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HOẠT ĐỘNG HỢP TÁC
DOANH NGHIỆP

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LỊCH LÀM VIỆC CỐ VẤN HỌC TẬP

Nhằm hướng dẫn, hỗ trợ, giải đáp sinh viên trong quá trình học tập, xây dựng kế hoạch và có phương pháp học tập hiệu quả.

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KHOA CÔNG NGHỆ THÔNG TIN

TRƯỜNG ĐH KHOA HỌC TỰ NHIÊN (ĐHQG-HCM)

Giới thiệu

~30

Năm

Chào mừng đến với..

Khoa Công nghệ thông tin

Khoa Công nghệ thông tin được thành lập theo quyết định số 3818/GD-ĐT ngày 13/12/1994 của Bộ Trưởng Bộ GD&ĐT, dựa trên Bộ môn Tin học (thuộc Khoa Toán, Trường Đại học Tổng hợp TP.HCM). Trải qua hơn 28 năm hoạt động, Khoa đã phát triển vững chắc và được Chính phủ bảo trợ để trở thành một trong những khoa Công nghệ thông tin hàng đầu trong hệ thống giáo dục đại học của Việt Nam.

4500+

Sinh viên đại học

500+

Học viên cao học

100+

Giảng viên

News & Events

Chương trình đào tạo

Đại học chính quy

Chương trình chuẩn

Chương trình đào tạo cử nhân của Khoa Công nghệ Thông tin đã được đánh giá theo bộ tiêu chuẩn AUN-QA và được đánh giá cao nhất cả nước trong đợt đánh giá ngoài tháng 12/2009.

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Chương trình tiên tiến

Chương trình đào tạo ngành Khoa học máy tính và giảng dạy hoàn toàn bằng tiếng Anh. Chương trình đang là sự lựa chọn ưu tiên của nhiều sinh viên có thành tích học tập xuất sắc ở bậc phổ thông, hoặc tại các cuộc thi học thuật trong nước và quốc tế.

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Chất lượng cao

Chuẩn đầu ra của chương trình được xây dựng theo cách tiếp cận CDIO, đảm bảo người học được trang bị đầy đủ kiến thức, kỹ năng và thái độ, đáp ứng nhu cầu xã hội khi tốt nghiệp. Chương trình được xây dựng cân đối dựa trên việc trang bị vững vàng kiến thức nghề nghiệp và phát triển kỹ năng cá nhân, kỹ năng mềm và ngoại ngữ cho sinh viên.

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Chương trình khác

Ngoài ra, Khoa CNTT còn có các chương trình khác như: Chương trình cử nhân liên kết với ĐH Claude Bernard Lyon 1 (Việt - Pháp), Đào tạo từ xa qua mạng...

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Đào tạo Sau đại học

Chương trình cung cấp những kiến thức và kỹ năng chuyên sâu thuộc lĩnh vực Khoa học Máy tính như Trí tuệ nhân tạo, Khoa học dữ liệu, Máy học, Thị giác máy tính, Xử lý ngôn ngữ tự nhiên, An ninh thông tin, và Vạn vật kết nối (IoT). Học viên còn được đào tạo các kỹ năng cá nhân, kỹ năng nhóm, kỹ năng quản lý, phương pháp nghiên cứu khoa học cũng như cách thức triển khai và xây dựng các hệ thống thông minh hiệu quả.

Chương trình cung cấp những kiến thức và kỹ năng chuyên sâu thuộc lĩnh vực hệ thống thông tin (HTTT), đặc biệt là các chủ đề nâng cao liên quan đến kiến trúc tổng thể HTTT của tổ chức. Chương trình đào tạo các chuyên gia, các kỹ sư, các phân tích viên có tầm nhìn sâu và rộng nhằm giúp các tổ chức, cơ quan xây dựng và thực thi chiến lược phát triển và ứng dụng HTTT của mình.

Chương trình cung cấp những kiến thức và kỹ năng chuyên sâu thuộc lĩnh vực trí tuệ nhân tạo (TTNT). Khác với ngành Khoa học máy tính, chương trình ngành TTNT tập trung chuyên sâu về TTNT với các môn học như TTNT nâng cao, Học máy nâng cao, Khai thác dữ liệu lớn, Học máy với dữ liệu đồ thị, và TTNT trên vạn vật.

