We often hear people say that smoking causes lung cancer. Is this statement really true? Apart from smoking, there are other factors (confounders) such as age, gender, and environmental factors that can contribute to the development of lung cancer. This is a classic example of Causal Inference. Causal Inference in statistics is often confused with prediction analysis in Machine Learning and computer science due to their technical similarities, ranging from PCA and SVM to Neural Networks. The fundamental difference between these two fields lies not in the methods used but in the purpose of utilizing the data. Statistics primarily focuses on inference, while Machine Learning concentrates on prediction. Data Science, a burgeoning field, emerges at the intersection of these two domains. We are well-acquainted with the applications of Machine Learning in Data Science, but what about the role of causal inference in this field? Firstly, we need to distinguish between two concepts: Inference and Prediction. Inference is the process of understanding the factors and mechanisms behind observed data and generalizing them. On the other hand, Prediction aims to determine what phenomenon will occur for a new data point. For instance, using the Random Forest machine learning algorithm to predict the progression of diabetes based on biochemical markers yields a prediction. Conversely, identifying which markers are crucial and have the most impact on disease progression, using techniques like feature_importance, can be considered causal inference. As the number of features increases, improving the accuracy of the model, inferring the influence of each feature on the outcome becomes challenging due to issues like high dimensionality and interaction effects. Causal inference guides us in making interventions or controlling what is happening in reality. In many cases, intervention and control are the most valuable outcomes Data Science can provide. Particularly in medical research, we not only want to predict a patient's lifespan based on their biochemical status when diagnosed with a disease, but we also need to propose interventions that can prolong that time. Even though predictions, even highly accurate ones, become nearly meaningless if we cannot infer the mechanisms influencing the output variable. Performing causal inference analysis is not easy; it requires a significant amount of time, resources, and human effort to produce meaningful results in the end. In practice, the application of causal inference or prediction analysis depends on the job requirements and the intended use of the data. In business and technical environments, prediction is often more common, predicting user behavior, prices, customer segments, fraud detection, etc. where intervening to change the outcome is almost irrelevant compared to analyzing what the outcome is. In contrast, in research-oriented environments such as science, healthcare, and social sciences, emphasizing intervention and understanding how they affect the outcomes is crucial to eliminate and minimize negative factors. Therefore, causal inference plays a significant role not only in applied Data Science fields but also in bringing stability and progress to individuals and society. So, if you were to work in the field of Data Science, which aspect would you prefer: causal inference or prediction? References: Linh Nghiem, Data Science: Inference or Prediction?: https://linhnghiem.org/2019/11/03/khoa-hoc-du-lieu-suy-luan-hay-du-doan/?fbclid=IwAR3viwLdBeq8GNwJ4-0eDvGRasZ2uy3o2j6RgHuDk8FeQwgBHyYPDsXcwnU Causal Inference for The Brave and True: https://matheusfacure.github.io/python-causality-handbook/landing-page.html?fbclid=IwAR1mpqr0iZdXJQ-EBlHKH25zaYssB_J5lAt51RVZniwgMRApanW7cS5og4s Detailed Information: https://www.facebook.com/dsociety.uit.ise/posts/pfbid0Nzbs8pLFfVCHDwEfgg5GicZCBs6WfgHJkZS9ozvpcBJgeiesej3ATpRhQTwNqUhYl Hai Bang - Communication Collaborator, University of Information Technology English version: Phan Huy Hoang

Title: "Study and Implementation of AQI Predictive Recommendation System Based on Artificial Intelligence" has been published at the International Conference on Intelligent Systems and Data Science (ISDS 2023). Authors: Nguyen Van Luc – 19521811 – HTTT2019 - Main Author Le Van Anh Duc - 19521374 – HTTT2019 – Co-Author Nguyen Thi Viet Huong – 19521595 – TMDT2019 – Co-Author Supervisors: Assoc. Prof. Dr. Nguyen Dinh Thuan Mr. Nguyen Minh Nhut – 17520867 – Computer Networks major, class of 2017 Summary: As air quality continues to deteriorate in many large cities worldwide, there is a growing concern about updating and monitoring air quality information. Based on the Air Quality Index (AQI) of Ho Chi Minh