感知移动平台周围的完整环境，需要多个此类传感器来捕获完整的360度范围。当前，用于创建此数据的处理算法不足以以相当快的更新速率为多个传感器这样做。此外，需要一个灵活而健壮的框架，以轻松地将多个成像声纳传感器实现到任何设置中，并为数据提供多种应用程序类型。在本文中，我们提出了一个专为这种新型传感方式而设计的传感器网络框架。此外，提出了在图形处理单元上的处理算法的实现，以减少计算时间，以便以足够高的更新速率实时处理一个或多个成像声纳传感器。

估计六级自由人体姿势的系统已有二十年多了。诸如运动捕获摄像机，高级游戏外围设备以及最近的深度学习技术和虚拟现实系统等技术都显示出令人印象深刻的结果。但是，大多数提供高精度和高精度的系统都是昂贵的，并且不容易操作。最近，已经进行了研究以使用HTC Vive虚拟现实系统估算人体姿势。该系统显示出准确的结果，同时将成本保持在1000美元以下。该系统使用光学方法。通过在接收器硬件上使用照片二极管来跟踪两个发射器设备发射红外脉冲和激光平面。以前开发了使用这些发射器设备与低成本定制接收器硬件结合使用的系统，但需要手动测量发射机设备的位置和方向。这些手动测量可能很耗时，容易出错，并且在特定设置中不可能。我们提出了一种算法，以使用自定义接收器/校准硬件的任何选择的环境中自动校准发射机设备的姿势。结果表明，校准在各种设置中起作用，同时比手动测量所允许的更准确。此外，校准运动和速度对结果的精度没有明显的影响。

在自主移动平台的各种和动态室内环境中导航仍然是一项复杂的任务。尤其是当条件恶化时，典型的传感器模式可能无法最佳运行，随后为安全导航控制提供了INAPT输入。在这项研究中，我们提出了一种使用分层控制系统具有单个或几个声纳传感器的移动平台进行动态室内环境导航的方法。这些传感器可以在雨，雾，灰尘或污垢等条件下运行。不同的控制层，例如避免碰撞和行为后的走廊，根据声流图像的融合中的声流队列被激活。这项工作的新颖性使这些传感器可以自由放置在移动平台上，并提供了基于移动平台周围分区系统设计最佳导航结果的框架。本文介绍的是使用的声流模型以及分层控制器的设计。在模拟中的验证旁边，在真实的办公室环境中使用具有一个，两个或三个声纳传感器的真实移动平台在真实的办公室环境中实现了实施和验证，并通过2D导航实时实时。在模拟和实际实验中均已验证了多个传感器布局，以证明控制器和传感器融合的模块化方法最佳起作用。这项工作的结果显示了具有动态对象的室内环境的稳定且安全的导航。

在空间上导航和动态环境是自主代理的关键任务之一。在本文中，我们提出了一种新颖的方法，该方法可以通过一个或多个3D-sonar传感器导航移动平台。移动移动平台，然后在其上移动任何3D-sonar传感器，将随着传感器读取中回声反射的时间而创建签名变化。提出了一种方法，可以为任何运动类型创建这些签名变化的预测模型。此外，该模型是自适应的，可用于移动平台上一个或多个声纳传感器的任何位置和方向。我们建议使用这种自适应模型并将所有感官读数融合来创建一个分层的控制系统，允许移动平台执行一组原始运动，例如避免碰撞，避免障碍物，避开障碍物，跟随墙壁和走廊跟随行为，以动态移动环境导航环境其中的对象。本文描述了整个导航模型的基本理论基础，并在模拟环境中验证了它，结果表明该系统稳定，并为一个或多个声纳传感器的多种测试空间配置提供了预期的行为，可以完成自主导航任务。

在模拟中设计和验证传感器应用程序和算法是现代开发过程中的重要一步。此外，现代的开源多传感器仿真框架正在朝着视频游戏引擎（例如虚幻引擎）的使用。在这种实时软件中，对激光雷达等传感器的仿真可能很难。在本文中，我们根据其物理特性和与环境的相互作用进行了GPU加速模拟。我们根据传感器的性质以及光束撞击表面的表面材料和入射角提供了深度和强度数据的产生。它针对真实的激光雷达传感器进行了验证，并证明是准确和精确的，尽管高度依赖于用于材料特性的光谱数据。

As demand for large corpora increases with the size of current state-of-the-art language models, using web data as the main part of the pre-training corpus for these models has become a ubiquitous practice. This, in turn, has introduced an important challenge for NLP practitioners, as they are now confronted with the task of developing highly optimized models and pipelines for pre-processing large quantities of textual data, which implies, effectively classifying and filtering multilingual, heterogeneous and noisy data, at web scale. One of the main components of this pre-processing step for the pre-training corpora of large language models, is the removal of adult and harmful content. In this paper we explore different methods for detecting adult and harmful of content in multilingual heterogeneous web data. We first show how traditional methods in harmful content detection, that seemingly perform quite well in small and specialized datasets quickly break down when confronted with heterogeneous noisy web data. We then resort to using a perplexity based approach but with a twist: Instead of using a so-called "clean" corpus to train a small language model and then use perplexity so select the documents with low perplexity, i.e., the documents that resemble this so-called "clean" corpus the most. We train solely with adult and harmful textual data, and then select the documents having a perplexity value above a given threshold. This approach will virtually cluster our documents into two distinct groups, which will greatly facilitate the choice of the threshold for the perplexity and will also allow us to obtain higher precision than with the traditional classification methods for detecting adult and harmful content.

Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively.

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how -- by exploiting the (potentially) partial cardinal and partial probabilistic information -- more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.

The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall little organization of the many aspects of fraud analytical research. The focus of academics ranges from identifying fraudulent credit card payments to spotting illegitimate insurance claims. In addition, there is a wide range of methods and research objectives. This paper aims to provide an overview of fraud analytics in research and aims to more narrowly organize the discipline and its many subfields. We analyze a sample of almost 300 records on fraud analytics published between 2011 and 2020. In a systematic way, we identify the most prominent domains of application, challenges faced, performance metrics, and methods used. In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research. One of the key challenges in fraud analytics is access to public datasets. To further aid the community, we provide eight requirements for suitable data sets in research motivated by our research. We structure our sample of the literature in an online database. The database is available online for fellow researchers to investigate and potentially build upon.