分类是数据挖掘和机器学习领域中研究最多的任务之一，并且已经提出了文献中的许多作品来解决分类问题，以解决多个知识领域，例如医学，生物学，安全性和遥感。由于没有单个分类器可以为各种应用程序取得最佳结果，因此，一个很好的选择是采用分类器融合策略。分类器融合方法成功的关键点是属于合奏的分类器之间多样性和准确性的结合。借助文献中可用的大量分类模型，一个挑战是选择最终分类系统的最合适的分类器，从而产生了分类器选择策略的需求。我们通过基于一个称为CIF-E（分类器，初始化，健身函数和进化算法）的四步协议的分类器选择和融合的框架来解决这一点。我们按照提出的CIF-E协议实施和评估24种各种集合方法，并能够找到最准确的方法。在文献中最佳方法和许多其他基线中，还进行了比较分析。该实验表明，基于单变量分布算法（UMDA）的拟议进化方法可以超越许多著名的UCI数据集中最新的文献方法。

自学学习（SSL）已成为深度学习研究的一个非常活跃的领域，在该领域中，它被大量用作分类和其他任务的预培训方法。但是，该领域的进步速度的迅速速度是一个代价：培训管道之间的培训管道差异很大，这是一个潜在的至关重要的混杂因素。在这里，我们表明，实际上，超参数和数据增强策略的选择可能会对性能产生巨大影响。为了阐明这些被忽视的因素并有助于最大化SSL的功能，我们将这些组件过度参数化并通过贝叶斯优化优化它们，从而显示了S​​IMSIAM SSL方法的多个数据集的改进。意识到数据增强对SSL的重要性，我们还引入了一种新的自动数据增强算法，小组声名格，该算法考虑了增强组的组并优化了跨组的采样。与为监督学习设计的算法相反，在我们考虑的所有数据集中，团体声明达到了一致的线性评估准确性。总体而言，我们的结果表明数据增强对SSL的重要性和可能低估的作用。

鉴于新的数据集D和低计算预算，我们应该如何选择预培训的模型来微调D，并设置微调的超参数而不冒险过度拟合，尤其是在D小的情况下？在这里，我们扩展了自动化的机器学习（AUTOML），以最好地做出这些选择。我们与域无关的元学习方法学习了一个零拍的替代模型，在测试时，该模型允许选择正确的深度学习（DL）管道（包括预训练的模型和微调的超参数）仅给定描述d的琐碎元功能，例如图像分辨率或类的数量。为了训练这种零射模型，我们在大量数据集中收集了许多DL管道的性能数据，并在此数据上收集了元训练，以最大程度地减少成对排名目标。我们在Chalearn AutoDL挑战基准的视觉轨道的严格时间限制下评估我们的方法，显然优于所有挑战竞争者。

尽管深度神经网络能够在各种任务上实现优于人类的表现，但他们臭名昭著，因为他们需要大量的数据和计算资源，将其成功限制在可用的这些资源的领域。金属学习方法可以通过从相关任务中转移知识来解决此问题，从而减少学习新任务所需的数据和计算资源的数量。我们组织了元数据竞赛系列，该系列为世界各地的研究小组提供了创建和实验评估实际问题的新元学习解决方案的机会。在本文中，我们在竞争组织者和排名最高的参与者之间进行了合作，我们描述了竞争的设计，数据集，最佳实验结果以及Neurips 2021挑战中最高的方法，这些方法吸引了15进入最后阶段的活跃团队（通过表现优于基线），在反馈阶段进行了100多次代码提交。顶级参与者的解决方案是开源的。汲取的经验教训包括学习良好的表示对于有效的转移学习至关重要。

本文报告了Chalearn的Autodl挑战系列的结果和后攻击分析，这有助于对自动学习（DL）进行分类，以便在各种环境中引入的深度学习（DL），但缺乏公平的比较。格式化所有输入数据模型（时间序列，图像，视频，文本，表格）作为张量，所有任务都是多标签分类问题。代码提交已在隐藏的任务上执行，具有限制时间和计算资源，推动快速获取结果的解决方案。在此设置中，DL方法占主导地位，但流行的神经结构搜索（NAS）是不切实际的。解决方案依赖于微调预培训的网络，架构匹配数据模块。挑战后测试没有透露超出强加时间限制的改进。虽然没有组件尤其原始或新颖，但是一个高级模块化组织出现了“Meta-Learner”，“数据摄入”，“模型选择器”，“模型/学习者”和“评估员”。这种模块化使得消融研究，揭示了（离坡）元学习，合奏和高效数据管理的重要性。异构模块组合的实验进一步证实了获胜解决方案的（本地）最优性。我们的挑战队遗产包括一个持久的基准（http://utodl.chalearn.org），获胜者的开放源代码，以及免费的“autodl自助服务”。

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.

How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.