The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.
In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.
In comparison to traditional vehicular edge computing (VEC) architectures, the air-ground integrated vehicular edge computing (AGI-VEC) network, which combines uncrewed aerial vehicles (UAV) and high-altitude platforms (HAP), offers significant advantages. These include seamless coverage, long-distance transmission, reduced latency, enhanced throughput, and alleviation of network congestion, all of which contribute to a more efficient service experience for the Internet of vehicles. To minimize the task offloading delay, this paper investigates a joint multi-computation equipment selection, resource allocation, and UAV trajectory design problem based on energy harvesting in the AGI-VEC network. In order to solve this problem, we first reformulate the problem into a Markov decision process (MDP). And then, we propose a hybrid dual-clip multi-agent proximal policy optimization (DC-MAPPO) algorithm based on the multi-agent proximal policy optimization (MAPPO) algorithm, which introduces the idea of dual-clip (DC), and the adaptive discount factor to enhance the stability and convergence speed of the algorithm. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms in reducing task offloading delay.
Outdoor fires (OFs) pose significant threats to human safety, property, and ecological stability. However, existing detection algorithms often suffer from performance degradation in complex real-world environments, resulting in high false alarm and missed detection rates. To address these limitations, we propose a novel Convolutional Neural Network (CNN)-Mamba Dual-branch Fusion Network (CMDFNet). The framework consists of a CNN branch for fine-grained local feature extraction and a Vision Mamba (VMamba) branch for efficient global context modeling. To further enhance representation, we introduce an eight-directional selective scanning strategy for irregular flame contour perception, a selective state space mechanism for dynamic temporal adaptation, and a CNN-VMamba fusion block to facilitate deep interaction between local and global features. Extensive experiments show that CMDFNet achieves superior performance, surpassing state-of-the-art methods by 2.0% in detection accuracy and 2.1% in recall, while maintaining high computational efficiency. The model has also been deployed at over 20 monitoring sites, where it successfully detected more than 1600 OF events, further confirming its effectiveness and robustness in complex, real-world environments. Code is available at: https://github.com/hejinsome/CMDFNet.
