Unmanned Aerial Vehicle (UAV) search has been widely applied in critical mission scenarios such as battlefield reconnaissance and disaster response. Due to uncertainties caused by target maneuverability and environmental threats, the development of multi-UAV distributed cooperative search methods to overcome the limitations of single-UAV perception has become imperative. This study addresses the decision optimization problem for multi-UAV cooperative dynamic target search in complex adversarial environments and proposes a systematic decision optimization framework. Firstly, an environmental perception system model is constructed by integrating a multiple distribution hypothesis target motion prediction model with a multi-dimensional search map model, which incorporates target existence probability, environmental uncertainty, and pheromone-inspired coordination signals, and achieves situational updates through dynamic Bayesian inference. Secondly, a distributed model predictive control (DMPC) architecture with motion encoding mechanisms and a lightweight communication protocol is designed to effectively alleviate communication resource dependence. Furthermore, Genetic Programming (GP) algorithm is combined with DMPC to autonomously generate cooperative search strategies that satisfy multiple conditional constraints. Simulation results demonstrate that the proposed method significantly outperforms traditional search methods in terms of dynamic target search timeliness and adaptability to complex environments, particularly exhibiting stronger robustness in adversarial scenarios.

Federated learning (FL) is a promising distributed learning approach to enable intelligent Internet of Things (IoT) applications. However, FL is vulnerable to model poisoning attacks in which malicious clients abate the accuracy of the global model by committing crafted local model updates to the server. Existing defense methods either rely on a validation data set or simply remove the detected malicious clients from the subsequent training process to handle attacks from a large number of malicious clients. Thus, the performance of existing methods deteriorates drastically in many scenarios where the data distributions of clients are unpredictable. To address these deficiencies, we propose a framework called FL overwatch (FLOW) to efficiently defend against model poisoning attacks taking advantages of the local model updates in current and historical training iterations. On one hand, FLOW detects malicious clients in each iteration by measuring the cosine distances between the local model updates of clients, such that malicious updates are eliminated from the current aggregation. On the other hand, FLOW gracefully punishes the previously identified malicious clients rather than removes them from the whole training process. As a result, FLOWcan embrace a richer reliable set of local model updates than existing methods in aggregation. Extensive experiments on widely used benchmark data sets show that FLOW can achieve higher success defending ratio and higher accuracy of global models over existing Byzantinerobust FL methods under typical untargeted attacks and targeted attacks. Furthermore, FLOW also shows significant effectiveness in defending against adaptive attacks tailored to FLOW.

This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan.

Federated Learning (FL) is a collaborative machine learning paradigm allowing participants to train a global model collaboratively without sharing training data. The distributed nature makes FL vulnerable to the untargeted or backdoor model poisoning attacks (MPAs). Hence, lots of defense methods are proposed to secure FL. However, existing defenses are ineffective in defending against the emerging stealthy layer-space MPA, since the defenses either focus on the model space or ignore the disparities between the layers. In this paper, we propose a novel layer-space defense method called FLGuardian that can protect the global model from the state-of-the-art MPAs. FLGuardian first employs a new layer-wise detection to find out the benign clients for each layer through pairwise cosine distances and pairwise Euclidean distances combined with a clustering algorithm. Then, FLGuardian assigns a trust score for each client according to the detection results of all the layers, where a deeper layer in the model brings a higher weight in the scoring. Finally, we select several clients with the highest scores for updating the global model. Experimental results show that FLGuardian excels nine typical defense methods against seven state-of-the-art MPAs in most cases. Particularly, under LPattack, the emerging layer-space backdoor MPA, FLGuardian secures Backdoor Success Rate (BSR) below 3% while other defenses have over 93% BSRs on CIFAR-10. Moreover, FLGuardian remains robust against adaptive attacks tailored to FLGuardian.

The triplet-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.

Automated document processing and circulation systems face critical challenges in achieving reliable retrieval accuracy and robust classification performance, particularly in security-critical organizational environments. Traditional approaches suffer from fundamental limitations, including fixed fusion strategies in hybrid retrieval systems, inability to model inter-document relationships in classification tasks, and lack of confidence estimation for result reliability. This paper introduces AttenFlow, a novel context-aware architecture that revolutionizes document management through two core technical innovations. First, we propose the retriever consensus confidence fusion (RCCF) method, which addresses the limitations of conventional hybrid retrieval approaches by introducing consensus-based fusion strategies that dynamically adapt to retriever agreement levels while providing confidence estimates for results. RCCF measures the consensus between different retrievers through sophisticated ranking and scoring consistency metrics, enabling adaptive weight assignment that amplifies high-consensus results while adopting conservative approaches for uncertain cases. Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. The approach incorporates adversarial training to enhance robustness against potential security threats, making it particularly suitable for critical document processing applications. Comprehensive experimental evaluation across multiple benchmark datasets demonstrates the substantial effectiveness of our innovations. RCCF achieves improvements of up to 16.9% in retrieval performance metrics compared to traditional fusion methods while providing reliable confidence estimates. AM-HDGAT for text demonstrates superior classification performance with an average F1-score improvement of 2.23% compared to state-of-the-art methods, maintaining 82.4% performance retention under adversarial attack scenarios. Real-world deployment validation shows a 34.5% reduction in manual processing time and 95.7% user satisfaction scores, establishing AttenFlow as a significant advancement in intelligent document management technology.