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Student Program Classification Using Gated Graph Attention Neural Network

M. Lu, Y. Wang, D. Tan, L. Zhao
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Abstract syntax tree, attention mechanism, code understanding, function call graph, data flow, graph neural network, student program classification.  ...  To learn from student programs, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the Abstract Syntax Tree (AST), and applies an improved  ...  GATED GRAPH NEURAL NETWORK Gated Graph Neural Network (GGNN) is an extension of GNN by replacing function nn in Equation (2) with the GRU [21] function shown in Equation (3) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3063475">doi:10.1109/access.2021.3063475</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yswxt2mnw5czvjoodjaksbr7oq">fatcat:yswxt2mnw5czvjoodjaksbr7oq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210729081938/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09367198.pdf?tp=&amp;arnumber=9367198&amp;isnumber=9312710&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/13/0e/130eca63a4ef8c7e4678cca38a473a926ceeeb31.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3063475"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Program Classification Using Gated Graph Attention Neural Network for Online Programming Service [article]

Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
<span title="2019-03-09">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address this problem, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the AST,and applies an improved GNN model to the integrated graph  ...  , so as to achieve the state-of-art program classification accuracy.  ...  PRELIMINARIES This section mainly introduces some basic concepts used in this paper, including graph, graph neural network, gated graph neural network. A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.03804v1">arXiv:1903.03804v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3kexjtwb4jeiznkgdiowijsytm">fatcat:3kexjtwb4jeiznkgdiowijsytm</a> </span>
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Constructing Knowledge Graphs for Online Collaborative Programming

Yuanyi Zhen, Lanqin Zheng, Penghe Chen
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Knowledge graph, entity recognition, relation extraction, deep neural network model, online collaborative learning, collaborative programming.  ...  This study aimed to automatically construct knowledge graphs for online collaborative programming.  ...  The neural relation extraction methods include convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention mechanism, graph convolutional networks (GCNs), adversarial training, reinforcement  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3106324">doi:10.1109/access.2021.3106324</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qlc55kphwrdsfberphxjkvdmzy">fatcat:qlc55kphwrdsfberphxjkvdmzy</a> </span>
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Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

Qu, Li, Wu, Zhang, Wang
<span title="2019-12-16">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement.  ...  First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students' learning processes.  ...  Attention Mechanism We used the attention-based student achievement prediction model to evaluate the importance of the LSTM neural network and sequence patterns adapter.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9245539">doi:10.3390/app9245539</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5pll4lm7orf3rg6koee6vv634m">fatcat:5pll4lm7orf3rg6koee6vv634m</a> </span>
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Modular Tree Network for Source Code Representation Learning [article]

Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
<span title="2021-04-01">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In recent years, neural networks have already shown success in this area, but most existing models did not make full use of the unique structural information of programs.  ...  We evaluate our model on two tasks: program classification and code clone detection.  ...  Graph-based models: we also compare our models with gated graph neural networks (GGNN) [21] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.00196v1">arXiv:2104.00196v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3w4cjpzujjbgrlkgnrryu3qsci">fatcat:3w4cjpzujjbgrlkgnrryu3qsci</a> </span>
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A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

Qianyu Cao, Hanmei Hao, Syed Hassan Ahmed
<span title="2021-07-02">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation.  ...  After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework  ...  Acknowledgments is study was supported by Trial and Practice of Technology Enhanced College English Teaching, sponsored by Sichuan Social Science Research Program (SC16WY006).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/3274326">doi:10.1155/2021/3274326</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2jw7infktff3ro67eo5d76iwde">fatcat:2jw7infktff3ro67eo5d76iwde</a> </span>
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College English Flipped Classroom Teaching Model Based on Big Data and Deep Neural Networks

Heli Chang, Shah Nazir
<span title="2021-05-24">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw4azkpu65d2thmrwfkoawyxse" style="color: black;">Scientific Programming</a> </i> &nbsp;
Therefore, this article proposes a novel college English flipped classroom teaching model based on big data and deep neural networks.  ...  autonomous learning ability and its influencing factors through interviews, and to predict and effectively analyze the weight of influencing factors through the deep neural network.  ...  As a kind of LSTM improved recursive neural network, GRU gated circulation unit has a simpler gate structure, which reduces a lot of matrix multiplication computation in the operation of neural network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9918433">doi:10.1155/2021/9918433</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w7wtoawowvenbfmob5cz5se3uy">fatcat:w7wtoawowvenbfmob5cz5se3uy</a> </span>
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Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Nurul A. Asif, Yeahia Sarker, Ripon K. Chakrabortty, Michael J. Ryan, Md. Hafiz Ahamed, Dip K. Saha, Faisal R. Badal, Sajal K. Das, Md. Firoz Ali, Sumaya I. Moyeen, Md. Robiul Islam, Zinat Tasneem
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints.  ...  INDEX TERMS graph neural network, geometric deep learning, graph-structured network, non-euclidean space.  ...  Prediction Learning Xu et al. (2019) [91] GRU+Attention Module Node Classification Bai et al. (2019) [92] Attention Network Graph Classification Al et al. (2019) [93] GNN + Attention Module Graph  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3071274">doi:10.1109/access.2021.3071274</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yufufsrgqvgyzhxljgkmejuyhe">fatcat:yufufsrgqvgyzhxljgkmejuyhe</a> </span>
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A Deep Learning Approach for a Source Code Detection Model Using Self-Attention

