对抗性培训（AT）被认为是对抗对抗攻击最可靠的防御之一。然而，模型培训以牺牲标准精度，并不概括为新的攻击。最近的作用表明，在新型威胁模型中的新威胁模型或神经感知威胁模型中，对普遍威胁模型的对抗样本进行了泛化改进。然而，前者需要确切的流形信息，而后者需要算法放松。通过这些考虑因素，我们利用了具有规范化流的底层歧管信息，确保了确切的歧管的假设保持。此外，我们提出了一种名为联合空间威胁模型（JSTM）的新型威胁模型，其可以作为神经感知威胁模型的特殊情况，这些威胁模型不需要额外放松来制作相应的对抗性攻击。在JSTM下，我们培养了新的对抗性攻击和防御。混合策略提高了神经网络的标准准确性，但与AT结合时牺牲了鲁棒性。为了解决这个问题，我们提出了强大的混合策略，其中我们最大限度地提高了内插图像的逆境，并获得了鲁棒性和预装配。我们的实验表明，内插关节空间对抗性训练（IJSAT）在CiFar-10/100，Om-ImageNet和CiFar-10-C数据集中实现了标准精度，鲁棒性和泛化的良好性能。 IJSAT也是灵活的，可以用作数据增强方法，以提高标准精度，并与诸多换取以提高鲁棒性的方法相结合。

最近的研究表明，对对抗性攻击的鲁棒性可以跨网络转移。换句话说，在强大的教师模型的帮助下，我们可以使模型更加强大。我们问是否从静态教师那里学习，可以模特“学习”和“互相教导”来实现更好的稳健性？在本文中，我们研究模型之间的相互作用如何通过知识蒸馏来影响鲁棒性。我们提出了互联土训练（垫子），其中多种模型一起培训并分享对抗性示例的知识，以实现改善的鲁棒性。垫允许强大的模型来探索更大的对抗样本空间，并找到更强大的特征空间和决策边界。通过对CIFAR-10和CIFAR-100的广泛实验，我们证明垫可以在白盒攻击下有效地改善模型稳健性和最优异的现有方法，使$ \ SIM为8％的准确性增益对香草对抗培训（在PGD-100袭击下。此外，我们表明垫子还可以在不同的扰动类型中减轻鲁棒性权衡，从$ l_ \ infty $，$ l_2 $和$ l_1 $攻击中带来基线的基线。这些结果表明了该方法的优越性，并证明协作学习是设计强大模型的有效策略。

随着机器学习数据的策展变得越来越自动化，数据集篡改是一种安装威胁。后门攻击者通过培训数据篡改，以嵌入在该数据上培训的模型中的漏洞。然后通过将“触发”放入模型的输入中的推理时间以推理时间激活此漏洞。典型的后门攻击将触发器直接插入训练数据，尽管在检查时可能会看到这种攻击。相比之下，隐藏的触发后托攻击攻击达到中毒，而无需将触发器放入训练数据即可。然而，这种隐藏的触发攻击在从头开始培训的中毒神经网络时无效。我们开发了一个新的隐藏触发攻击，睡眠代理，在制备过程中使用梯度匹配，数据选择和目标模型重新培训。睡眠者代理是第一个隐藏的触发后门攻击，以对从头开始培训的神经网络有效。我们展示了Imagenet和黑盒设置的有效性。我们的实现代码可以在https://github.com/hsouri/sleeper-agent找到。

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Based on WHO statistics, many individuals are suffering from visual problems, and their number is increasing yearly. One of the most critical needs they have is the ability to navigate safely, which is why researchers are trying to create and improve various navigation systems. This paper provides a navigation concept based on the visual slam and Yolo concepts using monocular cameras. Using the ORB-SLAM algorithm, our concept creates a map from a predefined route that a blind person most uses. Since visually impaired people are curious about their environment and, of course, to guide them properly, obstacle detection has been added to the system. As mentioned earlier, safe navigation is vital for visually impaired people, so our concept has a path-following part. This part consists of three steps: obstacle distance estimation, path deviation detection, and next-step prediction, done by monocular cameras.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.

Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer vision datasets. This approach works well with a large number of insulator images, but can result in unreliable models in the low data regime. The current literature mainly focuses on detecting the presence or absence of insulator caps, which is a relatively easy detection task, and does not consider detection of finer faults such as flashed and broken disks. In this article, we formulate three object detection tasks for insulator and asset inspection from aerial images, focusing on incipient faults in disks. We curate a large reference dataset of insulator images that can be used to learn robust features for detecting healthy and faulty insulators. We study the advantage of using this dataset in the low target data regime by pre-training on the reference dataset followed by fine-tuning on the target dataset. The results suggest that object detection models can be used to detect faults in insulators at a much incipient stage, and that transfer learning adds value depending on the type of object detection model. We identify key factors that dictate performance in the low data-regime and outline potential approaches to improve the state-of-the-art.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~(SAR) data. Our proposed method is a probabilistic model trained with unsupervised learning techniques, reconstruction, and contrastive learning. The change map is generated with the help of the distribution difference between pre-incident and post-incident data. Our proposed CD model is evaluated on flood detection data. We verified the efficacy of our model on 8 different flood sites, including three recent flood events from Copernicus Emergency Management Services and six from the Sen1Floods11 dataset. Our proposed model achieved an average of 64.53\% Intersection Over Union(IoU) value and 75.43\% F1 score. Our achieved IoU score is approximately 6-27\% and F1 score is approximately 7-22\% better than the compared unsupervised and supervised existing CD methods. The results and extensive discussion presented in the study show the effectiveness of the proposed unsupervised CD method.