Learning Journey on Informatics 2025

Abdin M, Aneja J, Awadalla H, Awadallah A, Awan A A, Bach N, et al. (2024). Phi-3 technical report: A highly capable language model locally on your phone. arXiv:2404.14219 v4 [cs.CL].

Abu-Mostafa, Y. S., Magdon-Ismail, M., and Lin, H.-T. (2012). Learning From Data: A Short Course. AMLBook.

Aftan S, Shah H (2023). A survey on BERT and its applications. Em 2023 20th Learning and Technology Conference (LT), p. 161–166.

Ahmad, K., Iqbal, W., El-Hassan, A., Qadir, J., Benhaddou, D., Ayyash, M., and Al-Fuqaha, A. (2024). Data-Driven Artificial Intelligence in Education: A Comprehensive Review. IEEE Transactions on Learning Technologies, 17:12–31.

Ainslie J, Lee Thorp J, de Jong M, Zemlyanskiy Y, Lebrón F, Sanghai S (2023). GQA: Training generalized multi-query transformer models from multi-head checkpoints. arXiv:2305.13245 [cs.CL].

Albrecht, M. R., Celi, S., Dowling, B., and Jones, D. (2023). Practically-exploitable cryptographic vulnerabilities in matrix. Proceedings of the IEEE Symposium on Security and Privacy, pages 164–181.

Almeida, S. D. C. D. D. and Soares, T. A. (2022). Os impactos da lei geral de proteção de dados-lgpd no cenário digital. Perspectivas em Ciência da Informação, 27:26–45.

Amdahl, G. M. (1967). Validity of the single processor approach to achieving large scale computing capabilities. In Proceedings of the April 18-20, 1967, spring joint computer conference, pages 483–485.

Andrews, G. R. (1999). Foundations of Parallel and Distributed Programming. Addison-Wesley Longman Publishing Co., Inc., USA, 1st edition.

Anil R, Dai A M, Firat O, Johnson M, Lepikhin D, Passos A, et al. (2023). PaLM 2 technical report. arXiv:2305.10403 [cs.CL].

ArgerichMF, Patiño MartínezM(2024). Measuring and improving the energy efficiency of large language models inference. IEEE Access, 12:80194–80207.

Authority, L. (2024). Security audit report: vodozemac. Technical report, Least Authority.

Bahdanau D, Cho K, Bengio Y (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

Bai X,Wang A, Sucholutsky I, Griffiths T L (2024). Measuring implicit bias in explicitly unbiased large language models. arXiv preprint arXiv:2402.04105.

Banerjee S, Lavie A (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Em Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, p. 65–72.

Barbetta, P. A., Reis, M. M., and Bornia, A. C. (2010). Estatística Para Cursos de Engenharia e Informática. Atlas, São Paulo, 3 edition.

Barocas, S., Hardt, M., and Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org. Disponível em: [link].

Baroni M (2021). On the proper role of linguistically-oriented deep net analysis in linguistic theorizing. CoRR, abs/2106.08694.

Barros, J., Moraes, L. O., Oliveira, F., and Delgado, C. A. D. M. (2025). Large language models generating feedback for students of introductory programming courses. In Proceedings of the Artificial Intelligence in Education Conference, Palermo, Italy. Springer. A ser publicado.

Bechara, J. and Lechner, U. (2024). Digital sovereignty and open-source software: A discussion paper. Digital Policy, Regulation and Governance.

Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., and Santos, E. A. (2022). Programming is hard - or at least it used to be: Educational opportunities and challenges of ai code generation. Arxiv. Available at [link].

Belle, V. and Papantonis, I. (2021). Principles and practice of explainable machine learning. Frontiers in Big Data, 4.

Birhane, A. (2021). Algorithmic injustice: a relational ethics approach. Patterns, 2(2).

Biström, M., Adolfsson, K. K., and Stocchetti, M. (2024). Opensource software and digital sovereignty: A technical case study on alternatives to mainstream tools. Technology and Regulation.

Boehm, H.-J. (2005). Threads cannot be implemented as a library. SIGPLAN Not., 40(6).

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020). Language models are few-shot learners. arXiv. Available at [link].

Buolamwini, J. and Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018), pages 77–91. PMLR.

Burns, A. and Wellings, A. (1998). Concurrency in Ada (2nd ed.). Cambridge University Press, USA.