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Khoa học máy tính
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Hệ thống thông tin
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Trí tuệ nhân tạo
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Thông tin tuyển sinh

Research

Featured results

The Faculty of Information Technology gathers a strong research team with experts with experience in basic research as well as developing solutions for application of artificial intelligence, machine learning, big data, optimization, human-machine interaction, computer vision, language processing, and software engineering.

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2024

A personalized learning path recommendation solution based on knowledge map mining

Recommendation systems (RS) are extensively used in various fields, especially in education, where intelligent e-learning platforms suggest personalized learning paths (PLP) tailored to learners and educational resources. Despite ongoing efforts to offer highly personalized recommendations, challenges like data sparsity and cold-start issues remain. Recently, the development of knowledge graph (KG)-based RS has attracted considerable attention. KGs can utilize semantic relationships between entities within a unified graph structure to address these...

Authors
  • Thu Minh Tran Nguyen
  • Duong Thien Nguyen

2024

Lung Nodule Detection based on deep learning integrated with the Attention mechanism in CT Images

This research introduces a sophisticated technique for segmenting lung nodule in CT scans, employing the MHA-SEPA model, which is built upon the ResUNet++ framework. The MHA-SEPA architecture combines Multi-Head Attention mechanisms with SEBlock and Position Attention approaches to improve feature extraction by giving priority to important spatial and channel information. This method greatly enhances the precision of lung nodule segmentation, especially for tiny and difficult...

Authors
  • Thai Hoang Le
  • Khai Dinh Lai
  • and Thuy Thanh Nguyen

2024

Courses Recommender System for the IT field based on a context-aware knowledge model

This paper presents an approach for a knowledge-based recommender system that provides relevant courses based on learners’ profiles, requirements, and career needs. The framework integrates an automatic data collection process, ensuring that the knowledge base reflects the latest job market and course information. The recommendation method relies on a set of rules that combine various matching techniques, incorporating user requirements, skill and knowledge...

Authors
  • Pham Nguyen Cuong
  • Le Nguyen Hoai Nam
  • Pham Thi Xuan Hien

2024

Enhancing embeddings based on graph reasoning and time-aware attention networks on temporal knowledge graphs

Temporal Knowledge Graphs (TKGs) organize dynamic real-world facts, adding a time dimension to the multi-relational graph structure of Knowledge Graphs (KGs). We leverage the expressive power of graph convolutional networks (GCNs) for modeling TKGs, recognizing similarities with handling graph-structured data and utilizing complex geometry. Our approach emphasizes compositional interactions between relations and entities, integrating a diachronic mechanism to enhance representation with both graph structure and temporal...

Authors
  • Thanh Le
  • Bac Le
  • and Loc Tran

2024

An Approach for Estimating the Influence of Neighboring Users on the Target User in Recommender Systems

Recommendation systems play a crucial role in helping users navigate information overload, particularly in today's digital era. Their primary objective is to predict users' preferences for items. Latent factor-based recommendation systems achieve this by aligning users and items under latent factors. Previous studies mainly focused on devising effective objective functions for learning these latent factors. However, the accuracy of latent factors also depends on their initialization and the order of the collected data fed into the...

Authors
  • Le Nguyen Hoai Nam
  • Ho Thi Hoang Vy
  • Do Thi Thanh Ha
  • Thi My Hang Vu
  • Ho Le Thi Kim Nhung
  • Cuong Pham-Nguyen
  • Tiet Gia Hong

2024

Speaker verification and identification for secure and high utility Vietnamese virtual assistant

This paper presents our work in building a Vietnamese dataset for command and speaker recognition problems. We built a website that allows users of mobile devices or personal computers to be able to provide their voice samples easily. We collected more than fifteen thousand utterances of nineteen Vietnamese commands from more than two hundred volunteers. The commands are primarily used for applications on edge devices that interact with users via...

Authors
  • Minh-Nhut Ngo
  • Long-Quoc Le

2024

A secure user authentication scheme for crypto-wallet in IoT environment

Presently, the prevalent environment is the Internet of things (IoT) since it can connect a large number of different gadgets in an increasingly wide range, and lead to the risk of information leakage. In addition, the field of crypto-currency is a hot topic with many sophisticated thefts, so the progressive utilization of crypto-wallets should protect transactions from eavesdropping. Clearly, IoT could be a promising and challenging environment for quick and secure cryptocurrency...

