Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learne...
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By treating users’ interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering (CF) based recommendation. Recently, researchers have introduced Graph Contrastive Lear...
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A major challenge is to construct ceramic membranes with tunable structures and functions for water ***,a novel corrosion-resistant polymer-derived silicon oxycarbide(SiOC)ceramic membrane with designed architectures ...
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A major challenge is to construct ceramic membranes with tunable structures and functions for water ***,a novel corrosion-resistant polymer-derived silicon oxycarbide(SiOC)ceramic membrane with designed architectures was fabricated by a phase separation method and was applied in organic removal via adsorption and oxidation for the first *** pore structure of the as-prepared SiOC ceramic membranes was well controlled by changing the sintering temperature and polydimethylsiloxane content,leading to a pore size of 0.84–1.62μm and porosity of 25.0–43.8%.Corrosion resistance test results showed that the SiOC membranes sustained minimal damage during 24 h exposure to high-intensity acid–base conditions,which could be attributed to the chemical inertness of *** rhodamine 6G(R6G)as the model pollutant,the SiOC membrane demonstrated an initial eff ective removal rate of 99%via adsorption;however,the removal rate decreased as the system approached adsorption *** peroxymonosulfate was added into the system,efficient and continuous degradation of R6G was observed throughout the entire period,indicating the potential of the as-prepared SiOC membrane in oxidation-related ***,this work provides new insights into the construction of novel polymer-derived ceramic membranes with well-defined structures and functions.
The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency in...
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The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.
Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risk...
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Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.
A novel magnetic field sensor based on long-period fiber gratings (LPFG) coated with terbium oxide nanofilms is proposed. Theoretical analysis and numerical simulation of the four-layer cylindrical waveguide's hyb...
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Phenotypic prediction before crop planting and harvest facilitates plant phenotyping analysis and the implementation of precision agriculture, which is crucial for food security policy formulation, crop management, an...
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Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in...
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An entailment tree is a structured reasoning path that clearly demonstrates the process of deriving hypotheses through multiple steps of inference from known premises. It enhances the interpretability of QA systems. E...
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With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human *** emotional dialogue systems usually use an external emotional di...
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With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human *** emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate ***,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the *** propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional *** from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer ***,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and *** content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features ***,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the *** results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.
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