知识图（kgs）已被证明是构建数据的可靠方法。他们可以提供有关文化遗产收藏的丰富情境信息。但是，文化遗产库库远非完整。他们通常会缺少重要的属性，例如地理位置，尤其是对于雕塑，移动或室内实体，例如绘画。在本文中，我们首先提出了一个框架，用于从各种数据源及其连接的多跳知识中汲取有关有形文化遗产实体的知识。其次，我们提出了一个多视图学习模型，用于估计给定的文化遗产实体之间的相对距离，该模型基于实体的地理和知识联系。

计算能力和大型培训数据集的可用性增加，机器学习的成功助长了。假设它充分代表了在测试时遇到的数据，则使用培训数据来学习新模型或更新现有模型。这种假设受到中毒威胁的挑战，这种攻击会操纵训练数据，以损害模型在测试时的表现。尽管中毒已被认为是行业应用中的相关威胁，到目前为止，已经提出了各种不同的攻击和防御措施，但对该领域的完整系统化和批判性审查仍然缺失。在这项调查中，我们在机器学习中提供了中毒攻击和防御措施的全面系统化，审查了过去15年中该领域发表的100多篇论文。我们首先对当前的威胁模型和攻击进行分类，然后相应地组织现有防御。虽然我们主要关注计算机视觉应用程序，但我们认为我们的系统化还包括其他数据模式的最新攻击和防御。最后，我们讨论了中毒研究的现有资源，并阐明了当前的局限性和该研究领域的开放研究问题。

本文维持了当时征服真正人类的语境中的视觉技能的学习机的位置，其中少数人类对象监督仅由声乐相互作用和指向辅助辅助。这可能需要关于愿景的计算过程的新基础，并通过在简单的人机语言相互作用下在自己的视觉环境中涉及视觉描述的任务中的最终目的。挑战由开发机器组成，该计算机学会在不需要处理视觉数据库的情况下。这可能会向真正正交的竞争轨道打开大门，关于视觉的深度学习技术，不依赖于庞大的视觉数据库的积累。

后门攻击在训练期间注入中毒样本，目的是迫使机器学习模型在测试时间呈现特定触发时输出攻击者所选的类。虽然在各种环境中展示了后门攻击和针对不同的模型，但影响其有效性的因素仍然不太了解。在这项工作中，我们提供了一个统一的框架，以研究增量学习和影响功能的镜头下的后门学习过程。我们表明，后门攻击的有效性取决于：（i）由普通参数控制的学习算法的复杂性; （ii）注入训练集的后门样品的一部分; （iii）后门触发的大小和可见性。这些因素会影响模型学会与目标类别相关联的速度触发器的存在的速度。我们的分析推出了封路计空间中的区域的有趣存在，其中清洁试验样品的准确性仍然很高，而后门攻击无效，从而提示改善现有防御的新标准。

在过去的十年中，由于航空图像引起的物体的规模和取向的巨大变化，对象检测已经实现了自然图像中的显着进展，而不是在空中图像中。更重要的是，缺乏大规模基准已成为在航拍图像（ODAI）中对物体检测发展的主要障碍。在本文中，我们在航空图像（DotA）中的物体检测和用于ODAI的综合基线的大规模数据集。所提出的DOTA数据集包含1,793,658个对象实例，18个类别的面向边界盒注释从11,268个航拍图像中收集。基于该大规模和注释的数据集，我们构建了具有超过70个配置的10个最先进算法的基线，其中已经评估了每个模型的速度和精度性能。此外，我们为ODAI提供了一个代码库，并建立一个评估不同算法的网站。以前在Dota上运行的挑战吸引了全球1300多队。我们认为，扩大的大型DOTA数据集，广泛的基线，代码库和挑战可以促进鲁棒算法的设计和对空中图像对象检测问题的可再现研究。

聚类算法在决策和明智的自动化过程中发挥着基本作用。由于这些应用的广泛使用，对抗对抗性噪声的这种算法的鲁棒性分析已经成为势在必行的。然而，据我们所知，目前只有少数作品目前解决了这个问题。在尝试填补这一差距，在这项工作中，我们提出了一种黑匣子对抗性攻击，用于制作对抗性样本来测试聚类算法的稳健性。我们将问题作为一个受约束的最小化程序，一般的结构，并且根据她的能力约束，攻击者定制。我们不假设有关受害者聚类算法的内部结构的任何信息，并且我们允许攻击者仅将其查询为服务。在没有任何衍生信息的情况下，我们通过抽象遗传算法（AGA）的自定义方法进行优化。在实验部分中，我们展示了不同单一和集群聚类算法对不同情景的制作的对抗样本的敏感性。此外，我们使用最先进的方法进行了对我们的算法的比较，显示我们能够达到或甚至优于其性能。最后，为了突出生成噪声的一般性质，我们表明我们的攻击即使针对SVMS，随机林和神经网络等监督算法也可转移。

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of wellannotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect 2806 aerial images from different sensors and platforms. Each image is of the size about 4000 × 4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using 15 common object categories. The fully annotated DOTA images contains 188, 282 instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral. To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.

Autoregressive processes naturally arise in a large variety of real-world scenarios, including e.g., stock markets, sell forecasting, weather prediction, advertising, and pricing. When addressing a sequential decision-making problem in such a context, the temporal dependence between consecutive observations should be properly accounted for converge to the optimal decision policy. In this work, we propose a novel online learning setting, named Autoregressive Bandits (ARBs), in which the observed reward follows an autoregressive process of order $k$, whose parameters depend on the action the agent chooses, within a finite set of $n$ actions. Then, we devise an optimistic regret minimization algorithm AutoRegressive Upper Confidence Bounds (AR-UCB) that suffers regret of order $\widetilde{\mathcal{O}} \left( \frac{(k+1)^{3/2}\sqrt{nT}}{(1-\Gamma)^2} \right)$, being $T$ the optimization horizon and $\Gamma < 1$ an index of the stability of the system. Finally, we present a numerical validation in several synthetic and one real-world setting, in comparison with general and specific purpose bandit baselines showing the advantages of the proposed approach.

Behavioral Cloning (BC) aims at learning a policy that mimics the behavior demonstrated by an expert. The current theoretical understanding of BC is limited to the case of finite actions. In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions. We start by deriving a novel bound on the performance gap based on Wasserstein distance, applicable for continuous-action experts, holding under the assumption that the value function is Lipschitz continuous. Since this latter condition is hardy fulfilled in practice, even for Lipschitz Markov Decision Processes and policies, we propose a relaxed setting, proving that value function is always Holder continuous. This result is of independent interest and allows obtaining in BC a general bound for the performance of the imitator policy. Finally, we analyze noise injection, a common practice in which the expert action is executed in the environment after the application of a noise kernel. We show that this practice allows deriving stronger performance guarantees, at the price of a bias due to the noise addition.

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. arm). We study a particular case of the rested and restless bandits in which the arms' expected payoff is monotonically non-decreasing. This characteristic allows designing specifically crafted algorithms that exploit the regularity of the payoffs to provide tight regret bounds. We design an algorithm for the rested case (R-ed-UCB) and one for the restless case (R-less-UCB), providing a regret bound depending on the properties of the instance and, under certain circumstances, of $\widetilde{\mathcal{O}}(T^{\frac{2}{3}})$. We empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a real-world dataset. Finally, using synthetic and real-world data, we illustrate the effectiveness of the proposed approaches compared with state-of-the-art algorithms for the non-stationary bandits.