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.
Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have use...
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Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have used two-dimensional (2D) emotional stimulus materials. Virtual reality (VR) has been utilized in many fields, and its superiority over 2D in emotion recognition has been proven. However, relevant research on VR scenes is lacking in the field of personality recognition. In this study, based on the psychological principle that emotional arousal can expose an individual’s personality, we attempt to explore the feasibility and effect of using electrocardiogram (ECG) signals in response to VR emotional stimuli for personality identification. For this purpose, a VR-2D emotion-induction experiment was conducted in which ECG signals were collected, and physiological datasets of emotional personalities were constructed through preprocessing and feature extraction. Statistical analysis of the emotion scale scores and ECG features of the participants showed that the VR group had a higher number of significantly correlated features. Meanwhile, VR- and 2D-based personality recognition models were constructed using machine learning algorithms. The results showed that the VR-based personality recognition model achieved better results for the four personality dimensions, with a maximum accuracy of 79.76%. These findings indicate that VR not only enhances the physiological correlation between emotion and personality but also improves the classification accuracy of personality recognition.
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an...
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ISBN:
(数字)9783540322771
ISBN:
(纸本)9783540305101
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an international forum for the presentation and discussion of research trends on the theory, method, and design of Grid and cooperative computing as well as their scienti?c, engineering and commercial applications. It has become a major annual event in this area. The First International Conference on Grid and Cooperative computing (GCC2002)***2003received550submissions,from which 176 regular papers and 173 short papers were accepted. The acceptance rate of regular papers was 32%, and the total acceptance rate was 64%. GCC 2004 received 427 main-conference submissions and 154 workshop submissions. The main conference accepted 96 regular papers and 62 short papers. The - ceptance rate of the regular papers was 23%. The total acceptance rate of the main conference was 37%. For this conference, we received 576 submissions. Each was reviewed by two independent members of the International Program Committee. After carefully evaluating their originality and quality, we accepted 57 regular papers and 84 short papers. The acceptance rate of regular papers was 10%. The total acc- tance rate was 25%.
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.
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