无监督的域适应（UDA）显示出近年来工作条件下的轴承故障诊断的显着结果。但是，大多数UDA方法都不考虑数据的几何结构。此外，通常应用全局域适应技术，这忽略了子域之间的关系。本文通过呈现新的深亚域适应图卷积神经网络（DSAGCN）来解决提到的挑战，具有两个关键特性：首先，采用图形卷积神经网络（GCNN）来模拟数据结构。二，对抗域适应和局部最大平均差异（LMMD）方法同时应用，以对准子域的分布并降低相关子域和全局域之间的结构差异。 CWRU和Paderborn轴承数据集用于验证DSAGCN方法的比较模型之间的效率和优越性。实验结果表明，将结构化子域与域适应方法对准，以获得无监督故障诊断的准确数据驱动模型。

In this research, we are about to present an agentbased model of human muscle which can be used in analysis of human movement. As the model is designed based on the physiological structure of the muscle, The simulation calculations would be natural, and also, It can be possible to analyze human movement using reverse engineering methods. The model is also a suitable choice to be used in modern prostheses, because the calculation of the model is less than other machine learning models such as artificial neural network algorithms and It makes our algorithm battery-friendly. We will also devise a method that can calculate the intensity of human muscle during gait cycle using a reverse engineering solution. The algorithm called Boots is different from some optimization methods, so It would be able to compute the activities of both agonist and antagonist muscles in a joint. As a consequence, By having an agent-based model of human muscle and Boots algorithm, We would be capable to develop software that can calculate the nervous stimulation of human's lower body muscle based on the angular displacement during gait cycle without using painful methods like electromyography. By developing the application as open-source software, We are hopeful to help researchers and physicians who are studying in medical and biomechanical fields.

Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted masks remain because of transformers' patch partitioning operations. In this work, we propose a new U-shaped architecture for medical image segmentation with the help of the newly introduced focal modulation mechanism. The proposed architecture has asymmetric depths for the encoder and decoder. Due to the ability of the focal module to aggregate local and global features, our model could simultaneously benefit the wide receptive field of transformers and local viewing of CNNs. This helps the proposed method balance the local and global feature usage to outperform one of the most powerful transformer-based U-shaped models called Swin-UNet. We achieved a 1.68% higher DICE score and a 0.89 better HD metric on the Synapse dataset. Also, with extremely limited data, we had a 4.25% higher DICE score on the NeoPolyp dataset. Our implementations are available at: https://github.com/givkashi/Focal-UNet

This paper presents the ARCAD simulator for the rapid development of Unmanned Aerial Systems (UAS), including underactuated and fully-actuated multirotors, fixed-wing aircraft, and Vertical Take-Off and Landing (VTOL) hybrid vehicles. The simulator is designed to accelerate these aircraft's modeling and control design. It provides various analyses of the design and operation, such as wrench-set computation, controller response, and flight optimization. In addition to simulating free flight, it can simulate the physical interaction of the aircraft with its environment. The simulator is written in MATLAB to allow rapid prototyping and is capable of generating graphical visualization of the aircraft and the environment in addition to generating the desired plots. It has been used to develop several real-world multirotor and VTOL applications. The source code is available at https://github.com/keipour/aircraft-simulator-matlab.

As social media grows faster, harassment becomes more prevalent which leads to considered fake detection a fascinating field among researchers. The graph nature of data with the large number of nodes caused different obstacles including a considerable amount of unrelated features in matrices as high dispersion and imbalance classes in the dataset. To deal with these issues Auto-encoders and a combination of semi-supervised learning and the GAN algorithm which is called SGAN were used. This paper is deploying a smaller number of labels and applying SGAN as a classifier. The result of this test showed that the accuracy had reached 91\% in detecting fake accounts using only 100 labeled samples.

在过去的几年中，卷积神经网络（CNN）在各种现实世界的网络安全应用程序（例如网络和多媒体安全）中表现出了有希望的性能。但是，CNN结构的潜在脆弱性构成了主要的安全问题，因此不适合用于以安全为导向的应用程序，包括此类计算机网络。保护这些体系结构免受对抗性攻击，需要使用挑战性攻击的安全体系结构。在这项研究中，我们提出了一种基于合奏分类器的新型体系结构，该结构将1级分类（称为1C）的增强安全性与在没有攻击的情况下的传统2级分类（称为2C）的高性能结合在一起。我们的体系结构称为1.5级（Spritz-1.5c）分类器，并使用最终密度分类器，一个2C分类器（即CNNS）和两个并行1C分类器（即自动编码器）构造。在我们的实验中，我们通过在各种情况下考虑八次可能的对抗性攻击来评估我们提出的架构的鲁棒性。我们分别对2C和Spritz-1.5c体系结构进行了这些攻击。我们研究的实验结果表明，I-FGSM攻击对2C分类器的攻击成功率（ASR）是N-Baiot数据集训练的2C分类器的0.9900。相反，Spritz-1.5C分类器的ASR为0.0000。

