对话策略学习是面向任务的对话系统（TDS）中的关键组成部分，该系统决定在每个回合处给定对话状态的系统的下一个动作。加强学习（RL）通常被选为学习对话策略，将用户作为环境和系统作为代理。已经创建了许多基准数据集和算法，以促进基于RL的对话策略的制定和评估。在本文中，我们调查了RL规定的对话政策的最新进展和挑战。更具体地说，我们确定了主要问题，并总结了基于RL的对话政策学习的相应解决方案。此外，我们通过将最新方法分类为RL中的基本元素，对将RL应用于对话政策学习的全面调查。我们认为，这项调查可以阐明对话管理未来的研究。

Recognition of facial expression is a challenge when it comes to computer vision. The primary reasons are class imbalance due to data collection and uncertainty due to inherent noise such as fuzzy facial expressions and inconsistent labels. However, current research has focused either on the problem of class imbalance or on the problem of uncertainty, ignoring the intersection of how to address these two problems. Therefore, in this paper, we propose a framework based on Resnet and Attention to solve the above problems. We design weight for each class. Through the penalty mechanism, our model will pay more attention to the learning of small samples during training, and the resulting decrease in model accuracy can be improved by a Convolutional Block Attention Module (CBAM). Meanwhile, our backbone network will also learn an uncertain feature for each sample. By mixing uncertain features between samples, the model can better learn those features that can be used for classification, thus suppressing uncertainty. Experiments show that our method surpasses most basic methods in terms of accuracy on facial expression data sets (e.g., AffectNet, RAF-DB), and it also solves the problem of class imbalance well.

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.

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies.

Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.

可重新配置的智能表面（RIS）可以显着增强TERA-HERTZ大量多输入多输出（MIMO）通信系统的服务覆盖范围。但是，获得有限的飞行员和反馈信号开销的准确高维通道状态信息（CSI）具有挑战性，从而严重降低了常规空间分裂多次访问的性能。为了提高针对CSI缺陷的鲁棒性，本文提出了针对RIS辅助TERA-HERTZ多用户MIMO系统的基于深度学习的（DL）基于速率的多访问（RSMA）方案。具体而言，我们首先提出了基于DL的混合数据模型驱动的RSMA预编码方案，包括RIS的被动预编码以及模拟主动编码和基本站（BS）的RSMA数字活动预码。为了实现RIS的被动预码，我们提出了一个基于变压器的数据驱动的RIS反射网络（RRN）。至于BS的模拟主动编码，我们提出了一个基于匹配器的模拟预编码方案，因为BS和RIS采用了Los-Mimo天线阵列结构。至于BS的RSMA数字活动预码，我们提出了一个低复杂性近似加权的最小均方误差（AWMMSE）数字编码方案。此外，为了更好地编码性能以及较低的计算复杂性，模型驱动的深层展开的主动编码网络（DFAPN）也是通过将所提出的AWMMSE方案与DL相结合的。然后，为了在BS处获得准确的CSI，以实现提高光谱效率的RSMA预编码方案，我们提出了一个CSI采集网络（CAN），具有低飞行员和反馈信号开销，下行链接飞行员的传输，CSI在此处使用CSI的CSI反馈。 （UES）和BS处的CSI重建被建模为基于变压器的端到端神经网络。

拟议的控制方法使用基于自适应的馈电控制器来为CDPR建立一个被动输入输出映射，该映射与线性不变的严格阳性真实反馈控制器一起使用，以确保稳健的闭环输入输出稳定性和渐进式姿势轨迹通过消极定理跟踪。所提出的控制器的新颖性是其配方用于一系列有效载荷态度参数化，包括任何无约束的态度参数化，四元组或方向余弦矩阵（DCM）。通过用刚性和柔性电缆的CDPR进行数值模拟，证明了所提出的控制器的性能和鲁棒性。结果证明了仔细定义CDPR的姿势误差的重要性，CDPR的姿势误差是在使用Quaternion和dcm时以乘法方式执行的，并且在使用不受约束的态度参数时（例如Euler-andle-angle序列）时以特定的添加剂方式执行。

集成感应和通信（ISAC）代表范式转移，以前竞争的无线传输是共同设计的，可通过共同使用硬件平台来提高光谱，能源和硬件效率来和谐地运行。但是，由于诸如褪色和堵塞之类的对抗性因素，ISAC无融合可能会遭受高感知不确定性的影响。本文提出了一个多点ISAC（MPISAC）系统，该系统通过利用多雷达数据冗余来融合来自多个ISAC设备的输出，以实现更高的感应性能。此外，我们建议通过功能选择模块有效地探索传感和通信之间的性能权衡，该功能选择模块可适应地确定ISAC设备的工作状态（即传感或通信）。我们方法的症结在于采用融合模型，该模型通过假设检验和最佳投票分析来预测融合精度。仿真结果表明，MPISAC优于各种基准方案，并表明所提出的方法可以有效地跨越ISAC系统中的权衡区域。

应用于设备的射频指纹〜（RFF）的深度学习（DL）由于其非凡的分类性能而引起了物理层认证的极大关注。传统的DL-RFF技术通过采用最大似然估计〜（MLE）训练，倾向于过度拟合培训数据集中嵌入的通道统计信息。这限制了他们的实际应用，因为收集足够的培训数据来捕获所有可能的无线渠道环境的特征是具有挑战性的。为了应对这一挑战，我们提出了一个DL表示的DL框架学习〜（DRL），该框架首先学会通过对抗学习将输入信号分解为相关的组件和设备 - iRretrelevant组件。然后，它通过在给定的培训数据集中洗牌以训练后续的RFF提取器来综合一组增强信号。所提出的框架中的隐式数据增强在RFF提取器上实施了正则化，以避免在不收集未知通道的其他数据的情况下，可能会过度拟合设备 - IRRELELERVENT的通道统计。实验验证了所提出的方法，称为DR-RFF，就不明复杂的传播环境的普遍性而言，均优于常规方法，例如，即使所有训练数据都在简单的直接线上收集，即使所有训练数据都收集到分散多径褪色通道，即使 - 见面〜（LOS）传播路径。

被广泛采用的缩减采样是为了在视觉识别的准确性和延迟之间取得良好的权衡。不幸的是，没有学习常用的合并层，因此无法保留重要信息。作为另一个降低方法，自适应采样权重和与任务相关的过程区域，因此能够更好地保留有用的信息。但是，自适应采样的使用仅限于某些层。在本文中，我们表明，在深神经网络的构件中使用自适应采样可以提高其效率。特别是，我们提出了SSBNET，该SSBNET是通过将采样层反复插入Resnet等现有网络构建的。实验结果表明，所提出的SSBNET可以在ImageNet和可可数据集上实现竞争性图像分类和对象检测性能。例如，SSB-Resnet-RS-200在Imagenet数据集上的精度达到82.6％，比基线RESNET-RS-152高0.6％，具有相似的复杂性。可视化显示了SSBNET在允许不同层专注于不同位置的优势，而消融研究进一步验证了自适应采样比均匀方法的优势。