Authors
  • Truong Toan Thinh
  • Tran Minh Triet
  • Duong Anh Duc

2024

Integration of pre-trained Vision – Language model into Vietnamese Visual Question Answering

In recent decades, artificial intelligence has made significant progress in understanding and interacting with images. One of the impor- tant applications of this technology is Visual Question Answering (VQA), a research field that requires computers to understand and answer questions about images in a natural manner. Despite extensive research and development in VQA for English, there have been very few similar efforts made for other...

Authors
  • Le Thanh Tung
  • Nguyen Tien Huy
  • Nguyen Cong Phu

2024

CollaXRSearch: A Collaborative Virtual Reality System for Lifelog Retrieval

In lifelog data search, despite automatic supports for identifying relevant pieces of information, the processes of inputting queries and filtering information from the generated results still heavily rely on human searchers. With the rapid increase in volume of such data, these tasks could become both mentally and physically tedious for an individual to perform. In this paper, we present CollaXRSearch, a collaborative virtual reality (VR) information retrieval...

Authors
  • Khanh-Duy Le
  • Duy-Nam Ly
  • Dinh-Thuan Duong-Le
  • Gia-Huy Vuong
  • Van-Son Ho
  • Van-Tu Ninh
  • Minh-Triet Tran

2024

ArmorDroid: A Rule-Set Customizable Plugin for Secure Android Application Development

Although Android is a popular mobile operating system, its app ecosystem could be safer. The lack of awareness and concern for security issues in apps is one of the main reasons for this. Given the current situation, developers have yet to receive sufficient security knowledge. Therefore, we have researched and proposed a tool to support security coding. Based on the idea of...

Authors
  • Truong Phuoc Loc
  • Tran Minh Triet
  • Tran Anh Duy
  • Le Cong Binh
  • Nguyen Le Bao Thi

2024

Speaker authentication using deep neural network

This research deals with three challenges for speaker verification (SV): adaptivity, accuracy, and replay attack. We propose a framework consisting of three independent components: wakeword detector, one time password (OTP) block, and speaker identificator. With this architecture, we can customize each component without significant interference to the whole structure. Via these components, the final representation provides a meaningful information about the speaker to help the system verifies...

Authors
  • Chau Thanh Duc
  • Do Duc Hao
  • Van Khai Nguyen

2024

VIDES: Virtual Interior Design via Natural Language and Visual Guidance

Interior design is crucial in creating aesthetically pleasing and functional indoor spaces. However, developing and editing interior design concepts requires significant time and expertise. We propose Virtual Interior DESign (VIDES) system in response to this challenge. Leveraging cutting-edge technology in generative AI, our system can assist users in generating and editing indoor scene concepts quickly, given user text description and visual...

Authors
  • Le Trung Nghia
  • Tran Minh Triet
  • Hoang Nhu Vinh
  • Le Minh Hien

2024

Vehicle Object Tracking Based on Fusing of Deep learning and Re-Identification

Object tracking is a popular problem for automatic surveillance systems as well as for the research community. The requirement of an object tracking problem is to predict the output including the object position at the current frame based on the input the position of the object at the previous frame. To present the comparison and experiment of some object tracking methods based on deep learning and suggestions for improvement between them in this...

Authors
  • Vo Hoai Viet
  • Huynh Nhat Duy

2024

EAPC: Emotion and Audio Prior Control framework for the emotional and temporal Talking Face Generation

Generating realistic talking faces from audio input is a challenging task with broad applications in fields such as film production, gaming, and virtual reality. Previous approaches, employing a two-stage process of converting audio to landmarks and then landmarks to a face, have shown promise in creating vivid videos. However, they still face challenges in maintaining consistency due to misconnections between information from the previous audio frame and the current audio...

Authors
  • Cao Xuan Nam
  • Tran Minh Triet
  • Dang Hoai Thuong
  • Trinh Quoc Huy
  • Nguyen Do Quoc Anh
  • Ho Van Son

2023

A syntax-aware deep-learning model for biomedical semantic role labelling

A deep learning model for biomedical semantic role labeling was build. Semantic role labeling is a useful task that enables the computer to comprehend the key facts expressed in each sentence, and is a necessary first step in the resolution of several other semantic-related tasks, such as event extraction, entity extraction, and Q-A systems... Semantic role labeling is a domain-dependent...

