Journal of research and development on information and communication technology
Tạp chí của Bộ Thông tin và Truyền thông (nay là Bộ Khoa học và Công nghệ)
A Method Mining Correlation High Utility Itemsets with Negative Profits
High-utility itemset (HUI) mining is an important task in the data mining process. Recently, many algorithms have been proposed to mine sets of HUIs. Most algorithms only work with data sets with positive return values. However, in the real world, the set of business items often includes both positive (+) and negative (-) profit values. To solve this problem, an algorithm is needed to find HUIs with negative returns in the database. The issue is how to mine efficiently correlation high utility itemsets with negative profits. Given the transactional database D and the profit table of products, how to find the best correlation high utility itemsets on the database with items having negative profits. To address these issues, the paper proposes a COHUIs_CoHUN method to mine correlation high utility itemsets with negative profits.
Evaluation of Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
Reconfigurable intelligent surfaces (RIS) have emerged as a promising technique for wireless communication in 5G and beyond. In an environment with lots of physical obstacles, RIS provides an alternative solution to the coverage problem by beam-forming signal towards the desired direction of the receiver. Due to the fixed constellation diagram, traditional modulation schemes cannot be used for the RIS-aided communication system with the receiver movement. In this work, we aim to design and evaluate an autoencoder-based communication model with RIS support, which can adapt to changes in the environment and the receiver’s position. Specifically, we use neural networks to present the encoder and decoder of the system and the parameters of these networks are trained to minimize the reconstruction error at the receiver. The simulation setup is based on characteristics of the environment and real RIS. Performance results show the benefits of RIS when a physical obstruction is present between the transmitter and receiver. Moreover, we prove that the selection of RIS codeword plays an important role in system performance.
An Improvemant of the IGEPVO-k Reversible Data Hiding Method
PVO-K method is an expansion of PVO method in which all 𝑘 largest (𝑙 smallest pixels) are used as a single unit to embed a bit. In PVOK-method, each pixel of an original image is modified by one at most. GePVO-K method is again a development of the PVOK-method by embedding k bits in k largest pixels and 𝑙 bits in 𝑙 smallest pixels. However, this method has two drawbacks. First, each pixel of the original image is changed by two at most, so marked image quality is not good. Second, the location map consisting of two bits for each pixel is so large that in some cases compression code length of the map is greater method’s embedding capacity, and then data embedding capacity is zero. Both drawbacks are overcome in the iGePVO-K method. Specifically, in this method, each pixel only is changed by one at most and the two-bit location map as in iGePVO-K method is replaced by a one-bit map and a sequence of binary flags. This paper is a development of the iGePVO-K method by discarding the sequence of binary flags. That means it is not necessary to embed flags with the data in the original image. Therefore, the proposed method not only has a larger data embedding capacity but also has a better image quality than the methods mentioned above.
Proposing of Imaging Graph Neural Network with Defined Security Pattern for Improving Smart Contract Vulnerability Detection
A smart contract is a special set of protocols based on blockchain technology to implement the terms or agreements between the parties in the contract. Lots of smart contracts are built and deployed everyday. However, to ensure the safety of smart contracts is still a big challenge. Smart contracts are built to carry out transactions directly related to cryptocurrencies, therefore, the loss of security of smart contracts leads to huge financial losses. Common methods being used to check and to verify smart contract security are heavily dependent on hard rules defined by experts, leading to low detection accuracy and non-scalable, which can be bypassed by experienced attackers. In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract. Then we extract graph features from normalized graphs, and combine graph features with defined security patterns to create combined features. Finally, we implemented normalization to gray scale image and feed it to the Convolutional Neural Network (CNN) to learn for vulnerability detection. Results show significantly improved accuracy compared to previous methods or other models. Specifically, 96,42%, 90,12%, 79% for reentrancy, timestamp dependence and infinite loop.
Research on Deep Learning and Its Application in Stock Price Prediction
The article studies the problem of forecasting the closing price of a stock based on historical data of a previous day. The paper uses and compares algorithms based on deep learning such as LSTM, BiLSTM, and CNN. The dataset includes data on price, trading volume and some technical indicators related to VCB, MSN, and HPG shares. The results show that CNN performs better for predicting the next day’s closing price than the other architectures.
