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All Outputs (28)

Reputation Gaming in Crowd Technical Knowledge Sharing (2025)
Presentation / Conference Contribution
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (2025, April). Reputation Gaming in Crowd Technical Knowledge Sharing. Presented at IEEE/ACM International Conference on Software Engineering (ICSE 2025), Ottawa, Ontario, Canada

Stack Overflow 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 offers, for the first time, a comprehensive study of the repo... Read More about Reputation Gaming in Crowd Technical Knowledge Sharing.

Dementia Friendly Buildings—Approach on Architectures (2025)
Journal Article
Ghamari, M., Suvish, Dehkordi, A. A., See, C. H., Sami, A., Yu, H., & Sundaram, S. (2025). Dementia Friendly Buildings—Approach on Architectures. Buildings, 15(3), Article 385. https://doi.org/10.3390/buildings15030385

Dementia’s escalating incidence, coupled with its economic burden, highlights the need for architectural designs and forms that benefit people living with dementia. This research explores strategies and design principles that focus on establishing su... Read More about Dementia Friendly Buildings—Approach on Architectures.

Reputation Gaming in Crowd Technical Knowledge Sharing (2024)
Presentation / Conference Contribution
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (2025, April). Reputation Gaming in Crowd Technical Knowledge Sharing. Presented at 47th IEEE/ACM International Conference on Software Engineering (ICSE), Ottawa, Canada

Stack Overflow 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 offers, for the first time, a comprehensive study of the repo... Read More about Reputation Gaming in Crowd Technical Knowledge Sharing.

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.

A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings (2024)
Presentation / Conference Contribution
Nazemi, A., Sami, A., Sami, M., & Hussain, A. (2024, September). A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings. Presented at 3rd COG-MHEAR Workshop on Audio-Visual Speech Enhancement (AVSEC), Kos Island, Greece

This study addresses the challenge of speech enhancement within an audio-only context. Our proposed framework extracts speaker embeddings and voice signals, subsequently integrating these components to synthesise a voice based on the extracted data.... Read More about A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings.

Iterative Speech Enhancement with Transformers (2024)
Presentation / Conference Contribution
Nazemi, A., Sami, A., Sami, M., & Hussain, A. (2024, September). Iterative Speech Enhancement with Transformers. Presented at 3rd COG-MHEAR Workshop on Audio-Visual Speech Enhancement (AVSEC), Kos, Greece

Enhancing audio quality in audio-video speech enhancement (AVSE) is a crucial step in improving the performance of speech recognition systems, particularly by integrating visual and auditory data to create more robust and accurate models. This study... Read More about Iterative Speech Enhancement with Transformers.

State of Practice: LLMs in Software Engineering and Software Architecture (2024)
Presentation / Conference Contribution
Jahic, J., & Sami, A. (2024, June). State of Practice: LLMs in Software Engineering and Software Architecture. Presented at 21st IEEE International Conference on Software Architecture (ICSA 2024): 3rd International Workshop on Software Architecture and Machine Learning, Hyderabad, India

Large Language Models (LLMs) are finding their way into Software Engineering by assisting with tasks such as code generation. Furthermore, LLMs might have a potential to perform even more complex tasks, such as suggesting architectural design. Howeve... Read More about State of Practice: LLMs in Software Engineering and Software Architecture.

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.

A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing (2024)
Presentation / Conference Contribution
Benzaïd, C., Taleb, T., Sami, A., & Hireche, O. (2023, December). A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing. Presented at GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia

Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses t... Read More about A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing.

A case study of fairness in generated images of Large Language Models for Software Engineering tasks (2023)
Presentation / Conference Contribution
Sami, M., Sami, A., & Barclay, P. (2023, October). A case study of fairness in generated images of Large Language Models for Software Engineering tasks. Presented at 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME), Bogotá, Colombia

Bias in Large Language Models (LLMs) has significant implications. Since they have revolutionized content creation on the web, they can lead to more unfair outcomes, lack of inclusivity, reinforcement of stereotypes and ethical and legal concerns. No... Read More about A case study of fairness in generated images of Large Language Models for Software Engineering tasks.

FortisEDoS: A Deep Transfer Learning-Empowered Economical Denial of Sustainability Detection Framework for Cloud-Native Network Slicing (2023)
Journal Article
Benzaïd, C., Taleb, T., Sami, A., & Hireche, O. (2024). FortisEDoS: A Deep Transfer Learning-Empowered Economical Denial of Sustainability Detection Framework for Cloud-Native Network Slicing. IEEE Transactions on Dependable and Secure Computing, 21(4), 2818-2835. https://doi.org/10.1109/tdsc.2023.3318606

Network slicing is envisaged as the key to unlocking revenue growth in 5G and beyond (B5G) networks. However, the dynamic nature of network slicing and the growing sophistication of DDoS attacks rises the menace of reshaping a stealthy DDoS into an E... Read More about FortisEDoS: A Deep Transfer Learning-Empowered Economical Denial of Sustainability Detection Framework for Cloud-Native Network Slicing.

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.

Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset (2022)
Presentation / Conference Contribution
Khoshnoud, F., Nasab, A. R., Toudeji, Z., & Sami, A. (2022, May). Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset. Presented at MSR '22: 19th International Conference on Mining Software Repositories, Pittsburgh, US

In pull-based development systems, code reviews and pull request comments play important roles in improving code quality. In such systems, reviewers attempt to carefully check a piece of code by different unit tests. Unfortunately, sometimes they mis... Read More about Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset.

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.

EfficientMask-Net for face authentication in the era of COVID-19 pandemic (2022)
Journal Article
Azouji, N., Sami, A., & Taheri, M. (2022). EfficientMask-Net for face authentication in the era of COVID-19 pandemic. Signal, Image and Video Processing, 16(7), 1991-1999. https://doi.org/10.1007/s11760-022-02160-z

Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobi... Read More about EfficientMask-Net for face authentication in the era of COVID-19 pandemic.

Organ-specific or personalized treatment for COVID-19: rationale, evidence, and potential candidates (2022)
Journal Article
Mousavi, S. Z., Rahmanian, M., & Sami, A. (2022). Organ-specific or personalized treatment for COVID-19: rationale, evidence, and potential candidates. Functional and Integrative Genomics, 22(3), 429-433. https://doi.org/10.1007/s10142-022-00841-z

Although extrapulmonary manifestations of coronavirus disease 2019 (COVID-19) are increasingly reported, no effective therapeutic strategy for these multisystemic complications is available due to a poor understanding of the pathophysiology of COVID-... Read More about Organ-specific or personalized treatment for COVID-19: rationale, evidence, and potential candidates.

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.