يعرض 1 - 20 نتائج من 94 نتيجة بحث عن '"conocimiento implícito"', وقت الاستعلام: 0.69s تنقيح النتائج
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    Dissertation/ Thesis

    المؤلفون: He, Qiaoling

    المساهمون: University/Department: Universitat Rovira i Virgili. Departament d'Estudis Anglesos i Alemanys

    Thesis Advisors: Oltra Massuet, Maria Isabel

    المصدر: TDX (Tesis Doctorals en Xarxa)

    وصف الملف: application/pdf

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    Conference
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    Academic Journal
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    Academic Journal
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    Academic Journal

    المصدر: Propósitos y Representaciones. Journal of Educational Psychology; Vol. 8 No. 3 (2020): Setiembre - Diciembre: Gestión educacional y competencias docentes; e518 ; Propósitos y Representaciones; ##issue.vol## 8 ##issue.no## 3 (2020): Setiembre - Diciembre: Gestión educacional y competencias docentes; e518 ; Propósitos y Representaciones; Vol. 8 Núm. 3 (2020): Setiembre - Diciembre: Gestión educacional y competencias docentes; e518 ; 2310-4635 ; 2307-7999 ; 10.20511/pyr2020.v8n3

    وصف الملف: application/pdf

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    Dissertation/ Thesis

    المؤلفون: Díaz Chica, Luis Felipe

    المساهمون: Garzón A, Wilmer, Benavides Navarro, Luis Daniel

    وصف الملف: 101 páginas; application/pdf

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A general path-based repre- sentation for predicting program properties; Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., J., S., Fadhel, M.A., Al-Amidie, M., Farhan, L., 2021. Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions - journal of big data.; he future of cloud development - Ampt — getampt.com. https://www. getampt.com/blog/introducing-ampt/.; Aviv, I., Gafni, R., Sherman, S., Aviv, B., Sterkin, A., Bega, E., 2023. Infrastructure from code: The next generation of cloud lifecycle automation. IEEE Software 40, 42–49.; Babar, M., Gorton, I., Jeffery, R., 2005. Capturing and using software architec- ture knowledge for architecture-based software development, in: Fifth Interna- tional Conference on Quality Software (QSIC’05), pp. 169–176.; Becker, M., Liang, S., Frank, A., 2021. 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Expert Systems with Applications; Within-project defect prediction of infrastructure-as-code using product and process metrics. IEEE Transactions on Software Engineering 48, 2086–2104; Dalla Palma, S., Di Nucci, D., Tamburri, D.A., 2020. Ansiblemetrics: A python library for measuring infrastructure-as-code blueprints in ansible.; De Lauretis, L., 2019. From monolithic architecture to microservices architecture, in: 2019 IEEE International Symposium on Software Reliability Engineering Work- shops; Du, X., Cai, Y., Wang, S., Zhang, L., 2016. Overview of deep learning, in: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation; Fadlullah, Z.M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., Mizutani, K., 2017. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems; Fehling, C., Leymann, F., Retter, R., Schupeck, W., Arbitter, P., 2014. Cloud computing patterns. 2014 ed., Springer, Vienna, Austria.; Feitosa, D., Penca, M.T., Berardi, M., Boza, R.D., Andrikopoulos, V., 2023. Mining for cost awareness in the infrastructure as code artifacts of cloud-based applica- tions: an exploratory study.; Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., Zhou, M., 2020. Codebert: A pre-trained model for programming and natural languages.; Galassi, A., Lippi, M., Torroni, P., 2021. Attention in natural language processing. IEEE Transactions on Neural Networks and Learning Systems; Gamma, E., Helm, R., Larman, C., Johnson, R., Vlissides, J., 2005. Valuepack: Design Patterns:Elements of Reusable Object-Oriented Software with Applying UML and Patterns:An Introduction to Object-Oriented Analysis and Design and References 83 Iterative Development. Addison Wesle; Georgousis, S., Kenning, M.P., Xie, X., 2021. Graph deep learning: State of the art and challenges. IEEE Access 9, 22106–22140; Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014.; Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T., 2018.; Guerriero, M., Garriga, M., Tamburri, D.A., Palomba, F., 2019. Adoption, support, and challenges of infrastructure-as-code: Insights from industry, in: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME); Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J., 2022. Unixcoder: Unified cross-modal pre-training for code representation; Clement, C., Drain, D., Sundare- san, N., Yin, J., Jiang, D., Zhou, M., 2021. Graphcodebert: Pre-training code representations with data flow; Hao, W., Bie, R., Guo, J., Meng, X., Wang, S., 2018. Optimized cnn based image recognition through target region selection.; Hasan, M.M., Bhuiyan, F.A., Rahman, A., 2020. Testing practices for infrastructure as code, in: Proceedings of the 1st ACM SIGSOFT International Workshop on Languages and Tools for Next-Generation Testing, Association for Computing Machinery, New York, NY, USA. p. 7–12; Joshi, A.V., 2020. Amazon’s Machine Learning Toolkit: Sagemaker. Springer In- ternational Publishing, Cham. pp. 233–243. URL; Kagdi, H., Collard, M.L., Maletic, J.I., 2007. A survey and taxonomy of approaches for mining software repositories in the context of soft- ware evolution. Journal of Software Maintenance and Evolution: Re- search and Practice 19, 77–131.; Kaliyar, R.K., 2020. A multi-layer bidirectional transformer encoder for pre-trained word embedding: A survey of bert, in: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 336–340.; Karamanolakis, G., Mukherjee, S., Zheng, G., Awadallah, A.H., 2021. Self-training with weak supervision. CoRR abs/2104.05514; arras, T., Aila, T., Laine, S., Lehtinen, J., 