Proposals and Implementations of MDS Diffusion Layer Dynamic Algorithms for AES Block Cipher
Block ciphers are an interesting cryptographic topic that is widely used today. Therefore, the issue of improving the security of block ciphers is increasingly concerned today. There have been many research directions to animate block ciphers to improve their security against well-known strong attacks. In [1], we proposed two diffusion layer dynamic algorithms for SPN block ciphers. In this paper, we will execute these two algorithms with recursive MDS matrices of size 4, 8, and 16, then evaluate the cryptographic properties of the obtained dynamic MDS matrices including branch number, number of fixed points, coefficient of fixed points, number of XOR and Xtime operations. Then, we apply these two dynamic algorithms to dynamically modify the AES block cipher and evaluate the execution speed of the dynamic AES block ciphers. We propose two new diffusion layer dynamic algorithms, implement these two algorithms and modify dynamically AES block cipher according to these two algorithms, and evaluate the speed of dynamic AES block ciphers. We also evaluate the security and implementation resources for dynamically modified AES block ciphers. The proposed diffusion layer dynamic algorithms contribute to improving the security of the AES block cipher against many current strong attacks.
Proposal of Dynamic Algorithms Combining a Substitution Layer and a Key Addition Layer for Block Ciphers using SHA3-256
In recent years, one research direction that has garnered significant attention from scientists is enhancing the security of SPN block ciphers against strong cryptanalysis attacks through dynamic methods. Researchers have focused on proposing dynamic implementations of substitution layers, diffusion layers, and key addition layers, as well as combi nations of these layers. These proposals are often based on mathematical foundations such as chaotic mappings, DNA techniques, affine transformations, or specially structured MDS matrices, among others. However, none of these studies have utilized the mathematical foundation of hash functions or combined substitution and key addition layers, nor have they leveraged the results of these layers as dynamic parameters for other layers. Therefore, in this study, we propose two dynamic algorithms that combine the key addition layer and substitu tion layer using the cryptographic hash function SHA3-256. Additionally, the dynamic method for the substitution layer is implemented using parameters derived from the key addition layer. We use the proposed dynamic algorithms to dynamically modify the AES block cipher while evaluating the security and randomness standards of the dynamic AES block cipher. With the two proposed dynamic algorithms, not only is there no increase in the expanded key size, but the security of the modified dynamic AES algorithm can also be significantly enhanced to 288
An Approach for Dealing with Sequential Data in Intelligent Tutoring Systems
The use of educational data to gain deeper insights into learners’ interaction histories with Intelligent Tutoring Systems (ITS) is receiving increasing attention, especially in the context of online learning and the growing demand for digital transformation in education. Predicting learners’ academic performance through the analysis and evaluation of their recorded activities in ITS plays a critical role in supporting educational administrators and instructors. An understanding of learners’ abilities helps refine teaching methods and optimize learning environments, ultimately en hancing educational quality. Our research improves upon our previous work, which utilized only LSTM, by incorporating an ensemble model- CL-PSP, combining CNN and LSTM networks. Specifically, the study focuses on predicting learn ers’ CFA capability- the likelihood of learners answering correctly on their first attempt. Knowledge evolves over time, observed in users’ interaction preferences within session based recommendation systems. CL-PSP leverages critical factor in shaping learners’ academic performance in two educational datasets, KDD Cup 2010 and Assistment 2017. Several enhancements, including a revised ensemble architec ture, improved error measurement, and refinements in data preprocessing. Results demonstrate that the proposed model significantly outperforms existing models, achieving superior performance with a lower Root Mean Square Error (RMSE). On the KDD Cup 2010 dataset, the model achieves a mini mum RMSE of 0.375, while notable improvements are also observed on the Assistment 2017 dataset, further underscoring the model’s effectiveness and robustness. The experimental results underscore the feasibility and considerable potential of utilizing session-based data in ITS to enhance both learning performance and educational quality.
