边缘计算是一个将数据处理服务转移到生成数据的网络边缘的范式。尽管这样的架构提供了更快的处理和响应，但除其他好处外，它还提出了必须解决的关键安全问题和挑战。本文讨论了从硬件层到系统层的边缘网络体系结构出现的安全威胁和漏洞。我们进一步讨论了此类网络中的隐私和法规合规性挑战。最后，我们认为需要一种整体方法来分析边缘网络安全姿势，该姿势必须考虑每一层的知识。

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users' viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users' experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

Achieving knowledge sharing within an artificial swarm system could lead to significant development in autonomous multiagent and robotic systems research and realize collective intelligence. However, this is difficult to achieve since there is no generic framework to transfer skills between agents other than a query-response-based approach. Moreover, natural living systems have a "forgetfulness" property for everything they learn. Analyzing such ephemeral nature (temporal memory properties of new knowledge gained) in artificial systems has never been studied in the literature. We propose a behavior tree-based framework to realize a query-response mechanism for transferring skills encoded as the condition-action control sub-flow of that portion of the knowledge between agents to fill this gap. We simulate a multiagent group with different initial knowledge on a foraging mission. While performing basic operations, each robot queries other robots to respond to an unknown condition. The responding robot shares the control actions by sharing a portion of the behavior tree that addresses the queries. Specifically, we investigate the ephemeral nature of the new knowledge gained through such a framework, where the knowledge gained by the agent is either limited due to memory or is forgotten over time. Our investigations show that knowledge grows proportionally with the duration of remembrance, which is trivial. However, we found minimal impact on knowledge growth due to memory. We compare these cases against a baseline that involved full knowledge pre-coded on all agents. We found that knowledge-sharing strived to match the baseline condition by sharing and achieving knowledge growth as a collective system.

多机器人和多代理系统通过系统的局部行为集成在组中表现出集体（Swarm）智能。分享有关任务和环境知识的代理商可以提高个人和任务水平的绩效。但是，这很难实现，部分原因是缺乏用于在代理之间转移一部分知识（行为）的通用框架。本文提出了一个新的知识表示框架和一种称为KT-BT：通过行为树的知识转移的转移策略。 KT-BT框架遵循通过在线行为树框架进行查询反应加速机制，在该框架中，代理对未知条件进行广播查询，并使用条件性能控制子流量以适当的知识做出响应。我们嵌入了一种称为StringBT的新型语法结构，该结构编码知识，从而实现行为共享。从理论上讲，我们研究了KT-BT框架的特性，与异质系统相比，整个小组的高知识同质性具有高度知识的性质，而没有能力共享知识。我们在模拟的多机器人搜索和救援问题中广泛验证了我们的框架。结果表明，在各种情况下，成功传递知识转移并提高了群体绩效。我们进一步研究了机会和沟通范围对一组代理商中群体绩效，知识传播和功能异质性的影响，并提供有趣的见解。

药物的因果模型已用于分析机器学习系统的安全性方面。但是，识别代理是非平凡的 - 通常只是由建模者假设而没有太多理由来实现因果模型 - 建模失败可能会导致安全分析中的错误。本文提出了对代理商的第一个正式因果定义 - 大约是代理人是制度，如果他们的行为以不同的方式影响世界，则可以改善其政策。由此，我们得出了第一个用于从经验数据中发现代理的因果发现算法，并提供了用于在因果模型和游戏理论影响图之间转换的算法。我们通过解决不正确的因果模型引起的一些混乱来证明我们的方法。

在过去的十年中，水下事物的互联网（IOUT）在环境监测和勘探，国防应用等应用程序中取得了迅速的动力。传统的IOUT系统使用机器学习（ML）方法，这些方法满足了可靠性，效率和及时性的需求。但是，对进行的各种研究的广泛审查突出了IOUT框架中数据隐私和安全性的重要性，这是实现任务关键应用程序中预期结果的主要因素。联邦学习（FL）是一个有安全的，分散的框架，是机器学习的最新发展，它将有助于满足IOUT中常规ML方法所面临的挑战。本文概述了FL在IOUT中的各种应用，其挑战，开放问题并指示未来研究前景的方向。

在两个非辅助变形形状之间建立对应关系是视觉计算中最根本的问题之一。当对现实世界中的挑战（例如噪声，异常值，自我结合等）挑战时，现有方法通常会显示出弱的弹性。另一方面，自动描述器在学习几何学上有意义的潜在嵌入方面表现出强大的表现力。但是，它们在\ emph {形状分析}中的使用受到限制。在本文中，我们介绍了一种基于自动码头框架的方法，该方法在固定模板上学习了一个连续形状的变形字段。通过监督点在表面上的变形场，并通过小说\ emph {签名距离正则化}（SDR）正规化点偏面的正规化，我们学习了模板和Shape \ Emph {卷}之间的对齐。经过干净的水密网眼培训，\ emph {没有}任何数据启发，我们证明了在受损的数据和现实世界扫描上表现出令人信服的性能。

我们在HOL4互动定理证明书的顶部实施了自动战术证据Tacticeoe。Tactice从人类证据中学习，数学技术适用于每个证明情况。然后在蒙特卡罗树搜索算法中使用这种知识来探索有前途的策略级证明路径。在一个CPU上，时间限制为60秒，Tactictoe在Hol4的标准图书馆中证明了7164定理的66.4％，而自动调度的电子箴言解决了34.5％。通过结合Tactice和电子证明者的结果，成功率上升至69.0％。