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学者姓名：杜永萍
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Abstract ：
summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset.
Keyword ：
Graph-network Graph-network Domain knowledge Domain knowledge Abstractive summarization Abstractive summarization Pre-trained language model Pre-trained language model
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| GB/T 7714 | Du, Yongping , Zhao, Yiliang , Yan, Jingya et al. UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain [J]. | METHODS , 2022 , 203 : 160-166 . |
| MLA | Du, Yongping et al. "UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain" . | METHODS 203 (2022) : 160-166 . |
| APA | Du, Yongping , Zhao, Yiliang , Yan, Jingya , Li, Qingxiao . UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain . | METHODS , 2022 , 203 , 160-166 . |
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Abstract ：
Sequential recommendation becomes a critical task in many application scenarios, since people's online activities are increasing. In order to predict the next item that users may be interested, it is necessary to take both general and dynamic preferences of the user into account. Existing approaches typically integrate the useritem or item-item feature interactions directly without considering the dynamic changes of the user's long-term and short-term preferences, which also limits the capability of the model. To address these issues, we propose a novel unified framework for sequential recommendation task, modeling users' long and short-term sequential behaviors at each time step and capturing item-to-item dependencies in higher-order by hierarchical attention mechanism. The proposed model considers the dynamic long and short-term user preferences simultaneously, and a joint learning mechanism is introduced to fuse them for better recommendation. We extensively evaluate our model with several state-of-the-art methods by different validation metrics on three real-world datasets. The experimental results demonstrate the significant improvement of our approach over other compared models.
Keyword ：
Behavior sequences Behavior sequences Hierarchical attention Hierarchical attention Feature interactions Feature interactions Long and short-term preferences Long and short-term preferences Sequential recommendation Sequential recommendation
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| GB/T 7714 | Du, Yongping , Peng, Zhi , Niu, Jinyu et al. A unified hierarchical attention framework for sequential recommendation by fusing long and short-term preferences [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 201 . |
| MLA | Du, Yongping et al. "A unified hierarchical attention framework for sequential recommendation by fusing long and short-term preferences" . | EXPERT SYSTEMS WITH APPLICATIONS 201 (2022) . |
| APA | Du, Yongping , Peng, Zhi , Niu, Jinyu , Yan, Jingya . A unified hierarchical attention framework for sequential recommendation by fusing long and short-term preferences . | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 201 . |
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Abstract ：
Sentiment classification can explore the opinions expressed by people and help them make better decisions. With the increasing of multimodal contents on the web, such as text, image, audio and video, how to make full use of them is important in many tasks, including sentiment classification. This paper focuses on the text and image. Previous work cannot capture the fine-grained features of images, and those models bring a lot of noise during feature fusion. In this work, we propose a novel multimodal sentiment classification model based on gated attention mechanism. The image feature is used to emphasize the text segment by the attention mechanism and it allows the model to focus on the text that affects the sentiment polarity. Moreover, the gating mechanism enables the model to retain useful image information while ignoring the noise introduced during the fusion of image and text. The experiment results on Yelp multimodal dataset show that our model outperforms the previous SOTA model. And the ablation experiment results further prove the effectiveness of different strategies in the proposed model. (C) 2022 Elsevier B.V. All rights reserved.