Biomedical Entity-Aware Semantic Role La belling via Model Merging
Semantic Role Labeling (SRL) is essential for extracting structured knowledge from biomedical texts, yet it remains challenging due to the complexity of biomedical terminology. This paper introduces a novel method that enhances biomedical SRL by directly integrating Named Entity Recognition (NER) knowledge using model merging. NER plays a crucial role in anchoring domain-specific ar guments, allowing the model to better distinguish seman tic roles in biomedical contexts. Unlike prior approaches that rely on multitask learning or syntax-aware features, our method merges pretrained SRL and NER models in a way that preserves entity knowledge while aligning with semantic role structures. Experimental results demonstrate that our approach surpasses state-of-the-art multitask and syntax-aware SRL models in both accuracy and training speed. The integration of biomedical entity knowledge proves especially effective for predicates with high proportions of entity-based arguments, highlighting the value of direct NER to-SRL knowledge transfer in advancing biomedical semantic understanding.
Distillation-Centric Approaches in Visual Question Answering with Mixture of Experts
Recent advancements in computer vision and nat ural language processing were applied to the Visual Question Answering task. Nonetheless, a significant proportion of mod els exhibiting high accuracy possess extensive architectural components. This has a significant impact on the process of bringing the technology to practical applications such as assistive devices for the blind and visually impaired, and other related fields. Our research focuses on compressing the Visual Question Answering model on the Vietnamese dataset by utilizing the knowledge distillation method. Furthermore, in order to enhance precision, we have also developed a Mixture of ViVQA Experts system that will adapt to each type of question for improving accuracy while increasing only a few parameters and not wasting time retraining the entire system from scratch. With a total of 204M parameters, this approach has reduced the size by 24.51% compared to the original model while only reducing accuracy by 6.59% on the overall test set. More specifically, we have made accuracy improvements on each question type: “number” increased by 1.35% and “color” increased by 0.48% compared to our dis tillation model. The code and pretrained models are available at: https://github.com/huynhhoanghuy/Distillation ViVQA.
Efficient Mining of Concise Patterns for Fre quent High-Utility Occupancy Itemsets Using Extended Pruning Strategies
Concise representations of frequent high utility occupancy itemsets (FHUOIs), including maximal FHUOIs, closed FHUOIs, and generators of FHUOIs, are essential in utility-driven pattern mining. These representations offer several advantages over the complete set of FHUOIs, such as reduced size, improved efficiency, lower storage costs, and eas ier analysis. Notably, maximal FHUOIs allow for the recovery of all FHUOIs, while closed FHUOIs and generators enable the generation of non-redundant high utility occupancy rules and the efficient reconstruction of all FHUOIs along with their key information. Despite their significance, existing methods either mine closed FHUOIs and generators separately or lack a solution for mining all maximal FHUOIs. To bridge this gap, this paper introduces two novel algorithms MaxFHUOI Miner and CGFHUOI-Miner. The former efficiently extracts only maximal FHUOIs using extended pruning strategies that eliminate non-maximal itemsets early, while the latter simul taneously mines closed FHUOIs and generators by employing innovative pruning techniques to eliminate non-closed and non-generator itemsets at three levels of the prefix tree without performing subset checks. Extensive experiments on real world and synthetic datasets demonstrate that the proposed algorithms outperform existing and baseline methods in both speed and memory efficiency, particularly for low minimum support and utility occupancy thresholds in dense databases.
Enhancing Recommender Systems: A New Approach Using Collaborative Filtering with Bayesian Optimization and Gaussian Processes
Recommendation systems frequently make use of collaborative filtering (CF). CF derives its strength from the fact that in order to profile its users, it does not require a lot of knowledge about them, but it depends on earlier users’ ratings in which they were choosing products to recommend. Although there has been progress in modeling users as well as items, calibrating CF algorithms’ hyperparameters will continue to be a difficult task. This paper has come up with a new way of doing this by the use of Bayesian optimization with Gaussian processes throughout hyperparameter tuning. The method in question automatically works on hyperparameters to make two fundamental and straightforward CF algorithms have solid results against three famous datasets (Netflix Prize, MovieLens 1M, MovieLens 10M), and two other datasets (Douban and Jester dataset 2). This solution not only exhibits solid performance in controlled experiments but also provides a simplified approach for practitioners, minimizing time intensive manual adjustments while ensuring low overhead, therefore necessitating relatively low computational and devel opmental resources. This results in expedited deployment and simplified integration into practical systems, hence enhancing the dependability and scalability of recommendation engines.
