Existing Unmanned Aerial Vehicle (UAV) clusters lack a formalized model, fail to consider external interference factors, and overlook the need for dynamic cluster maintenance to ensure stability and coordination in open scenarios. To address these issues, we propose an UAV collaborative cluster formation method suitable for open scenarios, which can form an energy-efficient, reliable, and stable UAV cluster even in external interferences. First, we present a UAV node promotion method based on mobility similarity and connectivity. Then, we formalize a collaborative UAV cluster model based on the energy efficiency, reliability, and stability among UAV nodes. Next, we propose a formation method for UAV clusters based on Pareto optimality and provide a maintenance method for clusters. Extensive simulation results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) approaches. Specifically, in open scenarios, our method improves average cluster efficiency by up to 6.9%, enhances average cluster reliability by 12.4%, enhances average cluster stability by 14.2%, and extends the average cluster survival time and the average node survival time by 18.1% and 17.2% compared to the best-performing baseline, verifying the superior effectiveness and robustness of the proposed framework.

Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have succeeded in low-resource perspectives, challenges persist in effectively harnessing information and optimizing computational efficiency. In this work, we present a novel prompt-based method to enhance low-resource NER without exhaustive template tuning. First, we construct knowledge-enriched prompts by integrating representative entities and background information to provide informative supervision tailored to each entity type. Then, we introduce an efficient reverse generative framework inspired by question answering (QA), which avoids redundant computations. Finally, we reduce costs by generating entities from their types while retaining model reasoning capacity. Experiment results demonstrate that our method outperforms other baselines on three datasets under few-shot settings.

In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor.