Nanfu Zong | AI & Materials | Best Researcher Award

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Dr. Nanfu Zong | AI & Materials | Best Researcher Award

Leader of Digital Intelligence Research Institute at Ben Gang Group Corporation | China

Dr. Nanfu Zong is a visionary leader and pioneering researcher recognized for his transformative work at the intersection of artificial intelligence and the iron and steel industry. As the Director of the Digital Intelligence Research Institute at Ben Gang Group Corporation and a senior engineer of distinction, he has driven the intelligent, high-end, and green evolution of steel production through advanced AI integration. His expertise spans digital modeling, intelligent control, and sustainable process optimization, bridging theoretical innovation with industrial practice. With a robust research portfolio encompassing over forty SCI-indexed journal publications, sixteen major projects, and fifteen patents, Dr. Zong has significantly advanced digital manufacturing intelligence and industrial innovation. His leadership has inspired collaborations with global research powerhouses such as the University of Leicester, Tsinghua University, and major steel enterprises, reinforcing his role as a key figure in the digital transformation of metallurgical engineering. An active member of professional societies and editorial boards, he contributes to shaping the future of intelligent manufacturing through thought leadership and scientific rigor. His research excellence has earned him numerous accolades, including top provincial awards for scientific achievement, underscoring his impact on both academia and industry. Through his strategic vision and pioneering spirit, Dr. Zong continues to redefine how artificial intelligence can revolutionize traditional industries, promoting efficiency, sustainability, and innovation within the global steel sector.

Profile: Scopus

Featured Publications

Zong, N. F., Jing, T., & Gebelin, J. (2025). Intelligent empowerment for green steel manufacturing: Artificial intelligence‐driven process optimization.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning for tandem cold rolling: Exploring innovations, challenges, and industrial applications.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning techniques for the comprehensive analysis of the continuous casting processes: Slab defects.

Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

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Prof. Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

Distinguished Professor at University of the West of England | United Kingdom

Professor Quanmin Zhu is a distinguished academic and leading authority in control systems engineering, currently serving at the School of Engineering, University of the West of England, Bristol. With a career grounded in rigorous research and scholarly excellence, he has significantly advanced the fields of complex system modelling, identification, and control through both theoretical innovation and practical application. His prolific contributions include the authorship of over 300 peer-reviewed publications and the editorial oversight of major works with prestigious publishers such as Springer and Elsevier. A Chartered Engineer and Fellow of the Institution of Engineering and Technology (FIET) as well as the Higher Education Academy (FHEA), Professor Zhu is widely recognized for his commitment to bridging the gap between academic research and industrial practice. His expertise has been instrumental in shaping methodologies that enhance system performance, reliability, and adaptability across diverse engineering domains. As Editor of Elsevier’s Emerging Methodologies and Applications in Modelling, Identification and Control series, he continues to influence emerging directions in modern control theory and intelligent systems. Professor Zhu’s academic leadership, combined with his dedication to mentorship and collaboration, underscores his enduring impact on the global engineering community and his role in fostering innovation at the intersection of computation, automation, and control science.

Profile: Google Scholar

Featured Publications

Azar, A. T., & Zhu, Q. (2015). Advances and applications in sliding mode control systems.

Zhu, Q., & Azar, A. T. (2015). Complex system modelling and control through intelligent soft computations.

Li, S., Zhang, Y., & Zhu, Q. (2005). Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem.

Billings, S. A., & Zhu, Q. M. (1994). Nonlinear model validation using correlation tests.

Chen, J., Zhu, Q., & Liu, Y. (2020). Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs.

