Paper Title: “TrustFedHealth: Federated Learning with Homomorphic Encryption and Blockchain for Heart Disease Prediction in Smart Healthcare” Student Contributors: Bùi Tấn Hải Đăng (Talent - Information Security 2020) Phan Hữu Luân (High Quality - Information Security 2020.1) Vương Đinh Thanh Ngân (High Quality - Information Security ATCL2020.1) Supervisors: TS. Phạm Văn Hậu ThS Phan Thế Duy Paper Summary: In recent years, advances in the Internet of Medical Things (IoMT) or smart devices have enabled automatic human health monitoring. The use of smart healthcare devices can not only reduce the burden on hospitals but also save costs, travel time, and provide home-based disease diagnosis, such as stroke rates. IoMT generates a large amount of data that can be used to train machine learning (ML) models for accurate disease diagnosis. However, data used to train ML models is often privacy-preserving, making sharing difficult and requiring high security. To overcome this challenge, this paper proposes a collaborative framework called TrustFedHealth to train heart disease prediction models based on ML. TrustFedHealth uses Federated Learning (FL) to enable training with decentralized data stored separately on multiple machines. Additionally, Mobile Edge Computing (MEC) is integrated into the model as a solution to optimize communication time between different elements of the system and reduce congestion. Homomorphic Encryption (HE) is also combined with FL to protect the security of model updates between client and aggregate server. Furthermore, Blockchain (BC) is leveraged to address the traceability of contributions and ensure the transparency of model updates. Through evaluation results on the MIT-BIH Arrhythmia Database from Physionet, TrustFedHealth provides a promising method for training ML-based heart disease prediction models while maintaining privacy and security. "We would like to express our gratitude to Dr. Phạm Văn Hậu and Mr. Phan Thế Duy for their dedicated support and assistance throughout our research." The International Conference on Advanced Technologies for Communications (ATC) is an annual scientific conference specializing in electronics, communications, information technology, and related fields. The conference opens up opportunities for exchange, research presentation, technology transfer, and introduction of products from businesses operating in these fields. The conference has been held annually since 2008, attracting over 200 papers with about 150 scientists from many countries worldwide. ATC 2023 was hosted by the University of Information Technology and Communication Vietnam - Korea and took place in Da Nang City, Vietnam from 19-21/10/2023. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02R4eK85sSVFrnAoRPfZrtzsgihuaWYXvK7QJW5dvRMWi8WptdUfT1ow7rwebL3ArAl Written by: Hai Bang Translated by: Ngoc Diem

Dear all students and guiding professors, The Department of Training and Scientific Research (DTSĐH&KHCN) announces the list of student research projects registered for approval in the second round of 2023. The department requests all students and guiding professors to check the information for any discrepancies and provide feedback to the department (qlkhcn@uit.edu.vn) before December 11, 2023. Attached file: k01.nckh-sv-xetduyet-dec2023-dot2.pdf   For more details, please visit: https://khcn.uit.edu.vn/danh-sach-de-tai-nckh-sv-dang-ky-xet-duyet-dot-2-2023 Written by: Ha Bang Translated by: Ngoc Diem

A group of students majoring in Information Security (Computer Networks and Communications Faculty) participated in presenting their research results on the topic of Blockchain at the 12th International Conference on Computational Data and Social Networks (CSoNet 2023) held from December 11-13, 2023, in Hanoi. Conference Paper: "A Multi-chain Interoperable Data and Access System for Healthcare" Students: Nguyen Bui Kim Ngan (Information Security - Talent Program 2020), Nguyen Binh Thuc Tram (Information Security - Talent Program 2020) Supervisors: M.S. Tran Tuan Dung, Dr. Pham Van Hau Conference Information: The 12th International Conference on Computational Data and Social Networks (CSoNet 2023) provides an interdisciplinary forum to gather researchers and scholars from the field of artificial intelligence related to large-scale data networks and applications in economics. The conference covers topics such as large-scale network computing, data network analysis, mining, security, privacy, as well as deep learning and information propagation on social and economic networks. The goal and content of the CSoNet 2023 workshop are to create a forum where scientists discuss new and important computational issues, focusing on fundamental platforms, developing theoretical technology, and their real-world applications related to analysis, modeling, and application in economic networks. The conference has attracted significant interest from scientists domestically and internationally. The organizers received scientific papers from scholars, researchers, and lecturers from Vietnam and 18 other countries, including the United States, the United Kingdom, South Korea, France, Germany, Ireland, Poland, Norway, Russia, China, Japan, India, Taiwan (China), Hong Kong (China), Saudi Arabia, Singapore, Thailand, Iran, among others. 