Cai T, Herner K, Yang T, Wang M, Acosta Flechas M, Harris P, et al. (2023). Accelerating machine learning inference with GPUs in ProtoDUNE data processing. Computing and Software for Big Science, 7(1).

Campbell, M., Jr., A. J. H., and hsiung Hsu, F. (2000). Deep blue. IBM Journal of Research and Development, 44(3):213–228.

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133):20180080.

Cavaleiro, G. G. H. and Du Bois, A. R. (2014). Ferramentas modernas para programação multicore. In Salgado, A. C., Lóscio, B. F., Alchieri, E., and Barreto, P. S., editors, Atualizações em Informática 2014, volume 1, pages 41–83. SBC, Porto Alegre. 144

Cavaleiro, G. G. H. and Santos, R. R. (2007). Multiprogramação leve em arquiteturas multi-core. In Kowaltowski, T. and Breitman, K. K., editors, Atualizações em Informática 2007, pages 327–379. PUC-Rio, Rio de Janeiro.

Cavalheiro, G. G. H. (2009). Programação com pthreads. In Mattos, J. C. B., Da Rosa Junior, L. S., and Pilla, M. L., editors, Desafios e Avanços em Computação: O Estado da Arte, pages 137–151. ditora e Gráfica Universitária - PREC UFPel, Pelotas.

Chandra, R., Dagum, L., Kohr, D., Maydan, D., McDonald, J., and Menon, R. (2001). Parallel programming in OpenMP. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, et al. (2024). A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol., 15(3).

Chen, B., Zhang, Z., Langrené, N., and Zhu, S. (2024). Unleashing the potential of prompt engineering in large language models: A comprehensive review. arXiv. Available at [link].

Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, et al. (2023). PaLM: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1–113.

Chung H W, Hou L, Longpre S, Zoph B, Tay Y, Fedus W, et al. (2024). Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70):1–53.

Conduit (2025). Conduit: Lightweight matrix homeserver in rust. [link]. Acessado em 2025.

Cortiz D, Zubiaga A (2021). Ethical and technical challenges of AI in tackling hate speech. The International Review of Information Ethics, 29.

Cox-Buday, K. (2017). Concurrency in Go: Tools and Techniques for Developers. O’Reilly Media, Inc., 1st edition.

Cui, M., Gu, C., and Nan, G. (2023). Crosschat: Instant messaging across different apps on mobile devices. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing.

Culler, D. E., Singh, J., and Gupta, A. (1999). Parallel Computer Architecture: A Hardware/Software Approach. Morgan Kaufmann.

Cunha, L. A. (1980). Educação e desenvolvimento social no Brasil. Francisco Alves, Rio de Janeiro.

Dagdelen J, Dunn A, Lee S, Walker N, Rosen A S, Ceder G, Persson K A, Jain A (2024). Structured information extraction from scientific text with large language models. Nature Communications, 15(1):1418.

Dao T, Fu D, Ermon S, Rudra A, Ré C (2022). FlashAttention: Fast and memory-efficient exact attention with IO-awareness. Em Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, editors, Advances in Neural Information Processing Systems, volume 35, p. 16344–16359. Curran Associates, Inc.

de Carvalho, A. C. P. L. F., Menezes, A. G., and Bonidia, R. P. (2024). Ciência de Dados - Fundamentos e Aplicações. LTC.

de Oliveira F. Moraes, L. (2016). Classificação de linfomas utilizando uma abordagem baseada em Árvores de decisão. Mestrado, Universidade Federal do Rio de Janeiro (UFRJ).

de Oliveira N R, Campos G, Rodrigues I, Watanabe J A C, Couto R S, Moraes I M, Medeiros D, Mattos D M F (2025). Quantization effects on large language models for intent-based network management. Em Proceedings of the 17th International Conference on Network and Telecommunications Management Systems (NTMS), Paris, France. IEEE. A ser apresentado.

de Sousa, J. B. (2025). Modelos de linguagem em larga escala na geração de feedbacks para alunos de cursos introdutórios de programação da ufrj. Trabalho de conclusão de curso, Universidade Federal do Rio de Janeiro (UFRJ), Escola Politécnica, Rio de Janeiro.

de Vries A (2023). The growing energy footprint of artificial intelligence. Joule, 7(10):2191–2194.

Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A., and Sarsa, S. (2024). Computing education in the era of generative ai. Communications of the ACM, 67(2):56–67.

Deranty J P, Corbin T (2024). Artificial intelligence and work: a critical review of recent research from the social sciences. Ai & Society, 39(2):675–691.

