Multi-modal MR image (MRI) is commonly used for brain tumor research, as it provides complementary imaging information to guide the segmentation of different brain regions. However, due to the complex relationships between modalities, simple 3D networks struggle to capture the nonlinear information that is both similar and complementary across modalities. Clinically, radiologists compare information across modalities to identify the most indicative slices and emphasize comparisons between intra-slice and neighboring slices, while fusing distant slices is often redundant. To address these challenges, we propose a Neighboring Slice and Modality-Aware Network (NSMA-Net) for multi-modal brain tumor segmentation. Specifically, we group modalities and apply a Neighboring Slice Adaptive Attention (NSAA) module to each group, enabling the lateral fusion of pixel-level features from neighboring slices. Furthermore, we design a Multi-Dimensional Calibration and Interaction (MDCI) Module to extract modality-specific information while utilizing complementary information from other modalities calibrated through multi-dimensional attention. We conduct extensive experiments on the BraTS2020 and BraTS2021 challenge datasets. Experimental results demonstrate that compared to state-of-the-art models, NSMA-Net achieves superior segmentation performance on both benchmark datasets. The source code is available at https://github.com/qiefanwumi/NSMA-Net.

Ubiquitous networked hosts and Internet of Things (IoT) devices have enabled critical network applications in both enterprise and industrial environments. However, network threats have proliferated, constantly challenging normal network operations and data security of IoT devices. In facing these challenges, defenders have increasingly adopted Deep Reinforcement Learning (DRL) approaches, aiming to leverage their self-learning and adaptability to bolster network security. Despite these efforts, existing approaches often exhibit significant performance bottlenecks when navigating the complexities of network scenarios. This paper introduces a novel algorithm, the Normalized Graph-Attention Proximal Policy Optimization (NGA-PPO), which synergistically integrates Graph Attention Networks (GAT) with the existing Proximal Policy Optimization (PPO) framework. By harnessing a weighted mechanism to amalgamate intricate network connectivity data, NGA-PPO empowers intelligent defense agents to dynamically decipher dependencies and interactions within complex network architectures, thereby facilitating precise and prompt defense actions. Comprehensive experiments within the Yawning Titan network security simulation platform reveal that NGA-PPO outperforms other methods, delivering substantial improvements in performance and exhibiting distinct advantages in robustness and generalization capabilities. Specifically, as network scenarios become more complex, existing algorithms face significant performance limitations, whereas NGA-PPO consistently demonstrates high performance, thereby affirming its efficacy and viability in mitigating complex network threats.

Temporal Knowledge Graph Question Answering (TKGQA) deals with the intricate task of processing and understanding time-evolving facts, and responding to natural language queries that involve sophisticated temporal constraints. Current Large Language Models (LLM) exhibit notable limitations in converting natural language queries into temporal reasoning tasks within complex TKGQA contexts. To mitigate these limitations, this paper proposes a pioneering approach named Symbolic Temporal Query for LLM TKGQA (SymbTQL). This novel framework bolsters LLM capacity to tackle complex temporal challenges through a tripartite process: initially, LLM is fine-tuned with a limited set of annotated data to heighten its comprehension of temporal constraints within TKGQA tasks; this is followed by employing a symbolic logic chain reasoning framework that breaks down intricate natural language queries into a sequence of simpler sub-questions; ultimately, the accuracy of the queries is ensured by aligning the generated query statements with actual data using sentence similarity encoding. Experimental evidence indicates that SymbTQL outperforms numerous baselines by 5.1% and 3.5% on the MultiTQ and CronQuestions datasets, respectively, and excels by 15.4% in managing multi-step complex temporal challenges, with ablation studies confirming its strong generalizability and logical coherence.

Industrial control networks are frequent targets of cyber attacks, calling for autonomous defense strategies to combat threats effectively and promptly. We propose to leverage Reinforcement Learning (RL) to train defenders how to select the most appropriate actions in response to attackers’ behavior. We model the defender and attacker as agents in RL environments and define action space and reward function. We focus on evaluating value-based and policy gradient-based RL algorithms and propose enhancements to Proximal Policy Optimization (PPO) method through normalization. We then leverage CybORG, a simulation tool to study how the RL algorithms perform to achieve autonomous defense. Our extensive experiments reveal that the proposed normalized PPO outperforms other models in terms of stability, robustness, and average rewards. The results also demonstrate the effectiveness of the defender in protecting the operational server upon changing the attacker’s position. Additionally, increasing the number of attackers had a significant impact on policy-based algorithms, particularly PPO, with decreased reward values highlighting the increased vulnerability of the network.

Abstract In order to solve the problem of difficult and low precision in the detection of safety helmet wearing in the complex pose of construction worker, a detection method of safety helmet wearing based on pose estimation is proposed. In the pose estimation model of OpenPose, the residual network optimized feature extraction is introduced to obtain the skeletal point information of the construction worker, and then the pose of the construction worker is estimated based on the skeletal point information, three‐point localization method is proposed for the front and back pose, and skin colour detection method is proposed for the side pose, and then to determine the head region. The YOLO v4 is used to detect the safety helmet region, and then the construction worker's safety helmet wearing is judged according to whether the head region intersects the safety helmet region or not. Experimental results show that the detection accuracy of the method is higher than other methods, and the adaptability to the environment is stronger.