Chrysoula Florou | computer programming | Best Researcher Award

Ms. Chrysoula Florou | computer programming | Best Researcher Award

PHD Doctor, University of Thessaly, Greece

👩‍💻 Chrysoula Florou is a dedicated researcher and educator specializing in the intersection of education and programming concepts. With a strong background in computer engineering and a passion for enhancing primary education through innovative tools, she has made significant contributions to improving self-assessment practices for young learners. Beyond academia, she serves as an Officer in the Hellenic Coast Guard, showcasing her versatile professional capabilities.

Publication Profile

Scopus

Education

🎓 Chrysoula’s academic journey reflects her commitment to excellence, earning a Ph.D. (2010–2025) in Electrical and Computer Engineering from the University of Thessaly with a thesis on self-assessment in programming education (“Excellent”). She holds two master’s degrees: one in Informatics focusing on Security and Big Data (2016–2018, “Excellent”) and another in Computer Science and Technology (2009–2011, “Excellent”). Additionally, she completed a diploma in Computer Engineering (2004–2009, “Very Good”) and a certified program in E-Learning Training (2012).

Experience

🧑‍🏫 Chrysoula has extensive teaching experience in programming, database applications, and multimedia at institutions like the University of Thessaly, TEI of Lamia, and DIEK Volos. In parallel, she contributed to European research projects like “cMinds” and worked in IT roles for the Ministry of Health and National Bank of Greece. Since 2011, she has served as an Officer in the Hellenic Coast Guard, balancing her technical expertise with public service.

Awards and Honors

🏆 Chrysoula has earned numerous accolades for her academic excellence, including distinctions for her master’s theses and diploma projects. Her research outcomes have been recognized by leading journals and conferences in the education and programming domains.

Research Focus

🔬 Chrysoula’s research centers on the integration of educational technologies in programming pedagogy, particularly for primary education. She explores self-assessment tools, enhancing teacher facilitation roles, and leveraging innovative software like Scratch for fostering computational thinking in young learners.

Conclusion

🌟 Chrysoula Florou exemplifies the harmony between academia, research, and public service. Her impactful work in education technology and programming continues to inspire both educators and students, shaping the future of learning in primary education.

Publications

The Role of Educators in Facilitating Students’ Self-Assessment in Learning Computer Programming Concepts: Addressing Students’ Challenges and Enhancing Learning.  Journal of Education and Information Technologies, Springer Nature Educ. DOI: 10.1007/s10639-024-13172-2

An autodidactic programming curriculum application for early education: Pilot studies and improvement suggestions.  Proceedings of the 40th SEFI Annual Conference 2012 – Engineering Education 2020: Meet the Future.

3rd graders’ experience on using an autodidactic programming software: A phenomenological perspective. Conference Paper.

 

 

GAN XU | Computer Science | Best Researcher Award

Mr. GAN XU | Computer Science | Best Researcher Award

PhD. candidate, capital university of economics and business, China

Xu Gan is a Doctoral student at Capital University of Economics and Business, Beijing, specializing in Rural Finance, Financial Theory and Policy, and Supply Chain Finance. With a solid academic background and practical experience, Xu has contributed significantly to research in financial services and blockchain technology. 🏛️📚

Profile

Scopus

 

Education

Xu Gan completed a Master of Finance from Capital University of Economics and Business in Beijing (2018-2021) and a Bachelor of Biotechnology from Beijing Union University (2006-2010). Xu also has an extensive academic background in these fields, providing a strong foundation for research and analysis. 🎓📈

Experience

Xu has actively participated in several high-profile research projects, including the National Social Science Foundation of China’s research on rural financial services and the China Mobile Communication Federation’s blockchain technology applications in finance. Xu was responsible for designing research sub-topics and writing research reports. 📊🔬

Research Interests

Xu Gan’s research interests include Rural Finance, Financial Theory and Policy, and Supply Chain Finance. Xu is focused on improving rural financial services and exploring innovative financial technologies such as blockchain. 🌾💡

Awards

Xu has been recognized for academic excellence with several honors: Beijing Outstanding Graduates (2020), Outstanding Graduate of Beijing Union University (2020), and several awards for excellent papers at various financial and rural finance forums. 🏅📜

Publications

Yang, GZ., Xu, G., Zhang, Y., et al. Financial density of village banks and income growth of rural residents. Economic Issues, 2021, 504(08): 89-94.

Zhang, F., Xu, G., Zhang, XY, Cheng, X. Knowledge mapping analysis of seven decades of rural finance research in China. Rural Finance Research, 2020(01): 10-20.

Zhang, F., Xu, G., Cheng, X. A review of blockchain applications in the financial sector. Technology for Development, 2019, 15(08): 865-871.

Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

Dr. Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

Shenzhen University, China

🎓 Dr. H. Sadia Nawaz is a distinguished researcher and academic in Computer Vision, with a Ph.D. from Ocean University of China. Her work focuses on Temporal Activity Detection using Natural Language. With a background in both teaching and research, she has contributed significantly to the field of computer vision and continues to pursue a career as a postdoctoral researcher. Her extensive knowledge and dedication to the subject make her a valuable asset to any research group.

Profile

Scopus

 

Education

📚 Dr. H. Sadia Nawaz holds a Ph.D. in Computer Science from Ocean University of China, specializing in Temporal Moment Localization using Natural Language. She also completed her Master’s in Information Science and Technology at the University of Sargodha and an MS in Computer Science and Technology with a focus on Cyber Security at the University of Lahore. Her academic background provides a strong foundation for her research and teaching roles.

Experience

💼 Dr. Nawaz has a robust teaching background, having served as a Programming Teaching Assistant at Heriot-Watt University and Ocean University of China, and as a Computer Science Lecturer at Superior University of Lahore and the University of Sargodha. Her roles in academia have honed her skills in teaching, mentoring, and research, contributing to her expertise in computer vision and machine learning.

Research Interests

🔍 Dr. Nawaz’s research interests include Activity Localization in Videos via Language, Temporal Moment Detection, and Natural Language Processing. She is particularly focused on moment detection in complex scenarios and the integration of machine learning methods with computer vision techniques to enhance video understanding and activity localization.

Awards

🏅 Dr. H. Sadia Nawaz has been recognized for her research contributions in the field of computer vision, particularly for her work on Temporal Moment Localization using Natural Language. Her research has been published in high-impact journals, reflecting her expertise and commitment to advancing the field.

Publications

Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora

IVTEN – Integration of Visual-Textual Entities for Temporal Activity Localization via Language in Video

Revelation of Novel Paradigm in Temporal Moment Localization by Using Natural Language in Video

Multi-level Edges and Context Arranger Architecture Using Referencing Expressions for Temporal Moment Localization via Natural Language

A Frame-Based Feature Model for Violence Detection from Surveillance Cameras Using ConvLSTM Network