City and hourly pollution levels from April to June 2023, this study utilizes time series analysis to forecast AQI using Recurrent Neural Network (RNN), Linear Regression (LR), Long Short-Term Memory (LSTM), and Q-Learning deep learning algorithms. Subsequently, we have developed a mobile application called SmartAQI, a social platform to recommend hourly locations with good air quality, display air quality for the next seven days, and identify the SmartAQI locations in Ho Chi Minh City. We evaluated machine learning algorithms using RMSE, MAE, and MAPE to predict AQI. The experimental results show that the RNN model achieves the highest accuracy in this experiment. We express our gratitude to Assoc. Prof. Dr. Nguyen Dinh Thuan and Mr. Nguyen Minh Nhut for their dedicated guidance, pointing out limitations, and suggesting possible improvements, which significantly contributed to the success of our research. The International Conference on Intelligent Systems and Data Science (ISDS 2023) will be held in Can Tho on November 11-12, 2023. The conference aims to attract researchers from both domestic and international institutions to participate and present recent outstanding research in the field of Computer Science and Information Technology. This conference provides an opportunity for scientists to meet, exchange ideas, and collaborate. ISDS 2023 also serves as a platform for students to report and explore new findings in the field of Computer Science and Information Technology. The conference reviews advanced and original research issues in intelligent systems and data science topics. For more detail, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid0XqELYFwYiDuP... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Title: "DeFL-BC: Empowering Reliable Cyberattack Detection through Decentralized Federated Learning and Poisoning Attack Defense" Link to the paper: https://drive.google.com/file/d/1OceAmIbHjbcO-5EwXCQqpY6tDGTdZ1eK/view?fbclid=IwAR1IqfV8GSNav_w38cwWA6DvvKXZ89_4VB8J0BJ4RtoNIjs2pVpZUbRZyyk Student: Nguyen Duy Hoang – 19521537 – ATCL2019.2 Supervisors: M.S. Phan The Duy M.S. Tran Tuan Dung Abstract: Federated Learning (FL) is a powerful method for training machine learning models without requiring the sharing of personal data. Each party trains a local version of the model on their own data. The training results from all parties are aggregated to create a global model. Federated Learning is particularly useful in situations with sensitive and hard-to-share data. However, federated learning faces challenges related to poisoning attacks, where a malicious party can manipulate or inject noise into the data during training, potentially harming the machine learning system. Therefore, integrating blockchain technology into federated learning becomes essential. Blockchain is a decentralized and tamper-proof system that reduces the risk of system failures and protects against malicious collaborators and poisoned data through consensus mechanisms. This work presents a collaborative and trust assurance model for network attack detection using Federated Learning (FL) in edge computing environments. This study leverages the capabilities of Federated Learning (FL) and blockchain technologies, specifically the Hyperledger Fabric platform, to construct a decentralized Federated Learning model, named DeFL-BC framework that can encourage contribution from the community and resist to poisoning attacks. Our framework aims to detect and prevent network attacks effectively in the Industrial Internet of Things (IIoT) contexts with the knowledge sharing from collaborative participants without privacy leakage. The experiments conducted on two datasets, Edge-IIoTset and CIC- IDS2018 demonstrate that the proposed framework is robust inreducing a single point of failure of the system, and showing outstanding performance under poisoning attacks when the accuracy is up to more than 90% and addressing limited computing resources and intermittent network connectivity in cyberattack scenarios. Furthermore, the integration of blockchain technology enhances security and resilience, effectively mitigating the risks of the single point of failure in conventional FL approaches. I would like to express my sincere gratitude to M.S. Phan The Duy and M.S. Tran Tuan Dung for their continuous support and motivation throughout the research, as well as their valuable input that helped improve the study. The RIVF conference is an international event on Information Technology and Communication, a major international scientific event that brings together scientists and researchers in the fields of computer science and communication technology in Vietnam and worldwide under the theme "Research, Innovation, and Vision for the future," abbreviated as RIVF. The RIVF conference is listed in the prestigious conference list recommended by SCOPUS and ISI Web of Science. RIVF has been organized 16 times, and the 2023 edition marks its 16th occurrence. The RIVF 2023 conference focuses on the following main topics: Image, language, and speech processing; Communication & Computer Networks, Network Security; Distributed systems, Internet of Things, Cloud computing; Artificial Intelligence, Data Science, Big Data Analysis, Intelligent Computing; Software engineering, Information Systems, Computational models. RIVF conferences began in 2003 as a joint effort of professors Patrick Bellot, Marc Bui, Duong Nguyen Vu, and colleagues from various countries, including Professor Nguyen Dinh Tri and professors from the Institute of Francophonie for Informatics (IFI) in Hanoi. By 2007, RIVF had transformed from a French-speaking computer science conference organized in Vietnam into an international conference of the IEEE (Institute of Electrical and Electronics Engineers) covering both IT & Communication Technology, with significantly improved quality. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid07TPxkX9E3... Hai Bang - Media Collaborator, University of Information Technology Nhat Hien - Translation Collaborator, University of Information Technology

Article: "Lite FPN_SSD: A Reconfiguration SSD with Adapting Feature Pyramid Network for Small Object Detection" Student: Nguyen Dang Duc Manh – 22520847 – KHMT2022.2 Supervisor: Dr. Duong Viet Hang Summary of the article: Detecting small objects poses a significant challenge in the field of computer vision due to the low-resolution and blurred features caused by pooling operations across convolutional layers. While single-stage detection algorithms have partially addressed this issue, they still contain redundant features, leading to resource wastage and increased computation time. This research primarily focuses on the SSD algorithm and an enhanced version, Lite FPN-SSD (Lite Single Shot MultiBox Detector with Feature Pyramid Network), proposed to overcome SSD's limitations. The Lite FPN-SSD model utilizes the FPN (Feature Pyramid Network) architecture combined with SSD to create a unified module for controlling features at both shallow and deep levels. With its lightweight design and a slight increase in parameters, the proposed model ensures high efficiency for real-time applications. Extensive experiments were conducted on VOC, VEDAI, and SOHAS datasets, demonstrating impressive results of the proposed models compared to the original SSD and its variants. Notably, with just an additional 2 million parameters, the proposed model achieved an average precision (mAP) of 78.36% on the VOC dataset, approaching another architecture that achieved an mAP of 78.40% but required more than 2.6 million parameters. I express my deep gratitude to Dr. Duong Viet Hang, my supervisor, for guiding me throughout this research. She has consistently supported and answered my queries during the research process and publication of this international scientific paper. Moreover, she has been a source of motivation, inspiring my passion and dedication to complete this study. I also want to extend my thanks to the Research Fund of the University of Information Technology (UIT-VNUHCM) for the financial support provided for my research project. The SOICT 2023 Conference (Symposium on Information and Communication Technology) is an annual international conference focusing on new research in critical areas such as AI Foundations and Big Data, Network Communication and Security, Image and Natural Language Processing, Software Engineering and Digital Technology, Blockchain, and Operations Research trends. The conference, technically sponsored by ACM, is the 12th international edition and will take place in Ho Chi Minh City. It is organized by the School of Information and Communication Technology - Hanoi University of Science and Technology, University of Science - Vietnam National University Ho Chi Minh City, Laboratory Informatics, Modelling and Optimisation System (LIMOS), The French National Centre for Scientific Research (CNRS), and Institute of Mathematics - Vietnam Academy of Science and Technology, on December 7-8, 2023. The goal of the SOICT conference is to provide an academic forum for researchers and postgraduate students to share the latest research findings and identify challenges in future computer science. The conference is listed in the reputable Scopus conference list. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02Vjnp15cby... Ha Bang - Media Collaborator, University of Information Technology Translation: Nhat Hien

