Journal Articles - Computer and Information Technology - 2020

Bài toán nhận dạng khuôn mặt có vai trò vô cùng quan trọng trong nhiều ứng dụng thực tiễn, có thể kể đến là chức năng facw tagging trên facebook. Nhận dạng khuôn mặt cũng được ứng dụng trong một số hệ thống về quản lý nhân sự và các hệ thống phát hiện tội phạm. Mục tiêu của bài viết là nghiên cứu về các phương pháp trích chọn thuộc tính nổi tiếng với các đặc trưng HOG, Eigenfaces, Fisherfaces, LBPH ... và đánh giá hiệu quả của các phương pháp này trong bài toán nhận dạng khuôn mặt.

Graph is a powerful mathematical tool applied in many fields as transportation, communication,informatics, economy, in ordinary graph the weights of edges and vertexes are considered independentlywhere the length of a path is the sum of weights of the edges and the vertexes on this path. However, inmany practical problems, weights at a vertex are not the same for all paths passing this vertex, but dependon coming and leaving edges. The paper develops a model of extended network that can be applied tomodelling many practical problems more exactly and effectively. The main contribution of this paper isalgorithm finding maximal flows on extended networks

Wireless Power Transfer (WPT) is a significant technique for Internet of Things (IoT) networks. Recently, more interest has been focused on multiple access technique without orthogonal signals for wireless communication. Non-orthogonal Multiple Access (NOMA) scheme is proposed to allow users the access point in IoT network. In this paper, we propose the power beacon which is able to feed energy to power-constraint relay node to further support transmission from the source to destinations in IoT networks. In this article, a NOMA system is benefited by with WPT and antenna selection technique. The system improvement can be achieved through the exact closed-form expressions of outage probability (OP). The performance gap among two users is evaluated using model of the Rayleigh fading channels. Furthermore, we compare NOMA with traditional scheme to highlight advantage of such IoT system.

One of the significant challenges to 5G networks is how to serve a proliferation of dense mobile users (MUs) various video applications and services (VASs) at high delivery capacity under a scarcity of resources. In this paper, we exploit the resources of dense MUs to establish a set of device-to-device (D2D) multi-hop multi-path (MHMP) communications that can assist the macro base station (MBS) to offload the videos. Particularly, there are three types of MUs including cellular MUs (CUs), source MUs (SUs), and relay MUs, which are willing to share the downlink spectrum resources, provide the cached videos, and forward the videos, respectively. A downlink resource allocation for D2D MHMP communications (DRA-DMC) optimization problem is formulated to find the optimal allocation pairs of CUs and D2D hops in each path from the SUs to a destination MU (DU). Consequently, the DU can receive the videos flexibly from both the MBS and the SUs over D2D MHMP communications by reusing the downlink resources of the CUs, at maximum delivery capacity. Simulation results are performed to demonstrate the benefits of the DRA-DMC solution compared to other conventional schemes.

Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models

Deep learning methods such as recurrent neural network and long short-term memory have attracted a great amount of attentions recently in many fields including computer vision, natural language processing and finance. Long short-term memory is a special type of recurrent neural network capable of predicting future values of sequential data by taking the past information into account. In this paper, the architectures of various long short-term memory networks are presented and the description of how they are used in sequence prediction is given. The models are evaluated based on the benchmark time series dataset. It is shown that the bidirectional architecture obtains the better results than the single and stacked architectures in both the experiments of different time series data categories and forecasting horizons. The three architectures perform well on the macro and demographic categories, and achieve average mean absolute percentage errors less than 18%. The long short-term memory models also show the better performance than most of the baseline models.

A question answering (QA) system based on natural language processing and deep learning is a prominent area and is being researched widely. The Long Short-Term Memory (LSTM) model that is a variety of Recurrent Neural Network (RNN) used to be popular in machine translation, and question answering system. However, that model still has certainly limited capabilities, so a new model named Bidirectional Encoder Representation from Transformer (BERT) emerged to solve these restrictions. BERT has more advanced features than LSTM and shows state-of-the-art results in many tasks, especially in multilingual question answering system over the past few years. Nevertheless, we tried applying multilingual BERT model for a Vietnamese QA system and found that BERT model still has certainly limitation in term of time and precision to return a Vietnamese answer. The purpose of this study is to propose a method that solved above restriction of multilingual BERT and applied for question answering system about tourism in Vietnam. Our method combined BERT and knowledge graph to enhance accurately and find quickly for an answer. We experimented our crafted QA data about Vietnam tourism on three models such as LSTM, BERT fine-tuned multilingual for QA (BERT for QA), and BERT+vnKG. As a result, our model outperformed two previous models in terms of accuracy and time. This research can also be applied to other fields such as finance, e-commerce, and so on.