Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and ex...
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Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection Network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
PRIMA is a series of workshops on agent computing and multi-agent systems, integrating the activities in Asia and Pacific Rim countries. Agent computing and multi-agent systems are computational systems in which sever...
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ISBN:
(数字)9783540368601
ISBN:
(纸本)9783540367079
PRIMA is a series of workshops on agent computing and multi-agent systems, integrating the activities in Asia and Pacific Rim countries. Agent computing and multi-agent systems are computational systems in which several autonomous or se- autonomous agents interact with each other or work together to perform some set of tasks or satisfy some set of goals. These systems may involve computational agents that are homogeneous or heterogeneous, they may involve activities on the part of agents having common or distinct goals, and they may involve participation on the part of humans and intelligent agents. The aim of PRIMA 2006 was to bring together Asian and Pacific Rim researchers and developers from academia and industry to report on the latest technical advances or domain applications and to discuss and explore scientific and practical problems as raised by the participants. PRIMA 2006 received 203 submitted papers. Each paper was reviewed by two internationally renowned Program Committee members. After careful reviews, 39 regular papers and 57 short papers were selected for this volume. We would like to thank all the authors who submitted papers to the workshop. We are very grateful to all Program Committee members and reviewers for their splendid work in reviewing the papers. Finally, we thank the editorial staff of Springer for publishing this volume in the Lecture Notes in Artificial Intelligence series.
The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT service...
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The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.
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