Information technology systems are always at risk of being attacked. Attackers can compromise security features such as integrity, availability, and confidentiality of information in different states (storage state, usage state, transmission state). Understanding various attack methods and information security safeguards becomes crucial in maintaining the stable operation of information systems. In the textbook "Introduction to Information Assurance and Security", the author presents fundamental knowledge related to attack methods and information security safeguards... The textbook has been processed by the library and students can borrow it to meet the requirements of the corresponding course. For more detail, visit: https://www.facebook.com/LibUIT.Fanpage/posts/pfbid0U5aW6DsXBzT... Ha Bang - Media Collaborator, University of Information Technology Translation: Nhat Hien

On October 28, 2023, the Youth Union and Student Union of University of Information Technology (UIT) held their year-end conference to summarize the activities of the Youth Union, the youth movement, and student activities during the academic year 2022-2023. During this conference, the Faculty of Computer Engineering and the Department's Youth Union were recognized and rewarded for their outstanding contributions and achievements throughout the year. The Faculty of Computer Engineering received a commendation from the University's Board of Directors and, notably, a flag of commendation for their "Positive Contributions to Youth Union and Youth Movement Activities" for the academic year 2022-2023. The Department's Youth Union was honored with a commendation from the University's Student Union Executive Committee for their "Positive Contributions to Student Union and Student Movement Activities" for the same academic year. Additionally, many individuals who are members of the Faculty Computer Engineering Department's Youth Union and Youth Union leadership received commendations from various organizations, including the Ho Chi Minh City Youth Union Committee, the University's Board of Directors, the University's Student Union Executive Committee, and the University's Student Union for their excellent achievements and positive contributions to the youth and student movements at VNUHCM - University of Information Technology. These accolades serve as a clear testament to the collective efforts and dedication of the Computer Engineering Department's Youth Union and the individuals who are members of the Youth Union leadership. They continually strive to improve themselves, bring practical and positive values to their fellow members, students in the department, and embody the spirit of "Comprehensive - Creative - Service." We extend our best wishes to the Computer Engineering Department's Youth Union and all the members who are part of the Youth Union leadership, as well as the collaborators and volunteers associated with the Youth Union. May you continue to achieve even greater success, spreading more values and positive messages throughout the academic year 2023-2024. Hạ Băng - Collaborator, Information Technology University Communication Team English version: Phan Huy Hoang

Paper titled "DoppelSearch: A Novel Approach to Content-Based Video Retrieval for AI Challenge HCMC 2023" Link: https://doi.org/10.1145/3628797.3628950  The dedicated team of students behind this innovative research includes: Phan Nguyễn Hữu Phong – 22521090 – KHTN2022 - Main Author Trần Đình Khoa – 22520689 – ATTN2022 – Co-author Trần Kim Ngọc Ngân – 22520002 – KHTN2022 – Co-author Trần Đức Lương – 19521815 – ANTN2019 – Co-author Lê Hoàng Phúc – 20521762 – CNCL2020 – Co-author Esteemed instructors: Associate Professor Nguyễn Hữu Quyền Dr. Phạm Văn Hậu Summary: Searching for videos, a task considered crucial in the fields of computer vision and image recognition, has found widespread applications in various sectors such as education, entertainment, security, and healthcare. However, this task encounters challenges due to the complexity of video data, instability in feature extraction methods, and differences in meaning between videos and text. In this paper, we introduce a novel content-based video search method called DoppelSearch. We leverage the CLIP (Contrastive Language-Image Pre-training) model architecture for classifying and labeling video segments, providing specific content-based video search capabilities for users. Our approach utilizes the ViT-b/32 model for feature extraction and employs feature embeddings, combined with the Faiss library, to enhance search performance. Experimental results demonstrate the high accuracy and fast retrieval times of our model, opening new possibilities for content-based