Yanjun Xu | Blockchain | Best Researcher Award

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Dr. Yanjun Xu | Blockchain | Best Researcher Award

Senior Engineer at Tongji University, China

Yanjun Xu is a Senior Engineer and current Ph.D. candidate at Tongji University, Shanghai. With a strong foundation in operating systems and high-performance computing, he has contributed to advancing software-hardware co-simulation in robotics and optimizing kernel compilation processes. His work spans research, development, and practical innovation in virtual simulation and blockchain-integrated educational tools.

🔹Professional Profile:

Orcid Profile

🎓Education Background

  • Bachelor’s Degree – Ocean University of China, 2010

  • Master’s Degree – Xi’an Polytechnic University, 2013

  • Ph.D. Candidate – Tongji University

💼 Professional Development

Yanjun Xu currently serves as a Senior Engineer at Tongji University. He has been actively involved in high-precision software and hardware co-simulation for robot development and has led virtual simulation projects. His work includes kernel-level system optimization, compiler enhancement, and integration of emerging technologies in real-world applications.

🔬Research Focus

  • Operating Systems

  • High-Performance Computing

  • Robotic Simulation

  • Compiler Optimization

  • Blockchain Applications in Education

📈Author Metrics:

  • Citation Index: 80

  • Books Published: 1

    • ISBN: 978-7-115-55608-0

  • Key Publications (SCI/Scopus):

    • Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications

    • CGNet: Improving Contour-guided Capability for RGB-D Semantic Segmentation

    • GANet: Geometry-aware Network for RGB-D Semantic Segmentation

    • A Blockchain-Based Certificate Management System for Online Education

🏆Awards and Honors:

  • Active Member, Shanghai Blockchain Association

  • Contributor to the Open Atom Foundation Community

  • Recognized for contributions to open-source compiler development and system optimization

📝Publication Top Notes

📝 1. A Two-Way Dynamic Adaptive Pricing Resource Allocation Model Based on Combinatorial Double Auctions in Computational Network

  • Journal: Computer Communications

  • Publication Date: April 2025

  • Type: Journal Article

  • DOI: 10.1016/j.comcom.2025.108170

  • Contributors: Yanjun Xu, Chunqi Tian, Wei Wang, Lizhi Bai, Xuhui Xia

  • Source: Crossref

  • Summary:  This paper proposes a two-way dynamic adaptive pricing model for resource allocation using a combinatorial double auction mechanism in computational networks. The model is designed to handle the complexity of multi-party bidding and resource matching efficiently. By incorporating adaptive pricing strategies, the system dynamically balances supply and demand in real time, improving overall allocation efficiency. The method shows promising performance in cloud and edge computing scenarios, especially where resources are heterogeneously distributed and demand is volatile.

📝 2. Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications

  • Journal: International Journal on Semantic Web and Information Systems (IJSWIS)

  • Publication Date: December 28, 2024

  • Type: Journal Article

  • DOI: 10.4018/IJSWIS.365912

  • Contributors: Yanjun Xu, Chunqi Tian, Yaoru Sun, Haodong Zhang

  • Source: Crossref

  • Summary:  This research introduces a collaborative motivation framework that enhances user engagement in semantic web platforms by leveraging similar interest behavior. The proposed system models semantic relevance and behavioral similarity to personalize recommendations and group formation. This approach is particularly beneficial in social knowledge-sharing systems and e-learning platforms, where collaborative filtering alone may fall short. The framework shows strong potential for increasing user satisfaction and platform interactivity.

Conclusion:

Dr. Yanjun Xu exhibits all the hallmarks of an emerging global research leader. His technical depth, innovation in blockchain and systems research, publication record, and active community engagement make him a strong contender for the Best Researcher Award.

He bridges practical engineering challenges with advanced academic research, particularly in virtual simulation, semantic web systems, and blockchain-integrated education technologies. With continued growth in international collaborations and broader dissemination of his applied research, Dr. Xu is on a clear path to becoming a thought leader in computational systems and blockchain innovation.

✅ Recommendation: Highly suitable for the Best Researcher Award.

Xiuhua Lu | Blockchain | Best Researcher Award

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Assoc. Prof. Dr. Xiuhua Lu | Blockchain | Best Researcher Award

Master Supervisor at Qufu Normal university, China📖

Xiuhua Lu, an Associate Professor at the School of Cyber Science and Engineering, Qufu Normal University, hails from Taian, Shandong, China. Born in October 1979, she has dedicated her career to advancing knowledge in lattice cryptography, blockchain, and federated learning. Her expertise in mathematics and information security has established her as a prominent figure in the field of cybersecurity and applied cryptography.

