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All Outputs (23)

Reputation Gaming in Crowd Technical Knowledge Sharing (2024)
Journal Article
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (online). Reputation Gaming in Crowd Technical Knowledge Sharing. ACM transactions on software engineering and methodology, 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.

Academic Staff AI Literacy Development Through LLM Prompt Training (2024)
Book Chapter
Drumm, L., & Sami, A. (in press). 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

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.

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.

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.

A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 (2021)
Journal Article
Azouji, N., Sami, A., Taheri, M., & Müller, H. (2021). A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19. Computers in Biology and Medicine, 139, Article 104927. https://doi.org/10.1016/j.compbiomed.2021.104927

The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect... Read More about A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19.

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.

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.

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.

Malware detection based on mining API calls
Presentation / Conference Contribution
Sami, A., Yadegari, B., Rahimi, H., Peiravian, N., Hashemi, S., & Hamze, A. (2010, March). Malware detection based on mining API calls. Presented at The 2010 ACM Symposium, Sierre, Switzerland

Financial loss due to malware nearly doubles every two years. For instance in 2006, malware caused near 33.5 Million GBP direct financial losses only to member organizations of banks in UK. Recent malware cannot be detected by traditional signature b... Read More about Malware detection based on mining API calls.

A case study of fairness in generated images of Large Language Models for Software Engineering tasks
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.

A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing
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.

Investigating Markers and Drivers of Gender Bias in Machine Translations
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.

State of Practice: LLMs in Software Engineering and Software Architecture
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.