Human-technology collaboration relies on verbal and non-verbal communication. Machines must be able to detect and understand the movements of humans to facilitate non-verbal communication. In this article, we introduce ongoing research on human activity recognition in intralogistics, and show how it can be applied in industrial settings. We show how semantic attributes can be used to describe human activities flexibly and how context informantion increases the performance of classifiers to recognise them automatically. Beyond that, we present a concept based on a cyber-physical twin that can reduce the effort and time necessary to create a training dataset for human activity recognition. In the future, it will be possible to train a classifier solely with realistic simulation data, while maintaining or even increasing the classification performance.

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To fulfill this, the so-called Self-Adaptive Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO), which features a two-stage online model management strategy, is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art non-surrogate or single surrogate assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems, which shows the feasibility and advantage of using multiple surrogate models in optimizing discrete problems.

Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design. One of these suites is the black-box optimization benchmarking (BBOB) suite of 24 single-objective noiseless functions, which has been a standard for over a decade. Within this problem suite, different instances of a single problem can be created, which is beneficial for testing the stability and invariance of algorithms under transformations. In this paper, we investigate the BBOB instance creation protocol by considering a set of 500 instances for each BBOB problem. Using exploratory landscape analysis, we show that the distribution of landscape features across BBOB instances is highly diverse for a large set of problems. In addition, we run a set of eight algorithms across these 500 instances, and investigate for which cases statistically significant differences in performance occur. We argue that, while the transformations applied in BBOB instances do indeed seem to preserve the high-level properties of the functions, their difference in practice should not be overlooked, particularly when treating the problems as box-constrained instead of unconstrained.

Within an operational framework, covers used by a steganographer are likely to come from different sensors and different processing pipelines than the ones used by researchers for training their steganalysis models. Thus, a performance gap is unavoidable when it comes to out-of-distributions covers, an extremely frequent scenario called Cover Source Mismatch (CSM). Here, we explore a grid of processing pipelines to study the origins of CSM, to better understand it, and to better tackle it. A set-covering greedy algorithm is used to select representative pipelines minimizing the maximum regret between the representative and the pipelines within the set. Our main contribution is a methodology for generating relevant bases able to tackle operational CSM. Experimental validation highlights that, for a given number of training samples, our set covering selection is a better strategy than selecting random pipelines or using all the available pipelines. Our analysis also shows that parameters as denoising, sharpening, and downsampling are very important to foster diversity. Finally, different benchmarks for classical and wild databases show the good generalization property of the extracted databases. Additional resources are available at github.com/RonyAbecidan/HolisticSteganalysisWithSetCovering.

异常检测描述了发现与正常值空间不同的异常状态，实例或数据点的方法。工业流程是一个领域，需要在其中找到质量增强异常数据实例的预期模型。但是，主要的挑战是在这种环境中没有标签。本文有助于以数据为中心的工业生产中人工智能的方式。借助来自汽车组件的增材制造的用例，我们提出了基于深度学习的图像处理管道。我们将域随机化和合成数据的概念整合在循环中，这显示了深度学习进展及其在现实世界中的工业生产过程中的桥接结果。

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。

在高维和不完整的矩阵中提取潜在信息是一个重要且具有挑战性的问题。潜在因子分析（LFA）模型可以很好地处理高维矩阵分析。最近，已经提出了粒子群优化（PSO）组合的LFA模型，以高效率调节超参数。但是，PSO的掺入会导致过早问题。为了解决这个问题，我们提出了一个顺序的Adam-unjusting-Antennae BAS（A2BAS）优化算法，该算法完善了由PSO成立的LFA模型获得的潜在因素。 A2BAS算法由两个子算法组成。首先，我们设计了一种改进的BAS算法，该算法可调节甲虫的触角并使用Adam进行尺寸。其次，我们实施了改进的BAS算法，以顺序优化所有行和列潜在​​因子。通过对两个实际高维矩阵的实验结果，我们证明我们的算法可以有效地解决过早的收敛问题。

可解释的人工智能（XAI）越来越多地用于分析神经网络的行为。概念激活使用人解剖概念来解释神经网络行为。这项研究旨在评估回归概念激活的可行性，以解释多模式体积数据的检测和分类。概念验证证明是在前列腺发射断层扫描/计算机断层扫描（PET/CT）成像的转移性前列腺癌患者中证明的。多模式的体积概念激活用于提供全球和局部解释。敏感性为80％，为每位患者的假阳性为1.78。全球解释表明，检测集中在CT上的解剖位置和PET上的检测信心。当地的解释显示出有望有助于区分真实积极因素和误报。因此，这项研究证明了使用回归概念激活来解释多模式体积数据的检测和分类的可行性。

在本文中，我们研究了如何在视觉和语言（V+L）表示学习中使用蒙版的信号建模。与其独立开发蒙面语言建模（MLM）和蒙面图像建模（MIM），我们建议建立关节蒙面的视觉和语言建模，其中一种模态的掩盖信号是在另一种方式的帮助下重建的。这是由图像文本配对数据的性质和文本传达几乎相同的信息但以不同格式传达的。在另一种模态下进行的一种模式的掩盖信号重建也可以隐式学习语言令牌和图像贴片之间的跨模式对齐。我们对各种V+L任务的实验表明，该建议的方法不仅可以通过使用大量数据来实现最先进的性能，而且还可以通过有限的培训数据的制度优于其他竞争对手。

人类决策受到许多系统错误的困扰。可以通过提供决策辅助工具来指导决策者参与重要信息并根据理性决策策略将其集成，从而避免使用这些错误。设计这样的决策辅助工具曾经是一个乏味的手动过程。认知科学的进步可能会使将来自动化这一过程。我们最近引入了机器学习方法，以自动发现人类决策的最佳策略，并自动向人们解释这些策略。通过这种方法构建的决策辅助工具能够改善人类决策。但是，遵循该方法产生的描述非常乏味。我们假设可以通过将自动发现的决策策略作为一系列自然语言指示来克服这个问题。实验1表明，人们确实确实比以前的方法更容易理解此类程序说明。在这一发现的鼓励下，我们开发了一种将我们先前方法的输出转化为程序指示的算法。我们应用了改进的方法来自动为自然主义计划任务（即计划旅行）和自然主义决策任务（即选择抵押）生成决策辅助工具。实验2表明，这些自动产生的决策AID可显着改善人们在计划公路旅行和选择抵押贷款方面的表现。这些发现表明，AI驱动的增强可能有可能改善现实世界中的人类决策。