Outputs (13)

Academic Staff AI Literacy Development Through LLM Prompt Training (2024)
Book Chapter
Drumm, L., & Sami, A. (2024). Academic Staff AI Literacy Development Through LLM Prompt Training. In X. O’Dea, & D. Tsz Kit Ng (Eds.), Effective Practices in AI Literacy Education: Case Studies and Reflections (41-49). Emerald. https://doi.org/10.1108/978-1-83608-852-320241005

A foundation in artificial intelligence (AI) literacy among all academic staff is essential for supporting students’ AI literacy effectively. As tools like ChatGPT increasingly influence academic work, educators need to understand prompt engineering... Read More about Academic Staff AI Literacy Development Through LLM Prompt Training.

Reputation Gaming in Crowd Technical Knowledge Sharing (2024)
Journal Article
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (2025). Reputation Gaming in Crowd Technical Knowledge Sharing. ACM transactions on software engineering and methodology, 34(1), Article 10. https://doi.org/10.1145/3691627

Stack Overrow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the incentive system prone to manipulation. This paper ooers, for the rst time, a comprehensive study of the reported... Read More about Reputation Gaming in Crowd Technical Knowledge Sharing.

Investigating Markers and Drivers of Gender Bias in Machine Translations (2024)
Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March). Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics (2023)
Journal Article
Feizpour, E., Tahayori, H., & Sami, A. (2023). CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics. Journal of Software: Evolution and Process, 35(12), Article e2569. https://doi.org/10.1002/smr.2569

Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive... Read More about CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics.

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique (2022)
Journal Article
Moezzi, S. A. R., Ghaedi, A., Rahmanian, M., Mousavi, S. Z., & Sami, A. (2023). Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique. Journal of Digital Imaging, 36(1), 80-90. https://doi.org/10.1007/s10278-022-00692-x

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniqu... Read More about Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.

Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods (2022)
Journal Article
Forootani, A., Rastegar, M., & Sami, A. (2022). Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods. Electric Power Systems Research, 210, Article 108119. https://doi.org/10.1016/j.epsr.2022.108119

Accurate short-term forecasting of the individual residential load is a challenging task due to the nonlinear behavior of the residential customer. Moreover, there are a large number of features that have impact on the energy consumption of the resid... Read More about Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods.

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations (2021)
Journal Article
Arshia, A. H., Shadravan, S., Solhjoo, A., Sakhteman, A., & Sami, A. (2021). De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations. Computers in Biology and Medicine, 139, Article 104967. https://doi.org/10.1016/j.compbiomed.2021.104967

The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates ca... Read More about De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

Particular matter prediction using synergy of multiple source urban big data in smart cities (2021)
Journal Article
Honarvar, A. R., & Sami, A. (2021). Particular matter prediction using synergy of multiple source urban big data in smart cities. Intelligent Decision Technologies, 15(3), 371-385. https://doi.org/10.3233/idt-200147

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are ve... Read More about Particular matter prediction using synergy of multiple source urban big data in smart cities.

Characterization and Prediction of Questions without Accepted Answers on Stack Overflow (2021)
Presentation / Conference Contribution
Yazdaninia, M., Lo, D., & Sami, A. (2021, May). Characterization and Prediction of Questions without Accepted Answers on Stack Overflow. Presented at 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC), Madrid, Spain

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is... Read More about Characterization and Prediction of Questions without Accepted Answers on Stack Overflow.

An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2021)
Presentation / Conference Contribution
Verdi, M., Sami, A., Akhondali, J., Khomh, F., Uddin, G., & Karami Motlagh, A. (2021, May). An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples. Presented at 43rd International Conference on Software Engineering, Online

Software developers share programming solutions in Q&A sites like Stack Overflow, Stack Exchange, Android forum, and so on. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code... Read More about An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples.

Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks (2021)
Journal Article
Khalafi, Z. S., Dehghani, M., Khalili, A., Sami, A., Vafamand, N., & Dragicevic, T. (2022). Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks. IEEE Transactions on Industrial Electronics, 69(5), 4697-4706. https://doi.org/10.1109/tie.2021.3080212

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation may become inaccurate, which can eventually lead to wrong supervisory decision making. To detect cyber-attacks in pow... Read More about Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks.

How Do Users Answer MATLAB Questions on Q&A Sites? A Case Study on Stack Overflow and MathWorks (2021)
Presentation / Conference Contribution
Naghashzadeh, M., Haghshenas, A., Sami, A., & Lo, D. (2021, March). How Do Users Answer MATLAB Questions on Q&A Sites? A Case Study on Stack Overflow and MathWorks. Presented at 28th IEEE International Conference on Software Analysis, Evolution and Reengineering 2021 (SANER 2021), Honolulu, HI, USA

MATLAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. Thi... Read More about How Do Users Answer MATLAB Questions on Q&A Sites? A Case Study on Stack Overflow and MathWorks.

An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2020)
Journal Article
Verdi, M., Sami, A., Akhondali, J., Khomh, F., Uddin, G., & Karami Motlagh, A. (2022). An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples. IEEE Transactions on Software Engineering, 48(5), 1497-1514. https://doi.org/10.1109/tse.2020.3023664

Software developers share programming solutions in Q&A sites like Stack Overflow, Stack Exchange, Android forum, and so on. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code... Read More about An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples.