In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node at...
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In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.
Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on l...
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Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.
Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics researc...
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As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social *** the realm of image tampering localization,accurately localizing...
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As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social *** the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of *** issues impede the model’s universality and generalization capability and detrimentally affect its *** tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering *** proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream ***,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization *** comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature *** facilitates a more precise localization of tampered regions of various ***,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered *** strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy *** a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is *** evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validat
Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed dat...
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Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.
Anomaly detection in time series data (e.g., sensor data) is becoming a fundamental research problem that has various applications. Due to the complex inter-sensor relationships, it is challenging to detect anomalous ...
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Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the...
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Unlike the traditional unimodal sentiment analysis, multimodal sentiment analysis can jointly process features between different modalities. Existing multimodal sentiment analysis methods simply extract features from ...
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With the multi-medal resource springing up, the field of cross-modal research has witnessed rapid advancement. Among these, Cross-Modal Hash retrieval has attracted attention due to its time-saving capabilities during...
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Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskde...
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Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.
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