Automated driving technology has gained a lot of momentum in the last few years. For the exploration field, navigation is the important key for autonomous operation. In difficult scenarios such as snowy environment, the road is covered with snow and road detection is impossible in this situation using only basic techniques. This paper introduces detection of snowy road in forest environment using RGB camera. The method combines noise filtering technique with morphological operation to classify the image component. By using the assumption that all road is covered by snow and the snow part is defined as road area. From the perspective image of road, the vanishing point of road is one of factor to scope the region of road. This vanishing point is found with fitting triangle technique. The performance of algorithm is evaluated by two error value: False Negative Rate and False Positive Rate. The error shows that the method has high efficiency for detect road with straight road but low performance for curved road. This road region will be applied with depth information from camera to detect for obstacle in the future work.

量化人体取向的安全性是人类机器人相互作用的重要问题。了解人类运动的身体限制不断变化，可以改善对安全人类运动的检查，并通过实时风险评估带来有关人体定向的稳定性和正常性的基本信息。此外，这些信息可以用于合作机器人和监视系统中，以更自由地评估和互动。此外，工作空间区域可以更确定性地具有安全性的身体特征。基于这种动机，我们提出了一种新型的预测安全模型（PSM），该模型依赖于人类胸部的惯性测量单元的信息。 PSM涵盖了一个3型弹簧型摆锤模型，该模型基于安全运动数据集预测人类运动。通过将安全数据集和弹性弹簧抑制模型集成的方式，该方法可以在建议的方法可以在不同的安全水平下实现复杂的运动来获得人类的估计安全取向。我们在现实情况下进行了实验，以验证我们的新型模型。这种新颖的方法可以在不同的指导/辅助机器人和健康监测系统中使用，以支持和评估人类状况，尤其是长者。

网络脆弱性管理是网络安全操作中心（CSOC）的关键功能，该中心有助于保护组织免受计算机和网络系统上的网络攻击。对手比CSOC拥有不对称的优势，因为这些系统中的缺陷次数与安全团队的扩展率相比，在资源受限的环境中减轻它们的速度相比，其速度明显更高。当前的方法是确定性和一次性决策方法，在优先考虑和选择缓解漏洞时，这些方法不考虑未来的不确定性。这些方法还受到资源的亚最佳分布的约束，没有灵活性来调整其对脆弱性到达波动的响应的灵活性。我们提出了一个新颖的框架，深深的瓦尔曼，由深入的强化学习代理和整数编程方法组成，以填补网络脆弱性管理过程中的这一空白。我们的顺序决策框架首先确定在给定系统状态下不确定性下为缓解的近乎最佳的资源，然后确定最佳的缓解优先级漏洞实例。我们提出的框架优于当前方法在一年内观察到的模拟和现实世界脆弱性数据优先选择重要的组织特定漏洞。

检测假新闻对于确保信息的真实性和维持新闻生态系统的可靠性至关重要。最近，由于最近的社交媒体和伪造的内容生成技术（例如Deep Fake）的扩散，假新闻内容的增加了。假新闻检测的大多数现有方式都集中在基于内容的方法上。但是，这些技术中的大多数无法处理生成模型生产的超现实合成媒体。我们最近的研究发现，真实和虚假新闻的传播特征是可以区分的，无论其方式如何。在这方面，我们已经根据社会环境调查了辅助信息，以检测假新闻。本文通过基于混合图神经网络的方法分析了假新闻检测的社会背景。该混合模型基于将图形神经网络集成到新闻内容上的新闻和BI定向编码器表示的传播中，以了解文本功能。因此，这种提出的方​​法可以学习内容以及上下文特征，因此能够在Politifact上以F1分别为0.91和0.93的基线模型和八西八角数据集的基线模型，分别超过了基线模型，分别在八西八学数据集中胜过0.93

使用物理互动设备（如小鼠和键盘）阻碍了自然主义的人机相互作用，并增加了大流行期间表面接触的可能性。现有的手势识别系统不具备用户身份验证，使其不可靠。当前手势识别技术中的静态手势会引入较长的适应周期并降低用户兼容性。我们的技术非常重视用户识别和安全。我们使用有意义且相关的手势进行任务操作，从而获得更好的用户体验。本文旨在设计一个强大的，具有面部验证的手势识别系统，该系统利用图形用户界面，主要通过用户识别和授权专注于安全性。面部模型使用MTCNN和FACENET来验证用户，而我们的LSTM-CNN体系结构进行手势识别，并以五类的手势获得了95％的精度。通过我们的研究开发的原型已成功执行了上下文依赖性任务，例如保存，打印，控制视频播放器操作和退出以及无上下文的操作系统任务，例如睡眠，关闭和直观地解锁。我们的应用程序和数据集可作为开源。

Pennylane是用于量子计算机可区分编程的Python 3软件框架。该库为近期量子计算设备提供了统一的体系结构，支持量子和连续变化的范例。 Pennylane的核心特征是能够以与经典技术（例如反向传播）兼容的方式来计算变异量子电路的梯度。因此，Pennylane扩展了在优化和机器学习中常见的自动分化算法，以包括量子和混合计算。插件系统使该框架与任何基于门的量子模拟器或硬件兼容。我们为硬件提供商提供插件，包括Xanadu Cloud，Amazon Braket和IBM Quantum，允许Pennylane优化在公开访问的量子设备上运行。在古典方面，Pennylane与加速的机器学习库（例如Tensorflow，Pytorch，Jax和Autograd）接口。 Pennylane可用于优化变分的量子本素体，量子近似优化，量子机学习模型和许多其他应用。

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.