Sohong Dhar | Data Science | Analytics Excellence Award

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Dr. Sohong Dhar | Data Science | Analytics Excellence Award

Data Scientist at Jadavpur University | India

Dr. Sohong Dhar is a distinguished Information Scientist whose career bridges the fields of data science, digital marketing, and business analytics with remarkable proficiency. He is recognized for his ability to transform complex data into actionable insights that drive innovation, efficiency, and strategic growth across diverse industries. With expertise spanning machine learning, artificial intelligence, cloud computing, and advanced statistical analysis, he demonstrates an exceptional command of both theoretical and applied aspects of data-driven problem-solving. His multidisciplinary academic foundation, strengthened through advanced studies in data science and information science, has empowered him to approach challenges with analytical precision and creative foresight. Sohong has made impactful contributions to research, data modeling, and algorithmic development, delivering intelligent systems that enhance operational performance and decision-making processes. His fluency in multiple languages, combined with an understanding of literature and information systems, reflects a rare synthesis of technical acumen and intellectual versatility. He has collaborated effectively in cross-functional environments, employing platforms such as Microsoft Azure, SQL, and GCP to implement scalable and efficient data solutions. Beyond his technical mastery, Sohong’s work reflects a strong commitment to continuous learning, innovation, and excellence in the evolving domain of information and data science. His professional journey stands as a testament to the integration of analytical rigor, technological depth, and strategic thinking, establishing him as a forward-thinking expert dedicated to advancing the digital transformation landscape through intelligent, evidence-based insights and data-led decision frameworks.

Profile: Scopus

Featured Publications

Melba Kani, R., Karimli Maharram, V., Dhar, S., Samisha, B., Rajendran, P., & Ahmed, S. A. (2025). Automating grading to enhance student feedback and efficiency in higher education with a hybrid ensemble learning model.

Deepti, Nalluri, M., Mupparaju, C. B., Rongali, A. S., Dhar, S., & Ajitha, P. (2023). Retracted: Analyzing the impact of deep learning approaches on real-time data analysis in machine learning.

Nilay Kushawaha | Continual Learning for Robotics | Best Researcher Award

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Mr. Nilay Kushawaha | Continual Learning for Robotics | Best Researcher Award

PhD Scholar at Scuola Superiore Sant’Anna | Italy

Mr. Nilay Kushawaha is an innovative researcher in Artificial Intelligence and Robotics, specializing in continual learning, multimodal data fusion, and adaptive control for soft robotic systems. As a doctoral candidate at the Biorobotics Institute, Scuola Superiore Sant’Anna, his work bridges advanced AI modeling with experimental robotics, creating intelligent machines capable of learning and adapting in real time. His contributions reflect a deep understanding of neural computation, reinforcement learning, and data-driven control, with research outcomes published in leading journals such as IEEE Transactions on Neural Networks and Learning Systems and Advanced Robotics Research. Nilay’s approach combines theoretical insight with practical implementation, evident in his development of algorithms like SynapNet and AGPNN, which enhance robot perception and continual learning efficiency. His interdisciplinary expertise spans physics, machine learning, and robotic design, refined through global collaborations, including research at the National University of Singapore and Jefferson Lab in the USA. Recognized for academic excellence through multiple international scholarships and awards, Nilay also contributes to academic outreach by creating tutorials and coordinating robotics initiatives. His technical fluency in Python, C++, and ROS, along with proficiency in deep learning frameworks, complements his passion for intelligent system design. Dedicated to pushing the boundaries of bioinspired robotics, Nilay’s vision centers on developing autonomous systems capable of adaptive, human-like learning and perception. His research continues to contribute significantly to the advancement of continual learning in robotics, marking him as a promising scholar and innovator in intelligent autonomous systems.

Profile: ORCID

Featured Publications

Kushawaha, N., Fruzetti, L., Donato, E., & Falotico, E. (2024). SynapNet: A complementary learning system inspired algorithm with real-time application in multimodal perception.

Kushawaha, N., & Falotico, E. (2025). Continual learning for multimodal data fusion of a soft gripper.

Kushawaha, N., Perovic, G., Donato, E., & Falotico, E. (n.d.). AGPNN: A dynamic architecture-based continual reinforcement learning algorithm for robotic control.

