学习证明（POL）建议模型所有者使用机器学习培训检查站，以建立已经花费了必要的培训计算的证明。 POL FIREGO加密方法和贸易严格的安全性的作者通过适用于随机梯度下降和适应性变体，可扩展到深度学习。缺乏正式分析使攻击者可能能够为他们没有训练的模型提供证据。我们对为什么不能正式（DIS）正式分析POL协议可抵抗欺骗对手。为此，我们在POL中解开了证明验证的两个角色：（a）有效确定证明是否是有效的梯度下降轨迹，以及（b）确定优先级，使在培训完成后制作证明（即。 ，欺骗）。我们表明，有效的验证会导致接受合法证明和拒绝无效的证据之间的权衡，因为深度学习必然涉及噪音。没有针对这种噪声如何影响训练的精确分析模型，我们无法正式保证POL验证算法是否强大。然后，我们证明，建立优先级也可以鲁棒化地减少到学习理论中的一个开放问题：欺骗Pol Pol hoc hoc训练类似于在非凸X学习中找到具有相同终点的不同轨迹。但是，我们不严格地知道对最终模型权重的先验知识是否有助于发现此类轨迹。我们得出的结论是，在解决上述开放问题之前，可能需要更严重地依靠密码学来制定新的POL协议，并提供正式的鲁棒性保证。特别是，这将有助于建立优先级。作为我们分析的见解的副产品，我们还展示了对POL的两次新攻击。

缺乏精心校准的置信度估计值使神经网络在安全至关重要的领域（例如自动驾驶或医疗保健）中不足。在这些设置中，有能力放弃对分布（OOD）数据进行预测的能力，就像正确分类分布数据一样重要。我们介绍了$ P $ -DKNN，这是一种新颖的推理程序，该过程采用了经过训练的深神经网络，并分析了其中间隐藏表示形式的相似性结构，以计算与端到端模型预测相关的$ p $值。直觉是，在潜在表示方面执行的统计测试不仅可以用作分类器，还可以提供统计上有充分根据的不确定性估计。 $ P $ -DKNN是可扩展的，并利用隐藏层学到的表示形式的组成，这使深度表示学习成功。我们的理论分析基于Neyman-Pearson的分类，并将其与选择性分类的最新进展（拒绝选项）联系起来。我们证明了在放弃预测OOD输入和保持分布输入的高精度之间的有利权衡。我们发现，$ p $ -DKNN强迫自适应攻击者制作对抗性示例（一种最差的OOD输入形式），以对输入引入语义上有意义的更改。

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.

Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.