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学者姓名：杜永萍
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摘要 ：
Sequential models based on deep learning are widely used in sequential recommendation task, but the increase of model parameters results in a higher latency in the inference stage, which limits the real-time performance of the model. In order to make the model strike a balance between efficiency and effectiveness, the knowledge distillation technology is adopted to transfer the pre-trained knowledge from the large teacher model to the small student model. We propose a multi-stage knowledge distillation method based on interest knowledge, including interest representation knowledge and interest drift knowledge. In the process of knowledge transfer, expert distillation is designed to transform the knowledge dimension of student model to alleviate the loss of original knowledge information. Specially, curriculum learning is introduced for multistage knowledge learning, which further makes the teacher model effectively transfer the knowledge to the student model with limited ability. The proposed method on three real-world datasets including MovieLen-1M, Amazon Game and Steam datasets. The experimental results demonstrate that our method is superior to the other compared distillation method significantly and multi-stage learning makes the student model achieve the knowledge step by step for improvement.
关键词 ：
Model compression Model compression Interest drift Interest drift Knowledge distillation Knowledge distillation Multi-stage learning Multi-stage learning Sequential recommendation Sequential recommendation
引用：
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| GB/T 7714 | Du, Yongping , Niu, Jinyu , Wang, Yuxin et al. Multi-stage knowledge distillation for sequential recommendation with interest knowledge [J]. | INFORMATION SCIENCES , 2024 , 654 . |
| MLA | Du, Yongping et al. "Multi-stage knowledge distillation for sequential recommendation with interest knowledge" . | INFORMATION SCIENCES 654 (2024) . |
| APA | Du, Yongping , Niu, Jinyu , Wang, Yuxin , Jin, Xingnan . Multi-stage knowledge distillation for sequential recommendation with interest knowledge . | INFORMATION SCIENCES , 2024 , 654 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提出了一种基于部分标签特征学习的生活垃圾识别方法，针对数据缺少标签使模型难以挖掘数据特征，导致分类精度低的问题。本发明在真实垃圾图像数据的基础上，建立基于部分标签特征学习的生活垃圾识别方法，利用信息不确定性选择具有丰富未知信息的样本进行标注，实现特征的自适应学习，并降低学习成本，最终完成垃圾的准确分类。这种基于部分标签特征学习的生活垃圾识别方法在实际垃圾回收过程中，可以解决垃圾由于数据量大，标签难以获取，从而导致垃圾特征挖掘不充分的问题，实现了高精度垃圾分类，为垃圾回收行业提供技术支持。
引用：
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| GB/T 7714 | 韩红桂 , 范晓晔 , 李方昱 et al. 一种基于部分标签特征学习的生活垃圾识别方法 : CN202310557795.5[P]. | 2023-05-18 . |
| MLA | 韩红桂 et al. "一种基于部分标签特征学习的生活垃圾识别方法" : CN202310557795.5. | 2023-05-18 . |
| APA | 韩红桂 , 范晓晔 , 李方昱 , 杜永萍 . 一种基于部分标签特征学习的生活垃圾识别方法 : CN202310557795.5. | 2023-05-18 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提出基于卷积神经网络特征可视化的典型垃圾识别方法。其中，方法包括：建立卷积神经网络的典型垃圾类别决策模型，设计基于类别决策的典型垃圾特征激活映射策略，突破网络学习过程中的典型垃圾识别可视化技术，实现可解释的特征可视化卷积神经网络典型垃圾识别，为垃圾回收行业提供强有力的技术支持，对典型生活垃圾分类具有显著的应用和经济效益。因此，本发明的研究成果在典型生活垃圾回收领域具有广阔的应用前景。
引用：
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| GB/T 7714 | 韩红桂 , 张奇宇 , 李方昱 et al. 基于卷积神经网络特征可视化的典型垃圾识别方法 : CN202310185219.2[P]. | 2023-02-24 . |
| MLA | 韩红桂 et al. "基于卷积神经网络特征可视化的典型垃圾识别方法" : CN202310185219.2. | 2023-02-24 . |
| APA | 韩红桂 , 张奇宇 , 李方昱 , 杜永萍 , 吴玉锋 . 基于卷积神经网络特征可视化的典型垃圾识别方法 : CN202310185219.2. | 2023-02-24 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The automatic price evaluation is an important and challenging issue for the used mobile phone recycling. However, due to complicated relationships between attributes and price as well as insufficient sample data, current approaches cannot achieve accurate price evaluation for the used mobile phones, which significantly reduce the efficiency for the used mobile phone recycling. To this end, a tensor based approach is proposed in this paper, which can achieve accurate and efficient price evaluation for used mobile phones. In the proposed approach, first, the mutual information based attributes selection mechanism and the boxplot based sample selection mechanism are employed to select the most relevant attributes and suitable price samples from used mobile phone dataset. Then, a tensor model is constructed to establish multi-dimensional relationships between selected attributes and prices of used mobile phones. Finally, the missing values in the constructed tensor model is completed through the gradient descent and CANDECOMP/PARAFAC decomposition algorithms. The completed tensor model can be directly used for the price evaluation of used mobile phones based on their attributes without further calculation. Based on the real price dataset of used mobile phones, the experiments indicate that the proposed approach outperforms most of current approaches for the price evaluation for the used mobile phones.
