The trend of using smart teaching devices in education in Vietnam

This study examines the growing integration of intelligent educational tools within the educational framework of Vietnam, underscoring the nation's swift shift towards a technology-centered learning environment. With 72.1 million internet users in Vietnam, marked by a 98.1% adoption rate of smartphones, 58.8% usage of laptops, and 35.5% ownership of tablets, these sophisticated devices have assumed a central role in the field of education. Notably, there exist governmental initiatives aimed at reinforcing the legal infrastructure, serving as a testament to Vietnam's dedication to advancing education through technological means. Smart educational devices have brought about a transformation in teaching techniques, rendering lessons more captivating, efficient, and accessible. The authors conducted a survey among a randomly selected group of 255 participants, revealing that the entire cohort utilizes smart devices for educational or work-related purposes. Nonetheless, certain students tend to employ smartphones for entertainment, which has raised inquiries regarding their scholastic impact. The paper explores the role of laptops, accentuating their revolutionary contribution to education and the necessity for further investigation to gain deeper insights into their effects. Furthermore, it investigates the obstacles encountered, encompassing technology-related distractions in educational contexts and disparities in technology access. Ultimately, this research underscores the significance of addressing these impediments to fully exploit the potential of intelligent educational devices in Vietnam's educational landscape, with the added benefit of drawing comparisons with global trends to glean valuable insights for further enhancing the educational domain

Pointwise Convolutional Neural Networks

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is pointwise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.Comment: 10 pages, 6 figures, 10 tables. Paper accepted to CVPR 201

LAB-SCALE STUDY ON CO-DIGESTION OF KITCHEN WASTE, SLUDGE AND SEWAGE

Anaerobic digestion is widely used for biodegradable solid organic wastes in order to recover bio-energy in the form of biogas. Some previous studies presented that co-digestion of various substrates can improve biogas yields as well as enhanceperformance of organic wastes digestion, in comparison with digestion of sole solid waste. This study aimed to evaluate the performance of anaerobic mono-digestion and anaerobic co-digestion of the following mixtures: (a) sole kitchen waste (KW), (b) KW and sewage (SW), (c) sole sludge (SL)and (d) KW and SL. This study was conductedby four lab-scale anaerobic complete mixing reactors (numbered MH1 – MH4) in 4,5 liters working volume atorganic loading rate (OLR) 2,0 g(VS).L-1.d-1. The KW was collected from canteen B4 and SW was collected from effluent from septic tank C6 Building in Ho Chi Minh University of Technology (HCMUT). The results show that the reactor of sole KW obtained average total chemical oxygen demand (tCOD), soluble chemical oxygen demand (sCOD), total solid (TS), volatile solid (VS), total phosphorus (TP) and total Kjeldahl nitrogen (TKN) of 62 %, 62 %, 71 %, 72 %, 73 % and 45 %, respectively, whereas reactor of KW and SW co-digestion had were tCOD, sCOD, TS, VS, TP and TKN removal of 73 %, 78 %, 75 %, 79 %, 59 % and 57 %, respectively. Thus co-digestion of KW and SW revealed an efficient enhancement of digestion, instead of sole KW digestion. Similarly, TS (74 %) and VS removals (75 %) of co-digesting mixtures of SL and KW were higher than those of sole SL digestion (67 %). Furthermore, co-digestion of SL and KW obtained better performance in tCOD and sCOD removals (70 % and 76 %, respectively)

Case study of autostereoscopic image based on SIRDS algorithm

Single Image Random Dots Stereogram (SIRDS) is an interesting algorithm deployed to represent a three-dimensional scene. Results of the algorithm are normal two-dimensional pictures but they do carry the vivid depth information - the third dimension in the real three-dimensional world - that cannot be obtained explicitly with other traditional two-dimensional pictures. The novelties of this paper are twofold: first it gives readers a complete overview of the possibility of `seeing´ reconstructed three-dimensional objects; then the paper focuses on analyzing and improving the implementation of the SIRDS algorithm. Its drawbacks and, especially, its visibility are deeply discussed and tested. Our proposals for generating optimized autostereograms (products of SIRDS) - i.e. they clearly display the depth information of a scene with less artifact and easier to view - are also presented

Exotic States Emerged By Spin-Orbit Coupling, Lattice Modulation and Magnetic Field in Lieb Nano-ribbons

The Lieb nano-ribons with the spin-orbit coupling, the lattice modulation and the magnetic field are exactly studied. They are constructed from the Lieb lattice with two open boundaries in a direction. The interplay between the spin-orbit coupling, the lattice modulation and the magnetic field emerges various exotic ground states. With certain conditions of the spin-orbit coupling, the lattice modulation, the magnetic field and filling the ground state becomes half metallic or half topological. In the half metallic ground state, one spin component is metallic, while the other spin component is insulating. In the half topological ground state, one spin component is topological, while the other spin component is topological trivial. The model exhibits very rich phase diagram

A Text-based Approach For Link Prediction on Wikipedia Articles

This paper present our work in the DSAA 2023 Challenge about Link Prediction for Wikipedia Articles. We use traditional machine learning models with POS tags (part-of-speech tags) features extracted from text to train the classification model for predicting whether two nodes has the link. Then, we use these tags to test on various machine learning models. We obtained the results by F1 score at 0.99999 and got 7th place in the competition. Our source code is publicly available at this link: https://github.com/Tam1032/DSAA2023-Challenge-Link-prediction-DS-UIT_SATComment: Accepted by DSAA 2023 Conference in the DSAA Student Competition Sectio

Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system