2017. Progressive growing of gans for improved quality, stability, and variation. CoRR abs/1710.10196.; Keery, S., Harber, C., Young, M., 2019. Implementing Cloud Design Patterns for AWS: Solutions and design ideas for solving system design problems. Packt Pub- lishing, Limited; Kovalenko, V., Bogomolov, E., Bryksin, T., Bacchelli, A., 2019. Pathminer: A library for mining of path-based representations of code, in: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), pp. 13– 17; Land, L., Aurum, A., Handzic, M., 2001. Capturing implicit software engineering knowledge, in: Proceedings 2001 Australian Software Engineering Conference, pp. 108–114.; Linthicum, D.S., 2017. Cloud-native applications and cloud migration: The good, the bad, and the points between. IEEE Cloud Computing 4, 12–14; Liu, Y., Agarwal, S., Venkataraman, S., 2021. Autofreeze: Automatically freezing model blocks to accelerate fine-tuning; Maffort, C., Valente, M.T., Bigonha, M., Hora, A., Anquetil, N., Menezes, J., 2013. Mining Architectural Patterns Using Association Rules, in: International Con- ference on Software Engineering and Knowledge Engineering (SEKE’13), Boston, United States; Mistrik, I., Bahsoon, R., Ali, N., Heisel, M., Maxim, B., 2017. Software architecture for Big Data and the cloud.; Niu, C., Li, C., Ng, V., Ge, J., Huang, L., Luo, B., 2022. Spt-code: Sequence-to- sequence pre-training for learning source code representations, in: Proceedings of the 44th International Conference on Software Engineering, Association for Computing Machinery, New York, NY, USA. p. 2006–2018; Opdebeeck, R., Zerouali, A., Velázquez-Rodríguez, C., De Roover, C., 2021. On the practice of semantic versioning for ansible galaxy roles: An empiri- cal study and a change classification model. Journal of Systems and Software 182, 111059; Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., Ward, R., 2016. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, 694–707.; Perez., Q., Borgne., A.L., Urtado., C., Vauttier., S., 2021. Towards profiling runtime architecture code contributors in software projects, in: Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Soft- ware Engineering; Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., 2019a. Language models are unsupervised multitask learners.; Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al., 2019b. Language models are unsupervised multitask learners. OpenAI blog 1, 9.; Rahman, A., Mahdavi-Hezaveh, R., Williams, L., 2019. A systematic mapping study of infrastructure as code research. Information and Software Technology 108, 65–77; The RedMonk Programming Language Rankings: Jan- uary 2023 — redmonk.com; Rühling Cachay, S., Boecking, B., Dubrawski, A., 2021. End-to-end weak supervi- sion, in: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc. pp. 1845–1857; Salehinejad, H., Sankar, S., Barfett, J., Colak, E., Valaee, S., 2018. Recent advances in recurrent neural networks.; Savidis, A., Savvaki, K., 2021. Software architecture mining from source code with dependency graph clustering and visualization; Schmidt, F., MacDonell, S.G., Connor, A.M., 2014. An automatic architecture re- construction and refactoring framework, in: International Conference on Software Engineering Research and Applications.; Schuster, M., Paliwal, K., 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing 45, 2673–2681.; Sehovac, L., Grolinger, K., 2020. Deep learning for load forecasting: Sequence to sequence recurrent neural networks with attention. IEEE Access 8, 36411–36426; Sharma, A., Kumar, M., Agarwal, S., 2015. A complete survey on software archi- tectural styles and patterns. Procedia Computer Science 70, 16–28; Sharma, S., Sharma, S., Athaiya, A., 2017. Activation functions in neural networks. Towards Data Sci 6, 310–316; Shin, C., Li, W., Vishwakarma, H., Roberts, N.C., Sala, F., 2021. Universalizing weak supervision. CoRR abs/2112.03865; Shrestha, A., Mahmood, A., 2019. Review of deep learning algorithms and archi- tectures. IEEE Access 7, 53040–53065.; Siow, J.K., Liu, S., Xie, X., Meng, G., Liu, Y., 2022. Learning program seman- tics with code representations: An empirical study, in: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp.; Smite, D., Moe, N.B., Levinta, G., Floryan, M., 2019. Spotify guilds: How to succeed with knowledge sharing in large-scale agile organizations. IEEE Software 36, 51–57.; Sriram, A., Jun, H., Satheesh, S., Coates, A., 2017. Cold fusion: Training seq2seq models together with language models.; Sundararaman, D., Subramanian, V., Wang, G., Si, S., Shen, D., Wang, D., Carin, L., 2019. Syntax-infused transformer and bert models for machine translation and natural language understanding.; Taibi, D., El Ioini, N., Pahl, C., Niederkofler, J.R.S., 2020. Serverless cloud com- puting (function-as-a-service) patterns: A multivocal literature review, in: Pro- ceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER’20); Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2017. Attention is all you need; Wan Mohd Isa, W.A.R., Suhaimi, A.I.H., Noordin, N., Harun, A., Ismail, J., Teh, R., 2019. Cloud computing adoption reference model. 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    المؤلفون: Marie Nadeau, Carole Fisher

    المصدر: Recercat. Dipósit de la Recerca de Catalunya
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    Bellaterra: journal of teaching and learning language and literature; Vol. 4, Núm. 4 (2011): Vol.: 4 Núm.: 4; p. 1-31
    Bellaterra Journal of Teaching & Learning Language & Literature; Vol. 4, Núm. 4 (2011): Vol.: 4 Núm.: 4; p. 1-31
    Recercat: Dipósit de la Recerca de Catalunya
    Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
    Bellaterra Journal of Teaching & Learning Language & Literature, Vol 4, Iss 4 (2011)
    Dipòsit Digital de Documents de la UAB
    Universitat Autònoma de Barcelona

    وصف الملف: application/pdf