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141

Exploit those code reviews! bigger data for deeper learning
Robert Heumüller ; Sebastian Nielebock ; Frank Ortmeier
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. :1505-1509

Collaboration in softwar... Software and its enginee... 0202 electrical engineer... 02 engineering and techn... General and reference Computing methodologies …
Fachzeitschrift
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142

Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection.
Wang, Huanting ; Ye, Guixin ; Tang, Zhanyong ; et al.
IEEE Transactions on Information Forensics & Security; 2021, Vol. 16, p1943-1958, 16p

Fachzeitschrift
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143

DIACEFAL : Algoritmo para el diagnóstico cefalométrico utilizando radiografía lateral del cráneo. Proyecto de investigación
González Herrera, Sebastián ; Fernández Mc Cann, David Stephen ; Llano Sánchez, Elizabeth ; et al.

Cefalometría Cephalometry Aprendizaje automático Machine learning Inteligencia artificial Artificial Intelligence …
Dissertation
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144

On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection
Jiayi Hua ; Haoyu Wang
2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR). :530-534

0202 electrical engineer... 02 engineering and techn...
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145

The identification of DoS and DDoS attacks to IoT devices in software defined networks by using machine learning and deep learning models
JESUS ARTURO PEREZ DIAZ

info:eu-repo/classificat... info:eu-repo/classificat... info:eu-repo/classificat... info:eu-repo/classificat...
Dissertation
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146

Exploit those code reviews! : bigger data for deeper learning
Heumüller, Robert ; Nielebock, Sebastian ; Ortmeier, Frank

ddc:000 Computing methodologies General and reference Experimentation Software and its enginee... Collaboration in softwar...
Konferenz
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147

Unsupervised Translation of Programming Languages ; Traduction Non Supervisée de Langages de Programmation
Roziere, Baptiste ; Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE) ; Université Paris Dauphine-PSL ; et al.
https://theses.hal.science/tel-03852612 ; Neural and Evolutionary Computing [cs.NE]. Université Paris sciences et lettres, 2022. English. ⟨NNT : 2022UPSLD015⟩.

Transcompilation Programming languages Program synthesis Translation Neural networks Deep learning …
Dissertation
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148

Summarizing source code with transferred API knowledge
Hu, Xing ; Li, Ge ; Xia, Xin ; et al.
Hu, X, Li, G, Xia, X, Lo, D, Lu, S & Jin, Z 2018, Summarizing source code with transferred API knowledge. in J Lang (ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. Association for the Advancement of Artificial Intelligence (AAAI), Marina del Rey CA USA, pp. 2269-2275, International Joint Conference on Artificial Intelligence 2018, Stockholm, Sweden, 13/07/18. https://doi.org/10.24963/ijcai.2018/314

Machine Learning Transfer Adaptation Multi-task Learning Deep Learning Applications of Supervis...
Fachzeitschrift
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149

A deep language model for software code
Dam, Hoa Khanh ; Tran, Truyen ; Pham, Trang ; et al.

E-Ressource
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150

Deep just-in-time defect localization
QIU, Fangcheng ; GAO, Zhipeng ; XIA, Xin ; et al.
Research Collection School Of Computing and Information Systems

Databases and Informatio... Software Engineering
Fachzeitschrift
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151

Boosting component-based synthesis with control structure recommendation
Daiyan Wang ; Yating Zhang ; Wei Dong ; et al.
Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages. :19-28

0202 electrical engineer... 02 engineering and techn...
Fachzeitschrift
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152

CORE: Automating Review Recommendation for Code Changes
Lingling Fan ; Jing Kai Siow ; Sen Chen ; et al.
2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). :284-295

Software Engineering (cs... FOS: Computer and inform... Computer Science - Softw... Computer Science - Machi... 0202 electrical engineer... 02 engineering and techn... …
Fachzeitschrift
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153

scikit-multilearn: A scikit-based Python environment for performing multi-label classification.
Szymański, Piotr ; Kajdanowicz, Tomasz
Journal of Machine Learning Research. 2019, Vol. 20 Issue 2-29, p1-22. 22p.

CLASSIFICATION algorithm... PYTHON programming langu... MACHINE learning EMBEDDINGS (Mathematics) ACQUISITION of data SPARSE matrices
Fachzeitschrift
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154

Image analysis using deep-learning over a distributed platform such as spark in the Marenostrum
Cruz de la Cruz, Stalin Leonel ; Torres Viñals, Jordi ; Tous Liesa, Rubén ; et al.
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Recercat. Dipósit de la Recerca de Catalunya
instname

Imatges -- Anàlisi supercomputadoras spark Machine learning Aprenentatge automàtic deep learning …
Dissertation
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155

Improving Document Digitization with Machine Learning-Based OCR
Sri Charitha Pagadala ; Pulletikurthi Nithisha ; Pallikonda Rahul ; et al.
International Journal on Science and Technology. 16

Fachzeitschrift
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156

Leveraging Large Language Model for Information Retrieval-based Bug Localization
Asad, Moumita ; Yasir, Rafed Muhammad ; Geramirad, Armin ; et al.

Software Engineering (cs... FOS: Computer and inform...
Fachzeitschrift
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157

Applying Deep-Learning Techniques to Parallel Processes Scheduling within Heterogeneous Distributed Computational Environments
Onrubia Palacios, Jordi Ricard ; Guirado Fernández, Fernando ; Lérida Monsó, Josep Lluís ; et al.

Deep Learning Scheduling Distributed Computing Programació Computació distribuïda
Dissertation
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158

Monitoreo de afluencia de personas utilizando aprendizaje profundo
Bechara Echeverry, Lucas ; Parra Giraldo, Steven ; López Sotelo, Jesús Alfonso
instname:Universidad Autónoma de Occidente ; reponame:Repositorio Institucional UAO ; [1] "Script", Es.wikipedia.org. [En línea]. Disponible: https://es.wikipedia.org/wiki/Script. [Accedido: 18- Feb- 2019]. [2] "Tipado dinámico", Es.wikipedia.org. [En línea]. Disponible: https://es.wikipedia.org/wiki/Tipado_din %C3 %A1mico. [Accedido: 18- Feb2019]. [3] Marr, B. (2016). Forbes Welcome. Forbes.com. Accedido el 2 de febrero de 2018, Obtenido de; https://www.forbes.com/sites/bernardmarr/2016/12/29/4- amazing-ways-facebook-uses-deep-learning-to-learn-everything-aboutyou/2/1d8933493090. [4] A. Ouaknine, Review of Deep Learning Algorithms for Image Classification", Medium, 2018. [En linea]. Disponible en: https://medium.com/zylapp/review-ofdeep-learning-algorithms-for-image-classification-5fdbca4a05e2. [Accedido el:22- Jun- 2019]. [5] A. Krizhevsky, I. Sutskever and G. Hinton, ÏmageNet Classification with Deep Convolutional Neural Networks", Papers.nips.cc, 2019. [En linea]. Disponible: https://papers.nips.cc/paper/4824-imagenet-classificationwith-deep-convolutional-neural-networks. [Accedido: 22- Jun- 2019]. [6] ....

Ingeniería Mecatrónica Redes neuronales (Comput... Inteligencia artificial Deep learning Neural networks (Compute... Artificial intelligence
Dissertation
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159

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160

Learning Spark SQL
Sarkar, Aurobindo ; Sarkar, Aurobindo ; Sarkar, Aurobindo ; et al.

E-Ressource
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