video search for researchers, developers, and end-users. This paper not only introduces the applications of CLIP and ViT-b/32 models but also presents the specific feature extraction process and the use of Faiss for optimizing video search. The DoppelSearch method represents a significant advancement in the field of video search and promises diverse applications across various industries... We express our sincere gratitude to Dr. Phạm Văn Hậu, the Head of Information Security Division, for fostering such an academically stimulating environment for our students. Additionally, we thank Dr. Nguyễn Hữu Quyền, the head of TeamQ from Wanna.W1n Club, for his continuous support, guidance, and passion for research, which has inspired our students to excel in their research endeavors and personal development. We appreciate the instructors' dedicated teaching, insightful feedback, and valuable suggestions, which have significantly contributed to the success of this research project. The SOICT (Symposium on Information and Communication Technology) is a prestigious international scientific conference covering vital research areas such as AI Foundations and Big Data, Network Communication and Security, Image and Natural Language Processing, Software Engineering and Digital Technology, Blockchain, and Operations Research trends. The 12th SOICT conference will be held in Ho Chi Minh City, organized by the University of Information Technology and Communications - Hanoi University of Science and Technology, VNUHCM- University of Science, Laboratory Informatics, Modelling and Optimisation System (LIMOS), The French National Centre for Scientific Research (CNRS), and the Institute of Research and High Education in Mathematics (IRHÉMA). The conference will feature workshops and keynote speeches by world-class speakers. The proceedings of SOICT 2023 will be published and archived in the ACM Digital Library as part of the ACM International Conference Proceedings Series. SOICT 2023 will be indexed by DBLP, Ei Compendex, Scopus, and Clarivate Analytics Web of Science (ISI Web of Science). Once again, congratulations to the UIT student team for their outstanding achievement in the field of scientific research. Their dedication and innovation have made significant contributions to the academic community and the advancement of knowledge in information and communication technology. Detailed Information: https://www.facebook.com/UIT.Fanpage/posts/pfbid02w9adWr86UrYxME64oxJNBMskYvL7tFkRVWK9KCYESsZh2UJnL6YjPoUcWqX5WmXNl Hạ Băng - Collaborator, Information Technology University Communication Team English version: Phan Huy Hoang

Paper Title: "ZUni - The Application of Blockchain Technology in Validating and Securing Educational Credentials" Students: Nguyễn Ngọc Minh Châu – 19520023 – CNTT2019 – Primary Author. Trần Thảo Vinh – 19520023 – CNTT2019 – Co-author. Supervisors: MSc. Võ Tấn Khoa. MSc. Nguyễn Thị Anh Thư. Abstract: In today's rapidly evolving technological landscape, blockchain technology has found applications in various fields including finance, government, and education due to its decentralized, secure, and transparent nature. In the context of the Internet of Things (IoT), blockchain technology plays a vital role in enhancing the management and verification of educational information. We propose the ZUni system, offering a potential solution to apply blockchain technology to transform how individuals verify students' educational qualifications while ensuring data integrity and security. We utilize the InterPlanetary File System (IPFS) to store certificate information, Zero-Knowledge Proofs (ZKP), and Decentralized Identifiers (DID) on the Ethereum blockchain to establish clear and tamper-proof certificate profiles. This enables transparent verification of the authenticity of certificate information. This paper aims to contribute to a deeper understanding of blockchain technology applications based on IoT in the education system. It addresses common issues related to centralized systems used to manage student certificate data in educational institutions, such as data security vulnerabilities and the potential for errors during data input. ZUni can address these challenges by establishing a decentralized system through blockchain technology, issuing, storing, and verifying certificate information while ensuring students' data privacy securely. We express our gratitude to MSc. Võ Tấn Khoa and MSc. Nguyễn Thị Anh Thư for their guidance and support throughout our research and the publication of this international scientific paper. We also thank Ms. Nguyễn Hoàng Tú Anh and Mr. Đinh Ngọc Thạnh for their valuable feedback during our research. ICIT 2023 is an annual international conference focused on emerging research in the fields of Internet of Things (IoT), Intelligence of Things (IoT2/AIoT), and related areas in Information Technology. The conference is technically sponsored by Springer and is included in the prestigious Scopus conference list. Detailed Information: https://www.facebook.com/UIT.Fanpage/posts/pfbid02Q63vCseww6MDYX6pHpXweQqQL84qVFoFaouyqPpc8oqkamzQ4ZbAcBFNmgjsnqrLl Hạ Băng - Collaborator, Information Technology University Communication Team English version: Phan Huy Hoang

