Tópicos Especiais em Sistemas de Informação: Minicursos SBSI 2022

Autores

Rafael Dias Araújo (ed)
UFU
Mariangela Setti (ed)
UTFPR
Rita Cristina G. Berardi (ed)
UTFPR
Alexandre R. Graeml (ed)
UTFPR

Palavras-chave:

SBSI 2022, Sistemas de Informação

Sinopse

O Livro de Minicursos ministrados no XVIII Simpósio Brasileiro de Sistemas de Informação (SBSI 2022) aborda conteúdos relacionados a técnicas de processamento de linguagem natural, dentre elas, a utilização de algoritmos de deep learning (redes neurais profundas), e apresenta também uma abordagem para organizar hackathons corporativas. No primeiro capítulo, intitulado “Como Organizar uma Hackathon Corporativa?, apresenta um processo para condução de hackathons corporativas, permitindo que os participantes adotem essa ferramenta para melhorar os processos de trabalho, produtos e serviços de suas organizações, sob a ótica de inovação. Já o segundo e último capítulo, intitulado “Deep Learning para Processamento de Linguagem Natural”, é apresentado um levantamento de técnicas de processamento de linguagem natural, a fim de fornecer um panorama sobre os principais direcionamentos utilizados no processamento de textos atualmente.

Capítulos

Downloads

Não há dados estatísticos.

Referências

Antonio, N. P., Fornazin, M., Araujo, R. M., Santos, R. P. (2020) “Metodologia de Pesquisa - Estudo de Caso Interpretativo em Sistemas de Informação”. In: J. M. David, P. M. Menezes, S. Ávila e Silva. (Org.). Tópicos em Sistemas de Informação: Minicursos SBSI 2019, SBC, pp. 53-79, doi: 10.5753/sbc.480.9.03. https://doi.org/10.5753/sbc.480.9.03

Antonio, N. P., Fornazin, M., Araujo, R. M., Santos, R. P. (2021) “An Interpretative Case Study on the Scalability of Social Information Systems? The Case of "Bolsa Família" Program”, iSys - Revista Brasileira de Sistemas de Informação 14(4):100-130, doi: 10.5753/isys.2021.2003. https://doi.org/10.5753/isys.2021.2003

Arndt, J. M., Dibbern, J. (2006) “Co-Innovation in a Service Oriented Strategic Network”, In: Proceedings of the 2006 IEEE International Conference on Services Computing (SCC'06), Chicago, IL, USA, pp. 285-288, doi: 10.1109/SCC.2006.32. https://doi.org/10.1109/SCC.2006.32

Beltagy, I., Peters, M. E., and Cohan, A. (2020). Longformer: The long-document transformer. ArXiv, abs/2004.05150.

Bengio, Y., Ducharme, R., and Vincent, P. (2000). A neural probabilistic language model. Advances in Neural Information Processing Systems, 13.

Biffl, S., Aurum, A., Boehm, B., Erdogmus, H., Grünbacher, P. (2006) “Value-Based Software Engineering”. Springer-Verlag, doi: 10.1007/3-540-29263-2. https://doi.org/10.1007/3-540-29263-2

Boehm, B. (2006) “A View of 20th and 21st Century Software Engineering”, In: Proceedings of the 28th International Conference on Software Engineering, Shanghai, China, pp. 12-29, doi: 10.1145/1134285.1134288. https://doi.org/10.1145/1134285.1134288

Boley, H., Chang, E. (2007) “Digital Ecosystems: Principles and Semantics”, In: Proceedings of the 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, Cairns, QLD, Australia, pp. 398-403, doi: 10.1109/DEST.2007.372005. https://doi.org/10.1109/DEST.2007.372005

Boscarioli, C., Araujo, R. M., Maciel, R. S. (2017), “I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016-2026”, Comissão Especial de Sistemas de Informação (CESI), Sociedade Brasileira de Computação (SBC), doi: 10.5753/sbc.2884.0. https://doi.org/10.5753/sbc.2884.0

Bosch, J., Bosch-Sijtsema, P. (2010) “From Integration to Composition: On the Impact of Software Product Lines, Global Development and Ecosystems”, The Journal of Systems and Software 83(1):67-76, doi: 10.1016/j.jss.2009.06.051. https://doi.org/10.1016/j.jss.2009.06.051

Busby, B., Lesko, M., Federer, L. (2016) “Closing gaps between open software and public data in a hackathon setting: user-centered software prototyping”, F1000Research 5(2016):672, doi: 10.12688/f1000research.8382.2. https://doi.org/10.12688/f1000research.8382.2

Chesbrough, H. W. (2003) “Open Innovation: The New Imperative for Creating and Profiting From Technology”. Harvard Business School Press.