Divakaran D M, Peddinti S T (2024). Large language models for cybersecurity: New opportunities. IEEE Security & Privacy, p. 2–9.

Djeffal N, Kheddar H, Addou D, Mazari A C, Himeur Y (2023). Automatic speech recognition with BERT and CTC transformers: A review. Em 2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM), volume 1, p. 1–8.

Dobson, A. and Barnett, A. (2008). An Introduction to Generalized Linear Models. Chapman & Hall/CRC Texts in Statistical Science. Chapman and Hall/CRC.

Dubey A, Jauhri A, Pandey A, Kadian A, Al Dahle A, Letman A, et al. (2024). The Llama 3 herd of models.

Element (2025a). Element call integration with livekit. Accessed: 2025-05-22.

Element (2025b). Element docker demo. Accessed: 2025-05-22.

Element (2025c). Self-hosting element call. Accessed: 2025-05-22.

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.

European Commission (2020). White paper on artificial intelligence: A european approach to excellence and trust.

Falcon-LLM Team (2024). The Falcon 3 family of open models. Relatório técnico, Technology Innovation Institute (TII).

Fang G, Yin H, Muralidharan S, Heinrich G, Pool J, Kautz J, Molchanov P, Wang X (2024). MaskLLM: Learnable semi-structured sparsity for large language models. arXiv preprint arXiv:2409.17481.

Floridi, L. (2020). The fight for digital sovereignty: What it is, and why it matters, especially for the eu. Philosophy and Technology.

Foundation, M. (2020a). Docker-jitsi. Accessed: 2025-05-22.

Foundation, M. (2020b). Running matrix with jitsi. Accessed: 2025-05-22.

Foundation, M. (2023). Docker-jitsi. Accessed: 2025-05-22.

Foundation, M. (2024). Synapse metrics and monitoring. Acesso em 2025-05-16.

Foundation, M. (2025). An open network for secure, decentralised communication. Acessado em 2025.

Frantar E, Alistarh D (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. Em International Conference on Machine Learning, p. 10323–10337. PMLR.

Freire, P. (1970). Pedagogia do oprimido. Paz e Terra, Rio de Janeiro.

Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., and Wang, H. (2024). Retrieval-augmented generation for large language models: A survey.

Gašević, D., Dawson, S., and Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1):64–71.

Gemma Team, Kamath A, Ferret J, Pathak S, Vieillard N, Merhej R, et al. (2025). Gemma 3 technical report. arXiv:2503.19786 [cs.CL].

Ghielmetti N, Loncar V, Pierini M, Roed M, Summers S, Aarrestad T, et al. (2022). Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. Machine Learning: Science and Technology, 3(4):045011.

Gou J, Yu B, Maybank S J, Tao D (2021). Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789–1819.

Government, F. (2020). Tchap: The official messaging platform for french civil servants. Accessed: 2024-08-25.

Gross N (2023). What ChatGPT tells us about gender: A cautionary tale about performativity and gender biases in AI. Social Sciences, 12(8):435.

Gu Y, Dong L, Wei F, Huang M (2023). Minillm: Knowledge distillation of large language models. Em The Twelfth International Conference on Learning Representations.

Gunny A, Rankin D, Harris P, Katsavounidis E, Marx E, Saleem M, Coughlin M, Benoit W (2022). A software ecosystem for deploying deep learning in gravitational wave physics. Em Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, HPDC ’22, p. 9–17. ACM.

Guo D, Yang D, Zhang H, Song J, Zhang R, Xu R, et al. (2025). DeepSeek-R1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948.

Gupta, M., Bhargava, L., and Indu, S. (2021). Mapping techniques in multicore processors: current and future trends. The Journal of Supercomputing, 77(8):9308–9363.

Hardt, M., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), pages 3315–3323.

Hassany, M., Brusilovsky, P., Jiaze, K., Akhsueyinoglu, K., and Narayanan, A. B. L. (2024). Human-ai co-creation of worked examples for programming classes. In Workshops at the International Conference on Intelligent User Interfaces (IUI) 2024.

Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., and Liao, S. N. (2018). Predicting academic performance: a systematic literature review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 Companion, page 175–199, New York, NY, USA. ACM.

Henderson P, Sinha K, Angelard Gontier N, Ke N R, Fried G, Lowe R, Pineau J (2018). Ethical challenges in data-driven dialogue systems. Em Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’18, p. 123–129, New York, NY, USA. Association for Computing Machinery.