在这项综合研究中，通过基于能量环境分析添加入口空气冷却和再生冷却来评估涡轮轴发动机。首先，飞行器数量，飞行高度，主要周期中压缩机1的压缩比，主周期中涡轮-1的涡轮入口温度，涡轮-2的温度分数，辅助的压缩比循环和入口空气冷却系统中的进气温变化，这些功能性能参数的某些功能性能参数，配备了带有入口空气冷却系统的再生涡轮轴发动机周期，例如功率特异性的燃油消耗，功率输出，热效率和硝酸盐氧化物的质量流量（质量流量） NOX）通过使用氢作为燃料工作，研究了NO和NO2。因此，基于分析，开发了一个模型来预测带有冷却空气冷却系统基于深神经网络（DNN）的再生涡轮轴发动机周期的能量环境性能层。该模型提出的旨在预测含有NO和NO2的氮化物氧化物（NOX）的质量流量和质量流量。结果证明了综合DNN模型的准确性，具有适当的MSE，MAE和RMSD成本函数，用于验证测试和培训数据。同样，对于热效率和NOX发射质量流量，对于热效率的验证和NOX发射质量流量质量预测值及其测试数据，R和R^2都非常接近1。

基于变压器的模型用于实现各种深度学习任务的最新性能。由于基于变压器的模型具有大量参数，因此在下游任务上进行微调是计算密集型和饥饿的能量。此类型号的自动混合精液FP32/FP16微调以前已用于降低计算资源需求。但是，随着低位整数背面传播的最新进展，有可能进一步减少计算和记忆脚印。在这项工作中，我们探索了一种新颖的整数训练方法，该方法使用整数算术来进行正向传播和梯度计算，对基于变压器的模型中的线性，卷积，层和层和嵌入层的梯度计算。此外，我们研究了各种整数位宽度的效果，以找到基于变压器模型的整数微调所需的最小位宽度。我们使用整数层对流行的下游任务进行了微调和VIT模型。我们表明，16位整数模型与浮点基线性能匹配。将位宽度降低到10，我们观察到0.5平均得分下降。最后，将位宽度的进一步降低到8的平均得分下降为1.7分。

在过去的几十年中，人工智能的兴起使我们有能力解决日常生活中最具挑战性的问题，例如癌症的预测和自主航行。但是，如果不保护对抗性攻击，这些应用程序可能不会可靠。此外，最近的作品表明，某些对抗性示例可以在不同的模型中转移。因此，至关重要的是避免通过抵抗对抗性操纵的强大模型进行这种可传递性。在本文中，我们提出了一种基于特征随机化的方法，该方法抵抗了八次针对测试阶段深度学习模型的对抗性攻击。我们的新方法包括改变目标网络分类器中的训练策略并选择随机特征样本。我们认为攻击者具有有限的知识和半知识条件，以进行最普遍的对抗性攻击。我们使用包括现实和合成攻击的众所周知的UNSW-NB15数据集评估了方法的鲁棒性。之后，我们证明我们的策略优于现有的最新方法，例如最强大的攻击，包括针对特定的对抗性攻击进行微调网络模型。最后，我们的实验结果表明，我们的方法可以确保目标网络并抵抗对抗性攻击的转移性超过60％。

深度学习模型实现了最先进的结果，可以预测血糖轨迹，并提出了广泛的体系结构。但是，这种模型在临床实践中的适应性很慢，这主要是由于缺乏对所提供的预测的不确定性定量。在这项工作中，我们建议将未来的葡萄糖轨迹建模为基础分布的无限混合物（即高斯，拉普拉斯等）。这种变化使我们能够学习不确定性并在轨迹具有异质或多模式分布的情况下更准确地预测。为了估计预测分布的参数，我们利用了变压器体系结构。我们从经验上证明了我们方法在合成和基准葡萄糖数据集上的准确性和不确定性方面，既优于现有的最新技术。