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724–1734, Stroudsburg, PA, USA. Association for Computational Linguistics.

Chollet, F. (2017). Deep learning with Python. Simon and Schuster, Shelter Island, NY 11964, 1 edition.

Costa, L. A., Fontão, A., Santos, R. P. (2021) “Toward Proprietary Software Ecosystem Governance Strategies Based on Health Metrics”, IEEE Transactions on Engineering Management, doi: 10.1109/TEM.2021.3116531. https://doi.org/10.1109/TEM.2021.3116531

Cukierman, H. L., Teixeira, C., Prikladnicki, R. (2007) “Um Olhar Sociotécnico sobre a Engenharia de Software”, Revista de Informática Teórica e Aplicada 14(2):207-227, doi: 10.22456/2175-2745.5696. https://doi.org/10.22456/2175-2745.5696

Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1(Mlm):4171– 4186.

Díaz-Guerra, L., Daher Adegas, F., Stoustrup, J., and Monros, M. (2012). Adaptive control algorithm for improving power capture of wind turbines in turbulent winds. In 2012 American Control Conference (ACC), pages 5807–5812.

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2):179–211.

Ferreira, C. A., Werner, C. M. L., Barros, M. O. (2006) “Gerência de Carteiras de Componentes: Uma Abordagem Baseada em Valor”, In: Anais do VI Workshop de Desenvolvimento Baseado em Componentes (WDBC), Recife, Brasil, pp. 22-29.

Firth, J. R. (1957). A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis.

Fontão, A., Cleger-Tamayo, S., Wiese, I., Santos, R. P., Dias-Neto, A. C. (2021) “A Developer Relations (DevRel) model to govern developers in Software Ecosystems”, Journal of Software-Evolution and Process, e2389, doi: doi.org/10.1002/smr.2389. https://doi.org/doi.org/10.1002/smr.2389

Goldman, R., Gabriel, R. P. (2005) “Innovation Happens Elsewhere: Open Source as Business Strategy”. Morgan Kaufmann.

Goyal, P., Pandey, S., and Jain, K. (2018). Deep Learning for Natural Language Processing. Apress, Berkeley, CA.

Graciano Neto, V V., Santos, R. P., Viana, D., Araujo, R. (2020) “Towards a Conceptual Model to Understand Software Ecosystems Emerging from Systems-of-Information Systems”, In: Santos R., Maciel C., Viterbo J. (eds) Software Ecosystems, Sustainability and Human Values in the Social Web. WAIHCWS 2017, WAIHCWS 2018. Communications in Computer and Information Science, vol 1081. Springer, Cham. https://doi.org/10.1007/978-3-030-46130-0_1.

Han, D., Liu, Q., and Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95:43–56.

Hanssen, G. K. (2012) “A Longitudinal Case Study of an Emerging Software Ecosystem: Implications for Practice and Theory”, The Journal of Systems and Software 85(7):1455-1466, doi: 10.1016/j.jss.2011.04.020. https://doi.org/10.1016/j.jss.2011.04.020

Harris, Z. S. (1954). Distributional Structure. WORD, 10(2-3):146–162.

Hassan, H., Aue, A., Chen, C., Chowdhary, V., Clark, J., Federmann, C., Huang, X., Junczys-Dowmunt, M., Lewis, W., Li, M., et al. (2018). Achieving human parity on automatic chinese to english news translation. arXiv preprint arXiv:1803.05567.

Herala, A., Kokkola, J., Kasurinen, J., Vanhala, E. (2019) “Strategy for data: Open it or hack it?”, Journal of Theoretical and Applied Electronic Commerce Research 14(2): 33-46, https://dl.acm.org/doi/abs/10.5555/3289217.3289221.

Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8):1735–1780.

Iansiti, M., Levien, R. (2004) “Strategy as Ecology”, Harvard Business Review 82(3):68-78, 126, PMID: 15029791, https://hbr.org/2004/03/strategy-as-ecology.

Johnson, R. and Zhang, T. (2015). Effective use of word order for text categorization with convolutional neural networks. In Mihalcea, R., Chai, J. Y., and Sarkar, A., editors, NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015, pages 103–112. The Association for Computational Linguistics.

Kim, Y. (2014). Convolutional neural networks for sentence classification. In Moschitti, A., Pang, B., and Daelemans, W., editors, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1746–1751. ACL.

Kimbell, L. (2011) “Rethinking Design Thinking: Part I”, Design and Culture 3(3):285-306, doi: 10.2752/175470811X13071166525216. https://doi.org/10.2752/175470811X13071166525216

Kosmajac, D. and Kešelj, V. (2019). Automatic text summarization of news articles in serbian language. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), pages 1–6. IEEE.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90.

Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2020). Albert: A lite bert for self-supervised learning of language representations. ArXiv,abs/1909.11942.

Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692.

Luz, P. B. V., Fernandes, J., Valença, G., Santos, R. P. (2020) “Exploring Sustainability in Real Cases of Emerging Small-to-Medium Enterprises Ecosystems”, In: Santos R., Maciel C., Viterbo J. (eds) Software Ecosystems, Sustainability and Human Values in the Social Web. WAIHCWS 2017, WAIHCWS 2018. Communications in Computer and Information Science, vol 1081. Springer, Cham, doi: 10.1007/978-3-030-46130-0_3. https://doi.org/10.1007/978-3-030-46130-0_3

McCloskey, M. and Cohen, N. J. (1989). Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem. Psychology of Learning and Motivation - Advances in Research and Theory, 24(C):109–165.

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient Estimation of Word Representations in Vector Space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013b). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems.

Nolte, A., Pe-Than, E. P. P., Filippova, A., Bird, C., Scallen, S., Herbsleb, J. D. (2018) “You Hacked and Now What? Exploring Outcomes of a Corporate Hackathon”, In: Proceedings of the ACM Human-Computer Interaction 2, CSCW, Article 129 (November), 23 pages, doi: 10.1145/3274398. https://doi.org/10.1145/3274398

Paiva, E., Paim, A., and Ebecken, N. (2021). Convolutional neural networks and long short-term memory networks for textual classification of information access requests. IEEE Latin America Transactions, 19(5):826–833.

Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In 30th International Conference on Machine Learning, ICML 2013, number PART 3, pages 2347–2355. PMLR.

Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Stroudsburg, PA, USA. Association for Computational Linguistics.

Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. In NAACL HLT 2018 – 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, volume 1, pages 2227–2237, Stroudsburg, PA, USA. Association for Computational Linguistics.

Pe-Than, E. P P., Nolte, A., Filippova, A., Bird, C., Scallen, S., Herbsleb, J. D. (2019) “Designing Corporate Hackathons with a Purpose: The Future of Software Development”, IEEE Software 36(1):15-22, doi: 10.1109/MS.2018.290110547. https://doi.org/10.1109/MS.2018.290110547

Pinheiro, M. C., Chueri, L. O. V., Santos, R. P. (2020) “Identifying Topics and Difficulties on Collaboration in Social Innovation Environments”, In: Proceedings of the XVI Brazilian Symposium on Information Systems (SBSI’20), São Bernardo do Campo, Brazil, pp. 1-8 (Article 5), doi: 10.1145/3411564.3411581. https://doi.org/10.1145/3411564.3411581

Pinheiro, M. C., Chueri, L. O. V., Santos, R. P. (2021) “Investigando Colaboração em Ecossistemas”, In: Anais do VI Workshop sobre Aspectos Sociais, Humanos e Econômicos de Software (WASHES), Florianópolis, Brasil, pp. 11-20, doi: 10.5753/washes.2021.15885. https://doi.org/10.5753/washes.2021.15885

Porras, J., Khakurel, J., Ikonen, J., Happonen, A., Knutas, A., Herala, A., “Hackathons in Software Engineering Education - Lessons Learned from a Decade of Events”, In: Proceedings of the 2018 IEEE/ACM International Workshop on Software Engineering Education for Millennials (SEEM), Gothenburg, Sweden, pp. 40-47, https://ieeexplore.ieee.org/document/8442127.

Raatikainen, M., Komssi, M., Bianco, V. d., Kindstöm, K., Järvinen, J. (2013) “Industrial Experiences of Organizing a Hackathon to Assess a Device-centric Cloud Ecosystem”, In: Proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference, Kyoto, Japan, pp. 790-799, doi: 10.1109/COMPSAC.2013.130. https://doi.org/10.1109/COMPSAC.2013.130

Radfort, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). (OpenAI Transformer): Improving Language Understanding by Generative Pre-Training. OpenAI, pages 1–10.

Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.

Rosell, B., Kumar, S., Shepherd, J. (2014) “Unleashing innovation through internal hackathons”, In: Proceedings of the 2014 IEEE Innovations in Technology Conference, Warwick, RI, USA, pp. 1-8, doi: 10.1109/InnoTek.2014.6877369. https://doi.org/10.1109/InnoTek.2014.6877369

Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108.

