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| 1 | Novel polyphosphazenes containing charge-transporting agent and chromophore as pendant groups显示文摘 | Zhen Li Jingdong Luo Jun Li Caimao Zhan Jingui Qin | 2000 | Polymer Bulletin2000，，2： | 1 |
| 2 | Novel polyphosphazenes containing charge-transporting agent and chromophore as pendant groups显示文摘 | Zhen Li Jingdong Luo Jun Li Caimao Zhan Jingui Qin | 2000 | Polymer Bulletin2000，，2： | 1 |
| 3 | Research on Detection and Identification of Dense Rebar Based on Lightweight Network显示文摘Target detection technology has been widely used,while it is less applied in portable equipment as it has certain requirements for devices.For instance,the inventory of rebar is still manually counted at present.In this paper,a lightweight network that adapts mobile devices is proposed to accomplish the task more intelligently and efficiently.Based on the existing method of detection and recognition of dense small objects,the research of rebar recognition was implemented.After designing the multi-resolution input model and training the data set of rebar,the efficiency of detection was improved significantly.Experiments prove that the method proposed has the advantages of higher detection degree,fewer model parameters,and shorter training time for rebar recognition. | Fang Qu Caimao Li Kai Peng Cong Qu Chengrong Lin | 2020 | 国际计算机前沿大会会议论文集2020，，1： | 0 |
| 4 | User Attribute Prediction Method Based on Stacking Multimodel Fusion显示文摘The user’s age and gender play a vital role within the user portrait.In view of the lack of basic attribute information,such as the age and gender of users,this paper constructs an attribute prediction method based on stacking multimodel integration.The user’s browsing and clicking history is analyzed to predict the user’s basic attributes.First,LR,RF,XGBoost,and ExtraTree were selected as the base classifiers for the first layer of the stacking framework,and the training results of the first layer were input as new training data into the second layer LightGBM for training.Experiments show that the proposed model can improve the accuracy of prediction results. | Qiuhong Chen Caimao Li Hao Lin Hao Li Yuquan Hou | 2022 | 国际计算机前沿大会会议论文集2022，，2： | 0 |
| 5 | Factorization Machine Based on Bitwise Feature Importance for CTR Prediction显示文摘Click-through-rate(CTR)prediction is a crucial task in recommendation systems.The accuracy of CTR prediction is strongly influenced by the precise extraction of essential data and the modeling strategy chosen.The data of the CTR task are often very sparse,and Factorization Machines(FMs)are a class of general predictors working effectively with it.However,the performance of FMs can be limited by the fixed feature representation and the same weight of different features.In this work,we propose an improved Bitwise Feature Importance Factorization Machine(BFIFM)to improve the accuracy.The necessity of learning the degree of effect of the same feature under various situations is learned through the low-order intersection method,and the deep neural network(DNN)in our model is used in parallel to study high-order intersections.According to the final results obtained,the BFIFM model significantly outperforms other state-of-the-art models. | Hao Li Caimao Li Yuquan Hou Hao Lin Qiuhong Chen | 2022 | 国际计算机前沿大会会议论文集2022，，1： | 0 |
| 6 | Focusing on the Importance of Features for CTR Prediction显示文摘TraditionalCTR recommendation models have concentrated on howto learn low-order and high-order characteristics.The majority of them make many efforts at combining low-order and high-order functions.However,they ignore the importance of the attentionmechanism for learning input features.The ECABiNet model is proposed in this article to enhance the performance of CTR.On the one hand,the ECABiNet model can learn the importance of features dynamically via the LayerNorm and ECANET layers.On the other hand,through the use of a biinteraction layer and a DNN layer,it is capable of effectively learning the feature interactions.According to the experimental results on two public datasets,the ECABiNet model is more effective than the previous CTR model. | Yuquan Hou Caimao Li Hao Li Hao Lin Qiuhong Chen | 2022 | 国际计算机前沿大会会议论文集2022，，1： | 0 |
| 7 | Driving Factors for Subjective Relative Deprivation Alleviating Among Middle-Aged and Older Adults with Disabilities — China, 2023显示文摘Summary What is already known about this topic?Previous research has identified a link between economic deprivation,internet usage,and subjective relative deprivation in the general populace.However,few studies have explored the mediating role of internet usage in the relationship between economic deprivation and subjective relative deprivation,particularly in relation to middle-aged and older adults with disabilities. | Lei Zhang Niuniu Cui Xiaodong Zhang Shanwei Feng Caimao Li | 2023 | China CDC weekly2023，5，39： | 0 |
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