Yao Meng, Long Liu
<span title="">2020</span> <i title="Hindawi-Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
With the development of deep learning, many approaches based on neural networks are proposed for code clone.  ...  and improves the classification performance.  ...  Based on the common neural networks such as convolutional neural network, recursive neural network, and recurrent neural network, scholars exploit various deep encoders [11] [12] [13] to automatically  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/5027198">doi:10.1155/2020/5027198</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/a8882994dfc040c8be3d2f74a2cc9ca6">doaj:a8882994dfc040c8be3d2f74a2cc9ca6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3khymjwlzngwbnvv3h6zj7ym5y">fatcat:3khymjwlzngwbnvv3h6zj7ym5y</a> </span>
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An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Jing Zhang, Jamil Hussain
<span title="2021-11-30">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw4azkpu65d2thmrwfkoawyxse" style="color: black;">Scientific Programming</a> </i> &nbsp;
The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information.  ...  The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN).  ...  In order to evaluate the performance of this method, this method was compared with the following representative baseline methods: (1) GEXP3: using graph neural network and attention mechanism to predict  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/6191543">doi:10.1155/2021/6191543</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kaax4rlzxndrtbw7ml7fldrdxm">fatcat:kaax4rlzxndrtbw7ml7fldrdxm</a> </span>
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A Survey on Neural-symbolic Systems [article]

Dongran Yu, Bo Yang, Dayou Liu, Hui Wang
<span title="2021-11-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Combining the fast computation ability of neural systems and the powerful expression ability of symbolic systems, neural-symbolic systems can perform effective learning and reasoning in multi-domain tasks  ...  In recent years, neural systems have demonstrated superior perceptual intelligence through highly effective learning, but their reasoning capabilities remain poor.  ...  The knowledge graph transfer module models the classifier weights as a graph, and it uses a gated graph neural network (GGNN) to learn the representation of nodes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.08164v1">arXiv:2111.08164v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bc33afiitnb73bmjtrfbdgkwpy">fatcat:bc33afiitnb73bmjtrfbdgkwpy</a> </span>
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Inferring Users' Social Roles with a Multi-Level Graph Neural Network Model

Chunrui Zhang, Shen Wang, Dechen Zhan, Mingyong Yin, Fang Lou
<span title="2021-11-01">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
We further designed a novel social network representation learning algorithm in order to infer users' social statuses and roles in social networks through the use of an attention and gate mechanism on  ...  Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users' social statuses and roles.  ...  [22] proposed the use of graph wavelet neural network (GWNN) which replaces the graph Fourier transform with a graph wavelet transform for analyzing a graph network. Sun et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23111453">doi:10.3390/e23111453</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34828151">pmid:34828151</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8625403/">pmcid:PMC8625403</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v7m3muw52zbexc7adxbp4b3fvq">fatcat:v7m3muw52zbexc7adxbp4b3fvq</a> </span>
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ProGraML: Graph-based Deep Learning for Program Optimization and Analysis [article]

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
<span title="2020-03-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Message Passing Neural Networks propagate information through this structured representation, enabling whole-program or per-vertex classification tasks.  ...  We introduce ProGraML - Program Graphs for Machine Learning - a novel graph-based program representation using a low level, language agnostic, and portable format; and machine learning models capable of  ...  Our hope in developing PROGRAML is to provide a re-usable toolbox for representing and reasoning about programs that can be used for a wide variety of downstream tasks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.10536v1">arXiv:2003.10536v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p2unp2ggpjblpgq72aumicim5e">fatcat:p2unp2ggpjblpgq72aumicim5e</a> </span>
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Accurate Portraits of Scientific Resources and Knowledge Service Components [article]

Yue Wang and Zhe Xue and Ang Li
<span title="2022-04-11">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The solution is to construct accurate portraits of scientific and technological resources in combination with knowledge graph related technologies.  ...  There are a large number of management and classification standards for existing scientific and technological resources, but these standards are difficult to completely cover all entities and associations  ...  Acknowledgment This work is supported by National Key R&D Program of China (2018YFB1402600), the National Natural Science Foundation of China (61772083, 61877006, 61802028, 62002027).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.04883v1">arXiv:2204.04883v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/slsvxnz2obf2fc7imiol7skxce">fatcat:slsvxnz2obf2fc7imiol7skxce</a> </span>
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Paper titles

<span title="2020-09-28">2020</span> <i title="IEEE"> 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) </i> &nbsp;
convolutional networks Estimation of indoor position and motion direction for smartphones using DNN to BLE beacon signal strength Estimation of Person-Specific Visual Attention via Selection of Similar  ...  with Soft Dynamic Time Wrapping Prioritization method of link protection with convolutional neural networks Privacy-Preserving Delegation of Decision Tree Classification Product Quality Prediction  ... 
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