The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle ***,i...
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The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle ***,instead of being an isolated module,the incentive mechanism usually interacts with other *** on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real ***,we utilize an LSTM method to predict parking areas roughly for recommendations *** incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network *** order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking *** its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is ***,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.
The widespread adoption of wearable devices has led to a surge in the development of multi-device wearable human activity recognition (WHAR) systems. Nevertheless, the performance of traditional supervised learning-ba...
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The rapid advancement of artificial intelligence applications has resulted in the deployment of a growing number of deep neural networks (DNNs) on mobile devices. Given the limited computational capabilities and small...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like ...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small *** address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image *** adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different *** strengthens overlooked image details,extending the IAEN module’s *** the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation *** entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network *** lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image *** experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies...
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Although having achieved real-time performance on mesh construction, most of the existing LiDAR odometry and meshing methods have difficulties in dealing with cluttered scenes due to relying on explicit meshing techni...
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Although having achieved real-time performance on mesh construction, most of the existing LiDAR odometry and meshing methods have difficulties in dealing with cluttered scenes due to relying on explicit meshing techniques. To overcome these limitations, we propose a real-time mesh-based LiDAR odometry and mapping approach for large-scale scenes through implicit reconstruction and a parallel spatial-hashing scheme. In order to efficiently reconstruct the triangular meshes using the implicit function, we suggest an incremental voxel meshing strategy that depends on a novel voxel map fusing scans through a single traversal of the current frame. Moreover, we introduce a scalable partition module to compress space. By taking advantage of the rapid access to triangular meshes, we design a robust odometry method with location and feature-based data association to estimate the poses between the input point clouds and the recovered triangular meshes. The experimental results on four public datasets demonstrate the effectiveness of our proposed approach in recovering both accurate motion trajectories and environmental mesh maps. IEEE
Digital media interaction design mainly focuses on the user’s interactive experience in the digital media environment. By designing interaction methods that conform to human cognition and behavioral habits, it improv...
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Although deep convolution neural network(DCNN)has achieved great success in computer vision field,such models are considered to lack interpretability in *** of fundamental issues is that its decision mechanism is cons...
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Although deep convolution neural network(DCNN)has achieved great success in computer vision field,such models are considered to lack interpretability in *** of fundamental issues is that its decision mechanism is considered to be a“black-box”*** authors design the binary tree structure convolution(BTSC)module and control the activation level of particular neurons to build the interpretable DCNN ***,the authors design a BTSC module,in which each parent node generates two independent child layers,and then integrate them into a normal DCNN *** main advantages of the BTSC are as follows:1)child nodes of the different parent nodes do not interfere with each other;2)parent and child nodes can inherit ***,considering the activation level of neurons,the authors design an information coding objective to guide neural nodes to learn the particular information coding that is *** the experiments,the authors can verify that:1)the decision-making made by both the ResNet and DenseNet models can be explained well based on the"decision information flow path"(known as the decision-path)formed in the BTSC module;2)the decision-path can reasonably interpret the decision reversal mechanism(Robustness mechanism)of the DCNN model;3)the credibility of decision-making can be measured by the matching degree between the actual and expected decision-path.
The SM9 identity-based encryption(IBE) scheme is a cryptographic standard used in China, and has been incorporated into the ISO/IEC standard in 2021. This work primarily proposes a countermeasure to secure the SM9 IBE...
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The SM9 identity-based encryption(IBE) scheme is a cryptographic standard used in China, and has been incorporated into the ISO/IEC standard in 2021. This work primarily proposes a countermeasure to secure the SM9 IBE scheme if its implementation is tampered with or deviated from the standard *** attacks, known as subversion attacks, are feasible and powerful in real-world cryptographic application scenarios. Our goal is to design a subversion-resilient variant of the SM9 IBE scheme, primarily using the cryptographic reverse firewall(CRF) proposed by Mironov and Stephens-Davidowitz at EUROCRYPT 2015.A CRF can sanitize cryptographic transcripts to eliminate covert channels, necessitating that the underlying primitive be rerandomizable. Unfortunately, the rerandomizability of the SM9 IBE scheme is disabled for ensuring security against chosen ciphertext attack(CCA). Hence, we shift our focus to a relaxed version of CCA security called RCCA security, offering security guarantees comparable to CCA security while allowing for ciphertext rerandomization. For this purpose, we design an efficient and RCCA-secure variant of the SM9 IBE scheme with provable security that can integrate with CRFs to achieve subversion resilience.
In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000d...
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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.
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