在手术室（OR）中，活动通常与其他典型的工作环境不同。特别是，外科医生经常受到多种心理组织的约束，可能会对他们的健康和表现造成负面影响。这通常归因于相关的认知工作量（CWL）的增加，该工作量是由于处理意外和重复性任务以及大量信息以及潜在风险的认知超载而导致的。在本文中，建议在多种四个不同的手术任务中对CWL的多模式识别提出了两种机器学习方法。首先，使用基于转移学习概念的模型来确定外科医生是否经历任何CWL。其次，卷积神经网络（CNN）使用此信息来识别与每个手术任务相关的不同类型的CWL。建议的多模式方法考虑来自脑电图（EEG），功能近红外光谱（FNIRS）和瞳孔眼直径的相邻信号。信号的串联允许在时间（时间）和通道位置（空间）方面进行复杂的相关性。数据收集是由多种感应的AI环境来执行的，用于在Harms Lab开发的手术任务$ \＆$角色优化平台（Maestro）。为了比较拟议方法的性能，已经实施了许多最先进的机器学习技术。测试表明，所提出的模型的精度为93％。

如今，使用微创手术（MIS）进行了更多的手术程序。这是由于其许多好处，例如最小的术后问题，较少的出血，较小的疤痕和快速的康复。但是，MIS的视野，小手术室和对操作场景的间接查看可能导致手术工具发生冲突并可能损害人体器官或组织。因此，通过使用内窥镜视频饲料实时检测和监视手术仪器，可以大大减少MIS问题，并且可以提高手术程序的准确性和成功率。在本文中，研究，分析和评估了对Yolov5对象检测器的一系列改进，以增强手术仪器的检测。在此过程中，我们进行了基于性能的消融研究，探索了改变Yolov5模型的骨干，颈部和锚固结构元素的影响，并注释了独特的内窥镜数据集。此外，我们将消融研究的有效性与其他四个SOTA对象探测器（Yolov7，Yolor，Scaled-Yolov4和Yolov3-SPP）进行了比较。除了Yolov3-SPP（在MAP中具有98.3％的模型性能和相似的推理速度）外，我们的所有基准模型（包括原始的Yolov5）在使用新的内窥镜数据集的实验中超过了我们的顶级精制模型。

Demspter-Shafer证据理论中提出的不确定性量化的信念函数方法是基于对集合值观测的一般数学模型，称为随机集。设定值的预测是机器学习中不确定性的最自然表示。在本文中，我们介绍了一个基于对信仰功能的随机解释来模拟深度神经网络中的认知学习的概念。我们提出了一个新型的随机卷积神经网络，用于分类，该网络通过学习设置值的地面真实表示来为类别的分类产生分数。我们评估信仰功能的熵和距离度量的不同公式，作为这些随机集网络的可行损失函数。我们还讨论了评估认知预测质量和认知随机神经网络的表现的方法。我们通过实验证明，与传统的估计不确定性相比，认知方法可以产生更好的性能结果。

在自主驾驶系统中，感知 - 来自环境的特征和物体的识别 - 至关重要。在自主赛车中，高速和小幅度的距离需要快速准确的检测系统。在比赛期间，天气可能会突然变化，导致感知的显着降解，导致操作效果无效。为了改善恶劣天气的检测，基于深度学习的模型通常需要在这种条件下捕获的广泛数据集 - 这是一种繁琐，费力和昂贵的过程。然而，最新的Conscangan架构的发展允许在多种天气条件下合成高度现实的场景。为此，我们介绍了一种在自主赛车中使用合成的不利条件数据集（使用Cyclegan产生）来提高五个最先进的探测器的性能，平均为42.7和4.4地图百分比点分别存在夜间条件和液滴。此外，我们对五个对象探测器进行了比较分析 - 识别探测器的最佳配对和在挑战条件下自主赛车中使用的培训数据。

概率间隔是在不确定性下推理的有吸引力的工具。但是，与信仰功能不同，它们缺乏用于在实用工具理论框架中的决策中的自然概率转变。在本文中，我们提出了使用交叉路口概率，最初导致的变换，以便在不确定的几何方法的框架内进行信仰功能，作为最自然的这种转变。我们回顾其理由和定义，将其与其他概率间隔系统的其他候选者进行比较，讨论其作为一对简单的焦点的信任理由，并概述了概率间隔的可能决策框架，类似于可转移信仰功能的信仰模式。

作为自治车辆和自主赛车的竞争程度，所以需要更快，更准确的探测器。虽然我们的裸眼能够几乎立即提取上下文信息，但即使从远处地，图像分辨率和计算资源限制也使检测到较小的对象（即占用输入图像中小像素区域的对象）机器的真正具有挑战性的任务和一个广泛的研究领域。本研究探讨了如何修改流行的yolov5对象检测器以改善其在检测较小物体时的性能，具有自主赛车的特定应用。为实现这一目标，我们调查如何更换模型的某些结构元素（以及它们的连接和其他参数）可以影响性能和推理时间。在这样做时，我们提出了一系列模型，在不同的尺度上，我们命名为“YOLO-Z”，当时在50％iou的较小物体时，在地图上显示出高达6.9％的提高，以仅仅a与原始yolov5相比，推理时间增加3ms。我们的目标是为未来的研究提供调整流行检测器的可能性，例如YOLOV5以解决特定任务，并提供关于具体变化如何影响小对象检测的洞察。应用于自动车辆的更广泛背景的这种发现可以增加这些系统可用的上下文信息的量。

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.