Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning method...Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.展开更多
In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of cl...In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.展开更多
Purpose The purpose of the study was to investigate the acute effect of a beginner martial art class and aerobic exercise on executive function(EF)in college-aged young adults.There is overwhelming evidence that demon...Purpose The purpose of the study was to investigate the acute effect of a beginner martial art class and aerobic exercise on executive function(EF)in college-aged young adults.There is overwhelming evidence that demonstrates acute as well as long-term aerobic exercise improves EF.Nevertheless,there is limited research comparing externally paced exercise(EPE)to self-paced exercise(SPE)such as walking on improving EF.EPE requires greater cortical demand than SPE to execute a motor plan.Methods Eight men and eight women,aged 24.2±2.8 years,participated in a Repeated Measures Crossover Design.Pre-and post-testing of EF with the Stroop and Tower of London(ToL)and stress level were measured after each of the two 1-h conditions:the SPE consisted of a walk(aerobic exercise)and the EPE was a beginner martial art class.Results There were significant main effects for the martial art class for the Stroop’s mean reaction time for congruent trials(P=0.01)with a large-effect size.The mean reaction time for incongruent trials was significant(P=0.05)with a medium-effect size.The ToL’s mean solution time(P=0.003)and mean execution time(P=0.002)were also significant with large-effect sizes.Stress levels were not significantly improved following either condition.Conclusion The martial art class significantly improved all the major domains of EF,while aerobic exercise of a similar intensity did not demonstrate any measured significant changes.The physiological benefits of physical exercise are well documented;however,the cognitive enhancing capability of EPE should also be appreciated given the results of this study.展开更多
基金supported by the Key Research and Development Program of Zhejiang Province(No.2024C01071)the Natural Science Foundation of Zhejiang Province(No.LQ15F030006).
文摘Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.
基金Supported by the 13th 5-Year National Science and Technology Supporting Project(2018YFC2000302)。
文摘In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.
文摘Purpose The purpose of the study was to investigate the acute effect of a beginner martial art class and aerobic exercise on executive function(EF)in college-aged young adults.There is overwhelming evidence that demonstrates acute as well as long-term aerobic exercise improves EF.Nevertheless,there is limited research comparing externally paced exercise(EPE)to self-paced exercise(SPE)such as walking on improving EF.EPE requires greater cortical demand than SPE to execute a motor plan.Methods Eight men and eight women,aged 24.2±2.8 years,participated in a Repeated Measures Crossover Design.Pre-and post-testing of EF with the Stroop and Tower of London(ToL)and stress level were measured after each of the two 1-h conditions:the SPE consisted of a walk(aerobic exercise)and the EPE was a beginner martial art class.Results There were significant main effects for the martial art class for the Stroop’s mean reaction time for congruent trials(P=0.01)with a large-effect size.The mean reaction time for incongruent trials was significant(P=0.05)with a medium-effect size.The ToL’s mean solution time(P=0.003)and mean execution time(P=0.002)were also significant with large-effect sizes.Stress levels were not significantly improved following either condition.Conclusion The martial art class significantly improved all the major domains of EF,while aerobic exercise of a similar intensity did not demonstrate any measured significant changes.The physiological benefits of physical exercise are well documented;however,the cognitive enhancing capability of EPE should also be appreciated given the results of this study.