Kushawaha, N., Nazeer, S., Laschi, C., & Falotico, E. (n.d.). SMPL: A continual learning approach for dynamic modeling of modular soft robots.

Kushawaha, N., Pathan, R., Pagliarani, N., Cianchetti, M., & Falotico, E. (2025). Adaptive drift compensation for soft sensorized finger using continual learning.

Kushawaha, N., Alessi, C., Fruzetti, L., & Falotico, E. (2025). Domain translation of a soft robotic arm using conditional cycle generative adversarial network.

Zhongqiang Zhang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Zhongqiang Zhang | Mechanical Engineering | Best Researcher Award

Dean at Jiangsu University | China

Professor Zhongqiang Zhang is a distinguished scholar in mechanical engineering whose research bridges fundamental mechanics and advanced nanotechnology. He has made significant contributions to the understanding of solid–fluid interfacial phenomena, including boundary slip, interfacial friction, and their implications for the design of next-generation micro/nano fluidic systems. As a faculty member and academic leader at Jiangsu University, his work has advanced the frontiers of nanofluidics, soft robotics, and smart material design through innovative integration of theory, computation, and experiment. Professor Zhang’s pioneering studies on graphene-based membranes, droplet dynamics, and energy-efficient fluid systems have been published in leading international journals such as Science Advances, Chemical Engineering Journal, and ACS Applied Materials & Interfaces. His research has introduced novel mechanisms for unidirectional droplet transport and enhanced bubble collection, providing new pathways for desalination, energy harvesting, and biomedical applications. Recognized for his excellence and originality, he has received multiple prestigious honors, including the ICCES Outstanding Young Researcher Award and the Jiangsu Provincial Science and Technology Awards. His projects funded by the National Natural Science Foundation of China underscore his leadership in advancing electro-mechanical coupling and interface-driven transport in microstructured materials. Professor Zhang’s work embodies the synergy of mechanics, materials, and nanotechnology, continually inspiring innovation in sustainable energy systems and intelligent fluidic devices. His scholarly achievements reflect a sustained commitment to interdisciplinary research excellence, making him a leading figure shaping the global discourse in advanced mechanical and fluid systems engineering.

Profile: Google Scholar

Featured Publications

Cheng, G. G., Jiang, S. Y., Li, K., Zhang, Z. Q., Wang, Y., Yuan, N. Y., Ding, J. N., et al. (2017). Effect of argon plasma treatment on the output performance of triboelectric nanogenerator.

Zheng, Y., Ye, H., Zhang, Z., & Zhang, H. (2012). Water diffusion inside carbon nanotubes: Mutual effects of surface and confinement.

Zhang, H., Ye, H., Zheng, Y., & Zhang, Z. (2011). Prediction of the viscosity of water confined in carbon nanotubes.

Zhang, H., Zhang, Z., & Ye, H. (2012). Molecular dynamics-based prediction of boundary slip of fluids in nanochannels.

Zhang, Z., Li, S., Mi, B., Wang, J., & Ding, J. (2020). Surface slip on rotating graphene membrane enables the temporal selectivity that breaks the permeability–selectivity trade-off.

Vinay Chaudhri | Knowledge Engineering | Best Researcher Award

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Dr. Vinay Chaudhri | Knowledge Engineering | Best Researcher Award