Hendrycks D, Burns C, Basart S, Zou A, Mazeika M, Song D, Steinhardt J (2020). Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300.

Herlihy, M., Shavit, N., Luchangco, V., and Spear, M. (2020). The Art of Multiprocessor Programming. Morgan Kaufmann, 2nd edition.

Hernández-de Menéndez, M., Morales-Menendez, R., Escobar, C. A., and Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(3):1209–1230.

High-Level Advisory Body on AI (2024). Governing ai for humanity: Report of the high-level advisory body on artificial intelligence. Acesso em: 22 maio 2025.

Huang Y, Wan L J, Ye H, Jha M, Wang J, Li Y, Zhang X, Chen D (2024). Invited: New solutions on LLM acceleration, optimization, and application. Em Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC ’24, New York, NY, USA. Association for Computing Machinery.

INEP (2023). Sistema de avaliação da educação básica (saeb). [link]. Acesso em: 2 jun. 2025.

Izbicki, R. and dos Santos, T. M. (2020). Aprendizado de máquina: uma abordagem estatística.

Jacob, F., Beer, C., Henze, N., and Hartenstein, H. (2021). Analysis of the matrix event graph replicated data type. IEEE Access, 9:28317–28333.

Jadouli A, Amrani C E (2025). Deep learning with pretrained’internal world’layers: A Gemma 3-based modular architecture for wildfire prediction. arXiv preprint arXiv:2504.18562.

James, G.,Witten, D., Hastie, T., Tibshirani, R., and Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python. Springer, 1st edition.

Jawahar G, Sagot B, Seddah D (2019). What does BERT learn about the structure of language? Em ACL 2019-57th Annual Meeting of the Association for Computational Linguistics.

Jiang Z, Carlson B, Deiana A, Eastlack J, Hauck S, Hsu S C, et al. (2024). Machine learning evaluation in the global event processor FPGA for the ATLAS trigger upgrade. Journal of Instrumentation, 19(05):P05031.

Jiao J, Afroogh S, Xu Y, Phillips C (2025). Navigating LLM ethics: Advancements, challenges, and future directions. arXiv:2406.18841v4 [cs.CY].

Kaddour J, Harris J, Mozes M, Bradley H, Raileanu R, McHardy R (2023). Challenges and applications of large language models. arXiv preprint arXiv:2307.10169.

Kamiran, F. and Calders, T. (2012). Decision theory for discrimination-aware classification. IEEE Transactions on Knowledge and Data Engineering, 25(5):833–846.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G. (2023). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.

Kazemitabaar, M., Hou, X., Henley, A., Ericson, B. J., Weintrop, D., and Grossman, T. (2024). How novices use llm-based code generators to solve cs1 coding tasks in a self-paced learning environment. In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, Koli Calling ’23, New York, NY, USA. Association for Computing Machinery.

Khoda E E, Rankin D, Teixeira de Lima R, Harris P, Hauck S, Hsu S C, et al. (2023). Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. Machine Learning: Science and Technology, 4(2):025004.

Kingdom, U. (2018). Data protection act 2018. Accessed: 2025-01-20.

Klare, M. and Lechner, U. (2023). A reference model to strengthen digital sovereignty in companies. In 2023 IEEE International Conference on Digital Ecosystems (ICDE).

Kleiman, S., Shah, D., and Smaalders, B. (1996). Programming with Threads. Sun Soft Press ; Prentice Hall, Mountain View, Calif. ; Upper Saddle River, NJ.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.

Kotek H, Dockum R, Sun D (2023). Gender bias and stereotypes in large language models. Em Proceedings of The ACM Collective Intelligence Conference, CI ’23, p. 12–24. ACM.

Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2020). ALBERT: A Lite BERT for self-supervised learning of language representations. arXiv:1909.11942v6 [cs.CL].

Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2019). BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461.

Li L J, Zhou S L, Chao F, Chang X, Yang L, Yu X, Shang C, Shen Q (2023). Model compression optimized neural network controller for nonlinear systems. Knowledge-Based Systems, 265:110311.

Li, H., Wu, Y., Huang, R., Mi, X., Hu, C., and Guo, S. (2023). Demystifying decentralized matrix communication network: Ecosystem and security. In 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), pages 260–267.

Liang P, Bommasani R, Lee T, Tsipras D, Soylu D, Yasunaga M, et al. (2023). Holistic evaluation of language models. Transactions on Machine Learning Research. Featured Certification, Expert Certification.

