This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.

我们可以看到这一切吗？我们知道这一切吗？这些是我们当代社会中人类提出的问题，以评估我们解决问题的趋势。最近的研究探索了对象检测中的几种模型。但是，大多数人未能满足对客观性和预测准确性的需求，尤其是在发展中和发达国家中。因此，几种全球安全威胁需要开发有效解决这些问题的方法。本文提出了一种被称为智能监视系统（3S）的网络物理系统的对象检测模型。这项研究提出了一种2阶段的方法，突出了Yolo V3深度学习体系结构在实时和视觉对象检测中的优势。该研究实施了一种转移学习方法，以减少培训时间和计算资源。用于培训模型的数据集是MS COCO数据集，其中包含328,000个注释的图像实例。实施了深度学习技术，例如预处理，数据管道调查和检测，以提高效率。与其他新型研究模型相比，该模型的结果在检测监视镜头中的野生物体方面表现出色。记录了99.71％的精度，改进的地图为61.5。

大数据一词是为了指代传统数据处理技术无法处理的大量数据。大数据仍然是一个新颖的概念，在以下文献中，我们打算以明显的方式详细说明它。它从主题本身的概念及其属性以及处理该主题的两种一般方法开始。大数据为教育机构提供了一个机会，可以从战略上利用其信息技术资源来提高教育质量，指导学生提高完成率并改善学生的毅力和成果。本文探讨了与教育机构有关的大数据的属性，研究了影响大数据和分析机构中采用大数据的因素，并试图建立阻碍在高等教育机构中使用大数据的限制因素。在进行这项研究时采用了调查研究设计，问卷是用于数据收集的仪器。

在大多数领域，从人工智能和游戏到人类计算机互动（HCI）和心理学，面部表情识别是一个重要的研究主题。本文提出了一个用于面部表达识别的混合模型，该模型包括深度卷积神经网络（DCNN）和HAAR级联深度学习体系结构。目的是将实时和数字面部图像分类为所考虑的七个面部情感类别之一。这项研究中使用的DCNN具有更多的卷积层，恢复激活功能以及多个内核，以增强滤波深度和面部特征提取。此外，HAAR级联模型还相互用于检测实时图像和视频帧中的面部特征。来自Kaggle存储库（FER-2013）的灰度图像，然后利用图形处理单元（GPU）计算以加快培训和验证过程。预处理和数据增强技术用于提高培训效率和分类性能。实验结果表明，与最先进的实验和研究相比，分类性能有了显着改善的分类性能。同样，与其他常规模型相比，本文验证了所提出的体系结构在分类性能方面表现出色，提高了6％，总计高达70％的精度，并且执行时间较小，为2098.8S。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.