Santos, R. P. (2010) “Brechó-VCM: Uma Abordagem Baseada em Valor para Mercados de Componentes”. Dissertação, Mestrado em Engenharia de Sistemas e Computação, COPPE/UFRJ, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brasil, doi: 10.13140/RG.2.2.32705.07525. https://doi.org/10.13140/RG.2.2.32705.07525

Santos, R. P. (2013) “Engenharia e Gerenciamento de Ecossistemas de Software”. Exame de Qualificação, Doutorado em Engenharia de Sistemas e Computação, COPPE/UFRJ, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brasil, http://reuse.cos.ufrj.br/media/publicacoes/qualificacao/EQ_RodrigoSantos.pdf.

Santos, R. P. (2016) “Managing and Monitoring Software Ecosystem to Support Demand and Solution Analysis”. Tese, Doutorado em Engenharia de Sistemas e Computação, COPPE/UFRJ, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brasil, doi: 10.13140/RG.2.2.13391.18082. https://doi.org/10.13140/RG.2.2.13391.18082

Santos, R. P., Fontão, A. L., Fernandes, J. C. (2020) “Ecossistemas de Software”. In: Maciel, C.; Viterbo, J. (Org.). Computação e Sociedade: A Tecnologia, EdUFMT, v. 3, pp. 198-223.

Saravi, S., Joannou, D., Kalawsky, R. S., King, M. R. N., Marr, I., Hall, M., Wright, P. C. J., Ravindranath, R., Hill, A. (2018) “A Systems Engineering Hackathon – A Methodology Involving Multiple Stakeholders to Progress Conceptual Design of a Complex Engineered Product”, IEEE Access 6(2018):38399-38410, doi: 10.1109/ACCESS.2018.2851384 https://doi.org/10.1109/ACCESS.2018.2851384

Steglich, C., Santos, R. P., Marczak, S., Perin, M., Mosmann, L. H., Guerra, L. P., Souza, C. R. B., Figueira Filho, F. (2021) “An Investigation of the Strengths, Weaknesses, Opportunities and Threats in the Business Dimension for Develpers in Mobile Software Ecosystems”, iSys - Revista Brasileira de Sistemas de Informação 14(4):74-99, doi: 10.5753/isys.2021.2015. https://doi.org/10.5753/isys.2021.2015

Sun, C., Qiu, X., Xu, Y., and Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11856 LNAI(2):194–206.

Teixeira, C. A. N., Cukierman, H. L. (2007) “Por que Falham os Projetos de Implantação de Processos de Software?”, In: Anais do III Workshop Um Olhar Sociotécnico sobre a Engenharia de Software (WOSES), em conjunto com VI Simpósio Brasileiro de Qualidade de Software, Porto de Galinhas, Brasil, pp. 1-12.

Valença, G., Kneuper, R., Rebelo, M. (2020) “Privacy in Software Ecosystems-An Initial Analysis of Data Protection Roles and Challenges”, In: Proceedings of the 2020 46th Euromicro Conference on Software Engineering and Advanced Applications, Portoroz, Slovenia, pp. 120-123, doi: 10.1109/SEAA51224.2020.00028. https://doi.org/10.1109/SEAA51224.2020.00028

Valença, G., Lacerda N., Rebelo M. E., Alves C., de Souza C. R. B. (2019) “On the Benefits of Corporate Hackathons for Software Ecosystems – A Systematic Mapping Study”. In: Franch X., Männistö T., Martínez-Fernández S. (eds) Product-Focused Software Process Improvement. PROFES 2019. Lecture Notes in Computer Science, Springer, v. 11915, pp. 367-382, doi: 10.1007/978-3-030-35333-9_27. https://doi.org/10.1007/978-3-030-35333-9_27

Valença, G., Lacerda, N., de Souza, C. R. B., Gama, K. (2020) “A Systematic Mapping Study on the Organisation of Corporate Hackathons”, In: Proceedings of the 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, pp. 421-428, doi: 10.1109/SEAA51224.2020.00074. https://doi.org/10.1109/SEAA51224.2020.00074

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 2017-Decem(Nips):5999–6009.

Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning - ICML ’08, pages 1096–1103, New York, New York, USA. ACM Press.

Yadav, A. and Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6):4335–4385.

Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A. (2020). Big bird: Transformers for longer sequences. In Advances in Neural Information Processing Systems, volume 33, pages 17283–17297.

Zhang, X., Zhao, J. J., and LeCun, Y. (2015). Character-level convolutional networks for text classification. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 649–657.

Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., and Xie, X. (2016). Cooccurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pages 3697–3703.

Data de publicação

16/05/2022

Licença

Creative Commons License

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.

Detalhes sobre o formato disponível para publicação: Volume Completo

Volume Completo

ISBN-13 (15)

978-65-87003-90-0