Principal Scientist at Knowledge Systems Research LLC | United States

Dr. Vinay K. Chaudhri is a distinguished technology executive and research scientist renowned for his pioneering contributions in artificial intelligence, knowledge representation, and automated reasoning. His work bridges the gap between deep research and impactful real-world applications across domains such as education, finance, and law. As a thought leader in knowledge graphs, semantic reasoning, and large language models, Dr. Chaudhri has led groundbreaking projects that demonstrate the transformative potential of AI in enhancing learning outcomes, improving financial intelligence, and enabling computational understanding of complex legal structures. His tenure at organizations including SRI International, Stanford University, JPMorgan Chase, and Knowledge Systems Research has been marked by visionary leadership and innovation. At SRI, he played a key role in landmark AI initiatives like Project Halo and CALO—the latter evolving into Apple’s Siri—demonstrating his ability to architect systems that advance both academic inquiry and commercial technology. At Stanford, his efforts in intelligent education tools such as the HaloBook and Intelligent Textbook showcased AI’s capacity to personalize and elevate learning experiences, while his leadership in the LogicForAll initiative expanded logic education nationwide. In the finance sector, his work integrating NLP and graph-based AI systems has delivered multi-million-dollar impacts through enhanced analytics, compliance, and decision intelligence. A prolific researcher with numerous publications and awards, Dr. Chaudhri continues to shape the future of AI with a vision grounded in rigorous science, ethical innovation, and societal benefit, making him a respected figure in the global AI research and technology community.

Profile: Google Scholar

Featured Publications

Chaudhri, V. K., Farquhar, A., Fikes, R., Karp, P. D., & Rice, J. P. (1998). OKBC: A programmatic foundation for knowledge base interoperability.

Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education.

Burger, J., Cardie, C., Chaudhri, V., Gaizauskas, R., Harabagiu, S., Israel, D., … & Weischedel, R. (2001). Issues, tasks and program structures to roadmap research in question & answering (Q&A).

Karp, P. D., Chaudhri, V. K., & Thomere, J. (1999, July). XOL: An XML-based ontology exchange language.

Chaudhri, V. K., Farquhar, A., Fikes, R., Karp, P. D., & Rice, J. P. (1998). Open knowledge base connectivity 2.0.

Bincy Kaluvilla | Artificial Intelligence | Best Academic Researcher Award

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Dr. Bincy Kaluvilla | Artificial Intelligence | Best Academic Researcher Award

Head of Academics at Learners University College | United Arab Emirates

Dr. Bincy B. Kaluvilla is an accomplished academic leader and researcher whose work bridges the disciplines of sustainable finance, hospitality management, and real estate investment. Her professional journey reflects a deep commitment to academic excellence, innovation, and the advancement of sustainability-focused business education. As an experienced higher education professional, she has played a transformative role in shaping curricula and fostering strategic partnerships that align academic programs with contemporary industry practices. Her teaching portfolio encompasses subjects such as Real Estate Finance, Hospitality Accounting, and Corporate Finance, delivered across international undergraduate and postgraduate programs. A Fellow of the Higher Education Authority (UK) and a CPA Australia member, she brings a strong foundation in finance and accounting to her academic leadership. Her scholarly contributions span peer-reviewed journals, book chapters, and international conferences, exploring topics including ESG reporting, sustainable investment, AI integration in hospitality, and the evolving intersections of culture, ethics, and finance. Notable among her works are publications in Frontiers in Computer Science, Asia Pacific Journal of Tourism Research, Performance Measurement and Metrics, and Journal of Open Innovation. She has also contributed to edited volumes published by Springer Nature, IGI Global, Emerald, and Elsevier. Beyond research and teaching, Dr. Kaluvilla has led numerous corporate training programs for leading organizations such as the Jumeirah Group and Omran Group, promoting financial literacy and leadership within the hospitality sector. Her contributions have been recognized globally through awards and invitations to serve as visiting faculty at institutions in Malta, Japan, and China. Through her research, teaching, and leadership, she continues to champion sustainability, innovation, and excellence in global higher education and industry practice.

Profile: Google Scholar

Featured Publications

Kaluvilla, B. B., Kalarikkal, S. A., & Thamilvanan, G. (2025). AI-driven extraction and intelligent retrieval of missionary archives in Malabar: Advancing preservation and accessibility with machine learning.

Mulla, T., Kaluvilla, B. B., Zahidi, F., Alsabbah, S., & Tantry, A. (2025). “Your house looks like that show…”: Exploring consumers’ perceptions towards media-inspired home décor.

