COVID vaccine stigma: detecting stigma across social media platforms with computational model based on deep learning [0.03%]
COVID疫苗污名化：基于深度学习计算模型在社交媒体平台上的污名化检测
Nadiya Straton
Nadiya Straton
The study presents the first computational model of COVID vaccine stigma that can identify stigmatised sentiment with a high level of accuracy and generalises well across a number of social media platforms. The aim of the study is to unders...
FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks [0.03%]
带特征交互和意图感知注意力网络的知识增强推荐系统
Ruoyi Zhang,Huifang Ma,Qingfeng Li et al.
Ruoyi Zhang et al.
To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts an...
A novel approach based on rough set theory for analyzing information disorder [0.03%]
基于粗糙集理论分析信息紊乱的一种新方法
Angelo Gaeta,Vincenzo Loia,Luigi Lomasto et al.
Angelo Gaeta et al.
The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as low...
Thai-Vu Nguyen,Anh Nguyen,Nghia Le et al.
Thai-Vu Nguyen et al.
Domain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods train the model on training data and test that model on a completely separate dataset. The ad...
Hock-Ann Goh,Chin-Kuan Ho,Fazly Salleh Abas
Hock-Ann Goh
Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recentl...
From deterministic to stochastic: an interpretable stochastic model-free reinforcement learning framework for portfolio optimization [0.03%]
从确定性到随机性：用于投资组合优化的可解释的无模型强化学习框架
Zitao Song,Yining Wang,Pin Qian et al.
Zitao Song et al.
As a fundamental problem in algorithmic trading, portfolio optimization aims to maximize the cumulative return by continuously investing in various financial derivatives within a given time period. Recent years have witnessed the transforma...
TranGRU: focusing on both the local and global information of molecules for molecular property prediction [0.03%]
TranGRU：专注于分子的局部和全局信息以预测分子属性
Jing Jiang,Ruisheng Zhang,Jun Ma et al.
Jing Jiang et al.
Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently...
Daijun He,Jing Xiao
Daijun He
Solving Math Word Problems (MWPs) automatically is a challenging task for AI-tutoring in online education. Most of the existing State-Of-The-Art (SOTA) neural models for solving MWPs use Goal-driven Tree-structured Solver (GTS) as their dec...
IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships [0.03%]
IMGC-GNN:一种基于隐式关系的多粒度耦合图神经网络推荐方法
Qingbo Hao,Chundong Wang,Yingyuan Xiao et al.
Qingbo Hao et al.
In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of f...
Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19 [0.03%]
新冠肺炎疫情影响下的旅游需求预测及时序融合变压器模型研究
Binrong Wu,Lin Wang,Yu-Rong Zeng
Binrong Wu
An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Tempo...