具有数百万参数的过度参数化模型取得了巨大成功。在这项工作中，我们问：至少由于学习者的\ emph {计算}限制，对大型模型的需求至少可以部分原因吗？此外，我们问，这种情况是否加剧了\ emph {robust}学习？我们证明确实可能是这种情况。我们展示了与信息理论学习者所需的学习任务相比，计算有限的学习者需要\ emph {明显更多的模型参数。此外，我们表明，对于健壮的学习可能需要更多的模型参数。特别是，对于计算有限的学习者，我们扩展了Bubeck and Sellke [Neurips'2021]的最新结果，该结果表明，强大的模型可能需要更多的参数，并表明有限学习者可能需要更多的参数数量。然后，我们解决以下相关的问题：我们是否希望通过限制\ emph {fersversaries}来纠正强大计算界限学习的情况，以便为了获得更少的参数获得模型而在计算上进行计算？再次，我们证明这是可能的。具体而言，在Garg，Jha，Mahloujifar和Mahmoody [Alt'2020]的基础上，我们演示了一项学习任务，可以有效，强大地对计算界限的攻击者进行有效，强大的学习，同时对信息理论攻击者需要强大学习者要使用更多参数。

中毒攻击已成为对机器学习算法的重要安全威胁。已经证明对培训集进行小变化的对手，例如添加特制的数据点，可以损害输出模型的性能。一些更强大的中毒攻击需要全面了解培训数据。这种叶子打开了使用没有完全了解干净训练集的中毒攻击来实现相同的攻击结果的可能性。在这项工作中，我们启动了对上述问题的理论研究。具体而言，对于具有套索的特征选择的情况，我们表明全信息对手（基于培训数据的其余部分的工艺中毒示例）可从未获得培训集的最佳攻击者提供了更强的最佳攻击者数据分发。我们的分离结果表明，数据感知和数据疏忽的两个设置从根本上不同，我们不能希望在这些场景中始终达到相同的攻击或辩护。

在这项工作中，我们展示了普遍的多方中毒攻击，适应并适用于各方之间的任意交互模式的多方学习过程。更一般地说，我们介绍和研究$（k，p）$ - 中毒攻击，其中对手控制在[m] $中，以及每个损坏的派对$ p_i $，对手提交一些中毒数据$ \ mathcal {t}'_ i $代表$ p_i $，它仍然是``$（1-p）$ - 关闭''到正确的数据$ \ mathcal {t} _i $（例如，$ 1-p $ fillaction $ \ mathcal {t}'_ i $仍然诚实地生成）。我们证明，对于任何``bad'属性$ b $ b $ h $ h $（例如，特定测试示例上的$ h $失败或拥有具有任意小的持续概率的``lable''风险）没有攻击的情况，总是有一个$（k，p）$ - 中毒攻击，增加了$ \ mu $的$ \ mu $ to \ mu ^ {1-p \ cdot k / m}} = \ mu +ω（p \ cdot k / m）$。我们的攻击只使用干净的标签，它在线。更一般地说，我们证明，对于任何界限函数$ f（x_1，\ dots，x_n）\在$ n $ -step随机过程$ \ mathbf {x} =（x_1，\ dots ，x_n）$，可以覆盖每一个$ n $块的对手，甚至依赖概率$ p $可以通过至少$ \ω（p \ cdot \ mathrm {var} [f（\ mathbf { x}）]）$。

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.

With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to $3.99\% $ better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to $2.41\%$ higher accuracy than FedAvg in the case of testing the generalization of the models.

Learning models are highly dependent on data to work effectively, and they give a better performance upon training on big datasets. Massive research exists in the literature to address the dataset adequacy issue. One promising approach for solving dataset adequacy issues is the data augmentation (DA) approach. In DA, the amount of training data instances is increased by making different transformations on the available data instances to generate new correct and representative data instances. DA increases the dataset size and its variability, which enhances the model performance and its prediction accuracy. DA also solves the class imbalance problem in the classification learning techniques. Few studies have recently considered DA in the Arabic language. These studies rely on traditional augmentation approaches, such as paraphrasing by using rules or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets, namely AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.