At the end of the Vietnam War, after the U.S. veterans returned to their homeland, they were rejected by their own society and hence experienced an inner state of homelessness. Having faced everyday humiliation and monotonous life in their homeland, they longed for Vietnam where they were once important and desired to return there as tourists now. In this journey of life, they turned out to be existential characters who embody the predicament of existence. In this reflective piece, we use Martin Heidegger's critical phenomenological concepts of homelessness, being and dasein to make sense of the predicament of the veterans' return to Vietnam. Drawing on our extensive conversation with them, we reflect on their being: how they are exiled at their homeland, but felt at home upon return to Vietnam; and relatedly, how the ever-changing contours of nostalgia, homelessness, and homecoming bear upon their identity.

The purpose of the paper is to apply the Slutsky equation to the Bass diffusion model to examine the price and income elasticities of hotel demand in different tourist destinations including cultural, commercial, and coastal cities. The sample was a set of 120 points by ex-post data from the Smith Travel Research in Vietnam. Findings indicate the price in a leisure destination in the beginning of the tourist area life cycle is inelastic, whereas the prices in the business and culture destinations at the end of the cycle are elastic. This study identifies the hidden cost when demand increases in the three destinations, ultimately allowing hoteliers to effectively strategize when making price changes within their industry.

Many approaches are established based on different formalisms to develop database models for uncertain data, like the similarity-based approach, the fuzzy set theory, or the possibility theory. However, no database models can deal with numeric and linguistic data with intrinsic qualitative semantics and their inherent interactions and can handle linguistic queries in a proper formalism established based on the data’ topological neighborhood systems. For dealing with such datatypes, the study proposes a so-called linguistic database model, which requires equipping a formalism established based on an axiomatic formalization and quantification of the domains of the linguistic attributes. Such a formalism is essential for simulating the human domain expert’s capability in handling words and their inherent relationships with the attributes’ numeric values. It permits running with the data semantics of different types, including ordinary, linguistic, incomplete, and null values, in a unified formalism using the so-called uncertain (lambda,rho)-comparators, =_(lambda,rho), =<_(lambda,rho), >=_(lambda,rho), <_(lambda,rho), and >_(lambda,rho), where the integer pair (lambda,rho) indicates the uncertain comparators degree. Since the qualitative semantics of the words is specific, sophisticated, and entirely different from the ordinary datatypes, it demands equipping the proposed linguistic database with a particular metadata component to manage different multi-aspect linguistic semantics.

With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging. We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging. The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models. Moreover, we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy, sensitivity, and specificity.

On the playgrounds of America, every kid's goal is to score. In Asia, where children stitch soccer balls for six cents an hour, the goal is to survive. (Sydney Schanberg, 1996, Life Magazine).In th...

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

The Internet of Drone Things (IoDT) is envisioned as Future direction of Drones backend via Internet of Things, Smart Computer vision, Cloud Computing, advanced wireless communication, big data, and high-end security techniques. The utilization of drones is increasing in diverse fields from Agriculture to Industry, from Government to private organizations and from Smart Cities to Rural area monitoring. With IoDT based implementations, all the existing sectors will become intelligent and smart for performing Monitoring, surveillance, search and rescue and more. In this paper, we present, a conceptual presentation of new terminology, i.e., Internet of Drone Things (IoDT), along with its related technologies, applications, security issues and real-time implementation of IoDT by taking case studies of Agriculture and Smart Cities.

The contrast enhancement of mammograms at preprocessing stage optimizes the overall performance of a computer-aided detection (CAD) system for breast cancer. In the proposed approach, contrast enhancement is performed using a sigmoidal transformation mechanism followed by extracting a set of 14 Haralick features. For classification purposes, a support vector machine (SVM) classifier is used which sorts the input mammogram into either normal or abnormal subclasses. The performance of the classifier is estimated by calculating parameters like accuracy, specificity, and sensitivity. The performance of the proposed approach has been reported to be better in comparison to other existing approaches.

ObjectivesThe average alcohol consumption per capita among Vietnamese adults has consistently increased. Although alcohol-related disorders have been extensively studied, there is a paucity of research shedding light on this issue among Internet users. The study aimed to examine the severity of alcohol-related disorders and other associated factors that might predispose individuals towards alcohol usage in a sample of youths recruited online.MethodsAn online cross-sectional study was conducted with 1,080 Vietnamese youths. A standardized questionnaire was used. Respondent-driven sampling was applied to recruit participants. Multivariate logistic and Tobit regressions were utilized to identify the associated factors.ResultsAbout 59.5% of the males and 12.7% of the total youths declared that they were actively using alcohol. From the total sample, a cumulative total of 32.3% of the participants were drinking alcohol, with 21.8% and 25.0% of the participants being classified as drinking hazardously and binge drinkers, respectively. The majority of the participants (60.7%) were in the pre-contemplative stage.ConclusionsA high prevalence of hazardous drinking was recognized among online Vietnamese youths. In addition, we found relationships between alcohol use disorder and other addictive disorders, such as tobacco smoking and water-pipe usage. Our results highlighted that the majority of the individuals are not receptive to the idea of changing their alcohol habits, and this would imply that there ought to be more government effort towards the implementation of effective alcohol control policies.

Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

The online-advertising has been grown to focus on multimedia interactive model with through the Internet. Our Online Video Advertisement User-oriented (OVAU) system combined the machine learning model for face recognition from camera, multimedia streaming protocols, and video meta-data storage technology. face recognition (FR) is an importance phase which can to enhance the performance of our system. Feature Selection (FS) problem for FR is solved by MMAS-FS algorithms based-on PZMI and DWT features. The features set are represented by digraph G(E, V). Each node used to show the features, and the ability to choose a combination of features is presented the edges connecting between two adjacent nodes. The heuristic information extracted from the selected feature vector as ant's pheromone. The feature subset optimal is selected by the shortest length features and best presentation of classifier. The best subset used to classify the face recognition used Nearest Neighbor Classifier (NNC). The experiments were analyzed on FS shows that our algorithm can be easily applied without the priori information of features. The execution assessed of our calculation is more effective than previous approaches for Video-based recognition based on FS problem.