Bouchon, F., Kaluvilla, B. B., & Kolmorgon, K. (2025). Sustainable luxury hospitality: A reality beyond antagonistic terms? Innovations and trends in Maldivian luxury resorts.

Thomsen, K., Kaluvilla, B. B., & Zahidi, F. (2025). Sustainable wildlife tourism: Government guidelines and lodge contributions in Zambia.

Kaluvilla, B. B. (2025). Review of The Routledge handbook of religious and spiritual tourism, by D. H. Olsen & D. J. Timothy.

Muhammad Danish Ali | Bioinformatics | Best Researcher Award

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Mr. Muhammad Danish Ali | Bioinformatics | Best Researcher Award

PhD Scholar at Jeju National University Republic of korea | South Korea

Mr. Muhammad Danish Ali is a dedicated researcher and emerging scholar in computer science whose work bridges artificial intelligence, deep learning, and computer vision to address critical problems in medical imaging. As a PhD Research Scholar at Jeju National University, Republic of Korea, he is focused on developing meta-learning and ensemble-based deep neural frameworks for cancer detection and medical diagnostics. His academic foundation, rooted in strong research training from COMSATS University Islamabad and Gomal University, has shaped his analytical approach to solving real-world computational challenges. Danish has authored impactful papers in leading international journals, including works on breast cancer classification through meta-learning ensemble techniques, automatic melanoma diagnosis via adaptive fine-tuned convolutional networks, and advanced deep learning models for skin cancer classification. His research further extends to projects involving object detection, plant disease recognition, and explainable AI, showcasing a versatile command over both theoretical and applied aspects of machine learning. In addition to his scholarly pursuits, he contributes to academia as a lecturer and mentor, guiding students in computer science and fostering innovation through research-driven pedagogy. His technical proficiency spans Python, TensorFlow, Keras, MATLAB, and computer vision frameworks such as YOLO and GANs, reflecting his comprehensive skill set across AI technologies. Danish’s academic achievements and conference publications highlight his commitment to advancing computational intelligence and medical informatics. A passionate learner and innovator, he envisions leveraging AI-driven solutions to enhance healthcare diagnostics, promote automation, and contribute to scientific progress through collaborative global research.

Profile: Google Scholar

Featured Publications

Ali, M. D., Saleem, A., Elahi, H., Khan, M. A., Khan, M. I., Yaqoob, M. M., et al. (2023). Breast cancer classification through meta-learning ensemble technique using convolution neural networks.

Javid, M. H., Jadoon, W., Ali, H., & Ali, M. D. (2023). Design and analysis of an improved deep ensemble learning model for melanoma skin cancer classification.

Khan, M. A., Mazhar, T., Ali, M. D., Khattak, U. F., Shahzad, T., Saeed, M. M., et al. (2025). Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.

Ali, M. D., Mazhar, T., Shahzad, T., Rehman, W. U., Shahid, M., & Hamam, H. (2025). An advanced deep learning framework for skin cancer classification.

Ali, M. D., Han, I. C., & Kim, S. K. (2025). Advanced skin cancer detection using dual partial attention aware multiple convolutional framework

Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

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Dr. Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

Post-Doctoral Fellow at Emory University | United States

Dr. Bhavesh Kataria is a highly accomplished academician, researcher, and innovator in Computer Engineering, recognized globally for his leadership in Artificial Intelligence, Machine Learning, and Digital Image Processing. His professional journey spans academia and research institutions across India and the United States, including his role at Emory University, where he contributes to advanced AI-driven healthcare analytics and digital pathology solutions. With a Ph.D. focused on Optical Character Recognition of Sanskrit Manuscripts using Convolutional Neural Networks, Dr. Kataria has combined technical precision with deep domain expertise to address challenges in multilingual text recognition and medical imaging. His scholarly portfolio includes numerous publications in reputed international journals, multiple granted patents, and several authored books covering cutting-edge topics in AI, cloud computing, and web technologies. An active member of prestigious organizations such as IEEE and ACM, he serves on editorial boards of international journals and as a reviewer for globally recognized publishers like Springer Nature and Science Publishing Group. He has also chaired sessions and reviewed Ph.D. theses, contributing significantly to the academic ecosystem. Dr. Kataria’s pioneering innovations, such as AI-based network visualization tools, smart teaching devices, and healthcare monitoring systems, underscore his commitment to translational research and practical AI applications. Honored with awards including the Best Researcher Award and Teaching Excellence Award, he exemplifies a blend of scholarly excellence, innovation, and mentorship. His dedication to advancing intelligent systems and promoting interdisciplinary research continues to inspire global collaboration in emerging computational technologies.