Authors
  • Tuan Nguyen Hoai Duc
  • Nguyen Truong Son

2023

New Results on Erasure Combinatorial Batch Codes

We investigate in this work the problem of Erasure Combinatorial Batch Codes, in which n files are stored on m servers so that every set of n − r servers allows a client to retrieve at most k distinct files by downloading at most t files from each server. Previous studies have solved this problem for the special case of t =1 using Combinatorial Batch...

Authors
  • Le Phuc Lu
  • Dau Son Hoang
  • Ngo Dinh Hy
  • Nguyen Dinh Thuc

2023

Exploring the Role of Monolingual Data in Cross-Attention Pre-training for Neural Machine Translation

Recent advancements in large pre-trained language models have revolutionized the field of natural language processing (NLP). Despite the impressive results achieved in various NLP tasks, their effectiveness in neural machine translation (NMT) remains limited. The main challenge lies in the mismatch between the pre-training objectives of the language model and the translation task, where the language modeling task focuses on reconstructing the language without considering its semantic interaction with other...

Authors
  • Dinh Dien
  • Nguyen Hong Buu Long
  • Pham Vinh Khang

2023

Exploring Graph-based Transformer Encoder for Low-Resource Neural Machine Translation

The Transformer is commonly used in Neural Machine Translation (NMT), but it faces issues with over-parameterization in low-resource settings. This means that simply increasing the model parameters significantly will not lead to improved performance. In this study, we propose a graph-based approach that slightly increases the parameters while significantly outperforming the scaled version of the Transformer. We accomplish this by utilizing Graph Neural Networks to encode Universal Conceptual Cognitive Annotation...

Authors
  • Nguyen Hong Buu Long
  • Dinh Dien

2023

A new algorithm using integer programming relaxation for privacy-preserving in utility mining

High-utility itemset mining (HUIM) is an effective technique for discovering significant information in data. However, data containing sensitive and private information may cause privacy concerns. Therefore, privacy preserving utility mining (PPUM) has recently become a critical research area. PPUM is the process of transforming a quantitative transactional database into a sanitised one, thus ensuring that utility mining algorithms cannot discover sensitive...

Authors
  • Nguyen Ngoc Duc
  • Le Hoai Bac

2023

A Robust Approach for Hybrid Personalized Recommender Systems

The personalization of services for users is one of the most crucial objectives of digital platforms. This objective is accomplished by integrating automated recommendation components into information systems. The increasing computational power and storage capacity available today have opened up opportunities to deploy a combination of diverse approaches to enhance the accuracy of the recommendation process. Compared to previous research, the distinguishing feature of this study is the introduction of an approach that combines not only computational aspects but also data...

Authors
  • Le Nguyen Hoai Nam
  • Tiet Gia Hong

2023

Knowledge graph embedding by relational rotation and complex convolution for link prediction

Knowledge graphs are organized as triplets to represent facts from the real world and play an important role in various intelligent information systems. Because knowledge graphs are frequently constructed using manual or semi-automatic methods, they often miss connections between entities. Link prediction was created to solve this problem. Many recent state-of-the-art studies, such as those introducing the RotatE and RotatHS...

Authors
  • Le Ngoc Thanh
  • Le Hoai Bac

2023

Toward Deep Transfer Learning for Realistic Activity Recognition in Videos

Today, videos have become popular on the internet and specified in social network and media platforms such as Youbue, Ticktok, and Vimeo. Video understanding has attracted much attention in the research community in recent years. Automatically recognizing human activity in wild videos is a trending research topic with a wide range of applications in advertising, smarthome, and surveillance camera systems. Deep convolutional neural networks have become a new de facto visual recognition...

Authors
  • Vo Hoai Viet

2023

GENA: A knowledge graph for nutrition and mental health

While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental illnesses, even though a large amount of knowledge about these relationships is available in the form of unstructured text in biomedical literature articles. To address this...

