行人意图预测问题是估计目标行人是否会过马路。最先进的方法在很大程度上依赖于使用自我车辆的前置摄像头收集的视觉信息来预测行人的意图。因此，当视觉信息不准确时，例如，当行人和自我车辆之间的距离远处或照明条件不够好时，现有方法的性能会显着降低。在本文中，我们根据与行人的智能手表（或智能手机）收集的运动传感器数据的集成，设计，实施和评估第一个行人意图预测模型。提出了一种新型的机器学习体系结构，以有效地合并运动传感器数据，以加强视觉信息，以显着改善视觉信息可能不可靠的不利情况的性能。我们还进行了大规模的数据收集，并介绍了与时间同步运动传感器数据集成的第一个行人意图预测数据集。该数据集由总共128个视频剪辑组成，这些视频片段具有不同的距离和不同级别的照明条件。我们使用广泛使用的JAAD和我们自己的数据集训练了模型，并将性能与最先进的模型进行了比较。结果表明，我们的模型优于最新方法，特别是当行人的距离远（超过70m）并且照明条件不足时。

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

基于各种非负矩阵分解（NMF）方法为成本函数添加了新术语，以使模型适应特定任务，例如聚类或保留减少空间中的某些结构属性（例如，局部不变性）。附加的术语主要由高参数加权，以控制整体公式的平衡，以指导优化过程实现目标。结果是一种参数化的NMF方法。但是，NMF方法采用了无监督的方法来估计分解矩阵。因此，不能保证使用新的特征执行预测（例如分类）的能力。这项工作的目的是设计一个进化框架，以学习参数化NMF的超参数，并以监督的方式估算分解矩阵，以更适合分类问题。此外，我们声称，将基于NMF的算法分别应用于不同的类对，而不是将其应用于整个数据集，从而提高了矩阵分解过程的有效性。这导致训练具有不同平衡参数值的多个参数化的NMF算法。采用了交叉验证组合学习框架，并使用遗传算法来识别最佳参数值集。我们对真实和合成数据集进行的实验证明了所提出的方法的有效性。

由于对不同部门的电子芯片的需求不断增长，因此，半导体公司被授权离岸其制造流程。这一不必要的事情使他们对筹码的筹码有关，并引起了硬件攻击的创造。在这种情况下，半导体供应链中的不同实体可以恶意行事，并对从设备到系统的设计计算层进行攻击。我们的攻击是一个硬件特洛伊木马，在不受信任的铸造厂中插入了在面具的生成/制造过程中。特洛伊木马在制造，通过添加，删除或设计单元的变化中留下了脚印。为了解决这个问题，我们在这项工作中提出了可解释的视觉系统，用于硬件测试和保证（EVHA），可以检测以低成本，准确和快速的方式对设计的最小变化。该系统的输入是从正在检查的集成电路（IC）中获取的扫描电子显微镜（SEM）图像。系统输出是通过添加，删除或在单元格级的设计单元格中使用任何缺陷和/或硬件木马来确定IC状态。本文概述了我们的防御系统的设计，开发，实施和分析。

使用人工智能（AI）赋予无线网络中数据量的前所未有的数据量激增，为提供无处不在的数据驱动智能服务而开辟了新的视野。通过集中收集数据集和培训模型来实现传统的云彩中心学习（ML）基础的服务。然而，这种传统的训练技术包括两个挑战：（i）由于数据通信增加而导致的高通信和能源成本，（ii）通过允许不受信任的各方利用这些信息来威胁数据隐私。最近，鉴于这些限制，一种新兴的新兴技术，包括联合学习（FL），以使ML带到无线网络的边缘。通过以分布式方式培训全局模型，可以通过FL Server策划的全局模型来提取数据孤岛的好处。 FL利用分散的数据集和参与客户的计算资源，在不影响数据隐私的情况下开发广义ML模型。在本文中，我们介绍了对FL的基本面和能够实现技术的全面调查。此外，提出了一个广泛的研究，详细说明了无线网络中的流体的各种应用，并突出了他们的挑战和局限性。进一步探索了FL的疗效，其新兴的前瞻性超出了第五代（B5G）和第六代（6G）通信系统。本调查的目的是在关键的无线技术中概述了流动的技术，这些技术将作为建立对该主题的坚定了解的基础。最后，我们向未来的研究方向提供前进的道路。

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.