The paper titled "Machine Reading Comprehension for Vietnamese Customer Reviews: Task, Corpus And Baseline Models" has been accepted for presentation at the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC 37). Authors: Đỗ Phạm Phúc Tính – 20522020 – KHDL2020 – Co-author Cao Đình Duy Ngọc – 20521661 – KHDL2020 – Co-author Nguyễn Thành Nhân – 20521701 – KHDL2020 – Co-author Supervisors: MSc Nguyễn Văn Kiệt MSc Huỳnh Văn Tín Paper Summary: Customers spend significant time researching product information before making a purchase. Machine Reading Comprehension (MRC) on customer reviews can partially address this issue. However, benchmark corpora specific to the review domain in Vietnamese are lacking for effective MRC implementation. To fill this gap, the team proposed ViRe4MRC, the first benchmark corpus for evaluating review-based machine reading comprehension on customer reviews in Vietnamese. This corpus comprises 6,603 human-generated question-answer pairs from 2,174 customer reviews on smartphone and restaurant domains. The team also evaluated experimental results of monolingual language models: ViBERT, PhoBERT, and vELECTRA; multilingual language models: mBERT and XLM-RoBERTa (XLM-R). The XLM-R-Large model, as the best model, achieved 44.25% Exact Match (EM) and 78.13% F1. The corpus is available for research purposes. The team extends their gratitude to Mr. Nguyễn Văn Kiệt and Mr. Huỳnh Văn Tín for their guidance and support throughout the research and publication process. They also thank Mr. Vũ Quí San, a former student of the KHTN2018 class, for his orientation and assistance. The 37th Pacific Asia Conference on Language, Information and Computation (PACLIC 37) is a prestigious international conference in the field of theoretical analysis and natural language processing. Since 1982, the PACLIC conference series has provided a forum for researchers in various language research fields in the Asia-Pacific region to share discoveries and insights in formal and experimental language research. In 2023, the main PACLIC 37 conference will take place from December 2-4 at The Hong Kong Polytechnic University. Previous PACLIC proceedings have been indexed in Scopus (since PACLIC 19 in 2005) and listed in the ACL Anthology. Detailed Information: https://www.facebook.com/UIT.Fanpage/posts/pfbid07vowaL94RN41ECjJTKSkWPKgC3Lp27Xx9ivaVb56BAYtNnuurHCZCkk5eiuN6pRMl Hạ Băng - Collaborator, Information Technology University Communication Team English version: Phan Huy Hoang

Extracting Relationships between Entities in Vietnamese Texts Pham Minh Man - CH1802054 Extracting relationships is a crucial task in Natural Language Processing (NLP). It falls under the umbrella of information extraction, widely applied in various fields such as knowledge graphs, automatic question answering, text summarization, and more. With the continuous growth of data, especially textual data, this task has garnered significant attention from researchers both nationally and internationally. However, research on relationship extraction in Vietnamese texts is still limited compared to other languages like English and Chinese. This thesis delves deeper into this topic. Practical Applications include: Web Mining: Explores data from the web, analyzing competitors, extracting names of famous individuals, popular products, price comparisons, and customer sentiment analysis. Business Intelligence: Evaluates market information from new regulations in the business market, political information between countries, etc. Contributions to Science: Provides an overview of domestic and international research, the development trends of relationship extraction, and related tasks such as coreference resolution. Discusses and evaluates various relationship extraction methods and coreference resolution techniques, proposes future directions for research. Thesis Achievements: Offers a comprehensive understanding of the relationship extraction task, the current state of research both nationally and internationally, and trends in relationship extraction and related tasks such as coreference resolution. Compares combined models based on BERT for relationship extraction in Vietnamese. The