Libâneo, J. C. (1994). Pedagogia e pedagogos, para quê? Cortez, São Paulo.

Liffiton, M., Sheese, B. E., Savelka, J., and Denny, P. (2024). Codehelp: Using large language models with guardrails for scalable support in programming classes. In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, Koli Calling ’23, New York, NY, USA. ACM.

Lin C Y (2004). ROUGE: a package for automatic evaluation of summaries. Em Workshop on Text Summarization Branches Out, Post-Conference Workshop of ACL 2004, Barcelona, Spain, p. 74–81.

Lin J, Tang J, Tang H, Yang S, Chen W M, Wang W C, et al. (2024). AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration. Proceedings of Machine Learning and Systems, 6:87–100.

Lin T, Wang Y, Liu X, Qiu X (2022). A survey of transformers. AI open, 3:111–132.

Liu S, Lu Y, Fang W, Li M, Xie Z (2024a). OpenLLM-RTL: Open dataset and benchmark for LLM-aided design RTL generation. Em Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD ’24, p. 1–9. ACM.

Liu Z, Oguz B, Zhao C, Chang E, Stock P, Mehdad Y, et al. (2023). Llm-qat: Data-free quantization aware training for large language models. arXiv preprint arXiv:2305.17888.

Liu Z, Zhao C, Iandola F, Lai C, Tian Y, Fedorov I, et al. (2024b). MobileLLM: optimizing sub-billion parameter language models for on-device use cases. ICML’24. JMLR.org.

Loukas L, Stogiannidis I, Diamantopoulos O, Malakasiotis P, Vassos S (2023). Making LLMs worth every penny: Resource-limited text classification in banking. Em Proceedings of the Fourth ACM International Conference on AI in Finance, p. 392–400.

Luckesi, C. C. (2011). Avaliação da aprendizagem escolar: estudos e proposições. Cortez, São Paulo, 22 edition. Capítulos sobre políticas públicas de avaliação, incluindo o SAEB.

Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.

Ma X, Fang G, Wang X (2023). LLM-Pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702–21720.

Mahmud D, Hajmohamed H, Almentheri S, Alqaydi S, Aldhaheri L, Khalil R A, Saeed N (2025). Integrating LLMs with ITS: Recent advances, potentials, challenges, and future directions. IEEE Transactions on Intelligent Transportation Systems, 26(5):5674–5709.

Marx, B., Tate, B., and Valim, J. (2018). Adopting Elixir: From Concept to Production. Pragmatic Bookshelf, Dallas, TX.

Matrix Foundation (2025a). Dendrite: Next generation matrix homeserver. [link]. Acessado em 2025.

Matrix Foundation (2025b). Synapse: Reference matrix homeserver implementation. [link]. Acessado em 2025.

matrix nio (2025). nio: a multilayered matrix client library. Acessado em 2025.

Matrix.org (2022). Megolm group ratchet. [link]. libolm repository.

Mautrix (2025). mautrix-python: Matrix client library for bots and bridges. [link]. Acessado em 2025.

McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C. E. (1955). A proposal for the dartmouth summer research project on artificial intelligence. Memorandum, Dartmouth College. Disponível em: [link].

McCool, M., Robison, A. D., and Reinders, J. (2012). Structured Parallel Programming: Patterns for Efficient Computation. Morgan Kaufmann Publishers, an imprint of Elsevier.

Miao S, Liu M, Li P (2022). Interpretable and generalizable graph learning via stochastic attention mechanism. Em Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, p. 15524–15543. PMLR.

Ministério da Educação do Brasil (2017). Base nacional comum curricular. Acesso em: 22 maio 2025.

Misra, S., Barik, K., and Kvalvik, P. (2025). Digital sovereignty in the era of industry 5.0: Challenges and opportunities. Procedia Computer Science.

Moerel, L. and Timmers, P. (2021). Data sovereignty and open source governance.

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge, Massachusetts, illustrated edition.

Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., and Myers, B. (2024). Using an llm to help with code understanding. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE ’24, New York, NY, USA. Association for Computing Machinery.

Nascimento, B. L. C. and Silva, E. M. (2023). Lei geral de proteção de dados (lgpd) e repositórios institucionais: reflexões e adequações. Em Questão, 29:e–127314.

Naveed H, Khan A U, Qiu S, Saqib M, Anwar S, Usman M, et al. (2023). A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435.

