RESEARCH ON FACTORS INFLUENCING THE INTENT TO USE NETFLIX MOVIES IN VIETNAM

Abstract The Netflix movies market is steadily growing, especially during the complex COVID-19 pandemic. Consumers, instead of opting for free movie streaming services with potential risks and copyright violations, are choosing to pay for a better experience while emphasizing responsibility for protecting copyrights and supporting authors and producers. This research aims to examine the factors influencing the intent to use Netflix movie streaming services among surveyed individuals, primarily focusing on employees aged 18 to 22 in Vietnam. Participants were surveyed through online and offline questionnaires. The author conducted logistic regression analysis, treating the use of Netflix movies as the dependent variable, with five independent variables sourced from a literature review. Through online and offline survey questionnaires and multivariate regression models, the study identified and concluded the factors influencing employees' intent to use Netflix movie streaming services in Vietnam. Data were quantitatively analyzed using IBM SPSS 20.0. The research results identified five positively influencing factors on the intent to use Netflix movie streaming services: Price perception, Risk perception, Attitude, Ethical awareness, Subjective norms. Among these factors, Price perception had the strongest influence on the intent to use Netflix movies, while the Subjective norms factor was found to be insignificant. Consequently, the article suggests managerial implications for businesses to attract customers and promote the Netflix movies market

A RESEARCH ON MULTI-OBJECTIVE OPTIMIZATION OF THE GRINDING PROCESS USING SEGMENTED GRINDING WHEEL BY TAGUCHI-DEAR METHOD

In this study, the mutil-objective optimization was applied for the surface grinding process of SAE420 steel. The aluminum oxide grinding wheels that were grooved by 15 grooves, 18 grooves, and 20 grooves were used in the experimental process. The Taguchi method was applied to design the experimental matrix. Four input parameters that were chosen for each experiment were the number of grooves in cylinder surface of grinding wheel, workpiece velocity, feed rate, and cutting depth. Four output parameters that were measured for each experimental were the machining surface roughness, the system vibrations in the three directions (X, Y, Z). The DEAR technique was applied to determine the values of the input parameters to obtaine the minimum values of machining surface roughness and vibrations in three directions. By using this technique, the optimum values of grinding wheel groove number, workpiece velocity, feed-rate, cutting depth were 18 grooves, 15 m/min, 2 mm/stroke, and 0.005 mm, respectively. The verified experimental was performed by using the optimum values of input parameters. The validation results of surface roughness and vibrations in X, Y, Z directions were 0.826 (µm), 0.531 (µm), 0.549 (µm), and 0. 646 (µm), respectively. These results were great improved in comparing to the normal experimental results. Taguchi method and DEAR technique can be applied to improve the quality of grinding surface and reduce the vibrations of the technology system to restrain the increasing of the cutting forces in the grinding process. Finally, the research direction was also proposed in this stud

SEDIMENT BUDGET AND EROSION ASSESSMENT OF THE HAIHAU COASTAL ZONE, NAMDINH PROVINCE, NORTHERN VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection

The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense

Study of Multiple Photoneutron Reactions on 197Au Induced by 2.5 GeV Bremsstrahlung

We identified eight radionuclides $^{196}$Au, $^{195}$Au, $^{194}$Au, $^{193}$Au, $^{192}$Au, $^{191}$Au, $^{190}$Au, $^{189}$Au formed via the multiple photoneutron reactions $^{197}$Au$(\gamma ,kn)^{197 - k}$Au with 2.5 GeV\break bremsstrahlung. The yields of radionuclides that decay by emitting $\gamma$-ray were measured using high purity germanium (HPGe) detector coupled to a PC-based multichannel analyzer. In order to improve the accuracy of the experimental results the necessary corrections were made. The obtained results are compared with reference data and the variations of the $^{197}$Au($\gamma$,kn)$^{197 - k}$Au reaction yields according to incident bremsstrahlung energy and neutron multiplicity are also discussed

MACRO-ZOOPLANKTON ABUNDANCE IN RELATION TO METAL ACCUMULATION AND WATER QUALITY IN TRUC BACH LAKE

Urban lake pollution is one of the serious issues due to suffering of waste discharged from householders. However, there is a gap of knowledge about the diversity of zooplankton species and how metals accumulate in zooplankton in urban ecosystems. We addressed this by determining the rule of blooming macro-zooplankton in Truc Bach lake and levels of two essential metals: copper (Cu), and zinc (Zn) and of three non-essential metals: arsenic (As), and lead (Pb) in water samples were determined. The results showed that Cladocerans and copepods are macro-zooplankton dominant species in Truc Bach Lake. Water temperature significantly affects the variety of copepod blooming. Arsenic concentration in water collected from the lake exceeded the safety level of current Vietnamese regulation. As concentration in macro-zooplankton positively correlated with metal concentrations in the water (p 0.05) while the concentration of Cu, Zn, and Pb in water has no significant correlation with the metal in zooplankton’s body. The relative abundance of adult copepods in Truc Bach lake had a negative correlation with As concentration in water (p = 0.01). The higher As concentration in water, the lower relative abundance of copepods was found in the sample