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学者姓名：蒋宗礼
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Abstract ：
传统的协同过滤算法没有充分考虑用户和商品的交互信息,且面临数据稀疏、冷启动等问题,造成了推荐系统的结果不准确.在本文中提出了一种新的推荐算法,即基于融合元路径的图神经网络协同过滤算法.该算法首先由二部图嵌入用户和商品的历史互动,并通过多层神经网络传播获取用户和商品的高阶特征;然后基于元路径的随机游走来获取异质信息网络中的潜在语义信息;最后将用户和商品的高阶特征和潜在特征融合并做评分预测.实验结果表明,基于融合元路径的图神经网络协同过滤算法比传统的推荐算法有明显提升.
Keyword ：
图神经网络 图神经网络 协同过滤 协同过滤 元路径 元路径 推荐系统 推荐系统
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| GB/T 7714 | 蒋宗礼 , 田聪聪 . 基于融合元路径的图神经网络协同过滤算法 [J]. | 计算机系统应用 , 2021 , 30 (2) : 140-146 . |
| MLA | 蒋宗礼 等. "基于融合元路径的图神经网络协同过滤算法" . | 计算机系统应用 30 . 2 (2021) : 140-146 . |
| APA | 蒋宗礼 , 田聪聪 . 基于融合元路径的图神经网络协同过滤算法 . | 计算机系统应用 , 2021 , 30 (2) , 140-146 . |
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Abstract ：
提高课程教学站位,全面实现课程在人才培养体系中承担的任务.积极落实为党和国家培养人才的基本目标,全面构建人才成长的生态环境;树立标准意识,强化质量意识;科学规划,依据支撑毕业要求达成确定课程目标;科学施教,提升学生专业能力和科学意识;瞄准目标的达成实施科学评价,高效培养质量好、水平高的社会主义建设者和接班人.
Keyword ：
人才培养 人才培养 一流课程 一流课程 课程思政 课程思政 课程教学 课程教学 毕业要求 毕业要求 评价体系 评价体系 质量标准 质量标准
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| GB/T 7714 | 蒋宗礼 . 提高课程教学站位 [J]. | 中国大学教学 , 2021 , (1) : 35-41 . |
| MLA | 蒋宗礼 . "提高课程教学站位" . | 中国大学教学 1 (2021) : 35-41 . |
| APA | 蒋宗礼 . 提高课程教学站位 . | 中国大学教学 , 2021 , (1) , 35-41 . |
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Abstract ：
基于融合元路径的图神经网络协同过滤算法
Keyword ：
协同过滤 协同过滤 图神经网络 图神经网络 元路径 元路径 推荐系统 推荐系统
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| GB/T 7714 | 蒋宗礼 , 田聪聪 , 计算机系统应用 . 基于融合元路径的图神经网络协同过滤算法 [J]. | 蒋宗礼 , 2021 , 30 (2) : 140-146 . |
| MLA | 蒋宗礼 等. "基于融合元路径的图神经网络协同过滤算法" . | 蒋宗礼 30 . 2 (2021) : 140-146 . |
| APA | 蒋宗礼 , 田聪聪 , 计算机系统应用 . 基于融合元路径的图神经网络协同过滤算法 . | 蒋宗礼 , 2021 , 30 (2) , 140-146 . |
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Abstract ：
提高课程教学站位
Keyword ：
人才培养 人才培养 课程教学 课程教学 质量标准 质量标准 评价体系 评价体系 一流课程 一流课程 毕业要求 毕业要求 课程思政 课程思政
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| GB/T 7714 | 蒋宗礼 , 中国大学教学 . 提高课程教学站位 [J]. | 蒋宗礼 , 2021 , (1) : 35-41 . |
| MLA | 蒋宗礼 等. "提高课程教学站位" . | 蒋宗礼 1 (2021) : 35-41 . |
| APA | 蒋宗礼 , 中国大学教学 . 提高课程教学站位 . | 蒋宗礼 , 2021 , (1) , 35-41 . |
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Abstract ：
针对模式识别课程现行教材中数学表达内容多以及课堂教学中存在知识理解与转化度偏低、教师难教、学生难学的问题,以图式理论为指导,瞄准"知识贯通、能力提升"的教改目标,基于知识的递进理解规律,提出构建课程内容的"高阶""中阶""低阶"表达的多层级体系,并据此介绍与基础教材配套的辅助教材和应用案例建设,最后说明教学效果。
Keyword ：
课程设计 课程设计 高校教育 高校教育 教材建设 教材建设 模式识别 模式识别
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| GB/T 7714 | 徐勇 , 文杰 , 蒋宗礼 . 模式识别课程多层次知识表达设计与配套教材建设 [J]. | 计算机教育 , 2021 , PageCount-页数: 4 (09) : 191-194 . |
| MLA | 徐勇 等. "模式识别课程多层次知识表达设计与配套教材建设" . | 计算机教育 PageCount-页数: 4 . 09 (2021) : 191-194 . |
| APA | 徐勇 , 文杰 , 蒋宗礼 . 模式识别课程多层次知识表达设计与配套教材建设 . | 计算机教育 , 2021 , PageCount-页数: 4 (09) , 191-194 . |
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Abstract ：
Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and labor-intensive. Therefore, researchers have instead been seeking to develop effective computational algorithms to predict associations between diseases and RNAs. In this paper, we propose a novel model called Graph Attention Adversarial Network (GAAN) for the potential disease-RNA association prediction. To our best knowledge, we are among the pioneers to integrate successfully both the state-of-the-art graph convolutional networks (GCNs) and attention mechanism in our model for the prediction of disease-RNA associations. Comparing to other disease-RNA association prediction methods, GAAN is novel in conducing the computations from the aspect of global structure of disease-RNA network with graph embedding while integrating features of local neighborhoods with the attention mechanism. Moreover, GAAN uses adversarial regularization to further discover feature representation distribution of the latent nodes in disease-RNA networks. GAAN also benefits from the efficiency of deep model for the computation of big associations networks. To evaluate the performance of GAAN, we conduct experiments on networks of diseases associating with two different RNAs: MicroRNAs (miRNAs) and Long non-coding RNAs (lncRNAs). Comparisons of GAAN with several popular baseline methods on disease-RNA networks show that our novel model outperforms others by a wide margin in predicting potential disease-RNAs associations.
