Most of the existing ensemble clustering algorithms improve the performance by weighting the basic clusters to reduce the influence of low-quality basic clusters on the final clustering results. Low-quality base clust...
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Deepfake videos are rapidly spreading across the internet, posing significant threats to public interests. To counter this issue, researchers have developed various deep learning-based deepfake detection methods, whic...
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While data-driven approaches provide an effective solution for hybrid beamforming implementation, they currently fail to account for the path birth-death process (PBP), a phenomenon frequently encountered in practical...
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Existing methods in article recommendation fail to fully use the article information, or pay less attention to the correlations among articles and "User-Article"s, resulting in inaccurate recommendation perf...
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Knowledge graph embedding aims to embed triples into low-dimensional vector spaces. Through the attention mechanism, models based on the graph attention network can learn the information of the neighboring node, impro...
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Graph convolutional networks (GCNs) gain increasing attention on graph data learning tasks in recent years. However, in many applications, graph may come with an incomplete form where attributes of graph nodes are par...
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Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approache...
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Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.
Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consistin...
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Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.
Inductive knowledge graph embedding(KGE)aims to embed unseen entities in emerging knowledge graphs(KGs).The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring...
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Inductive knowledge graph embedding(KGE)aims to embed unseen entities in emerging knowledge graphs(KGs).The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring entities and relations with graph neural networks(GNNs).However,these methods rely on the existing neighbors of unseen entities and suffer from two common problems:data sparsity and feature ***,the data sparsity problem means unseen entities usually emerge with few triplets containing insufficient ***,the effectiveness of the features extracted from original KGs will degrade when repeatedly propagating these features to represent unseen entities in emerging KGs,which is termed feature smoothing *** tackle the two problems,we propose a novel model entitled Meta-Learning Based Memory Graph Convolutional Network(MMGCN)consisting of three different components:1)the two-layer information transforming module(TITM)developed to effectively transform information from original KGs to emerging KGs;2)the hyper-relation feature initializing module(HFIM)proposed to extract type-level features shared between KGs and obtain a coarse-grained representation for each entity with these features;and 3)the meta-learning training module(MTM)designed to simulate the few-shot emerging KGs and train the model in a meta-learning *** extensive experiments conducted on the few-shot link prediction task for emerging KGs demonstrate the superiority of our proposed model MMGCN compared with state-of-the-art methods.
Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in...
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Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital *** work mines real-world consumer feedbacks through social media topics,which is significant to product *** specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a *** primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset ***,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse *** this end,this work combines deep learning and survival analysis to predict the prevalent time of *** propose a specialized deep survival model which consists of two *** first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network ***,a specific loss function different from regular survival models is proposed to achieve a more reasonable *** experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.
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