深度学习（DL）在无线领域中找到了丰富的应用，以提高频谱意识。通常，DL模型要么是根据统计分布后随机初始初始初始初始初始初始初始初始初始初始化，要么在其他数据域（例如计算机视觉）（以转移学习的形式）上进行鉴定，而无需考虑无线信号的唯一特征。即使只有有限的带有标签的培训数据样本，自我监督的学习也能够从射频（RF）信号本身中学习有用的表示形式。我们通过专门制定一组转换以捕获无线信号特征来提出第一个自我监督的RF信号表示学习模型，并将其应用于自动调制识别（AMR）任务。我们表明，通过学习信号表示具有自我监督的学习，可以显着提高样本效率（实现一定准确性性能所需的标记样品数量）。这转化为大量时间和节省成本。此外，与最先进的DL方法相比，自我监管的学习可以提高模型的准确性，即使使用了一小部分训练数据样本，也可以保持高精度。

本文提出了对基于深度学习的无线信号分类器的信道感知对抗攻击。有一个发射器，发送具有不同调制类型的信号。每个接收器使用深神经网络以将其超空气接收信号分类为调制类型。与此同时，对手将对手扰动（受到电力预算的影响）透射到欺骗接收器，以在作为透射信号叠加和对抗扰动的叠加接收的分类信号中进行错误。首先，当在设计对抗扰动时不考虑通道时，这些逃避攻击被证明会失败。然后，通过考虑来自每个接收器的对手的频道效应来提出现实攻击。在示出频道感知攻击是选择性的（即，它只影响扰动设计中的信道中考虑的接收器），通过制作常见的对抗扰动来呈现广播对抗攻击，以在不同接收器处同时欺骗分类器。通过占通道，发射机输入和分类器模型可用的不同信息，将调制分类器对过空中侵犯攻击的主要脆弱性。最后，引入了基于随机平滑的经过认证的防御，即增加了噪声训练数据，使调制分类器鲁棒到对抗扰动。

Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Focal Loss, Rank & Sort Loss) have shown that forcing these two loss terms to interact with each other in non-conventional ways creates a useful inductive bias and improves performance. Inspired by these works, we focus on the correlation between classification and localization and make two main contributions: (i) We provide an analysis about the effects of correlation between classification and localization tasks in object detectors. We identify why correlation affects the performance of various NMS-based and NMS-free detectors, and we devise measures to evaluate the effect of correlation and use them to analyze common detectors. (ii) Motivated by our observations, e.g., that NMS-free detectors can also benefit from correlation, we propose Correlation Loss, a novel plug-in loss function that improves the performance of various object detectors by directly optimizing correlation coefficients: E.g., Correlation Loss on Sparse R-CNN, an NMS-free method, yields 1.6 AP gain on COCO and 1.8 AP gain on Cityscapes dataset. Our best model on Sparse R-CNN reaches 51.0 AP without test-time augmentation on COCO test-dev, reaching state-of-the-art. Code is available at https://github.com/fehmikahraman/CorrLoss

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.

在未知环境中存在动态障碍的情况下，避免碰撞是无人系统最关键的挑战之一。在本文中，我们提出了一种方法，该方法可以鉴定出椭圆形的障碍，以估计线性和角度障碍速度。我们提出的方法是基于任何对象的概念，可以由椭圆形表示。为了实现这一目标，我们提出了一种基于高斯混合模型，kyachiyan算法和改进算法的变异贝叶斯估计的方法。与现有的基于优化的方法不同，我们提出的方法不需要了解集群数量，并且可以实时操作。此外，我们定义一个基于椭圆形的特征向量以匹配两个及时的接近点帧。我们的方法可以应用于具有静态和动态障碍的任何环境，包括具有旋转障碍的环境。我们将算法与其他聚类方法进行比较，并表明当与轨迹计划器结合时，整体系统可以在存在动态障碍物的情况下有效地穿越未知环境。

这项工作总结了2022年2022年国际生物识别联合会议（IJCB 2022）的IJCB被遮挡的面部识别竞赛（IJCB-OCFR-2022）。OCFR-2022从学术界吸引了总共3支参与的团队。最终，提交了六个有效的意见书，然后由组织者评估。在严重的面部阻塞面前，举行了竞争是为了应对面部识别的挑战。参与者可以自由使用任何培训数据，并且通过使用众所周知的数据集构成面部图像的部分来构建测试数据。提交的解决方案提出了创新，并以所考虑的基线表现出色。这项竞争的主要输出是具有挑战性，现实，多样化且公开可用的遮挡面部识别基准，并具有明确的评估协议。

在这项研究中，我们解决了增强学习中有效探索的问题。最常见的探索方法取决于随机行动的选择，但是这些方法在稀疏或没有奖励的环境中无法很好地工作。我们提出了基于生成的对抗网络的固有奖励模块，该模块了解观察到的状态的分布并发送一个内在的奖励，该奖励是为无法分配的状态而计算出的，以使代理人领导未开发的状态。我们在超级马里奥兄弟（Super Mario Bros）中评估了我们的方法，以获取无奖励的环境，并在蒙特祖玛（Montezuma）的报仇中为稀疏的奖励设置进行了报复，并表明我们的方法确实能够有效地探索。我们讨论了一些弱点，并通过讨论未来的作品来得出结论。

该报告说明了基于音频和视频数据的最成功的AAL应用程序和功能的艺术状态，即（i）生命式和自我监控，（ii）对生命体征的远程监控，（iii）情绪状态识别，（（iv）食物摄入量监测，活动和行为认识，（v）活动和个人帮助，（vi）手势识别，（vii）秋季检测和预防，（viii）移动性评估和脆弱的识别以及（IX）认知和运动康复。对于这些应用程序方案，该报告说明了科学进步，可用产品和研究项目的状态。开放的挑战也被突出显示。