Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lac...
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Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lack of fine-grained instance-wise annotations, existing VAE methods can easily suffer from the posterior collapse problem. In this paper, we propose an innovative asymmetric VAE network by aligning enhanced feature representation（AEFR） for GZSL. Distinguished from general VAE structures, we designed two asymmetric encoders for visual and semantic observations and one decoder for visual reconstruction. Specifically, we propose a simple yet effective gated attention mechanism（GAM） in the visual encoder for enhancing the information interaction between observations and latent variables, alleviating the possible posterior collapse problem effectively. In addition, we propose a novel distributional decoupling-based contrastive learning（D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation space. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. The source code is available at https://***/seeyourmind/AEFR.
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
Pre-trained language models(PLMs),such as BERT,have achieved good results on many natural language processing(NLP)***,some studies have attempted to integrate factual knowledge into PLMs to adapt to vari-ous downstrea...
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Pre-trained language models(PLMs),such as BERT,have achieved good results on many natural language processing(NLP)***,some studies have attempted to integrate factual knowledge into PLMs to adapt to vari-ous downstream *** sentiment analysis tasks,sentiment knowledge,such as sentiment words,plays a significant role in determining the sentiment tendencies of *** Chinese sentiment analysis,historical stories and fables imbue words with richer connotations and more complex sentiments than those typically found in English,which makes senti-ment knowledge injection *** clearly,this knowledge has not been fully *** this paper,we propose EKBSA,a Chinese sentiment analysis model,which is based on the K-BERT model and utilizes a sentiment knowledge graph to achieve better results on sentiment analysis *** construct a high-quality sentiment knowledge graph,we collect a large number of sentiment words by combining several existing sentiment ***,in order to under-stand texts better,we enhance local attention through syntactic analysis and direct to EKBSA focus more on syntactical-ly relevant *** is compatible with BERT and existing structural *** results show that EKBSA achieves better performance on Chinese sentiment analysis *** upon EKBSA,we further change the gen-eral attention to the context attention and propose Context EKBSA,so that the model can adapt to sentiment analysis tasks in Chinese conversations and achieve good performance.
Communication is a key element for classroom teaching and group project management in higher education. In this paper, we describe in detail how an online tool, Slack, helps improve communication and collaboration in ...
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In the enormous field of Natural Language Processing (NLP), deciphering the intended significance of a word among a multitude of possibilities is referred to as word sense disambiguation. This process is essential for...
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Modern apps require high computing resources for real-time data processing, allowing app users (AUs) to access real-time information. Edge computing (EC) provides dynamic computing resources to AUs for real-time data ...
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Modern apps require high computing resources for real-time data processing, allowing app users (AUs) to access real-time information. Edge computing (EC) provides dynamic computing resources to AUs for real-time data processing. However, due to resources and coverage constraints, edge servers (ESs) in specific areas can only serve a limited number of AUs. Hence, the app user allocation problem (AUAP) becomes challenging in the EC environment. This paper proposes a quantum-inspired differential evolution algorithm (QDE-UA) for efficient user allocation in the EC environment. The quantum vector is designed to provide a complete solution to the AUAP. The fitness function considers the minimum use of ES, user allocation rate (UAR), energy consumption, and load balance. Extensive simulations and hypotheses-based statistical analyses (ANOVA, Friedman test) are performed to show the significance of the proposed QDE-UA. The results indicate that QDE-UA outperforms the majority of the existing strategies with an average UAR improvement of 112.42%, and 140.62% enhancement in load balance while utilizing 13.98% fewer ESs. Due to the higher UAR, QDE-UA shows 59.28% higher total energy consumption on average. However, the lower energy consumption per AU is evidence of its energy efficiency. IEEE
In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper...
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Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work ***-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security *** aim to investigate backdoo...
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Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work ***-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security *** aim to investigate backdoor attack methods for image categorization tasks,to promote the development of DNN towards higher *** on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples,and the meticulous data screening by developers,hindering practical attack *** overcome these challenges,this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation(GN-TUAP)*** approach restricts the direction of perturbations and normalizes abnormal pixel values,ensuring that perturbations progress as much as possible in a direction perpendicular to the decision hyperplane in linear *** limits anomalies within the perturbations improves their visual stealthiness,and makes them more challenging for defense methods to *** verify the effectiveness,stealthiness,and robustness of GN-TUAP,we proposed a comprehensive threat *** on this model,extensive experiments were conducted using the CIFAR-10,CIFAR-100,GTSRB,and MNIST datasets,comparing our method with existing state-of-the-art attack *** also tested our perturbation triggers using various defense methods and further experimented on the robustness of the triggers against noise filtering *** experimental outcomes demonstrate that backdoor attacks leveraging perturbations generated via our algorithm exhibit cross-model attack effectiveness and superior ***,they possess robust anti-detection capabilities and maintain commendable performance when subjected to noise-filtering methods.
Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained dev...
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Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature cl...
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The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature class activation maps,which can result in significant computational overhead and complicate the training *** this work,we investigate the semantic structure information concealed within the CNN network,and propose a semantic structure aware inference(SSA)method that utilizes this information to obtain high-quality CAM without any additional training ***,the semantic structure modeling module(SSM)is first proposed to generate the classagnostic semantic correlation representation,where each item denotes the affinity degree between one category of objects and all the ***,the immature CAM are refined through a dot product operation that utilizes semantic structure ***,the polished CAMs from different backbone stages are fused as the *** advantage of SSA lies in its parameter-free nature and the absence of additional training costs,which makes it suitable for various weakly supervised pixel-dense prediction *** conducted extensive experiments on weakly supervised object localization and weakly supervised semantic segmentation,and the results confirm the effectiveness of SSA.
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