Authors
  • Phan Thi Phuong Uyen
  • Nguyen Thi Hong Nhung
  • Dang Diem Linh

2023

A Multi-Factor Approach to Measure User Preference Similarity in Neighbor-Based Recommender Systems

Neighbor-based Collaborative filtering is one of the commonly applied techniques in recommender systems. It is highly appreciated for its interpretability and ease of implementation. The effectiveness of neighbor-based collaborative filtering depends on the selection of a user preference similarity measure to identify neighbor users. In this paper, we propose a user preference similarity measure named Multi-Factor Preference Similarity (MFPS). The distinctive feature of our proposed method is its efficient combination of the four key factors in determining user preference similarity: rating...

Authors
  • Ho Thi Hoang Vy
  • Tiet Gia Hong

2023

HybridMingler: Towards Mixed-Reality Support for Mingling at Hybrid Conferences

Mingling, the activity of ad-hoc, private, opportunistic conversations ahead of, during, or after breaks, is an important socializing activity for attendees at scheduled events, such as in-person conferences. The Covid-19 pandemic had a dramatic impact on the way conferences are organized, so that most of them now take place in a hybrid mode where people can either attend on-site or...

Authors
  • Le Khanh Duy
  • Ly Duy Nam
  • Le Quang Tri
  • Nguyen Hoang Long

2022

Multilingual Communication System with Deaf Individuals Utilizing Natural and Visual Languages

According to the World Federation of the Deaf, more than two hundred sign languages exist. Therefore, it is challenging to understand deaf individuals, even proficient sign language users, resulting in a barrier between the deaf community and the rest of society. To bridge this language barrier, we propose a novel multilingual communication system, namely MUGCAT, to improve the communication efficiency of sign language...

Authors
  • Le Trung Nghia
  • Huynh Tan Luc
  • Chu Chi Bien
  • Nguyen Ngoc Khoi Nguyen

2022

Meta-Learning and Personalization Layer in Federated Learning

Federated learning systems are confronted with two challenges: systemic and statistical. Non-IID data is acknowledged to be a primary component in causing statistical challenges. To address the federated learning system’s substantial performance loss on non-IID data, we offer the algorithm (which combines meta-learning methods and personalization layer approaches into a federated learning system). In terms of performance and personalization, has been shown in experiments to outperform typical federated learning...

Authors
  • Le Hoai Bac
  • Nguyen Bao Long
  • Cao Tat Cuong

2022

An approach to constructing a graph data repository for course recommendation based on IT career goals in the context of big data

Graph data is widely regarded as the next frontier in big data modeling for a variety of domains. Graph-based data models have been used in big data to organize messy or complicated data points based on their relationships. Graph analytic technologies for big data provide a framework for absorbing both structured and unstructured data from a variety of sources, allowing analysts to see the connections between entities in graphs and draw new...

Authors
  • Nguyen Tran Minh Thu
  • Pham Minh Tu

2022

RVT-Transformer: Residual Attention in Answerability Prediction on Visual Question Answering for Blind People

Answerability Prediction on Visual Question Answering is an attractive and novel multi-modal task that can be regarded as a fundamental filter to eliminate the low-qualified samples in practical systems. Instead of focusing on the similarity between images and texts, the critical concern in this task is to accentuate the conflict in visual and textual information. However, the fusion function of the multi-modal system unwittingly decreases the original features of image and text that are essential in answerability...

Authors
  • Le Thanh Tung
  • Nguyen Tien Huy

2022

Knowledge graph embedding by projection and rotation on hyperplanes for link prediction

Knowledge is increasingly completed due to connections formed in a knowledge graph, enabling a complete understanding of reality. Link prediction plays an important role in this process. Among the multiple methods that exist to tackle this problem, the geometry-based prediction method has attracted attention due to its intuitiveness and capacity to flexibly address various types of relations. We propose the rotation embedding of entities on separate relation-specific hyperplanes as an alternative to the translation...

Authors
  • Le Ngoc Thanh
  • Le Hoai Bac

2022

MemInspect2: OS-Independent Memory Forensics for IoT Devices in Cybercrime Investigations

In the age of rapid development of the Internet of Things (IoT) world, more and more cybersecurity incidents have emerged in many IoT devices and systems. Therefore, the need for cybercrime investigation, especially for IoT devices, has become more imperative than ever. Memory forensics, the approach that inspects the memory dump to understand the current state or behavior of the attacked...

