Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeli...
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Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by ...
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Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by restoring details and contextual information. However, the spatial discrepancy between different modalities in RGB-Thermal (RGB-T) semantic segmentation tasks complicates the process of multimodal feature fusion, leading to a loss of spatial contextual information and limited model performance. This paper proposes a channel-space fusion nonlinear spiking neural P system model network (CSPM-SNPNet) to address these challenges. This paper designs a novel color-thermal image fusion module to effectively integrate features from both modalities. During decoding, a nonlinear spiking neural P system is introduced to enhance multi-channel information extraction through the convolution of spiking neural P systems (ConvSNP) operations, fully restoring features learned in the encoder. Experimental results on public datasets MFNet and PST900 demonstrate that CSPM-SNPNet significantly improves segmentation performance. Compared with the existing methods, CSPM-SNPNet achieves a 0.5% improvement in mIOU on MFNet and 1.8% on PST900, showcasing its effectiveness in complex scenes.
To address challenges in uncrewed aerial vehicles (UAV) object detection including complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions, we propose FDM-DETR, a novel detection al...
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We consider the problem of optimizing the evacuation of a workplace environment by deciding the best arrangement of emergency exits. To this end, we consider a simulation-based approach that relies on the use of cellu...
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ISBN:
(纸本)9798400717291
We consider the problem of optimizing the evacuation of a workplace environment by deciding the best arrangement of emergency exits. To this end, we consider a simulation-based approach that relies on the use of cellular automata to model the collective behavior of the crowd. In order to obtain problem instances more akin to realistic workplace environments, a problem instance generator based on L-attributed grammars is devised and described in detail. Subsequently, we consider the use of evolutionary algorithms, an iterated greedy heuristic, and Nelder-Mead method to solve the problem. In-depth experimentation is reported. It is shown that the evolutionary algorithm is superior during training and that Nelder-Mead method is also competitive during test, raising interesting prospects for future hybrid strategies.
Dear editor,Aspect term extraction(ATE) is a sub-task of aspect-based sentiment analysis, which aims to extract opinionated aspect terms from user reviews. For example, in a laptop domain review: “Boot time is super ...
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Dear editor,Aspect term extraction(ATE) is a sub-task of aspect-based sentiment analysis, which aims to extract opinionated aspect terms from user reviews. For example, in a laptop domain review: “Boot time is super fast”, boot time is an aspect, and the sentiment towards it is positive, which can be inferred from super fast.
The paper proposes 'AdaptVR' a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challe...
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With the proliferation of cloud computing and the Internet of Things(IoT), ensuring privacy and robust data protection has become increasingly critical. Reversible data hiding in encrypted images (RDHEI) methods emerg...
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With the proliferation of cloud computing and the Internet of Things(IoT), ensuring privacy and robust data protection has become increasingly critical. Reversible data hiding in encrypted images (RDHEI) methods emerge as a promising solution to address these challenges. In this paper, we propose an asymmetric CNNbased predictor (ACNNP) that significantly outperforms other predictors in terms of prediction accuracy. Then, an adaptive mean predictor is proposed to cooperate with ACNNP. According to these two predictors, a two -stage prediction and embedding model is designed to further enhance the embedding capacity and make data embedding more flexible. Experimental results demonstrate that the proposed method exhibits a high embedding capacity and reversibility when compared to state-of-the-art methods. On the BOWS -2, BOSSBase, and UCID datasets, it obtains average embedding rates of 3.302, 3.461, and 2.69 bpp, respectively. Therefore, this method provides an effective solution for applications such as data integrity verification, privacy protection, and copyright preservation.
Existing personalized federated learning frameworks fail to significantly improve the personalization of user preference learning in next Point-Of-Interest (POI) recommendations, causing notable performance deficits. ...