Innovation of Mechanical Practice Model using Virtual Reality Technology and Pilot Butt Welding technique in Flat Position
Metal welding is one of the indispensable jobs in daily practice. Nowadays, there are many different welding techniques and methods depending on the object/metal to be welded. This article focuses on studying the principle of butt welding technique of metal plates in the flat position using traditional tools, machinery and equipment, thereby proposing a solution to innovate the practice model of butt welding technique in the flat position with the traditional method on real equipment to the practice method on virtual reality technology. Accordingly, the practical method will design and build 3D models of traditional equipment, tools, and instruments such as soldering irons, current regulators, metal iron blanks, protective gear, etc. using 3D graphic design technologies such as Unity 3D, Maya 3D, 3Ds Max, etc. and build virtual simulation exercises of the metal welding process in the form of a visual virtual reality model using the OpenSG graphics library and C# programming languages. The results of the article will build a virtual reality practice model for butt welding metal plates in a flat position with instructions for automatically explaining each step of the metal welding process. At the same time, the article will also provide assessments and analysis of the quality of the practice exercises at vocational training institutions in Thai Nguyen province.
Evidential Reasoning combine with Mass based Similarity for Imbalanced Classification
This paper introduces an integrated approach to address imbalanced classification problems using the Dempster-Shafer theory of evidence and mass-based dissim ilarity measurement. Traditional distance-based and density based classifiers often struggle with skewed datasets, leading to two key challenges: (1) misclassification bias, where conventional classifiers treat all instances equally despite the dominance of majority-class samples, and (2) variability in instance densities, which affects similarity assessments. To overcome these limitations, we introduce EMass, a novel classifier that replaces distance- and density-based similarity calculations with mass-based measures. Each neighbor of a considering instance is treated as an independent source of prior knowledge, represented through a basic probability assignment (BPA). We then apply Dempster’s rule of combi nation to aggregate multiple sources of information, producing a comprehensive probability estimate for classification. Experimental evaluations are conducted on 60 imbalanced data sets, comparing 12 algorithms using key performance metrics, including the F1 score, the Brier score, and the area under the curve (AUC). The results show the effectiveness of the proposed EMass classifier in improving the classification performance for imbalanced data sets.
viTBI-BERT: A Vietnamese Language Model for Prediction of Traumatic Brain Injury
Artificial Intelligence (AI) is increasingly uti lized for disease prediction by analyzing large and complex datasets. Traditionally, AI-based traumatic brain injury (TBI) prediction relies on multimodal data, including structured information like test results and unstructured data such as CT and/or MRI scans. However, physician conclusions in textform at admission and discharge represent an underutilizedsource of valuable insights into a patient’s condition. This paper proposes a novel approach to classifying TBI severity using these physicians’ written conclusions. We introduce viTBI-BERT, a model built on the ViHealthBERT backbone, incorporating pre-processing techniques such as acronym normalization, special character removal, word segmentation, and textual data augmentation. Following pre-processing, the data is passed through the pre-trained ViHealthBERT model, augmented with a fully connected layer and a softmax layer for classification. Evaluated on three sets of medical notes from a self-collected dataset of 503 Vietnamese patients, viTBI-BERT achieved a sensitivity of 71% in classifying four levels of injury severity. These findings demonstrate the potential of language based analysis, which, when integrated with structured clinical and subclinical data, could further improve TBI classification outcomes.
Low-Complexity FNN-Based Transmit Antenna Selec tion for Enhanced Performance in VASM Systems over Rician Fading Channels
Variable Active Antenna Spatial Modulation (VASM) is a spatial modulation variant designed to improve spectral efficiency and offer greater flexibility in system configuration. In this paper, we propose a feedforward neural network (FNN) framework to address the transmit antenna selection (TAS) problem, aiming to enhance performance in VASM systems operating over Rician fading channels. Computational results demonstrate that our novel FNN-TAS algorithm provides a significant reduction in computational costs compared to the standard Euclidean distance-based method. Additionally, simulation results indicate that the bit error rate (BER) of the VASM system increases with the Rician factor. However, the proposed FNN-TAS method effectively improves BER performance for VASM systems, particularly at high Rician factors, and outperforms conven tional TAS methods based on channel gain.