Keyword ：
Network representation Network representation Gaph convolution networks Gaph convolution networks Adversarial regularization Adversarial regularization Disease-related association Disease-related association
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| GB/T 7714 | Zhang, Jinli , Jiang, Zongli , Hu, Xiaohua et al. A novel graph attention adversarial network for predicting disease-related associations [J]. | METHODS , 2020 , 179 : 81-88 . |
| MLA | Zhang, Jinli et al. "A novel graph attention adversarial network for predicting disease-related associations" . | METHODS 179 (2020) : 81-88 . |
| APA | Zhang, Jinli , Jiang, Zongli , Hu, Xiaohua , Song, Bo . A novel graph attention adversarial network for predicting disease-related associations . | METHODS , 2020 , 179 , 81-88 . |
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Abstract ：
Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.
Keyword ：
Hierarchy construction Hierarchy construction Relax strategy Relax strategy Heterogeneous information networks Heterogeneous information networks Stacked denoising auto encoder Stacked denoising auto encoder
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| GB/T 7714 | Zhang, Jinli , Jiang, Zongli , Du, Yongping et al. Hierarchy construction and classification of heterogeneous information networks based on RSDAEf [J]. | DATA & KNOWLEDGE ENGINEERING , 2020 , 127 . |
| MLA | Zhang, Jinli et al. "Hierarchy construction and classification of heterogeneous information networks based on RSDAEf" . | DATA & KNOWLEDGE ENGINEERING 127 (2020) . |
| APA | Zhang, Jinli , Jiang, Zongli , Du, Yongping , Li, Tong , Wang, Yida , Hu, Xiaohua . Hierarchy construction and classification of heterogeneous information networks based on RSDAEf . | DATA & KNOWLEDGE ENGINEERING , 2020 , 127 . |
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Abstract ：
There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.
Keyword ：
meta path meta path meta graph meta graph classification classification heterogeneous information networks heterogeneous information networks
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| GB/T 7714 | Zhang, Jinli , Li, Tong , Jiang, Zongli et al. A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks [J]. | APPLIED SCIENCES-BASEL , 2020 , 10 (5) . |
| MLA | Zhang, Jinli et al. "A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks" . | APPLIED SCIENCES-BASEL 10 . 5 (2020) . |
| APA | Zhang, Jinli , Li, Tong , Jiang, Zongli , Hu, Xiaohua , Jazayeri, Ali . A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks . | APPLIED SCIENCES-BASEL , 2020 , 10 (5) . |
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Abstract ：
Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have difficulty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-significance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classification and link prediction. WMGCN's representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What's more, WMGCN'feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms.
Keyword ：
graph convolutional network graph convolutional network Heterogeneous networks Heterogeneous networks Neural networks Neural networks Heterogeneous network Heterogeneous network Task analysis Task analysis Predictive models Predictive models Semantics Semantics representation learning representation learning weighted meta-graph weighted meta-graph Licenses Licenses Convolution Convolution
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| GB/T 7714 | Zhang, Jinli , Jiang, Zongli , Chen, Zheng et al. WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks [J]. | IEEE ACCESS , 2020 , 8 : 40744-40754 . |
| MLA | Zhang, Jinli et al. "WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks" . | IEEE ACCESS 8 (2020) : 40744-40754 . |
| APA | Zhang, Jinli , Jiang, Zongli , Chen, Zheng , Hu, Xiaohua . WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks . | IEEE ACCESS , 2020 , 8 , 40744-40754 . |
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Abstract ：
The traditional end-to-end task-oriented dialogue models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. But in the case of large amounts of data, there are many types of questions. It performs poorly when answering multiple types of questions, memory information cannot effectively record all the sentence information of the context. In view of the above this, this article uses a modified transformer model to overcome the problems mentioned in dialogue tasks. Transformer is a model constructed using attention mechanisms, which completely discards the method of RNN (recurrent neural networks), and its structure includes two sub-parts of Encoder and decoder. It uses residual network, batch normalization, and self-attention mechanism to build the model structure, uses Positional Encoding to capture sentence information, which can speed up model training convergence and capture Longer sentence information. In this paper, we modified the activation function in the transformer and use label smoothing to optimize the training to make the model's expressive ability better than previous. © 2019 Published under licence by IOP Publishing Ltd.
Keyword ：
Intelligent computing Intelligent computing Decoding Decoding Convolutional neural networks Convolutional neural networks Signal encoding Signal encoding Recurrent neural networks Recurrent neural networks
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| GB/T 7714 | Jiang, ZongLi , Zhang, Shuo . Research on Task-oriented Dialogue Based on Modified Transformer [C] . 2020 . |
| MLA | Jiang, ZongLi et al. "Research on Task-oriented Dialogue Based on Modified Transformer" . (2020) . |
| APA | Jiang, ZongLi , Zhang, Shuo . Research on Task-oriented Dialogue Based on Modified Transformer . (2020) . |
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