Authors
  • Tran Anh Duy
  • Nguyen Quoc Trung
  • Nguyen Anh Minh

2022

Research and development of face recognition system for attendance and monitoring of students in the classroom and exams

One of the primary activities that lectures usually do is to take a roll call. This activity not only helps lecturers determine the participation of students but also detect strangers in the classroom. When the number of students increases, lectures take more time to monitor and check students’ attendance. We propose a student monitoring system based on facial recognition approaches to tackle that...

Authors
  • Pham Trong Nghia
  • Le Ngoc Thanh

2022

A semi-automatic approach for pasbio corpus data augmentation

A semi-automatic solution to build a biomedical semantic role corpus named PASBio+ was proposed. The corpus was annotated with a predicate argument structure, the important information that revealed the main content of a sentence. Because more than 86% of the arguments in the biomedical domain significantly differed from those in the general domain, this proposed corpus was labeled on top of 317 labeled sentences from...

Authors
  • Tuan Nguyen Hoai Duc

2022

Medical Prescription Recognition Using Heuristic Clustering and Similarity Search

The necessity to convert printed documents to facilitate the storage and retrieval of information is growing, particularly in the medi- cal and healthcare industries. In our last work, we presented a method to extract prescriptions from images using CRAFT and TESSERACT so that patients could quickly save and check up on their pharmaceutical use information. However, the slow processing speed and the limited number of medication names lead to it being...

Authors
  • Nguyen Ngoc Thao
  • Le Ngoc Thanh

2022

M.A.R – Micro Automatic Robot

Robotics is one of the important subjects in automation and modernizing our country. The automatic robot has a variety of sizes and shapes. This helps people in any context, like discovering small or dangerous areas in collapsed houses, and caves or check the cash, leak in oil or water pipelines. To create a passion for science and application for the...

Authors
  • Cao Xuan Nam
  • Nguyen Do Quoc Anh

2022

Binary Classification for Lung Nodule Based on Channel Attention Mechanism

In order to effectively handle the problem of tumor detection on the LUNA16 dataset, we present a new methodology for data augmentation to address the issue of imbalance between the number of positive and negative candidates in this study. Furthermore, a new deep learning model - ASS (a model that combines Convnet sub-attention with Softmax loss) is also proposed and evaluated on patches with different sizes of the...

Authors
  • Le Hoang Thai
  • Lai Dinh Khai

2022

Group Testing with Blocks of Positives and Inhibitors

The main goal of group testing is to identify a small number of specific items among a large population of items. In this paper, we consider specific items as positives and inhibitors and non-specific items as negatives. In particular, we consider a novel model called group testing with blocks of positives and inhibitors. A test on a subset of items is positive if the subset contains at least one positive and does not contain any...

Authors
  • Nguyen Dinh Thuc
  • Bui Van Thach

2022

ITCareerBot: A Personalized Career Counselling Chatbot

Nowadays, career counselling, which is a service designed to help people finding the right professional learning is emerging. In the information technology (IT) domain, this service is facing the challenge of changing very quickly in business environments, technologies and tools. Consequently, IT students and professionals often need additional knowledge and skills to fulfill market requirements and to target their professional goal in order to increase their opportunity for...

Authors
  • Pham Nguyen Cuong
  • Le Nguyen Hoai Nam
  • Nguyen Duy Cuong
  • Dinh Nguyen Hanh Dung

2021

Object-less Vision-language Model on Visual Question Classification for Blind People

Despite the long-standing appearance of question types in the Visual Question Answering dataset, Visual Question Classification does not received enough public interest in research. Different from general text clas-sification, a visual question requires an understanding of visual and textual features simultaneously. Together with the enthusiasm and novelty of Visual Question Classification, the most important and practical goal we concentrate on is to deal with the weakness of Object Detection on object-less...

Authors
  • Nguyen Tien Huy
  • Bui Huy Thong

2021

Using Bert embedding to improve memory-based collaborative recommender systems

The performance of memory-based collaborative filtering recommender systems will be severely affected when the users' item preference data is sparse. In this paper, we focus on solving this issue. Our idea is to use Bert Embedding to learn a new feature set, which is denser and more semantic, for representing users and items. In these new features, memory based collaborative filtering recommender systems work more ...

Authors
  • Ho Thi Hoang Vy
  • Le Nguyen Hoai Nam

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