41 papers were selected for presentation in 4 sessions in the following areas: machine learning and forecasting, optimization, security and blockchain, and social network analysis. 23 outstanding papers were accepted in the regular paper category, with an acceptance rate of 36%... The accepted conference papers will be published in the Lecture Notes in Computer Science series by Springer, indexed in ISI and SCOPUS. NEU will also have a special issue dedicated to the best papers presented at the conference in ISI-listed journals: the Journal of Combinatorial Optimization and IEEE Transactions on Network Science and Engineering. Link: https://csonet-conf.github.io/csonet23/index.html For more details, visit: https://www.facebook.com/inseclab/posts/pfbid032ePdTnDNbudg5sN... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Paper: "Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names" Paper link: Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names Student involved: Pham Tien Duong – 20521222 – Data Science 2020 – Main author Supervisor: MSc. Nguyen Thanh Luan Summary of the paper: Every human has their own name, a fundamental aspect of their identity and cultural heritage. The name can often convey a wealth of information, including details about an individual’s background, ethnicity, and, especially, their gender. By detecting gender through the analysis of names, researchers can unlock valuable insights into linguistic patterns and cultural norms, which can be applied to practical applications. In this paper, we release a novel large-scale dataset for Japanese name gender detection comprising 64,139 full names in romaji, hiragana, and kanji forms, along with their biological genders. Moreover, we propose Gendec, a machine learning-based framework for gender detection from Japanese names that leverages diverse approaches, including traditional machine learning techniques or state-of-the-art transfer learning approaches, to predict the gender associated with Japanese names accurately. Through a thorough investigation, the proposed framework is expected to be effective and serve potential applications in various domains. The ISDA conference is a reputable international conference held annually, focusing on research in Intelligent Systems and their real-world applications. The main theme of ISDA’23 is the application of large language models and generation in the real world. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid025YnynHGuk1YQB52YBoFrGxkdg... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Paper: "Transformer-based End-to-End Object Detection in Aerial Images" Authors: Hoang Ngoc Cu – 20520145 – IT2020 – Main author Hua Phu Thien – 20521946 – IT2020 – Co-author Supervisors: M.S. Vo Tan Khoa M.S. Nguyen Thi Anh Thu Prof. Dr. Nguyen Hoang Tu Anh Summary of the paper: Scholarship administration plays a critical role within higher educational systems, ensuring deserving students have the financial resources to pursue their education. Nevertheless, conventional scholarship administration methods often encounter fraud, mismanagement, restricted accessibility, and the difficult process of authenticating scholarship applicants. The potential of blockchain technology lies in revolutionizing scholarship administration by providing a decentralized, transparent, and secure structure. Despite its potential, blockchain faces scalability challenges, owing to its lower throughput and latency performance compared to non-blockchain systems. This study introduces a decentralized scholarship management model that leverages blockchain technology and zk-rollups, a layer 2 scaling solution. The implementation is on Polygon zkEVM, using Solidity for smart contracts and other contemporary technologies. The experiment conducted by the researchers revealed that the application utilizing zk-rollup technology necessitates transaction costs that are approximately 25 times lower and operates nearly 53% faster than the system implemented on layer 1. "The group sincerely thanks Ms. Nguyen Thi Anh Thu, Mr. Vo Tan Khoa, and Mr. Nguyen Tri for guiding the group on the process and development direction of our research. The group expresses gratitude to Ms. Nguyen Hoang Tu Anh and Mr. Dinh Ngoc Thanh for their support. The group would like to thank Mr. Nguyen Hoang Long for participating in technical support exchanges with the group." The International Conference on Intelligent Systems Design and Applications (ISDA) is a major international conference bringing together researchers, engineers, and practitioners who work in the areas of intelligent systems and its applications in industry and the real world. The conference proceedings will be published by Springer in the Lecture Notes in Networks and Systems, which is currently indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago, and are submitted for consideration in Web of Science. