Self-Regulated Sample Diversity in Large Language Models
(2024)
Presentation / Conference Contribution
Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024). Self-Regulated Sample Diversity in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (1891–1899)
Professor Sue Black's Outputs (4)
Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study (2020)
Presentation / Conference Contribution
Aduragba, O. T., Yu, J., Cristea, A. I., Hardey, M., & Black, S. (2020). Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study. In 2020 15th International Conference on Computer Science & Education (ICCSE) (162-168). https://doi.org/10.1109/iccse49874.2020.9201693The TechUPWomen programme takes 100 women from the Midlands and North of England, particularly from underrepresented communities, with degrees or experience in any subject area, retrains them in technology and upon graduation guarantees an interview... Read More about Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study.
Empirical comparison of text-based mobile apps similarity measurement techniques (2019)
Journal Article
Al-Subaihin, A., Sarro, F., Black, S., & Capra, L. (2019). Empirical comparison of text-based mobile apps similarity measurement techniques. Empirical Software Engineering, 24(6), 3290-3315. https://doi.org/10.1007/s10664-019-09726-5Context: Code-free software similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring similarity may affect both the efficiency and quality of... Read More about Empirical comparison of text-based mobile apps similarity measurement techniques.
Clustering Mobile Apps Based on Mined Textual Features (2016)
Presentation / Conference Contribution
Al-Subaihin, A., Sarro, F., Black, S., Capra, L., Harman, M., Jia, Y., & Zhang, Y. (2016). Clustering Mobile Apps Based on Mined Textual Features. In ESEM '16: Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. https://doi.org/10.1145/2961111.2962600