Keyword ：
Convolutional neural network Convolutional neural network Multimodal sentiment classification Multimodal sentiment classification Gated attention mechanism Gated attention mechanism Feature fusion Feature fusion
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| GB/T 7714 | Du, Yongping , Liu, Yang , Peng, Zhi et al. Gated attention fusion network for multimodal sentiment classification [J]. | KNOWLEDGE-BASED SYSTEMS , 2022 , 240 . |
| MLA | Du, Yongping et al. "Gated attention fusion network for multimodal sentiment classification" . | KNOWLEDGE-BASED SYSTEMS 240 (2022) . |
| APA | Du, Yongping , Liu, Yang , Peng, Zhi , Jin, Xingnan . Gated attention fusion network for multimodal sentiment classification . | KNOWLEDGE-BASED SYSTEMS , 2022 , 240 . |
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Abstract ：
基于孪生卷积神经网络的手机型号识别方法
Keyword ：
手机型号识别 手机型号识别 孪生卷积神经网络 孪生卷积神经网络 相似性评估 相似性评估 边缘检测 边缘检测 废旧手机回收 废旧手机回收 模型参数更新 模型参数更新
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| GB/T 7714 | 韩红桂 , 甄琪 , 任柯燕 et al. 基于孪生卷积神经网络的手机型号识别方法 [J]. | 韩红桂 , 2021 , 47 (2) : 112-119 . |
| MLA | 韩红桂 et al. "基于孪生卷积神经网络的手机型号识别方法" . | 韩红桂 47 . 2 (2021) : 112-119 . |
| APA | 韩红桂 , 甄琪 , 任柯燕 , 伍小龙 , 杜永萍 , 乔俊飞 et al. 基于孪生卷积神经网络的手机型号识别方法 . | 韩红桂 , 2021 , 47 (2) , 112-119 . |
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Abstract ：
In e-commerce platform, users conduct purchase behavior and write reviews for the purchased items. These reviews usually contain a lot of valuable information for recommendation, which can reflect the purchase preference of the user and the characteristic of the item. We propose the Hierarchical Attention Cooperative Neural Networks (HACN) model for recommendation. Hierarchical attention mechanism is adopted to enrich user's and item's feature representation from review texts. Two parallel networks based on review texts are used to model users and items respectively, which makes the generated features more purposeful. Further, the target ID embedding is introduced to capture the global entity relationship in the dataset. The experiments are performed on five real-world datasets of different domains from Amazon, and our proposed HACN model has achieved better results than the existing state-of-the-art methods. © 2021 Elsevier B.V.
Keyword ：
Recommender systems Recommender systems Hierarchical systems Hierarchical systems Electronic commerce Electronic commerce Sales Sales
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| GB/T 7714 | Du, Yongping , Wang, Lulin , Peng, Zhi et al. Review-based hierarchical attention cooperative neural networks for recommendation [J]. | Neurocomputing , 2021 , 447 : 38-47 . |
| MLA | Du, Yongping et al. "Review-based hierarchical attention cooperative neural networks for recommendation" . | Neurocomputing 447 (2021) : 38-47 . |
| APA | Du, Yongping , Wang, Lulin , Peng, Zhi , Guo, Wenyang . Review-based hierarchical attention cooperative neural networks for recommendation . | Neurocomputing , 2021 , 447 , 38-47 . |
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Abstract ：
Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network (B-CNN) is proposed in this paper. First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods.
Keyword ：
joint loss joint loss bilinear convolutional neural network bilinear convolutional neural network fine-grained image recognition fine-grained image recognition low-rank decomposition low-rank decomposition
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| GB/T 7714 | Han HongGui , Zhen Qi , Yang HongYan et al. Mobile phone recognition method based on bilinear convolutional neural network [J]. | SCIENCE CHINA-TECHNOLOGICAL SCIENCES , 2021 , 64 (11) : 2477-2484 . |
| MLA | Han HongGui et al. "Mobile phone recognition method based on bilinear convolutional neural network" . | SCIENCE CHINA-TECHNOLOGICAL SCIENCES 64 . 11 (2021) : 2477-2484 . |
| APA | Han HongGui , Zhen Qi , Yang HongYan , Du YongPing , Qiao JunFei . Mobile phone recognition method based on bilinear convolutional neural network . | SCIENCE CHINA-TECHNOLOGICAL SCIENCES , 2021 , 64 (11) , 2477-2484 . |
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Abstract ：
用户对项目评分数据的稀疏性是影响推荐质量的主要因素之一,提出了融合评分数据和评论文本的深度学习模型,通过引入辅助信息缓解评分数据稀疏性的影响.利用评论文本可以获取用户的偏好信息和项目特征,而评分数据中又包含了用户和项目之间的潜在关联.现有的融合模型对评分数据的处理大多数都是采用矩阵分解方法,为了更好地利用评分数据中的有效信息,文中利用卷积神经网络处理评论文本,并引入注意力机制提取评论信息中具有代表性的评论,从而更好地表征用户偏好和项目特征.利用深度神经网络处理评分数据提取其中的深度特征,将特征进行融合来预测出用户对项目的评分.文中在Amazon数据集上进行验证,以均方误差MSE作为评价指标,结果表明所提出的模型优于多个优秀的基线模型.