study compares models based on PhoBERT and XLM-RoBERTa with single models like PhoBERT, demonstrating that combining models yields better results than individual models. Proposes and evaluates new coreference resolution methods for Vietnamese texts to support the relationship extraction task, achieving F1 scores of 66.50%, 82.70%, and 76.26% using MUC, B3, and CEAFe metrics, respectively. Develops a demo system for relationship extraction in Vietnamese texts. Limitations: The thesis does not address the issue of data imbalance during the training of relationship extraction models in Vietnamese. The results of coreference resolution have not been integrated into the developed relationship extraction system. For more details, visit: https://fit.uit.edu.vn/index.php/tin-tuc/goc-hoc-tap/6485-rut-trich-quan-h-gi-a-cac-th-c-th-trong-van-b-n-ti-ng-vi-t Ha Bang - Media Collaborator, University of Information Technology Translation: Nhat Hien

Building a Real-Time License Plate Recognition System Using Big Data Technology Bui Thanh Phuong - CH1902016 Smart cities are becoming increasingly prevalent in urban areas worldwide, particularly in the field of transportation. To effectively manage traffic-related tasks, it is essential to implement a license plate recognition system. Furthermore, with the exponential growth of big data, processing and analyzing this data require support from big data tools and artificial intelligence. Therefore, to utilize and optimize the available resources, this project aims to propose and implement a system using the RetinaNet model trained with the BigDL big data processing library. This system aims to identify license plates of moving vehicles in real-time using a Jetson Nano embedded computer connected to a camera. Achievements: Proposed a smart traffic system using big data technology to recognize license plates of vehicles in real-time. Applied deep learning methods to increase the accuracy of license plate recognition on the big data platform. Utilized 2365 raw images with various sizes and qualities. Annotated 1750 images of vehicles in VOC format. Processed 30 video files ranging from 30 seconds to 1 minute in length, with resolutions from Full HD to 4K. Developed a browser extension to download vehicle images. For more details, visit: https://fit.uit.edu.vn/index.php/tin-tuc/goc-hoc-tap/6484-xay-d-ng-h-th-ng-nh-n-di-n-bi-n-s-xe-o-to-theo-th-i-gian-th-c-s-d-ng-cong-ngh-d-li-u-l-n Ha Bang - Media Collaborator, University of Information Technology Translation: Nhat Hien

Article Title: "EVB - Electronic Voting System Based on Blockchain Technology" Student Researchers: Duong Hoang Long – 19521779 – KTPM.CLC2019 – Main Author Nguyen Ngoc Minh Chau – 19520023 – CNTT2019 – Co-Author Supervisors: M.S. Vo Tan Khoa. M.S. Nguyen Thi Anh Thu. Abstract: Blockchain is one of the prominent technologies, the trend of blockchain in IoT application in free and fair elections creating positive changes for the public sector. IoT integration in blockchain-based electronic voting systems provides secure device authentication and real-time monitoring, ensuring security, transparency, and accuracy. We proposed solution for building an online voting system, called EVB, leverages Ethereum as a smart contract on a digital platform. Experimental results demonstrate the scalability potential of EVB by comparing processing performance and costs across different blockchain layers, including layer 1 - Ethereum, layer 2 - Polygon, and sidechain - BSC. The utilization of Merkle tree enhances security, controls user numbers, and reduces costs significantly. Additionally, the computational complexity of Optimistic Rollup suggests that switching to layer 2 is unnecessary. Account abstraction is applied to support user payments, thereby facilitating transaction fee support and laying the groundwork for the expansion of the EVB voting system on IoT environment. We would like to express our gratitude to M.S. Vo Tan Khoa and M.S. Nguyen Thi Anh Thu for their guidance and support throughout our research and the publication of this international scientific paper. We also extend our thanks to Ms. Nguyen Hoang Tu Anh and Mr. Dinh Ngoc Tha  nh for their valuable feedback during the research process. The ICIT 2023 conference is an annual international conference focused on new research in the field of Internet of Things (IoT), Intelligence of Things (IoT2/AIoT), and related areas of Information Technology, technically supported by Springer. The conference is listed in the reputable Scopus conference list. For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02fhcxzAH48sUWMb4... Hai Bang - Media Collaborator, University of Information Technology Translation: Nhat Hien