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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>
The online programing services, such as Github,TopCoder, and EduCoder, have promoted a lot of social interactions among the service users.  ...  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  ...  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>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825015522/https://arxiv.org/pdf/1903.03804v1.pdf" 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/74/3f/743f2a690d12285d7ddc925ecda39856e85fd9fc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.03804v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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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Detecting Malicious Accounts in Online Developer Communities Using Deep Learning

Qingyuan Gong, Jiayun Zhang, Yang Chen, Qi Li, Yu Xiao, Xin Wang, Pan Hui
<span title="">2019</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM &#39;19</a> </i> &nbsp;
An attention mechanism is used to integrate the information generated by the parallel neural networks.  ...  Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online.  ...  For example, Ma et al. [21] proposed to use recurrent neural networks to detect rumors from microblogs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3357384.3357971">doi:10.1145/3357384.3357971</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/GongZ00XWH19.html">dblp:conf/cikm/GongZ00XWH19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lb7eccbtjrgyxe4ke4vlvwhmba">fatcat:lb7eccbtjrgyxe4ke4vlvwhmba</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;
with graph 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  ...  Multiple Service Chains Outside the Box: New Style Discovering via Generative Adversarial Network for Shoes Design Performance Analysis of Programming Training based on Data Mining and High-interaction  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icce-taiwan49838.2020.9258179">doi:10.1109/icce-taiwan49838.2020.9258179</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2eheaztzhncixhbvp7nrbzml4m">fatcat:2eheaztzhncixhbvp7nrbzml4m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428234020/https://ieeexplore.ieee.org/ielx7/9257985/9257988/09258179.pdf" 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/f8/b0/f8b057e92c869e804f28adb7fed378fee868e1b5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icce-taiwan49838.2020.9258179"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
<span title="">2020</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;
The article by Kumar et al., ''Aspect-based sentiment classification using interactive gated convolutional network,'' proposes an interactive gated convolutional network (IGCN) that uses a bidirectional  ...  ., ''Neural tensor network for multi-label classification,'' proposes a new approach to multilabel classification, which employs a neural tensor network to explore the relations among the labels of neighbors  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3043060">doi:10.1109/access.2020.3043060</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qbqk5f4ojvadlazhk2mc343sra">fatcat:qbqk5f4ojvadlazhk2mc343sra</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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Information Fusion Algorithm for Big Data in Digital Publishing Industry Chain

Haixiang He, Zhihan Lv
<span title="2021-04-16">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
Meanwhile, this paper also studies the flow of digital publishing resources and empirically analyses the eco-efficiency of digital publishing eco-chain by using metabolic network analysis, population analysis  ...  services between publishing institutions and libraries in the digital era; and finally sorts out the theoretical foundations such as synergy theory, information integration service theory, and game theory  ...  of data, such as trees, graphs, and multidimensional data, and can be combined with convolutional neural networks [19] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9925567">doi:10.1155/2021/9925567</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5kzrcmwgva57mkhzupo4ci5xi">fatcat:d5kzrcmwgva57mkhzupo4ci5xi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210421021537/https://downloads.hindawi.com/journals/complexity/2021/9925567.pdf" 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/f9/11/f91115e5990cecdb4325b3c1d38280f5d4a955c1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9925567"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

Mingxiong Zhao, Han Wang, Jin Guo, Di Liu, Cheng Xie, Qing Liu, Zhibo Cheng
<span title="2019-07-05">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;
Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas.  ...  The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes.  ...  ., who utilized convolutional neural networks (CNNs) for relation classification [1, 21] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9132720">doi:10.3390/app9132720</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nzsd2rxbpzcbxpx5smyhmcsug4">fatcat:nzsd2rxbpzcbxpx5smyhmcsug4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200216022355/https://res.mdpi.com/d_attachment/applsci/applsci-09-02720/article_deploy/applsci-09-02720.pdf" 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/ff/0c/ff0c31cf983d1c1bb02434ab2cfa6dd3bf9ca424.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9132720"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

GAHNE: Graph-Aggregated Heterogeneous Network Embedding [article]

