Mª del Carmen de Castro-Cabrera | Computer Science | Innovative Research Award

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Innovative Research Award

Mª del Carmen de Castro-Cabrera
University of Cádiz, Spain

Mª del Carmen de Castro-Cabrera
Affiliation University of Cádiz
Country Spain
Scopus 49862833800
Documents 18
Citations 58
h-index 4
Subject Area Computer Science
Event International Phenomenological Research Awards
ORCID 0000-0003-4622-5275

Mª del Carmen de Castro-Cabrera is an Associate Professor in the Department of Computer Science and Engineering at the University of Cádiz, Spain, and a member of the UCASE software engineering research group. Her documented research activity centers on software engineering, particularly software testing, metamorphic testing, model-based testing, and related computational methods. Her publication record also includes work involving educational technologies and collaborative learning. [1]

Abstract

Mª del Carmen de Castro-Cabrera’s academic profile reflects sustained engagement with software engineering research at the University of Cádiz. Her work addresses software testing and associated techniques, including metamorphic testing, test-case prioritization, model-based testing, and constraint-based approaches. Her research record also extends to educational innovation and collaborative learning, providing an interdisciplinary connection between software engineering research and higher-education practice. [2]

Keywords

Software engineering; Software testing; Metamorphic testing; Model-based testing; Test-case prioritization; Constraint programming; Service-oriented architectures; Educational innovation.

Introduction

The research profile of Mª del Carmen de Castro-Cabrera is situated within computer science and software engineering, with particular attention to methods for improving software quality and testing processes. Her work has included research on testing service compositions, metamorphic testing, test-case prioritization, and combinations of model-based testing with constraint programming. Bibliographic records also document contributions to educational technology and assessment in higher education. [3]

Research Profile

Mª del Carmen de Castro-Cabrera is associated with the UCASE software engineering research group at the University of Cádiz. Her research activity has included participation in regional and national research projects concerned with software architectures, software design, formal modeling, testing, service-oriented systems, search-based software engineering, and data-processing applications. University research records identify her as a university professor and document continued collaboration within software engineering research. [1]

Research Contributions

The research contributions focus on software engineering, including metamorphic testing, WS-BPEL service testing, test-case prioritization, model-based testing, and constraint-based approaches for software verification and quality assurance. [3] [4] Her research also includes educational technology, focusing on wiki-based learning assessment, educational innovation, and software-testing practices that support collaborative and self-assessment in higher education.. [2] [5]

Publications

Mª del Carmen de Castro-Cabrera’s publications address software testing, metamorphic testing, test-case prioritization, model-based testing, constraint programming, service-oriented architectures, and educational technologies. Representative publications include research on wiki-based learning assessment, test-case prioritization, metamorphic testing with constraint solvers, WS-BPEL compositions, and model-based testing combined with constraint programming. These works illustrate continuity between methodological software-engineering research and applied educational or systems contexts. [2] [3] [4] [5] [6]

Research Impact

The supplied bibliometric profile records 18 Scopus documents, 58 citations, and an h-index of 4 for author ID 49862833800. A separate Google Scholar profile supplied for the researcher reports 59 documents, 115 citations, and an h-index of 5. Because bibliometric databases apply different coverage and counting methods, these figures should be interpreted as database-specific indicators rather than interchangeable measures of research output. [1] Her publication record demonstrates research activity extending across software testing and educational applications. Bibliographic records also document continuing collaboration within the University of Cádiz software engineering environment, including publications on model-based testing, constraint programming, and metamorphic testing. [6]

Award Suitability

The Innovative Research Award category aligns with her documented research in software engineering, software testing, metamorphic testing, test-case prioritization, and model-based approaches. Her participation in multiple research projects and publications demonstrates sustained academic engagement across software engineering and educational technology, supporting consideration within an innovation-focused research recognition framework based on the supplied record.