Profile

Scopus Profile

Orcid Profile

Education Background🎓

Xiuhua Lu holds a Bachelor of Science in Mathematics and Applied Mathematics from Shandong Normal University (1998–2002). She pursued a Master of Science in Applied Mathematics at Capital Normal University (2002–2005), deepening her analytical and mathematical prowess. Later, she earned a Doctor of Engineering in Information Security from Beijing University of Posts and Telecommunications (2011–2019), where she focused on advanced topics in cybersecurity and cryptography.

Professional Experience🌱

Xiuhua Lu began her academic journey as an Assistant Teacher at Langfang Normal University in 2005, progressing to Lecturer and subsequently Associate Professor during her 16-year tenure. Since January 2022, she has been serving as an Associate Professor at Qufu Normal University, where she continues to mentor students and lead research projects in the School of Cyber Science and Engineering.

Research Interests🔬

Xiuhua Lu’s research interests include lattice cryptography, blockchain technology, and federated learning. Her work focuses on developing secure and efficient solutions for modern challenges in data privacy, distributed systems, and cryptographic algorithms, positioning her as a key contributor to advancements in these areas.

Author Metrics

Xiuhua Lu has contributed significantly to her field through numerous research publications in lattice cryptography, blockchain applications, and federated learning. Her scholarly work is recognized for its innovative approach and practical applications, garnering citations and establishing her influence in cybersecurity and data privacy.

Publications Top Notes 📄

1. Quantum-Resistant Identity-Based Signature with Message Recovery and Proxy Delegation

Authors: Lu, X., Wen, Q., Yin, W., Panaousis, E., Chen, J.
Journal: Symmetry, 2019, 11(2), 272
Abstract: This paper presents a quantum-resistant identity-based signature scheme that incorporates message recovery and proxy delegation capabilities. Leveraging lattice-based cryptography, the proposed method ensures robust security against quantum attacks while enhancing efficiency in identity-based systems.
Citations: 6

2. Message Integration Authentication in the Internet-of-Things via Lattice-Based Batch Signatures

Authors: Lu, X., Yin, W., Wen, Q., Chen, L., Chen, J.
Journal: Sensors (Switzerland), 2018, 18(11), 4056
Abstract: This study explores lattice-based batch signatures for message authentication in IoT systems. The proposed scheme effectively integrates and authenticates multiple messages simultaneously, addressing security and computational efficiency in resource-constrained IoT environments.
Citations: 4

3. A Lattice-Based Unordered Aggregate Signature Scheme Based on the Intersection Method

Authors: Lu, X., Yin, W., Wen, Q., Jin, Z., Li, W.
Journal: IEEE Access, 2018, 6, pp. 33986–33994
Abstract: This paper introduces a lattice-based unordered aggregate signature scheme utilizing the intersection method. The method enhances computational efficiency and scalability while ensuring strong security foundations, suitable for applications requiring high-performance aggregate signing.
Citations: 23

4. The Prediction of PM2.5 Value Based on ARMA and Improved BP Neural Network Model

Authors: Zhu, H., Lu, X.
Conference: Proceedings of the 2016 International Conference on Intelligent Networking and Collaborative Systems (IEEE INCoS), 2016, pp. 515–517
Abstract: This research proposes a hybrid model combining ARMA and an improved BP neural network for accurate PM2.5 value predictions. The model demonstrates improved forecasting accuracy, contributing to better environmental monitoring and pollution control strategies.
Citations: 46

5. A Lattice-Based Signcryption Scheme Without Trapdoors

Authors: Lu, X., Wen, Q., Wang, L., Du, J.
Journal: Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2016, 38(9), pp. 2287–2293
Abstract: The authors develop a lattice-based signcryption scheme that eliminates the need for trapdoors, enhancing both security and practical applicability. The scheme is tailored for secure communication in post-quantum scenarios, balancing confidentiality, authenticity, and efficiency.
Citations: 11

Conclusion

Dr. Xiuhua Lu is an outstanding candidate for the Best Researcher Award, owing to her groundbreaking work in quantum-resistant cryptography, blockchain technology, and federated learning. Her scholarly contributions are marked by innovation, practical relevance, and academic rigor, aligning seamlessly with the award’s objectives. Addressing areas like expanded collaboration, outreach, and leadership in large-scale projects could further elevate her already stellar profile.

Recommendation:

Awarding Dr. Lu this recognition would highlight her pivotal role in advancing cybersecurity and cryptographic research, inspiring further contributions in these critical fields.