它在智能代理系统中起着核心作用，以模拟代理的认知状态及其变化。为此，已经提出了一些正式系统。其中，认知逻辑侧重于不同认知属性（例如知识，信仰，常识等）和认知行动（例如，公开公告，私人公告，异步公告等）的逻辑定律。所有这些系统都不涉及代理与其环境之间的交互行为。通过丰富众所周知的$ \ pi $ -calculus，本文介绍了电子库，该论文提供了一个概念框架，以模拟代理人与认知状态的认知相互作用。与通常的过程演算不同，始终安排电子库中的所有系统以在认知状态下运行。为了抽象地形式化认知状态，提出了一群假设。此外，基于这些假设，电子钙的行为理论是在两个不同的观点中开发的。

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.

Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severe adaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.

Partial differential equations (PDEs) are widely used for description of physical and engineering phenomena. Some key parameters involved in PDEs, which represents certain physical properties with important scientific interpretations, are difficult or even impossible to be measured directly. Estimation of these parameters from noisy and sparse experimental data of related physical quantities is an important task. Many methods for PDE parameter inference involve a large number of evaluations of numerical solution of PDE through algorithms such as finite element method, which can be time-consuming especially for nonlinear PDEs. In this paper, we propose a novel method for estimating unknown parameters in PDEs, called PDE-Informed Gaussian Process Inference (PIGPI). Through modeling the PDE solution as a Gaussian process (GP), we derive the manifold constraints induced by the (linear) PDE structure such that under the constraints, the GP satisfies the PDE. For nonlinear PDEs, we propose an augmentation method that transfers the nonlinear PDE into an equivalent PDE system linear in all derivatives that our PIGPI can handle. PIGPI can be applied to multi-dimensional PDE systems and PDE systems with unobserved components. The method completely bypasses the numerical solver for PDE, thus achieving drastic savings in computation time, especially for nonlinear PDEs. Moreover, the PIGPI method can give the uncertainty quantification for both the unknown parameters and the PDE solution. The proposed method is demonstrated by several application examples from different areas.

Prior works on Information Extraction (IE) typically predict different tasks and instances (e.g., event triggers, entities, roles, relations) independently, while neglecting their interactions and leading to model inefficiency. In this work, we introduce a joint IE framework, HighIE, that learns and predicts multiple IE tasks by integrating high-order cross-task and cross-instance dependencies. Specifically, we design two categories of high-order factors: homogeneous factors and heterogeneous factors. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work.

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7\% precision, 84\% mAp, and 44.8\% $IoU$.

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.

动态极化控制（DPC）对许多光学应用都是有益的。它使用可调节的波形来执行自动极化跟踪和操作。有效的算法对于在高速下实现无尽的极化控制过程至关重要。但是，基于标准梯度的算法尚未很好地分析。在这里，我们用基于雅各布的控制理论框架对DPC进行建模，该框架与机器人运动学有很多共同点。然后，我们将Stokes矢量梯度的状况作为Jacobian矩阵进行详细分析。我们将多阶段DPC识别为冗余系统，启用具有空空间操作的控制算法。可以找到一种有效的，无复位的算法。我们预计更多定制的DPC算法将在各种光学系统中遵循相同的框架。

在这项工作中，我们考虑了具有多个基站和间隔干扰的无线系统中的联合学习模型。在学习阶段，我们应用了一个不同的私人方案，将信息从用户传输到其相应的基站。我们通过在其最佳差距上得出上限来显示学习过程的收敛行为。此外，我们定义了一个优化问题，以减少该上限和总隐私泄漏。为了找到此问题的本地最佳解决方案，我们首先提出了一种计划资源块和用户的算法。然后，我们扩展了该方案，以通过优化差异隐私人工噪声来减少总隐私泄漏。我们将这两个程序的解决方案应用于联合学习系统的参数。在这种情况下，我们假设每个用户都配备了分类器。此外，假定通信单元的资源块比用户数量少。仿真结果表明，与随机调度程序相比，我们提出的调度程序提高了预测的平均准确性。此外，其具有噪声优化器的扩展版本大大减少了隐私泄漏的量。