Background: Internet addiction (IA) is a common problem found in young Asians. This study aimed to study the influence of IA and online activities on health-related quality of life (HRQOL) in young Vietnamese. This study also compared the frequencies of anxiety, depression and other addiction of young Vietnamese with and without IA.Methods: This study recruited 566 young Vietnamese (56.7% female, 43.3% male) ranging from 15 to 25 years of age via the respondent-driven sampling technique. Chi-squared, t-test and analysis of variance were used to compare young Vietnamese with and without IA. Regression analyses were used to examine the association between internet usage characteristics and HRQOL.Results: Results from this cross-sectional study showed that 21.2% of participants suffered from IA. Online relationship demonstrated significantly higher influences on behaviors and lifestyles in participants with IA than those without IA. Participants with IA were more likely to have problems with self-care, difficulty in performing daily routine, suffer from pain and discomfort, anxiety and depression. Contrary to previous studies, we found that there were no differences in gender, sociodemographic, the number of participants with cigarette smoking, water-pipe smoking and alcohol dependence between the IA and non-IA groups. IA was significantly associated with poor HRQOL in young Vietnamese.Conclusion: IA is a common problem among young Vietnamese and the prevalence of IA is the highest as compared to other Asian countries. Our findings suggest that gender may not play a key role in IA. This can be an emerging trend when both genders have equal access to the internet. By studying the impact of IA on HRQOL, healthcare professionals can design effective intervention to alleviate the negative consequences of IA in Vietnam.

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

In this research, we propose the online video contextual advertisement user-oriented system. Our system is a combination of video-based face recognition using machine learning models from the camera with multimedia communications and networking streaming architecture using Meta-data structure to video data storage. The real images captured by the camera will be analyzed based on predefined set of conditions to determine the appropriate object classes. Based on the defined object class, the system will access the multimedia advertising contents database and automatically select and play the appropriate contents. We analyse existing face recognition in videos and age estimation from face images approaches. Our experiment was analyzed and evaluated in performance when we integrate analyze age from the face identification in order to select the optimal approach for our system.

Synthetic Aperture Radar (SAR) images are known to be corrupted by granular noise known as speckle. This noise is inherently present in these images owing to acquisition constraints and is a major cause of visual quality degradation. The anisotropic diffusion approaches for despeckling are constrained in terms exercising control over the non-homogeneous regions. This paper proposes to improve the non-linear Anisotropic Diffusion (AD) filter for despeckling using Ant Colony Optimization (ACO) algorithm. The main essence of this work is to suppress speckle and preserve the structural content. The issue of residual speckle content has been minimized by optimal selection of AD parameter(s) using ACO algorithm. Experimental results advocate the performance improvement achieved and has been validated using objective measures of image quality evaluation.

Presently, the advertising has been grown to focus on multimedia interactive model with through the Internet. The Online Video Advertisement User-oriented (OVAU) system is combined of the machine learning model for face recognition from camera, multimedia streaming protocols, and video meta-data storage technology. Face recognition is an importance phase which can improve the efficiency performance of the OVAU system. The Feature Selection (FS) for face recognition is solved by MMAS-FS algorithm used PZMI feature. The heuristic information extracted from the selected feature vector as ant’s pheromone. The feature subset optimal is selected by the shortest length features and best presentation of classifier. The experiments were analyzed on face recognition show that our algorithm can be easily applied without the priori information of features. The performance evaluated of our algorithm is better than previous approaches for feature selection.

One of the biggest challenges for schools and institutions offering IT-training programs in Vietnam is how to minimize the amount of retraining by IT companies and corporations for new IT graduates due to the gap between college education and real-world practices in IT. In particular, how can our IT graduates acquire the right knowledge, skills and attitudes to fit in our IT labor market? There are many solutions to the above problem like restructuring the curricula, integrating the materials of higher-division courses, or adding in practical skill-set training, etc. However, the most effective solution from our experiences is to change our current methodology of teaching and learning IT in Vietnam, specially, enhancing our students' capability through more use of real-world IT projects. This is usually known as the Project-Based Learning (PjBL) approach in which students explore real-world problems or challenges, and simultaneously developing interdisciplinary skills while working in small collaborative groups or teams. Since project-based learning is involved with active and engaged learning, it inspires students to acquire a deeper knowledge of the subjects they are studying. In this paper, we will present some current alarming issues of the IT training and education situation in Vietnam and how the Faculty of Information Technology of Duy Tan University tackles those issues through its deployment of major IT Capstone projects as part of a university-wide Project-based Learning effort.

This paper introduces the Fixed Broadband Wireless Access (FBWA), being the cost effective solutions for wide area coverage and last mile delivery of a wide range of data, voice, video, multimedia, and high-speed Internet services. Its objective is to provide a comprehensive understanding of the FBWAS as follows. The first section provides reasons for introducing the FBWAS. The system definition, frequency reuses strategy, operation, network architecture, and various other FBWAS systems are discussed and explained in section 2. In the 3rd section, a typical digital radio system is first reviewed to point out the key parameters of the FBWAS. Based on this information, the equipments of all vendors, having participated the field trial, are considered and discussed. A detailed performance comparison is given. The 4th section proposes the key system performance metrics for evaluating a large FBWAS, having hundreds of cells and covering the entire country. Last is the conclusion and recommendation.