关键词 ：
Used mobile phones Used mobile phones Tensor Tensor CP decomposition CP decomposition Price evaluation Price evaluation Mutual information Mutual information
引用：
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| GB/T 7714 | Su, Xing , Shi, Xingyan , Du, Yongping et al. A tensor based price evaluation approach for the used mobile phone recycling [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 238 . |
| MLA | Su, Xing et al. "A tensor based price evaluation approach for the used mobile phone recycling" . | EXPERT SYSTEMS WITH APPLICATIONS 238 (2023) . |
| APA | Su, Xing , Shi, Xingyan , Du, Yongping , Han, Honggui . A tensor based price evaluation approach for the used mobile phone recycling . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 238 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it's more difficult to build large-scale datasets for specific domain question answering. Existing methods are limited by the lack of training data, and the performance is not as good as in open-domain settings, especially degrading when facing to the adversarial sample. We try to resolve the above issues. First, effective data augmentation strategies are adopted to improve the model training, including slide window, summarization and round-trip translation. Second, we propose a model weighting strategy for the final answer prediction in biomedical domain, which combines the advantage of two models, open-domain model QANet and BioBERT pre-trained in biomedical domain data. Finally, we give adversarial training to reinforce the robustness of the model. The public biomedical dataset collected from PubMed provided by BioASQ challenge is used to evaluate our approach. The results show that the model performance has been improved significantly compared to the single model and other models participated in BioASQ challenge. It can learn richer semantic expression from data augmentation and adversarial samples, which is beneficial to solve more complex question answering problems in biomedical domain.
关键词 ：
Training Training Context modeling Context modeling Data models Data models model weighting model weighting Training data Training data data augmentation data augmentation Biomedical question answering Biomedical question answering Task analysis Task analysis Biological system modeling Biological system modeling Predictive models Predictive models deep learning deep learning
引用：
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| GB/T 7714 | Du, Yongping , Yan, Jingya , Lu, Yuxuan et al. Improving Biomedical Question Answering by Data Augmentation and Model Weighting [J]. | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , 2023 , 20 (2) : 1114-1124 . |
| MLA | Du, Yongping et al. "Improving Biomedical Question Answering by Data Augmentation and Model Weighting" . | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 20 . 2 (2023) : 1114-1124 . |
| APA | Du, Yongping , Yan, Jingya , Lu, Yuxuan , Zhao, Yiliang , Jin, Xingnan . Improving Biomedical Question Answering by Data Augmentation and Model Weighting . | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , 2023 , 20 (2) , 1114-1124 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Aspect-based Sentiment Analysis (ABSA) aims to predict the sentiment polarity towards a particular aspect in a sentence. Most of the existing methods construct neural networks or fine-tune the pre-trained language models. However, it is difficult to fully utilize the knowledge of language model learned during pretraining process, and it usually performs worse in few-shot experiment. Prompt learning can alleviate the above problems effectively. Manual prompt template construction methods are adopted usually but it leads to high costs and low efficiency, and there are seldom prompt templates applicable to ABSA task. We propose a method for ABSA prompt template construction, named Average Gradient Search with External Knowledge Based on KNN. The vocabulary list of the pre-trained language model is built as a KD-Tree, and the KNN algorithm is used to search the best prompt template on KD-Tree. For the sentiment polarity prediction, the best prompt template is used to wrap the ABSA data, converting the classification task to generative task, which enables the language model to fully leverage the learned knowledge from the pre-training stage. Moreover, the constructed verbalizer incorporates external knowledge to provide label words of each class with extensive semantic coverage and lower subjective bias. The comparison experimental results on SemEval2014 Restaurant and Laptop datasets demonstrate that the proposed method outperforms the existing SOTA models. Further, the few-shot experimental results indicate that, with only a small number of training samples, the proposed method achieves comparable or even better performance than the baseline model trained on the full dataset.