Newell, A., Shaw, J. C., and Simon, H. A. (1956). The logic theory machine – a complex information processing system. In Proceedings of the Western Joint Computer Conference, pages 218–230. IRE-AIEE-ACM.

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

Núñez, C. P., Koslinski, M. C., and Fernández, S. J. (2019). Políticas de incentivo ao uso de dados educacionais: experiências no contexto internacional e brasileiro. Jornal de Políticas Educacionais, 13.

of Education, U. D. (1974). Family educational rights and privacy act (ferpa). 20 U.S.C. 1232g; 34 CFR Part 99.

Okoye, K. and Hosseini, S. (2024). Correlation Tests in R: Pearson Cor, Kendall’s Tau, and Spearman’s Rho, pages 247–277. Springer Nature Singapore, Singapore.

OpenAI, Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, et al. (2024). GPT-4 technical report. arXiv:2303.08774 [cs.CL].

Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, et al. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744.

Pacheco, P. (2022). An Introduction to Parallel Programming. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2st edition.

Parliament, E. and Council (2016). Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 – general data protection regulation. Accessed: 2025-01-20.

Penedo G, Malartic Q, Hesslow D, Cojocaru R, Cappelli A, Alobeidli H, et al. (2023). The RefinedWeb dataset for Falcon LLM: Outperforming curated corpora with web data, and web data only. arXiv:2306.01116 [cs.CL].

Peres, R. S. (2023). Grandes modelos de linguagem na resolução de questões de vestibular: O caso dos institutos militares brasileiros. Mestrado, Universidade Federal do Estado do Rio de Janeiro (UNIRIO).

Piaget, J. (1973). A epistemologia genética. Bertrand Brasil, Rio de Janeiro. Tradução de E. F. Souza. Obra original: *L’épistémologie génétique*, 1950.

Pilla, M. L., Santos, R. R., and Cavaleiro, G. G. H. (2009). Introdução á programação para arquiteturas multicore. In Dorneles, R. V. and Stein, B., editors, IX Escola Regional de Processamento de Alto Desempenho, pages 71–202. SBC, Porto Alegre.

Press O, Smith N A, LewisM(2022). Train short, test long: Attention with linear biases enables input length extrapolation. arXiv:2108.12409 [cs.CL].

Provost, F. and Fawcett, T. (2016). Data Science Para Negócios. Editora Alta Books, edição: 1 edition.

Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., and Chen, H. (2023). Reasoning with language model prompting: A survey. In Rogers, A., Boyd-Graber, J., and Okazaki, N., editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5368–5393, Toronto, Canada. Association for Computational Linguistics.

Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67.

Rahman, M. and Wang, Y. (2024). Implementation of dewinspired matrix-mesh communication protocol. In De, D. and Roy, S., editors, Dew Computing: The Sustainable IoT Perspectives, pages 105–115. Springer Nature Singapore.

Raiaan MA K, MuktaMS H, Fatema K, Fahad N M, Sakib S, Mim M M J, et al. (2024). A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE Access, 12:26839–26874.

Rauber, T. and Rünger, G. (2010). Parallel Programming for Multicore and Cluster Systems. Springer.

Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1135–1144.

Rodrigues, H. S., Moraes, L. O., Garcia, A. C. B., Alvares, R. V., dos Santos, R. P., and Delgado, C. (2025). Investigating artificial intelligence algorithms to predict college students’ academic performance: A systematic mapping study. In Anais do Workshop de Educação em Computação, Maceió, Brasil. A ser publicado.

Romero, C. and Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3):e1355.

Russell, S. and et al. (2022). Human rights in the age of ai. UNESCO Report.

Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., and Chadha, A. (2024). A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv. Available at [link].

Sanh V, Debut L, Chaumond J,Wolf T (2020). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 [cs.CL].

Sarlet, G. B. S. and Ruaro, R. L. (2021). A proteção de dados sensíveis no sistema normativo brasileiro sob o enfoque da lei geral de proteção de dados (lgpd) – l.13.709/2018. Revista de Direitos Fundamentais & Democracia, 26(2):81–106.

Shazeer N (2019). Fast transformer decoding: One write-head is all you need. arXiv:1911.02150 [cs.NE].

Silberschatz, A., Galvin, P. B., and Gagne, G. (2018). Operating System Concepts. Wiley, 10 edition.

Silva, G., Bianchini, C., and Costa, E. (2022). Programação Paralela e Distribuída com MPI, OpenMP e OpenACC para computação de alto desempenho.

