Advanced Machine Learning for Runtime Data Generation-Reference-Cited by-同舟云学术

Advanced Machine Learning for Runtime Data Generation

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:12th Latin-American Symposium on Dependable and Secure Computing
language:
Short-container-title:

Author:

Zamir Bukhtawar¹^ORCID,Campos João R.¹^ORCID,Vieira Marco¹^ORCID

Affiliation:

1. Department of Informatics Engineering, University of Coimba, Portugal

Funder

Agenda Mobilizadora Sines Nexus, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation and by the FCT, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit ? UIDB/00326/2020 or project code UIDP/00326/2020.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615366.3622793

Reference43 articles.

1. Mamdouh Alenezi and Mohammad Zarour . 2020. On the relationship between software complexity and security. arXiv preprint arXiv:2002.07135 ( 2020 ). Mamdouh Alenezi and Mohammad Zarour. 2020. On the relationship between software complexity and security. arXiv preprint arXiv:2002.07135 (2020).

2. Failure Analysis of Virtual and Physical Machines: Patterns, Causes and Characteristics

3. Ramiro Camino , Christian Hammerschmidt , and Radu State . 2018. Generating multi-categorical samples with generative adversarial networks. arXiv preprint arXiv:1807.01202 ( 2018 ). Ramiro Camino, Christian Hammerschmidt, and Radu State. 2018. Generating multi-categorical samples with generative adversarial networks. arXiv preprint arXiv:1807.01202 (2018).

4. Ramiro D Camino , Christian A Hammerschmidt , and Radu State . 2019. Improving missing data imputation with deep generative models. arXiv preprint arXiv:1902.10666 ( 2019 ). Ramiro D Camino, Christian A Hammerschmidt, and Radu State. 2019. Improving missing data imputation with deep generative models. arXiv preprint arXiv:1902.10666 (2019).

5. Fault Injection to Generate Failure Data for Failure Prediction: A Case Study