Keyword ：
评论文本 评论文本 推荐系统 推荐系统 深度学习 深度学习 评分矩阵 评分矩阵 注意力机制 注意力机制
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| GB/T 7714 | 王艳 , 彭治 , 杜永萍 . 融合评分矩阵和评论文本的深度学习推荐模型 [J]. | 计算机技术与发展 , 2021 , 31 (8) : 13-18 . |
| MLA | 王艳 et al. "融合评分矩阵和评论文本的深度学习推荐模型" . | 计算机技术与发展 31 . 8 (2021) : 13-18 . |
| APA | 王艳 , 彭治 , 杜永萍 . 融合评分矩阵和评论文本的深度学习推荐模型 . | 计算机技术与发展 , 2021 , 31 (8) , 13-18 . |
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Abstract ：
针对废旧手机回收过程中型号难以精确识别的问题,提出一种基于孪生卷积神经网络的废旧手机型号识别方法.首先,利用基于最大类间差分的边缘检测算法解析手机图像的区域特征,构建手机型号识别数据库;其次,构造一种共享权值孪生卷积网络(siamese convolutional neural network, S-CNN)的手机识别模型,实现废旧手机图像特征的快速提取;最后,设计一种自适应学习率的识别模型参数更新策略,提高手机型号识别的精度.将其应用于不同场景下废旧手机的分拣,实验结果表明该方法具有较好的快速性和准确性.
Keyword ：
手机型号识别 手机型号识别 相似性评估 相似性评估 模型参数更新 模型参数更新 边缘检测 边缘检测 废旧手机回收 废旧手机回收 孪生卷积神经网络 孪生卷积神经网络
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| GB/T 7714 | 韩红桂 , 甄琪 , 任柯燕 et al. 基于孪生卷积神经网络的手机型号识别方法 [J]. | 北京工业大学学报 , 2021 , 47 (02) : 112-119 . |
| MLA | 韩红桂 et al. "基于孪生卷积神经网络的手机型号识别方法" . | 北京工业大学学报 47 . 02 (2021) : 112-119 . |
| APA | 韩红桂 , 甄琪 , 任柯燕 , 伍小龙 , 杜永萍 , 乔俊飞 . 基于孪生卷积神经网络的手机型号识别方法 . | 北京工业大学学报 , 2021 , 47 (02) , 112-119 . |
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Abstract ：
基于对抗式分布对齐的跨域方面级情感分析
Keyword ：
跨域方面级情感分析 跨域方面级情感分析 梯度反转 梯度反转 交互注意力 交互注意力 对抗式训练 对抗式训练
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| GB/T 7714 | 杜永萍 , 刘杨 , 贺萌 et al. 基于对抗式分布对齐的跨域方面级情感分析 [J]. | 杜永萍 , 2021 , 34 (1) : 87-94 . |
| MLA | 杜永萍 et al. "基于对抗式分布对齐的跨域方面级情感分析" . | 杜永萍 34 . 1 (2021) : 87-94 . |
| APA | 杜永萍 , 刘杨 , 贺萌 , 模式识别与人工智能 . 基于对抗式分布对齐的跨域方面级情感分析 . | 杜永萍 , 2021 , 34 (1) , 87-94 . |
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Abstract ：
跨领域情感分类任务旨在利用富含情感标签的源域数据对缺乏标签的目标域数据进行情感极性分析.由此,文中提出基于对抗式分布对齐的跨域方面级情感分类模型,利用方面词与上下文的交互注意力学习语义关联,基于梯度反转层的领域分类器学习共享的特征表示.利用对抗式训练扩大领域分布的对齐边界,有效缓解模糊特征导致错误分类的问题.在Semeval-2014、Twitter数据集上的实验表明,文中模型性能较优.消融实验进一步表明捕获决策边界的模糊特征并扩大样本与决策边界间距离的策略可提高分类性能.
Keyword ：
梯度反转 梯度反转 对抗式训练 对抗式训练 跨域方面级情感分析 跨域方面级情感分析 交互注意力 交互注意力
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| GB/T 7714 | 杜永萍 , 刘杨 , 贺萌 . 基于对抗式分布对齐的跨域方面级情感分析 [J]. | 模式识别与人工智能 , 2021 , 34 (1) : 87-94 . |
| MLA | 杜永萍 et al. "基于对抗式分布对齐的跨域方面级情感分析" . | 模式识别与人工智能 34 . 1 (2021) : 87-94 . |
| APA | 杜永萍 , 刘杨 , 贺萌 . 基于对抗式分布对齐的跨域方面级情感分析 . | 模式识别与人工智能 , 2021 , 34 (1) , 87-94 . |
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