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Existing personalized federated learning frameworks fail to significantly improve the personalization of user preference learning in next Point-Of-Interest (POI) recommendations, causing notable performance deficits. These frameworks do not fully consider crucial factors such as: (1) how to thoroughly explore spatial-temporal relationships within user trajectories to deeply understand personalized behavior patterns, and (2) the neglect of collaborative signals among users with similar spatio-temporal distributions, which results in the loss of valuable shared information. To tackle these challenges, this paper introduces the Spatio-temporal Consistency Federated Learning (SCFL) framework, which capitalizes on the spatio-temporal consistency of trajectories to boost the personalized performance of POI recommendation models in FL. Specifically, we have developed the trajectory optimization module SCA for clients in isolation to extract deeper behavioral patterns from the spatio-temporal distribution of sparse trajectories. Additionally, we present a hierarchical aggregation strategy based on distribution consistency, utilizing intermediate entities called Edges to aggregate similar users, thereby enhancing the model's learning of shared information. Experimental validation across three real-world datasets (NYC, TKY and Gowalla) and two models (SASRec and SSEPT) with six scalability settings shows that SCFL substantially outperforms eight strong baselines. In six experimental configurations, SCFL achieves a personalized performance improvement of 10.65% over the best baselines. Additional experiments have also validated the superiority of SCFL from various perspectives.
Pose estimation in crowded scenes is key to understanding human behavior in real-life applications. Most existing CNN-based pose estimation methods often depend on the appearance of visible parts as cues to localize h...
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Pose estimation in crowded scenes is key to understanding human behavior in real-life applications. Most existing CNN-based pose estimation methods often depend on the appearance of visible parts as cues to localize human joints. However, occlusion is typical in crowded scenes, and invisible body parts have no valid features for joint localization. Introducing prior information about the human pose structure to infer the locations of occluded parts is a natural solution to this problem. In this paper, we argue that learning structural information based on human joints alone is not enough to address human body variations and could be prone to overfitting. From a perspective on the human pose as a dual representation of joints and limbs, we propose a pose refinement network, coined as dual graph network (DGN), to jointly learn its structural information of body joints and limbs by incorporating the cooperative constraints between two branches. Specifically, our DGN has two coupled graph convolutional network (GCN) branches to model the structure information of joints and limbs. Each stage in the branch is composed of a feature aggregator and a GCN module for inter-branch information fusion and intra-branch context extraction, respectively. In addition, to enhance the modeling capacity of GCN, we design an adaptive GCN layer (AGL) embedded in the GCN module to handle each pose instance based on its graph structure. We also propose a heatmap-guided sampling to leverage the features of the body parts to provide rich visual features for the inference of occluded parts. We perform extensive experiments on five challenging datasets to demonstrate the effectiveness of our DGN on pose estimation. Our DGN obtains significant performance improvement from 67.9 to 72.4 mAP in the CrowdPose dataset with the same CNN-based pose estimator and training strategy as the OPEC-Net. It shows that, compared to the OPEC-Net only considering joints, our DGN has a clear advantage due to the j
Dempster-Shafer (D-S) evidence theory is used to process multisource data fusion and uncertainty problems. When faced with strongly contradictory evidence, there are always some surprising phenomena. We propose a new ...
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Dempster-Shafer (D-S) evidence theory is used to process multisource data fusion and uncertainty problems. When faced with strongly contradictory evidence, there are always some surprising phenomena. We propose a new generalized distance based on Li et al.'s Hellinger distance in this study to assess the distinction between basic probability assignments (BPAs) to solve this *** basic structure of Li et al.'s Hellinger distance was kept in the generalized Hellinger distance, and certain advancements were achieved. The generalized Hellinger distance considers the differences between both focal elements and the subsets of the sets of belief functions, enabling a wider range of applications for it. Additionally, we present the proof of generalized Hellinger distance satisfied nonnegativeness, symmetry, definiteness and triangle inequality. Through several comparative examples, we know that the new distance has better universality than some well-known works. Finally, we suggest a novel generalized Hellinger distance-based multisource data fusion approach and use it to solve a real-word pattern classification problem.
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