Resolving Multi-Class Imbalance using Coun terfactual Data Augmentation
Data imbalance in multi-class classification, where some classes have significantly fewer samples than others, is a prevalent challenge in machine learning. This paper evaluates the effectiveness of Counterfactual Augmentation for Multi-Class (CFA-MC), a data augmentation technique that utilizes ”native counterfactuals” to generate synthetic samples for minority classes, compared to two popular oversampling techniques, SMOTE and ADASYN. We conduct experiments on multiple benchmark multi-class datasets and employ three different classifiers (Random Forest, k-Nearest Neighbors, and Multilayer Perceptron) to assess the performance of the three methods. Experimental results demonstrate that CFA MC consistently outperforms SMOTE and ADASYN in terms of macro-averaged F1-score, suggesting its ability to generate more plausible synthetic samples, improve decision boundary representation, and mitigate overfitting.
Predicting Stock Market Trends in Vietnam Using SVM, XGBoost, and LSTM Architec tures
Predicting the behavior of a stock market with higher accuracy has been a critical task, however, application of various cutting-edge Machine Learning (ML) and Deep Learning (DL) architectures have shown better results compared to that of classical statistical models. This study explores the Vietnamese stock market with the aim of predicting closing price movements. Various machine learning models, including Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks, are employed for the analysis. The data for this research has been gathered from various companies listed on the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNINDEX) in Vietnam. These datasets were systematically preprocessed and thoroughly analyzed to calculate performance metrics, including the Root Mean Squared Error (RMSE) for assessing prediction quality and Mean Absolute Percentage Error (MAPE) to gauge forecast accuracy. In this study, technical features such as Relative Strength Index (RSI), Moving Average Convergence Diver gence (MACD), Stochastic Oscillator (SO), among others, are employed. Additionally, Simple Moving Averages (SMA) and Weighted Moving Averages (WMA) are considered for various time frames. The significance of these features is systematically evaluated and determined. The experimental results indicate that the LSTM model outperforms both SVM and XGBoost in terms of prediction accuracy, as evidenced by its lower RMSE and MAPE values, as well as higher accuracy and F1-scores. However, the computational time of LSTM is significantly greater compared to that of SVM and XGBoost.
A Rich High-Order Mutation Testing Dataset for Software Fortification
High-order mutation (HOM) testing is a rigorous technique for evaluating the effectiveness of test suites by introducing mutations with multiple concurrent faults into the source code. In this study, we present the development and analysis of a comprehensive dataset tailored for HOM testing purposes. The dataset comprises 2,839,792 instances categorized into Survived and Killed classes, representing instances correctly identified as surviving and not surviving the mutation testing process, respectively. We employ four prominent machine learning algorithms—Logistic Regression, Random Forest Classifier, LightGBM, and XGBoost—to clas sify instances within these categories. Experimental results demonstrate varying levels of accuracy, precision, recall, and F1-score across the algorithms, with LightGBM and XGBoost exhibiting superior performance. These findings underscore the importance of high-quality datasets in facilitating effective HOM testing and provide valuable insights into the capabilities of machine learning algorithms in this context.
Applying Deep Learning Models for Weapon Detection in Public Environments Through Serveil-lance Cameras
In the context of public security, the ability to detect weapons in real time through surveillance cameras is of utmost importance. This study focuses on applying fine tuning techniques and data augmentation strategies to a Transformer-based model to enhance the capability of identifying weapons in public environments. The fine-tuning process optimized the model parameters, along with these augmentations, to improve weapon detection performance. Experimental results show that after fine-tuning, the model achieved an mAP0.5 score of up to 96.5%, representing a significant improvement in accuracy compared to previous object detection models. These results demonstrate the great potential for applying the model to real-time security surveil lance systems, effectively detecting and addressing threats.
Low-Barrier Object Detection for Mobile Ap plications
This paper investigates innovative applications of image processing and object recognition methods aimed at simplifying the creation and deployment of object detection models. By doing so, we seek to expand access to advanced computer vision technologies for small and medium-sized businesses. Our research leverages a combination of modern technologies including Flask for web development, Firebase for database management, and Kotlin and Jetpack Compose for mobile application development. We integrate these with automatic training methods provided by the Autodistill library, utilizing models such as Detic and YOLOv8. The results demonstrate that this technological combination significantly enhances the performance of our object detection models, contributing to AI solutions in the digital intelligence era. A notable advancement is Autodistill’s capability tobypass the traditional dataset creation step by automatically generating labeled datasets from unlabeled input data. This feature markedly improves the efficiency and effectiveness of both data preparation and model training processes. Overall, our findings underscore the potential of these integrated technologies to democratize access to sophisticated computer vision capabilities for smaller enterprises, fostering greater innovation and competitiveness in the marketplace.