Profiles: Scopus | ORCID

Featured Publications

Kataria, B., & Jethva, H. B. (2024, September 30). Decentralized security mechanisms for AI-driven wireless networks: Integrating blockchain and federated learning.

Kataria, B. (2024, June 2). Automated detection of tuberculosis using deep learning algorithms on chest X-rays.

Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep learning based image classification of lungs radiography for detecting COVID-19 using a deep CNN and ResNet 50.

Shivadekar, S., Kataria, B., Limkar, S., Wagh, K., Lavate, S., & Mulla, R. (2023, June 15). Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process.

Kataria, B., Jethva, H. B., Shinde, P. V., Banait, S. S., Shaikh, F., & Ajani, S. (2023, April 30). SLDEB: Design of a secure and lightweight dynamic encryption bio-inspired model for IoT networks.

Xiaolin Zhu | Computer Vision | Best Researcher Award

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Dr. Xiaolin Zhu | Computer Vision | Best Researcher Award

Lecturer at Xiangtan University | China

Dr. Xiaolin Zhu is a dynamic researcher and lecturer at the School of Automation and Electronic Information, Xiangtan University, China, specializing in advanced computer vision and deep learning. His scholarly pursuits focus on video understanding, group activity recognition, and multi-object tracking, with a strong commitment to developing intelligent algorithms that enhance human–machine perception and real-world visual interpretation. A prolific author, Dr. Zhu has published eight influential papers, including contributions in IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems for Video Technology, and Expert Systems with Applications, one of which has garnered over one hundred citations. His innovative research has also led to five granted Chinese patents and one software copyright, demonstrating his skill in translating theoretical insights into practical applications. Dr. Zhu has collaborated with top institutions, including the University of Technology Sydney and Shanghai Jiao Tong University, advancing cross-disciplinary innovation and producing four notable joint publications. As a member of professional organizations such as IEEE, the Chinese Association of Automation, and the Chinese Institute of Electronics, he remains an active contributor to the scientific community. His recent comprehensive review on deep learning-based group activity recognition offers a refined taxonomy of methodologies from 2016 to 2024, mapping out the evolution of the field through supervision types, network architectures, modeling mechanisms, and input modalities. Recognized for his rigorous analytical approach and consistent academic excellence, Dr. Zhu represents the new generation of AI scholars pushing the boundaries of visual intelligence and autonomous systems, making significant strides toward the future of intelligent surveillance, human activity analysis, and video-based behavioral prediction.

Profile: Google Scholar

Featured Publications

Zhang, X., & Zhu, X. (2019). Autonomous path tracking control of intelligent electric vehicles based on lane detection and optimal preview method.

Zhu, X., Zhou, Y., Wang, D., Ouyang, W., & Su, R. (2022). Mlst-former: Multi-level spatial-temporal transformer for group activity recognition.

Wu, D., Qu, Z. S., Guo, F. J., Zhu, X. L., & Wan, Q. (2019). Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods.

Zhu, X., Wang, D., Li, J., Su, R., Wan, Q., & Zhou, Y. (2024). Dynamical attention hypergraph convolutional network for group activity recognition.

Zhu, X., Wang, D., & Zhou, Y. (2023). Hierarchical spatial-temporal transformer with motion trajectory for individual action and group activity recognition.