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02dVS97aEnSihns9bTbMz... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Paper: "Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic" Paper link: Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic Students involved: Chau Tan – 20520926 – KHDL2020: Main author. Ngo Duc Vu – 20520950 – KHDL2020: Co-author. Nguyen Minh Tri – 20522052 - KHDL2020: Co-author. Nguyen Tran Anh Duc – 20521198 – KHDL2020: Co-author. Supervisor: M.S. Do Trong Hop Summary of the paper: Vehicle trajectory prediction is a crucial task in the field of intelligent transportation systems, with applications in traffic management, autonomous driving, and advanced driver assistance systems. In this project, we aim to demonstrate the capabilities of this technology by collecting and analyzing video footage of traffic in Thu Duc District, Ho Chi Minh city. Utilizing YOLOv7 for object detection and DeepSORT for object tracking, we are able to accurately gather the coordinates of vehicles in the scene. Using these coordinates, we then employ various hybrid models such as CNN-LSTM and CNN-GRU to predict the future trajectory of the vehicles. The results are then visualized and superimposed onto the original video footage to showcase the capabilities of these predictive models. "We would like to express our gratitude to Mr. Do Trong Hop for accompanying and guiding our group throughout the research and publication process of this international scientific paper." The ICIT 2023 conference is the International Conference on Intelligence of Things sponsored by Springer. This conference focuses on the latest technologies, applications, and research in the field of the Internet of Things, Intelligence of Things (IoT2/AIoT), and related areas of Information Technology. Accepted papers presented at the conference will be published by Springer in the Lecture Notes on Data Engineering and Communications Technologies series, indexed by SCOPUS (Q3). For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0D8N7c1Lr2FWZeM... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Congratulations to student Nguyen The Vo Quyen Anh, Student ID: 22520060 from the Faculty of Software Engineering, for excellently achieving the IELTS 7.5 certificate. We wish you continued success in your studies with the best achievements! UIT always encourages students to cultivate and enhance their language skills towards becoming global citizens and comprehensive role models. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0Puhe7FKG2U... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Congratulations to student Nguyen Duy Tan, Student ID: 22521304 from the Faculty of Computer Science, for excellently achieving the IELTS 6.5 certificate. We wish you continued success in your studies with the best achievements! UIT always encourages students to cultivate and enhance their language skills towards becoming global citizens and comprehensive role models. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02bHz6WbBbMeCCoVjNSqdQ... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Congratulations to student Le Tan Hoa, Student ID: 21522081 from the Faculty of Information Science and Engineering, for excellently achieving the JLPT N3 certificate. We wish you continued success in your studies with the best achievements! UIT always encourages students to cultivate and enhance their language skills towards becoming global citizens and comprehensive role models. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0CPzRe8sByR7W9yg5QPAev... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Paper: "Transformer-based End-to-End Object Detection in Aerial Images" Paper link: Transformer-based End-to-End Object Detection in Aerial Images Students involved: Le Duy Nguyen – KTPM2022.2 – Co-author Le Huu Do - KTPM2022.1 – Co-author Ngo Huong Giang – KTPM2022.1 – Co-author Doan Danh Du - KTPM2022.1 – Co-author Supervisors: Dr. Nguyen Tan Tran Minh Khang M.S. Vo Duy Nguyen Summary of the paper:Transformer models have achieved significant milestones in the field of Artificial Intelligence in recent years,primarily focusing on text processing and natural language processing. However, the application of these models in the domain of image processing, particularly on aerial images data, is actively research. This study concentrates on the experimental evaluation of Transformer-based models such as