Data Power Optimization Algorithm for Downlink Multi-ARS Small Cell Communication Systems
In this paper, we investigate a novel downlink Small Cell (SC) system based on the Cell Free (CF) model, with the deployment of Unmanned Aerial Vehicles (UAVs) as Aerial Relay Stations (ARSs) simultaneously serving users in a specific area. The new SC model concept is expressed in the user-centric idea that the user will choose only one ARS with the best conditions for communication, and the concept of “cell" or “cell boundary" will no longer exist. The Time Division Duplex (TDD) mechanism is used for system-wide communication protocol, and the Minimum Mean Squared Error (MMSE) technique is applied for downlink channel estimation. We establish the downlink user throughput expression and then propose a Bisection optimization algorithm to control power for downlink data transmission to improve system quality and save energy. We evaluate the downlink multi-ARS SC communication system with and without power optimization in different aspects, such as altering the number of users, the number of ARSs, the number of antennas, and the transmission channel estimation interval. The results also reveal that the system quality is significantly improved when applying the downlink power factor optimization problem.
Exploring the Feasibility of Meeting the MDS Criterion in Random Matrices and Searching for Efficient Random Involutory Hadamard MDS Matrices
Maximum distance separable codes (MDS codes) have been studied for a long time in error correcting code theory and have important applications in cryptography. There are many different methods have been studied to build MDS matrices such as: from MDS codes, Cauchy matrices, Hadamard matrices, Vandermonde matrices, circulant and circulant-like matrices etc. In this paper, the construction of MDS matrix from random matrices is studied, and the possi bility of satisfying the MDS condition of any random matrix is presented. Then, the method of random search of MDS matrices will be given. In addition, we propose algorithms to randomly search for efficient involutory Hadamard MDS matrices of size 4, and 8. These results will be meaningful to determine the possibility that MDS matrices can be obtained from random matrices and diversify the methods of searching for MDS matrices. The involutory Hadamard MDS matrices make the encryption and decryption processes identical, which makes them very efficient for both hardware and software implementation.
A Meta-Heuristic Approach for Enhancing Performance of Associative Classification
Associative Classification is an interesting ap proach in data mining to create more accurate and easily interpretable predictive systems. This approach is often built on both association rule mining and classification techniques, to find a set of rules called association rules for classification (CAR) of label attributes. There are many kinds of associative classification such as CPAR, CBA, CMAR but the accuracy is still low on large datasets, and the running time is not reasonable as well. This paper proposes a heuristic approach to significantly enhance the performance of Associative Clas sification algorithms in running time, reducing the rule set, and accuracy on large data. Experimental results show that heuristic searching the optimal data set makes the associative classification more useful on big data, and reasonable in practice.
A Review of Cyber Security Risk Assessment for Web System during Its Deployment and Operation
This paper presents the state of the arts in security risk assessment of web systems. The process of assessing security risks and the process of developing and operating information systems in general, web systems in particular, are depicted step by step, showing how the risk assessment is performed during the deployment and the operation of web systems. Based on this analysis, different methods related to the manual and automatic risk assessment are reviewed, focusing on the methods using probability theory and Bayesian networks. The techniques developed for quantitative and qualitative assessment are presented and compared in terms of their objectives, scopes, and results to pick out advantages and limits. Finally, the approaches dedicated to assessing the risks of web systems are presented.
Estimation for the Number of MDS Matrices, Recursive MDS Matrices and Symmetric Recursive MDS Matrices from the Reed-Solomon Codes
The diffusion layer of the SPN block ciphers is usually built on the basis of the MDS (Maximum Distance Separable) matrices which is the matrix of the maximum distance separable code (MDS code). MDS codes have long been studied in error correcting code theory and have ap-plications not only in coding theory but also in the design of block ciphers and hash functions. Thanks to that im-portant role, there have been many studies on methods of building MDS matrices. In particular, the recursive MDS matrices and the symmetric recursive MDS matrices have particularly important applications because they are very efficient for execution. In this paper, we will give an estimate of the number of MDS matrices, recursive MDS matrices and symmetric recursive MDS matrices built from Reed-Solomon codes. This result is meaningful in determining the efficiency from this method of building matrices based on the Reed-Solomon codes. From there, this method can be applied to find out many MDS matrices, secure and efficient symmetric recursive MDS matrices for execution to apply in current block ciphers. Furthermore, recursive MDS matrices can be efficiently implemented using Linear Feedback Shift Registers (LFSR), making them well suited for lightweight cryptographic algorithms, so suitable for limited resources application.
