近年来，轨迹优化方法已在现实世界机器人上达到了出色的性能水平。这些方法在很大程度上依赖于动力学的准确分析模型，但是物理世界的某些方面只能在有限的程度上捕获。另一种方法是利用机器学习技术从数据中学习系统的可区分动力学模型。在这项工作中，我们使用轨迹优化和模型学习，在没有精确的动力学分析模型的情况下，使用机器人系统执行高度动态和复杂的任务。我们表明，从仅在两个不同的机器人上的25分钟相互作用的数据中收集的数据，神经网络可以准确地对高度非线性行为进行建模：（i）波士顿动力学点和（ii）RC CAR。此外，我们使用神经网络的梯度来执行基于梯度的轨迹优化。在我们的硬件实验中，我们证明了我们所学的模型可以代表现场和无线电控制（RC）汽车的复杂动力学，并与轨迹优化方法结合使用良好的性能。

由于相机已成为许多安全关键系统和应用中的关键部分，例如自主车辆和监视，因此大量的学术和非学术作品已经表现出对其主要成分的攻击 - 图像传感器。然而，这些攻击仅限于粗粒，并且通常是可疑的注射，因为光被用作攻击载体。此外，由于光学攻击的性质，它们需要对手和目标相机之间的视线。在本文中，我们提出了一种对CCD图像传感器的新型传感器信号注入攻击，因为它们用于专业，科学，甚至军事环境。我们展示了如何使用电磁散发来操纵CCD图像传感器捕获的图像信息，该图像信息具有粒度下降到各个像素的亮度。我们研究了我们攻击的可行性，然后展示其在自动条码扫描场景中的影响。我们的结果表明，注入的失真可以扰乱自动化视觉的智能系统。

在本文中，我们描述了如何利用明亮的调制光源（例如，廉价，离心激光器）来利用CMOS图像传感器中的电子滚动快门。我们展示了七种不同CMOS相机的攻击，从IoT廉价到半专业监控摄像机，以突出滚动快门攻击的广泛适用性。我们模拟了影响不受控制的设置中滚动快门攻击的基本因素。然后，我们对对象检测任务的攻击作用进行了详尽的评估，研究了攻击参数的效果。我们验证了我们对两个独立相机收集的经验数据的模型，表明通过简单地使用来自相机数据表的信息，对手可以准确地预测注入的失真大小并相应地优化它们的攻击。我们发现，通过选择适当的攻击参数，对手可以通过最先进的探测器隐藏高达75％的物体。我们还调查了与NA \“{i} vers致盲攻击相比攻击的隐秘，表明常见的图像失真度量无法检测到攻击存在。因此，我们向骨干展示了一种新的，准确和轻巧的增强对象检测器的网络识别滚动快门攻击。总体而言，我们的结果表明，滚动快门攻击可以大大降低基于视觉智能系统的性能和可靠性。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

State-of-the-art language models are often accurate on many question-answering benchmarks with well-defined questions. Yet, in real settings questions are often unanswerable without asking the user for clarifying information. We show that current SotA models often do not ask the user for clarification when presented with imprecise questions and instead provide incorrect answers or "hallucinate". To address this, we introduce CLAM, a framework that first uses the model to detect ambiguous questions, and if an ambiguous question is detected, prompts the model to ask the user for clarification. Furthermore, we show how to construct a scalable and cost-effective automatic evaluation protocol using an oracle language model with privileged information to provide clarifying information. We show that our method achieves a 20.15 percentage point accuracy improvement over SotA on a novel ambiguous question-answering answering data set derived from TriviaQA.