DETR, DAB-DETR, and DINO on the challenging Visdrone dataset, which is also essential for aerial image data processing. The experimental results indicate that Transformer-based models exhibit substantial potential, especially in object detection on aerial image data. Nevertheless, their application is not without challenges, including low resolution, dense object occurrences, and environmental noise. This work provides an initial glimpse into both the capabilities and limitations of Transformer-based approaches within this domain, with the aim of stimulating further development and optimization for practical applications, including traffic monitoring, environmental protection, and various other domain "We sincerely thank the Faculty of Software Engineering, Multimedia Communications Laboratory, and the UIT-Together research group for creating conditions that helped us research and complete this paper." IJACSA (International Journal of Advanced Computer Science and Applications) is a reputable computer science journal representing leading research. The research, evaluations, and surveys published by IJACSA make significant contributions to the computer science industry and stimulate the interest of a large readership. The scope of the journal includes all major areas of computer science and related applications. The journal is indexed by Scopus, Scimagojr (Q3), and Web of Science (ESCI). Top of Form For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0uJudDhkHbEhZz... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Paper: "UIT-VehPredTraj: The Pioneer Dataset for Vehicle Trajectory Prediction Problem in Vietnamese Traffics" Paper link: UIT-VehPredTraj Authors: Chau Tan – 20520926 – KHDL2020: Main author. Ngo Duc Vu – 20520950 – KHDL2020: Co-author. Nguyen Tran Anh Duc – 20521198 – KHDL2020: Co-author. Supervisor: M.S. Do Trong Hop Summary of the paper: Predicting the trajectory of vehicles plays a crucial role in the field of intelligent transportation systems, autonomous driving, and advanced driver assistance systems. This article documents the development process of the UIT-VihPredTraj dataset, where advanced methods such as YOLOv7 for accurate object detection and DeepSORT for precise object tracking were utilized. Using these techniques, we effectively captured and recorded the precise coordinates of vehicles in the landscape, laying the foundation for a comprehensive vehicle trajectory dataset, specifically tailored for Vietnamese traffic. We also conducted a series of experiments using various deep learning models, including LSTM, GRU, CNN-LSTM, CNN-GRU, ResNet, and Transformer, to predict the future trajectories of vehicles. After analysis, the results showed that CNN-LSTM outperformed other methods, demonstrating superiority in accurately predicting the vehicle trajectories based on the collected dataset. Through this effort, we not only aim to advance in the field of vehicle trajectory prediction but also provide a valuable resource for the development of innovative automotive applications in Vietnam. "We would like to express our gratitude to Mr. Do Trong Hop for accompanying and guiding our group throughout the research and publication process of this scientific paper at the national conference." VNICT 2023 is the 26th National Conference on "Selected Issues in Information Technology and Communications" organized by the Institute of Information Technology - Vietnam Academy of Science and Technology and the Technical University - Logistics Department of the Ministry of Public Security. The conference took place in Bac Ninh on October 5-6, 2023, with the collaborative participation of the Club of IT Schools and Institutes in Vietnam (FISU). The conference is an annual forum for researchers, implementers, educators, and managers in the field of information technology and communications to exchange academic knowledge, share application experiences, and seek collaboration. The conference particularly encourages young scientists, researchers, and postgraduate students to present and exchange their research and study results. It also promotes the exhibition of scientific and technological products and the application of research results into practice. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0uLeznBTnHNP... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Congratulations to student Ly Van Nhat Tien, Student ID: 21521525, from the Faculty of Computer Science, for excellently obtaining the IELTS 7.5 certification. Wishing you continued success in your studies with the best achievements! UIT always encourages students to cultivate and enhance their language skills, aiming for a global citizen generation and comprehensive student role models. Top of Form For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02He2fbSEHc1NvY47RLS... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology