The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from depende...The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.展开更多
Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which ar...Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.展开更多
Customer churn prediction model refers to a certain algorithm model that can predict in advance whether the current subscriber will terminate the contract with the current operator in the future.Many scholars currentl...Customer churn prediction model refers to a certain algorithm model that can predict in advance whether the current subscriber will terminate the contract with the current operator in the future.Many scholars currently introduce different depth models for customer churn prediction research,but deep modeling research on the features of historical behavior sequences generated by users over time is lacked.In this paper,a customer churn prediction model based on user behavior sequences is proposed.In this method,a long-short term memory(LSTM)network is introduced to learn the overall interest preferences of user behavior sequences.And the multi-headed attention mechanism is used to learn the collaborative information between multiple behaviors of users from multiple perspectives and to carry out the capture of information about various features of users.Experimentally validated on a real telecom dataset,the method has better prediction performance and further enhances the capability of the customer churn prediction system.展开更多
为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法...为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法。该方法首先创新性地提出了站点实时关联度,可实现对目标站点客流量更精准的预测;其次,在公交站点的编码信息中融入线路站点信息、客流变化率、天气、日期等关联因素;接着,该方法依靠Attention机制计算站点实时关联度;核心算法中使用multi-headed机制、增加通道和残差连接进一步提升预测能力;最后,以苏州市公交数据进行验证。结果显示:在准确率上,对比多元线性回归的53.8%、GRU(Gated Recurrent Unit)的66.9%和LightGBM(Light Gradient Boosting Machine)的81.2%,本文提出的基于站点实时关联度的短时公交客流预测方法的准确率在90%以上,表明该方法具备优秀的短时公交客流预测能力。展开更多
基金supported by the National Natural Science Foundation of China under Grant 61976158 and Grant 61673301.
文摘The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.
文摘Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.
文摘Customer churn prediction model refers to a certain algorithm model that can predict in advance whether the current subscriber will terminate the contract with the current operator in the future.Many scholars currently introduce different depth models for customer churn prediction research,but deep modeling research on the features of historical behavior sequences generated by users over time is lacked.In this paper,a customer churn prediction model based on user behavior sequences is proposed.In this method,a long-short term memory(LSTM)network is introduced to learn the overall interest preferences of user behavior sequences.And the multi-headed attention mechanism is used to learn the collaborative information between multiple behaviors of users from multiple perspectives and to carry out the capture of information about various features of users.Experimentally validated on a real telecom dataset,the method has better prediction performance and further enhances the capability of the customer churn prediction system.
文摘为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法。该方法首先创新性地提出了站点实时关联度,可实现对目标站点客流量更精准的预测;其次,在公交站点的编码信息中融入线路站点信息、客流变化率、天气、日期等关联因素;接着,该方法依靠Attention机制计算站点实时关联度;核心算法中使用multi-headed机制、增加通道和残差连接进一步提升预测能力;最后,以苏州市公交数据进行验证。结果显示:在准确率上,对比多元线性回归的53.8%、GRU(Gated Recurrent Unit)的66.9%和LightGBM(Light Gradient Boosting Machine)的81.2%,本文提出的基于站点实时关联度的短时公交客流预测方法的准确率在90%以上,表明该方法具备优秀的短时公交客流预测能力。