Skillicorn, D. B. and Talia, D. (1998). Models and languages for parallel computation. ACM Comput. Surv., 30(2):123–169.

Skinner, B. F. (1953). Science and Human Behavior. Macmillan, New York.

Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., and Thompson, N. (2025). The ai risk repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence.

Sun M, Liu Z, Bair A, Kolter J Z (2023). A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695.

Sun, S., Yuan, R., Cao, Z., Li, W., and Liu, P. (2024). Prompt chaining or stepwise prompt? refinement in text summarization. In Ku, L.-W., Martins, A., and Srikumar, V., editors, Findings of the Association for Computational Linguistics: ACL 2024, pages 7551–7558, Bangkok, Thailand. Association for Computational Linguistics.

Sutter, H. and Larus, J. (2005). Software and the concurrency revolution: Leveraging the full power of multicore processors demands new tools and new thinking from the software industry. Queue, 3(7):54–62.

Sutton, R. S. and Barto, A. G. (2018). Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. The MIT Press, Cambridge, Massachusetts, 2nd edition.

Suzgun M, Scales N, Schärli N, Gehrmann S, Tay Y, Chung H W, et al. (2023). Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research.

Synapse (2025). Synapse: Welcome to the documentation repository for synapse, a matrix homeserver implementation developed by element. Accessed: 2025-01-09.

Tanenbaum, A. S. and Bos, H. (2022). Modern Operating Systems. Prentice Hall, 5 edition.

Tay Y, Dehghani M, Bahri D, Metzler D (2022). Efficient transformers: A survey. ACM Comput. Surv., 55(6).

Team, T. R. (2021). The Rust Programming Language. Rust Core Team. Official language guide.

Thomas, D. (2022). Programming Elixir 1.6: Functional |> Concurrent |> Pragmatic |> Fun. Pragmatic Bookshelf, Raleigh, North Carolina, 7 edition.

Tian K, Mitchell E, Zhou A, Sharma A, Rafailov R, Yao H, Finn C, Manning C D (2023). Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. arXiv preprint arXiv:2305.14975.

Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M A, Lacroix T, et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.

Troutwine, B. L. (2018). Hands-On Concurrency with Rust: Confidently build memory-safe, parallel, and efficient software in Rust. Packt, Birmingham.

UNESCO (2021a). Artificial Intelligence and Education: Guidance for Policy-makers. United Nations Educational, Scientific and Cultural Organization, Paris.

UNESCO (2021b). Recommendation on the ethics of artificial intelligence. Disponível em: [link].

UNESCO (2023). Governing Artificial Intelligence in Education: From Principles to Practice. United Nations Educational, Scientific and Cultural Organization, Paris.

UNESCO (2024). Generation ai: Navigating the opportunities and risks of artificial intelligence in education.

V. J M N, Giraldo D M, Segura S G, Corchado J M, la Prieta F D (2025). Bioinspired small language models in edge systems for bee colony monitoring and control. Internet of Things, 32:101633.

van Haastrecht, M., Brinkhuis, M., and Spruit, M. (2024). Federated learning analytics: Investigating the privacy-performance trade-off in machine learning for educational analytics. In Olney, A. M., Chounta, I.-A., Liu, Z., Santos, O. C., and Bittencourt, I. I., editors, Artificial Intelligence in Education, pages 62–74, Cham. Springer Nature Switzerland.

Vasconcelos, C. N. and Clua, E. W. G. (2017). Deep learning - teoria e prática. In Delicato, F. C., Pires, P. F., and Silveira, I. F., editors, Jornadas de Atualização em Informática 2017, chapter 6. Sociedade Brasileira de Computação.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I (2017). Attention is all you need. Em Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, p. 6000–6010, Red Hook, NY, USA. Curran Associates Inc.

Vygotsky, L. S. (1984). A formação social da mente: o desenvolvimento dos processos psicológicos superiores. Livros Técnicos e Científicos, São Paulo. Tradução de José Cipolla Neto, Luiz Silveira Menna Barreto e Solange Castro Afeche. Obra original de 1930-1934.

Wang J, Hu X, Hou W, Chen H, Zheng R, Wang Y, et al. (2023). On the robustness of chatgpt: An adversarial and out-of-distribution perspective. arXiv preprint arXiv:2302.12095.

Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., and Lim, E.-P. (2023). Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In Rogers, A., Boyd-Graber, J., and Okazaki, N., editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2609–2634, Toronto, Canada. Association for Computational Linguistics.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.

