SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Maria Silva(malu.maia@lsbd.ufc.br)
2 André Luís Mendonça(andre.luis@lsbd.ufc.br)
3 Eduardo Neto(eduardo.rodrigues@lsbd.ufc.br)
4 Iago Chaves(iagocc@gmail.com)
5 Carlos Caminha(caminha@ufc.br)
6 Felipe Brito(felipe.timbo@lsbd.ufc.br)
7 Victor de Farias(victor.farias@lsbd.ufc.br)
8 Javam Machado(javam.machado@lsbd.ufc.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Abbas, Y. and Malik, M. S. I. (2023). Defective products identification framework using online reviews. Electronic Commerce Research, 23(2):899–920.
2 Chavan, A., Magazine, R., Kushwaha, S., Debbah, M., and Gupta, D. (2024). Faster and lighter llms: A survey on current challenges and way forward. arXiv preprint arXiv:2402.01799.
3 Chaves, I. C., de Paula, M. R. P., Leite, L. G., Queiroz, L. P., Gomes, J. P. P., and Machado, J. C. (2016). Banhfap: A bayesian network based failure prediction approach for hard disk drives. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pages 427–432.
4 Cheng, Z., Han, S., Lee, P. P., Li, X., Liu, J., and Li, Z. (2022). An in-depth correlative study between dram errors and server failures in production data centers. In 2022 41st International Symposium on Reliable Distributed Systems (SRDS), pages 262–272. IEEE.
5 Hakami, A. (2024). Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance. Scientific Reports, 14(1):9645.
6 Lima, F. D. S., Pereira, F. L. F., Chaves, I. C., Gomes, J. P. P., and Machado, J. C. (2018). Evaluation of recurrent neural networks for hard disk drives failure prediction. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pages 85–90. IEEE.
7 Marella, R. (2023). ctransformers:python bindings for the transformer models implemented in c/c++ using ggml library. https://github.com/marella/ctransformers. Accessed: 2024-06-29.
8 Park, Y., Fan, S., and Hsu, C. (2020). A review on fault detection and process diagnostics in industrial processes. processes, 8 (9), 1123.
9 Queiroz, L. P., Rodrigues, F. C. M., Gomes, J. P. P., Brito, F. T., Chaves, I. C., Paula, M. R. P., Salvador, M. R., and Machado, J. C. (2016). A fault detection method for hard disk drives based on mixture of gaussians and nonparametric statistics. IEEE Transactions on Industrial Informatics, 13(2):542–550.
10 Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
11 Rombach, K. (2023). Fault Diagnostics under label and data scarcity. PhD thesis, ETH Zurich.
12 Schroeder, B. and Gibson, G. A. (2009). A large-scale study of failures in high-performance computing systems. IEEE transactions on Dependable and Secure Computing, 7(4):337–350.
13 Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning research, 9(11).
14 Xia, F., Song, H., Yan, L.-C., Li, Y., and Wang, L.-J. (2021). A survey on failure prediction in large-scale computing systems. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), pages 2028–2033. IEEE.
15 Xu, F., Han, S., Lee, P. P., Liu, Y., He, C., and Liu, J. (2021). General feature selection for failure prediction in large-scale ssd deployment. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pages 263–270. IEEE.
16 Young, A., Chen, B., Li, C., Huang, C., Zhang, G., Zhang, G., Li, H., Zhu, J., Chen, J., Chang, J., et al. (2024). Yi: Open foundation models by 01. ai. arXiv preprint arXiv:2403.04652.