Xiaohe Li, Lijie Wen, Chen Qian, Jianmin Wang
<span title="2020-12-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
results of downstream tasks based on graph convolutional neural networks.  ...  To address these limitations, we propose a novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE), which is designed to extract the semantics of HINs as comprehensively as possible to improve the  ...  Our proposed graph neural network with website5 . gated aggregator. - GAHNEpool . Our proposed graph neural network with B.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.12517v1">arXiv:2012.12517v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7efq3643fffsvdgevrpy7odfhm">fatcat:7efq3643fffsvdgevrpy7odfhm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201228091745/https://arxiv.org/pdf/2012.12517v1.pdf" 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/fb/9f/fb9fe486c52f2af45515831616db6b40ed1dc389.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.12517v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Toward Identifying APT Malware through API System Calls

Chaoxian Wei, Qiang Li, Dong Guo, Xiangyu Meng, Angel M. Del Rey
<span title="2021-12-09">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sdme5pnua5auzcsjgqmqefb66m" style="color: black;">Security and Communication Networks</a> </i> &nbsp;
Thus, we used transfer learning to perform multiple classifications of the APT family.  ...  By extracting the application programming interface (API) system calls, with the vector representation of features by combining dynamic LSTM and attention algorithm, we can obtain API at different APT  ...  [29] constructed a neural network based on the convolution and cyclic network layer to obtain the best features for malware classification. Schofield et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8077220">doi:10.1155/2021/8077220</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ycfzgdhajfeldpkjp7ccertoe">fatcat:3ycfzgdhajfeldpkjp7ccertoe</a> </span>
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End-to-end diagnosis of cloud systems against intermittent faults

Chao Wang, Zhongchuan Fu, Yanyan Huo
<span title="">2020</span> <i title="National Library of Serbia"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rhtuh2ifczhapmhplqzald63za" style="color: black;">Computer Science and Information Systems</a> </i> &nbsp;
The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods.  ...  Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing.  ...  We implement the online mode based on Back Propagation 14 Neural Network (BP) and Probabilistic Neural Network (PNN), respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/csis200620040w">doi:10.2298/csis200620040w</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/73wwxknvdrb2rnz6byb5tbsc3y">fatcat:73wwxknvdrb2rnz6byb5tbsc3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201119032646/http://www.doiserbia.nb.rs/img/doi/1820-0214/2020%20OnLine-First/1820-02142000040W.pdf" 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/14/df/14df61b4b908bed5f8322d4bf13e82f79b9e7e30.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/csis200620040w"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

Xinxin Lu, Hong Zhang, Wenzheng Bao
<span title="2021-05-22">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;
Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the  ...  Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed.  ...  (ii) We fill the input layer with pad_sequences to a fixed length, use a two-way gated recurrent network to extract information, and use the attention mechanism to highlight the key information of the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/6669664">doi:10.1155/2021/6669664</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7c72t7qwfzbm3gqwyl4nwoxejy">fatcat:7c72t7qwfzbm3gqwyl4nwoxejy</a> </span>
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Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism

Yong Fang, Shaoshuai Yang, Bin Zhao, Cheng Huang
<span title="2021-04-16">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmr4kpn2yreovpdxpdiqtjcrnu" style="color: black;">Information</a> </i> &nbsp;
Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism.  ...  Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks.  ...  Bi-LSTM + Attention [29] : LSTM is also a typical neural network which consists of logic gates like GRU, it is commonly used to train classification model for word embeddings.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info12040171">doi:10.3390/info12040171</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g6po5ahosjh5pkfuxweqft22ce">fatcat:g6po5ahosjh5pkfuxweqft22ce</a> </span>
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Applications of deep learning in stock market prediction: recent progress [article]

Weiwei Jiang
<span title="2020-02-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility.  ...  Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists.  ...  Also, the collection of online news or twitter data provides new sources of predicting stock market. More recently, graph neural networks using various knowledge graph data appear as new ideas.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.01859v1">arXiv:2003.01859v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdwsi5xpozfyjeg7oatfzwbg7a">fatcat:rdwsi5xpozfyjeg7oatfzwbg7a</a> </span>
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Learning to Solve Vehicle Routing Problems: A Survey [article]

Aigerim Bogyrbayeva, Meraryslan Meraliyev, Taukekhan Mustakhov, Bissenbay Dauletbayev
<span title="2022-05-05">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present the taxonomy of the studies for learning paradigms, solution structures, underlying models, and algorithms.  ...  Then this heatmap is used for branching in forwarding Dynamic Programming. [82, 83] uses neural networks to approximate the value functions for dynamic programming, which expedites the solution time.  ...  Neural Networks (GNNs) [36] and Graph Convolution Networks [37] to extract features of a graph and deploy Memory Augmented Neural Networks [38] and Recurrent Neural Networks (RNNs) [39] to pass  ... 
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