Conclusion

Mª del Carmen de Castro-Cabrera’s academic profile integrates university teaching and research in software engineering. Her work includes software testing, metamorphic testing, model-based approaches, constraint programming, service-oriented systems, and educational technology. Participation in research projects and scholarly publications demonstrates sustained contributions to software quality, testing methodologies, and applied computer science research. [1] [6]

References

  1. Elsevier. (n.d.). Scopus author details: Mª del Carmen de Castro-Cabrera, Author ID 49862833800. Scopus. https://www.scopus.com/authid/detail.uri?authorId=49862833800
  2. Palomo-Duarte, M., Dodero, J. M., García-Domínguez, A., Neira-Ayuso, P., et al. (2014). Scalability of assessments of wiki-based learning experiences in higher education. Computers in Human Behavior, 31, 638–650. DOI: https://doi.org/10.1016/j.chb.2013.07.033
  3. de Castro-Cabrera, M. C., García-Domínguez, A., & Medina-Bulo, I. (2020). Trends in prioritization of test cases: 2017–2019. Proceedings of the 35th Annual ACM Symposium on Applied Computing, 2005–2011. DOI: https://doi.org/10.1145/3341105.3374036
  4. de Castro-Cabrera, M. C., García-Domínguez, A., & Medina-Bulo, I. (2019). Using constraint solvers to support metamorphic testing. 2019 IEEE/ACM 4th International Workshop on Metamorphic Testing, 32–39. DOI: https://doi.org/10.1109/MET.2019.00013
  5. Castro-Cabrera, C., & Medina-Bulo, I. (2011). An approach to metamorphic testing for WS-BPEL compositions. Proceedings of the International Conference on e-Business, 137–142. DOI: https://doi.org/10.5220/0003611401370142
  6. Castro-Cabrera, C., & Medina-Bulo, I. (2012). Application of Metamorphic Testing to a Case Study in Web Services Compositions. Communications in Computer and Information Science, 168–181. DOI: https://doi.org/10.1007/978-3-642-35755-8_13

Pengfei Wei | Computer Science | Best Researcher Award

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Dr. Pengfei Wei | Computer Science | Best Researcher Award 

Senior Engineer at Guangdong University of Technology | China

Dr. Pengfei Wei is a Senior Engineer at Guangdong University of Technology, recognized for his pioneering contributions to the field of computer science, particularly in multimodal learning, knowledge tracing, edge artificial intelligence, and task-oriented dialogue systems. He holds a Ph.D. in Computer Science, where his research focused on integrating deep learning models with practical applications in intelligent education and human–machine interaction. Combining academic rigor with industrial innovation, he brings substantial experience from both enterprise research and academic development, bridging the gap between theory and real-world technology deployment. His work encompasses advanced methods such as visual-enhanced transformers for multimodal named entity recognition, genetic-inspired relation extraction, and the introduction of Kolmogorov–Arnold representations in knowledge tracing, which have improved model interpretability and performance in AI-based learning systems. In addition to his theoretical advancements, he has successfully led projects on real-time lab-safety analytics and large-scale AI deployment using Huawei Ascend, Nvidia, and TPU platforms, contributing to the broader industrial adoption of edge AI technologies. Dr. Pengfei Wei has authored numerous peer-reviewed papers in top-tier international journals and conferences, including Neural Networks, ICMR, and IJCAI, and serves as a reviewer for several prestigious publications such as Neural Networks, Pattern Recognition Letters, AAAI, and IJCNN. His collaborative initiatives with research teams and institutions have fostered multidisciplinary innovation, emphasizing the integration of AI with blockchain, big data, and education systems. A dedicated mentor and research leader, he actively supports student-led research and fosters the development of next-generation AI scholars. His professional memberships with the China Computer Federation (CCF) and the Association for Computing Machinery (ACM) reflect his strong engagement in the global computing community. Dr. Pengfei Wei’s research continues to push the boundaries of multimodal understanding and intelligent systems, driving transformative progress in computational learning and applied artificial intelligence. Through his sustained contributions, he remains committed to advancing the capabilities of intelligent technologies that enhance human productivity, knowledge discovery, and digital transformation.

Profile: Orcid | Google Scholar

Featured Publications:

  • Liao, W., B. Zeng, Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533.

  • Liao, W., Zeng, B., Liu, J., Wei, P., Cheng, X., & Zhang, W. (2021). Multi-level graph neural network for text sentiment analysis. Computers & Electrical Engineering, 92, 107096.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2022). Image-text interaction graph neural network for image-text sentiment analysis. Applied Intelligence, 52(10), 11184–11198.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Cheng, X. (2022). Taxi demand forecasting based on the temporal multimodal information fusion graph neural network. Applied Intelligence, 52(10), 12077–12090.

