Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.

联邦学习（FL）变得流行，并在训练大型机器学习（ML）模型的情况下表现出很大的潜力，而不会使所有者的原始数据曝光。在FL中，数据所有者可以根据其本地数据培训ML模型，并且仅将模型更新发送到模型更新，而不是原始数据到模型所有者进行聚合。为了提高模型准确性和培训完成时间的学习绩效，招募足够的参与者至关重要。同时，数据所有者是理性的，可能不愿意由于资源消耗而参与协作学习过程。为了解决这些问题，最近有各种作品旨在激励数据业主贡献其资源。在本文中，我们为文献中提出的经济和游戏理论方法提供了全面的审查，以设计刺激数据业主参加流程培训过程的各种计划。特别是，我们首先在激励机制设计中常用的佛罗里达州的基础和背景，经济理论。然后，我们审查博弈理论和经济方法应用于FL的激励机制的应用。最后，我们突出了一些开放的问题和未来关于FL激励机制设计的研究方向。

未来的互联网涉及几种新兴技术，例如5G和5G网络，车辆网络，无人机（UAV）网络和物联网（IOT）。此外，未来的互联网变得异质并分散了许多相关网络实体。每个实体可能需要做出本地决定，以在动态和不确定的网络环境下改善网络性能。最近使用标准学习算法，例如单药强化学习（RL）或深入强化学习（DRL），以使每个网络实体作为代理人通过与未知环境进行互动来自适应地学习最佳决策策略。但是，这种算法未能对网络实体之间的合作或竞争进行建模，而只是将其他实体视为可能导致非平稳性问题的环境的一部分。多机构增强学习（MARL）允许每个网络实体不仅观察环境，还可以观察其他实体的政策来学习其最佳政策。结果，MAL可以显着提高网络实体的学习效率，并且最近已用于解决新兴网络中的各种问题。在本文中，我们因此回顾了MAL在新兴网络中的应用。特别是，我们提供了MARL的教程，以及对MARL在下一代互联网中的应用进行全面调查。特别是，我们首先介绍单代机Agent RL和MARL。然后，我们回顾了MAL在未来互联网中解决新兴问题的许多应用程序。这些问题包括网络访问，传输电源控制，计算卸载，内容缓存，数据包路由，无人机网络的轨迹设计以及网络安全问题。

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.

This paper aims to improve the Warping Planer Object Detection Network (WPOD-Net) using feature engineering to increase accuracy. What problems are solved using the Warping Object Detection Network using feature engineering? More specifically, we think that it makes sense to add knowledge about edges in the image to enhance the information for determining the license plate contour of the original WPOD-Net model. The Sobel filter has been selected experimentally and acts as a Convolutional Neural Network layer, the edge information is combined with the old information of the original network to create the final embedding vector. The proposed model was compared with the original model on a set of data that we collected for evaluation. The results are evaluated through the Quadrilateral Intersection over Union value and demonstrate that the model has a significant improvement in performance.

在本文中，我们介绍了一个高质量的大规模基准数据集，用于英语 - 越南语音翻译，其中有508音频小时，由331k的三胞胎组成（句子长度的音频，英语源笔录句，越南人目标subtitle句子）。我们还使用强基础进行了经验实验，发现传统的“级联”方法仍然优于现代“端到端”方法。据我们所知，这是第一个大规模的英语 - 越南语音翻译研究。我们希望我们的公开数据集和研究都可以作为未来研究和英语语音翻译应用的起点。我们的数据集可从https://github.com/vinairesearch/phost获得

强化学习（RL）为可以在现实世界中自主互动的培训代理提供了潜力。但是，一个关键限制是RL算法对核心超参数和网络体系结构选择的脆弱性。此外，诸如不断发展的训练数据和增加的代理复杂性等非平稳性意味着不同的超参数和体系结构在不同的训练点上可能是最佳的。这激发了Autorl，这是一种试图自动化这些设计选择的方法。一类突出的Autorl方法是基于人群的培训（PBT），这在几个大型设置中导致了令人印象深刻的表现。在本文中，我们介绍了PBT式方法中的两项新创新。首先，我们采用基于信任区域的贝叶斯优化，从而可以全面覆盖高维混合参数搜索空间。其次，我们表明，使用世代相传，我们还可以在一次训练中共同学习体系结构和超参数。利用新的高度可行的Brax物理引擎，我们表明这些创新导致了巨大的性能增长，在即时学习整个配置的同时，大大优于调谐基线。代码可在https://github.com/xingchenwan/bgpbt上找到。

在社交媒体上传播谣言对社会构成了重要威胁，因此最近提出了各种谣言检测技术。然而，现有的工作重点是\ emph {what}实体构成谣言，但几乎没有支持理解\ emph {为什么}实体已被归类为这样。这样可以防止对检测的谣言以及对策设计的有效评估。在这项工作中，我们认为，可以通过过去检测到的相关谣言的例子来给出检测到的谣言的解释。一系列类似的谣言有助于用户概括，即了解控制谣言的探测的特性。由于通常使用特征声明的图表对社交媒体的谣言传播通常是建模的，因此我们提出了一种逐个示例的方法，鉴于谣言图，它从过去的谣言中提取了$ k $最相似和最多的子图。挑战是所有计算都需要快速评估图之间的相似性。为了在流式设置中实现该方法的有效和适应性实现，我们提出了一种新颖的图表学习技术，并报告了实施注意事项。我们的评估实验表明，我们的方法在为各种谣言传播行为提供有意义的解释方面优于基线技术。

我们为神经机翻译（NMT）提供了一个开源工具包。新工具包主要基于拱形变压器（Vaswani等，2017）以及下面详述的许多其他改进，以便创建一个独立的，易于使用，一致和全面的各个领域的机器翻译任务框架。它是为了支持双语和多语言翻译任务的工具，从构建各个语料库的模型开始推断新的预测或将模型打包给提供功能的JIT格式。