关键词 ：
Average Gradient Search Average Gradient Search Pre-trained language model Pre-trained language model External knowledge External knowledge Natural Language Processing Natural Language Processing Aspect-based Sentiment Analysis Aspect-based Sentiment Analysis Prompt learning Prompt learning
引用：
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| GB/T 7714 | Du, Yongping , Yin, Zihao , Xie, Runfeng et al. Prompt template construction by Average Gradient Search with External Knowledge for aspect sentimental analysis [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 238 . |
| MLA | Du, Yongping et al. "Prompt template construction by Average Gradient Search with External Knowledge for aspect sentimental analysis" . | EXPERT SYSTEMS WITH APPLICATIONS 238 (2023) . |
| APA | Du, Yongping , Yin, Zihao , Xie, Runfeng , Zhang, Qi . Prompt template construction by Average Gradient Search with External Knowledge for aspect sentimental analysis . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 238 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
Convolutional neural network Convolutional neural network Multimodal sentiment classification Multimodal sentiment classification Gated attention mechanism Gated attention mechanism Feature fusion Feature fusion
引用：
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提出了一种基于空间遗忘通道注意力卷积神经网络的生活垃圾分类方法，解决实际生活垃圾分类过程中垃圾种类样式繁多难以精准分类的问题。本发明设计特征空间遗忘注意力模块，可以让网络关注更多局部特征，从而使得网络学习到更多不同垃圾样式的复杂特征信息，有效提高网络对生活垃圾的分类精度。本发明应用在智能自动化视觉技术的生活垃圾回收过程，提高了生活垃圾的回收效率，同时也能够减少人工成本，提高全社会面的资源回收效益。
引用：
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| GB/T 7714 | 韩红桂 , 张奇宇 , 李方昱 et al. 一种基于空间遗忘通道注意力卷积神经网络的生活垃圾分类方法 : CN202211062731.X[P]. | 2022-08-31 . |
| MLA | 韩红桂 et al. "一种基于空间遗忘通道注意力卷积神经网络的生活垃圾分类方法" : CN202211062731.X. | 2022-08-31 . |
| APA | 韩红桂 , 张奇宇 , 李方昱 , 杜永萍 , 吴玉锋 . 一种基于空间遗忘通道注意力卷积神经网络的生活垃圾分类方法 : CN202211062731.X. | 2022-08-31 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明针对类别不断变化的数据难以使用模型准确分类的问题，提出了一种基于原型增量学习的垃圾分类方法，建立基于原型增量学习的垃圾分类模型，利用原型表征已知类别图像数据，减少对已知垃圾类别数据的存储，该方法能在垃圾组分不断变化时，对类样本不平衡的垃圾进行准确分类，同时该方法基于原型降低了对网络存储的需求，避免了训练增量分类网络时泄露用户隐私的可能，为垃圾回收行业提供技术支持。
引用：
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| GB/T 7714 | 韩红桂 , 范晓晔 , 李方昱 et al. 一种基于原型增量学习的垃圾分类方法 : CN202211063075.5[P]. | 2022-08-31 . |
| MLA | 韩红桂 et al. "一种基于原型增量学习的垃圾分类方法" : CN202211063075.5. | 2022-08-31 . |
| APA | 韩红桂 , 范晓晔 , 李方昱 , 杜永萍 . 一种基于原型增量学习的垃圾分类方法 : CN202211063075.5. | 2022-08-31 . |
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