  • Wei, P., Zeng, B., & Liao, W. (2022). Joint intent detection and slot filling with wheel-graph attention networks. Journal of Intelligent & Fuzzy Systems, 42(3), 2409–2420.

  • Wei, P., Ouyang, H., Hu, Q., Zeng, B., Feng, G., & Wen, Q. (2024). VEC-MNER: Hybrid transformer with visual-enhanced cross-modal multi-level interaction for multimodal NER. Proceedings of the International Conference on Multimedia Retrieval (ICMR 2024).

  • Wen, S., Zeng, B., Liao, W., Wei, P., & Pan, Z. (2021). Research and design of credit risk assessment system based on big data and machine learning. Proceedings of the IEEE 6th International Conference on Big Data Analytics (ICBDA 2021), 9–13.

laiba Sultan Dar | Time series analysis | Best Researcher Award

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Ms. laiba Sultan Dar | Time series analysis | Best Researcher Award 

Phd Student at Abdul wali khan university | Pakistan

Ms. Laiba Sultan Dar is a dedicated researcher and Ph.D. scholar at Abdul Wali Khan University, specializing in time series analysis and decomposition techniques. Her academic journey is marked by a strong foundation in applied mathematics and data analytics, enabling her to contribute effectively to statistical modeling, forecasting, and computational research. She has developed expertise in identifying patterns and trends within large datasets, applying modern analytical frameworks to address complex problems in areas such as economics, climate studies, and system optimization. Her research integrates both theoretical and empirical approaches, focusing on improving the precision and interpretability of time-dependent models. Through her doctoral studies, she has actively engaged in institutional research projects and contributed to scholarly publications in recognized international journals, including those indexed by Scopus and ScienceDirect. Her work demonstrates a balance between mathematical rigor and practical application, particularly in designing adaptive models for real-world data forecasting. In addition to her academic pursuits, Ms. Laiba Sultan Dar has shown growing involvement in research collaborations and interdisciplinary studies that bridge statistical science and technological innovation. Her ability to combine methodological sophistication with computational efficiency has made her research relevant to emerging domains such as artificial intelligence-driven data analysis and predictive modeling. She continuously enhances her research capabilities by incorporating advanced tools and techniques, fostering a deeper understanding of time series dynamics. With a citation index reflecting the recognition of her early scholarly efforts, she remains committed to producing impactful research that advances statistical methodologies and contributes to data-driven decision-making. Her intellectual curiosity and analytical acumen position her as a promising researcher in quantitative sciences. Ms. Laiba Sultan Dar’s long-term goal is to strengthen the integration of mathematical modeling with applied sciences, promoting innovation and scientific development in her field. Her commitment to excellence, collaborative mindset, and ongoing pursuit of research excellence make her a deserving candidate for recognition among emerging scholars in data analytics and statistical research.

Profile: Google Scholar

Featured Publications:

  • Dar, L., Akmal, A., Naseem, M. A., & Khan, K. U. D. (2011). Impact of stress on employees’ job performance in the business sector of Pakistan. Global Journal of Management and Business Research, 11(6), 1–4.

  • Dar, L. A., Naseem, M. A., Rehman, R. U., & Niazi, G. S. (2011). Corporate governance and firm performance: A case study of Pakistan oil and gas companies listed in Karachi Stock Exchange. Global Journal of Management and Business Research, 11(8), 1–10.

  • Malik, M., Wan, D., Dar, L., Akbar, A., & Naseem, M. A. (2014). The role of work-life balance in job satisfaction and job benefit. Journal of Applied Business Research (JABR), 30(5), 1627–1638.

  • Dar, L. S., Aamir, M., Khan, Z., Bilal, M., Boonsatit, N., & Jirawattanapanit, A. (2022). Forecasting crude oil price volatility by reconstructing EEMD components using ARIMA and FFNN models. Frontiers in Energy Research, 10, 991602.

  • Dar, L. S., Aamir, M., Bibi, S., & Bilal, M. (2025). A novel robust adaptive decomposition approach for solar energy potential using atmospheric transparency and UV radiation indicators. Journal of Radiation Research and Applied Sciences, 18(4), 101946.

  • Dar, L. S., Aamir, M., Hamraz, M., Faiz, N., Emam, W., & Tashkandy, Y. (2025). A robust adaptive signal decomposition method for enhanced mode extraction in financial time series. IEEE Access.