Deep Learning of Image Representations with Convolutional Neural Networks Autoencoder for Image Retrieval with Relevance Feedback
Image retrieval with traditional relevance feed-back encounters problems: (1) ability to represent hand-crafted features which is limited, and (2) inefficient with high-dimensional data such as image data. In this paper, we propose a framework based on very deep convolutional neural network autoencoder for image retrieval, called AIR (Autoencoders for Image Retrieval). Our proposed framework allows to learn feature vectors directly from the raw image and in an unsupervised manner. In addition, our framework utilizes a hybrid approach of unsupervised and supervised to improve retrieval performance. The experimental results show that our method gives better results than some existing methods on the CIFAR-100 image set, which consists of 60,000 images.
A Recommendation Method for an Online Programming Portal
An online programming portal is a valuable resource for Information Technology students to enhance their programming skills. It offers an environment where educators and learners can create problems, generate test data, program, and automatically evaluate tests. Numerous universities, both domestic and international, have achieved remarkable success by developing digital content for these portals. However, a common challenge for learners is locating problems that align with their expertise in the vast database covering various topics. In this paper, we propose a collaborative filtering recommendation approach integrated into the Online Pro gramming Portal. This method aims to suggest a suitable set of problems based on each user’s programming proficiency. Experiments conducted on the Online Programming Portal at the Post & Telecommunications Institute of Technologym (PTIT) demonstrate a significant enhancement in students’ online problem-solving results.
A Method for Change Impact Analysis of C# Projects
Change Impact Analysis (CIA) is a common technique for identifying the potential effects of a change in software on other components. Despite the popularity of C# in the software industry, there have not been any CIA methods for C# projects. This paper proposes a new method for performing static CIA in C# projects based on existing methods for object-oriented languages. The main idea behind the method is constructing a dependency graph by doing static source code analysis. This enables the use of WaveCIA, a CIA algorithm leveraging dependency graphs. The implementation of this method in a CIA tool has proven its effectiveness in determining impacted components in C# projects, which can be further used for regression testing. Change Impact Analysis (CIA) is a common technique for identifying the potential effects of a change in software on other components. This solution has been made to analyze the impact of code changes in different languages like Java, C/C++, etc. Despite the popularity of C# in the software industry, there have not been any CIA methods for C# projects. This paper proposes a new method for performing static CIAin C# projects based on existing methods for object-oriented languages, to generate dependency graphs of that project’s source code. The key idea behind the method is to construct a dependency graph by doing static source code analysis. This enables the use of Wave-CIA, a CIA method leveraging dependency graphs. The results of analyzing the source code to build the dependency graph between the components in the project have been shown in the CIA4CS tool. We did some tool testing with several C# projects and the results showed high coverage for components and dependencies, that has proven its effectiveness in determining impacted components in C# projects. Finally, we provide some methodological discussion at the end of the paper.
Vietnamese Scene Text Detection Method based on Deep Learning
This paper proposes an efficient method for detecting Vietnamese text in outdoor scene images. Essentially, the text detection method presented here is based on the idea of utilizing deep learning network architectures to learn various geometric properties in order to reconstruct polygonal representations of text regions. The effectiveness of the method has been evaluated on four real-world outdoor scene image datasets, including the ICDAR 2015, Total-Text, VinText, and VnSceneText datasets. Experimental results show that the proposed method can detect text of various shapes and sizes with high and consistent accuracy. Specifically, the method achieved Precision, Recall, and Hmean scores of 87.53%, 86.94%, and 87.23%, respectively, on the test datasets, 84.32%, 88.17%, and 86.20% on a different dataset, 85.63%, 87.94%, and 86.77% on yet another dataset, and 85.14%, 87.23%, and 86.17% on the last dataset. The experimental results indicate that this approach is feasible for detecting Vietnamese text in outdoor scene images.