Research on the applications of blockchain technology within tourism industry in Vietnam: Proposed model in Phu Quoc Island

In recent years, blockchain technology has garnered interest from a diverse range of industries and areas, mostly due to its enormous possibilities for transforming the way data is stored and utilized, enhancing security and transparency, and facilitating transactions. In light of the rapid advancement of blockchain technology and the trend toward increasing the awareness of its benefits in the tourism sector, the Vietnamese government, particularly Ministry of Culture- Sports and Tourism, has made significant efforts and played a pivotal role in trying to establish an ecosystem, facilitating blockchain technology to gradually integrate into tourism activities. This contemporary study was conducted to ascertain the level of government interest in fostering an environment conducive to the adoption of blockchain technology in Vietnam. To begin, the author collected secondary data on the number of seminars held and sanctioned by local governments in Vietnam about the use of blockchain technology in tourism since 2018. Second, fifteen tourism specialists who work in resorts, travel agencies, and tourism-related enterprises on Phu Quoc Island were chosen to collect primary data using a mix of open- and closed-ended questionnaires. The Delphi technique was used to evaluate the data collected in order to estimate the outcome of a future scenario involving the establishment of a blockchain system on Phu Quoc Island. The findings indicate that local governments are likely to be interested in expanding the legal framework for access to blockchain technology, and tourism organizations are willing to incorporate blockchain technology into their current operations if the legal framework allows for this new technology. According to the findings, there are two distinct views on which business scope should be prioritized first. For example, hotel and resort representatives prefer to integrate booking and luggage checking first, while travel agencies prefer automatic commission allocation for travel parties involved in the entire tourism procedure

Foveated Vision Models for Search and Recognition

Computer vision has made a significant progress in recent years thanks to advancement in neural network architectures and computing power. At the sensory level, the current machine vision systems sample the visual data uniformly to make predictions about the scene. This is in contrast with the human vision system that has high visual acuity only in a small central region, the fovea, and much coarser sampling away from the center. There has been a renewed interest, particularly in the context of active vision for robotics navigation and scene exploration, to develop biologically motivated methods that can leverage such foveated computations. While foveated vision offers computational savings at or near the region of interest, it requires eye movements to scan the scene for effective image understanding. The hypothesis is that methods that can leverage non-uniform sampling of the field of view together with eye-movements will lead to a new class of active vision systems that are optimized computationally for specific tasks of interest.Inspired by the above observations, this research provides, for the first time, a comprehensive study of the human visual search in the constrained setting of person identification in the wild. A novel video database is created that systematically tests how different parts of a person contribute towards eye-movements and person identification. Our study shows that the search errors can dominate the overall recognition accuracy in human subject experiments. This calls for new strategies for integrating eye tracking with foveated image representations. Towards this two specific approaches are investigated further.In the first approach, a deep neural network based method is developed to model eye movements. Using the long-short-term-memory to model the successive fixations. The proposed method outperforms state of the state of the art performance while simplifying the feature extraction procedure. The second approach focuses on the foveated image model that leverages multiple fixations. A convolutional neural network method is proposed that works directly with the foveated input images that achieves competitive recognition rates compared to standard neural networks operating on the same number of input pixels. Overall the thesis investigates the requirements and implementations that could support active foveated vision, and lays down the ground work for future studies in this area

Quality Assurance of Higher Education in Vietnam: The Impact of Autonomy Policy

The autonomous university model is recognized as a method of advanced university governance to improve training quality . In Vietnam, university autonomy has made many positive changes in training quality in recent years. This study examines if there is a difference in the QA activities of academic programs between two types of higher education institutions: the public universities with financial autonomy and the public universities without financial autonomy. A quantitative method was used to analyze the survey data from 593 participants. An independent T-test was used to analyze the differences between the two types of institutions. The results indicated statistical differences in most activities in seven research areas. The quantitative result provided strong evidence of the impact of autonomy policy on two types of higher education institutions, which was not addressed in the national report on autonomy policy in 2022. Some recommendations were made to improve the QA activities toward continuous quality improvement

Artifact elimination in ECG signal using wavelet transform

Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density