  • Saleem, Q., Dar, L., Shahid, M., & Rana, S. (2012). A quantitative analysis of the role of human resource development in economic growth in Pakistan. International Journal of Management Sciences and Business Research.

  • Bilal, M., Aamir, M., Abdullah, S., Mahmood, N., Khalil, U., Khalid, N., Ahmed, M., & Dar, L. (2022). Assessment of the COVID-19 pandemic’s impact on gasoline prices in Pakistan. VFAST Transactions on Mathematics, 10(2), 52–67.

Jiandong Ma | Computer Science | Best Researcher Award

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Mr. Jiandong Ma | Computer Science | Best Researcher Award

Student at Chinese Academy of Sciences, China.

🌍Jiandong Ma is a talented researcher specializing in signal and information processing, particularly in hardware-based network optimization and communication systems. With a Ph.D. from the University of Chinese Academy of Sciences and a Bachelor’s degree from the University of Electronic Science and Technology of China, Jiandong has demonstrated exceptional leadership and technical expertise. His project portfolio includes innovative contributions like FPGA-based RDMA NICs, packet reordering systems, and deep flow tables. Jiandong has successfully applied for multiple patents and published high-impact SCI papers. He has also been recognized with academic honors, including Merit Student of the University, showcasing his commitment to excellence in research and innovation.

Profile👤

Education 🎓

🎓Jiandong Ma holds a Ph.D. in Signal and Information Processing from the University of Chinese Academy of Sciences, where he worked in the National Network New Media Engineering Research Center. During his academic journey, Jiandong gained expertise in FPGA design and high-performance networking. He earned his Bachelor’s degree in Network Engineering from the University of Electronic Science and Technology of China, graduating from the prestigious Yingcai Honors College. His education laid a strong foundation for his innovative research in multipath networking, RDMA systems, and signal processing. 📘🎓

Experience💼

🩺Jiandong Ma has led several groundbreaking projects in high-performance networking. As the team leader, he developed a 100Gbps RDMA NIC with out-of-order packet handling and a packet reordering system for SD-WAN. His work improved performance metrics, reduced resource usage, and achieved scalability. Jiandong also optimized SDN switches for DDoS traffic filtering and led projects involving deep flow tables with flexible resource management. His hands-on experience spans FPGA design, Verilog programming, and flow control. Jiandong’s experience reflects technical depth, innovation, and leadership in advancing network infrastructure. 🛠️🚀

Awards and Honors 🏆

Jiandong Ma has earned multiple accolades, including the Merit Student of the University and recognition as a volunteer for the Academy of Sciences Public Science Day. His innovative contributions to RDMA systems and network optimization have been recognized through patents granted for technologies like DDoS filtering and packet reordering. Jiandong has also received acceptance for two high-impact SCI papers, demonstrating his academic excellence and dedication to solving real-world challenges in networking. His honors underscore his commitment to research and innovation. 🏅📜

Research Interests 🔬

🔬Jiandong Ma focuses on advancing high-performance networking, with expertise in FPGA-based solutions, RDMA NICs, and multipath communication systems. His research addresses challenges like packet reordering, deduplication, and dynamic resource management, with applications in SD-WAN and DDoS traffic filtering. Jiandong’s work leverages innovative designs to optimize throughput, scalability, and efficiency in communication networks. His groundbreaking contributions aim to bridge the gap between hardware and software, ensuring reliable and robust network systems. His research embodies a blend of technical depth and practical innovation. 🌟🔬

Conclusion 🔚 

Jiandong Ma is a strong candidate for the Best Researcher Award, given his innovative contributions, technical leadership, and academic impact. His patents and publications highlight his commitment to solving real-world challenges in high-performance networking. While enhancing his visibility and expanding his research focus could further elevate his profile, Jiandong’s existing achievements make him a highly deserving contender for this honor.

Publications Top Notes 📚

SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks

Authors: Ma, J.; Guo, Z.; Song, M.

Citations: 0

Year: 2024

A High-Performance FPGA-Based RoCE v2 RDMA Packet Parser and Generator

Authors: Sun, Z.; Guo, Z.; Ma, J.; Pan, Y.

Citations: 0

Year: 2024