Weidinger L, Uesato J, Rauh M, Griffin C, Huang P S, Mellor J, et al. (2022). Taxonomy of risks posed by language models. Em Proceedings of the 2022 ACM conference on fairness, accountability, and transparency, p. 214–229.

Weizenbaum, J. (1966). Eliza – a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1):36–45.

Wendling, M. I. et al. (2023). Diagnóstico do nível de maturidade da aplicação da lgpd nas escolas de educação infantil da rede municipal de passo fundo. Revista Brasileira de Políticas Públicas e Direito da Educação, 15(3):45–62.

Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., and Wilson, J. (2020). The what-if tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics, 26(1):56–65.

Williams, A. (2019). C++ Concurrency in Action, Second Edition. Manning, 2 edition.

Xu J, Li Z, Chen W, Wang Q, Gao X, Cai Q, Ling Z (2024). On-device language models: A comprehensive review. arXiv preprint arXiv:2409.00088.

Xue M, Liu D, Yang K, Dong G, Lei W, Yuan Z, Zhou C, Zhou J (2023). OccuQuest: Mitigating occupational bias for inclusive large language models. arXiv:2310.16517v1 [cs.CL].

Yan X, Zhang T, Li Z, Zhang Y (2025). Progressive binarization with semi-structured pruning for LLMs. arXiv preprint arXiv:2502.01705.

Yang A, Yu B, Li C, Liu D, Huang F, Huang H, et al. (2025). Qwen2.5-1m technical report. arXiv:2501.15383 [cs.CL].

Yang J, Jin H, Tang R, Han X, Feng Q, Jiang H, et al. (2024). Harnessing the power of LLMs in practice: A survey on chatgpt and beyond. ACM Trans. Knowl. Discov. Data, 18(6).

Yao Y, Duan J, Xu K, Cai Y, Sun Z, Zhang Y (2024a). A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly. High-Confidence Computing, p. 100211.

Yao Y, Duan J, Xu K, Cai Y, Sun Z, Zhang Y (2024b). A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly. High-Confidence Computing, 4(2):100211.

Yenduri G, Ramalingam M, Selvi G C, Supriya Y, Srivastava G, Maddikunta P K R, et al. (2024). GPT (generative pre-trained transformer)— a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 12:54608–54649.

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013). Learning fair representations. In International Conference on Machine Learning, pages 325–333. PMLR.

Zhang P, Zeng G, Wang T, Lu W (2024). TinyLlama: An opensource small language model. arXiv:2401.02385v2 [cs.CL].

Zhang Q, Liu Z, Pan S (2025). The rise of small language models. IEEE Intelligent Systems, 40(1):30–37.

Zhang T, Kishore V, Wu F, Weinberger K Q, Artzi Y (2020). BERTScore: Evaluating text generation with BERT. Em International Conference on Learning Representations.

Zhang, B. H., Lemoine, B., and Mitchell, M. (2018). Mitigating unfairness in machine learning with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 335–340.

Zhao W X, Zhou K, Li J, Tang T, Wang X, Hou Y, et al. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223, 1(2).

Zhao W, Peyrard M, Liu F, Gao Y, Meyer C M, Eger S (2019). MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. Em Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, China. Association for Computational Linguistics.

Zhao Y, Lin C Y, Zhu K, Ye Z, Chen L, Zheng S, et al. (2024). Atom: Low-bit quantization for efficient and accurate llm serving. Proceedings of Machine Learning and Systems, 6:196–209.

Zheng X, Zhang C, Woodland P C (2021). Adapting GPT, GPT-2 and BERT language models for speech recognition. Em 2021 IEEE Automatic speech recognition and understanding workshop (ASRU), p. 162–168. IEEE.

Zheng Z, Ning K, Zhong Q, Chen J, ChenW, Guo L,WangW,Wang Y (2025). Towards an understanding of large language models in software engineering tasks. Empirical Software Engineering, 30(2):50.

Zhu K, Wang J, Zhou J, Wang Z, Chen H, Wang Y, et al. (2024a). PromptRobust: Towards evaluating the robustness of large language models on adversarial prompts. Em Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis, LAMPS ’24, p. 57–68, New York, NY, USA. Association for Computing Machinery.

Zhu X, Li J, Liu Y, Ma C,WangW(2024b). A survey on model compression for large language models. Transactions of the Association for Computational Linguistics, 12:1556–1577.