• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.249-255
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• 2023

In this study, based on the awareness of the problem of current scientific guard systems of ROKA, We suggested a develoment direction for scientific guard system applying 3 elements of Revolution in Military Affairs by 2035. To this end, we analyzed challenges of current scientific guard systems and reviewed similar cases in other countries. Based on this, We suggested a develoment direction for scientific guard system, comprised of the concept of gurad operation, the organization of guard troops, and MUM-T(manned and unmanned teaming) by applying the framework of 3 elements of military innovation (operation concept, organization, weapon system). In order to overcome challenges at hand, we need a innovative scientific guard systems that applies MUM-T based on high technology along with agile&smart guard troops.

• Journal of Distribution Science
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• v.22 no.2
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• pp.71-82
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• 2024

Purpose: Despite significant research on decision-making, researchers struggle to comprehend the decision-making process. This paper aims to not only examine the relationship between problem-solving skills, cognitive competency, and decision-making but also develop measurement instruments for cognitive competency and problem-solving skills to better model decision-making. Research Design, Methodology and Approach: A cross-sectional study was conducted by surveying 292 university students in HCM City, Vietnam, via email sent randomly by Google Forms. This study identifies the conceptual framework and tests the hypotheses using a deductive approach. The SPSS program was used to evaluate the scales' reliability, and the SmartPLS program was used to assess the measurement and structural models. Results: The results show that the research model better modelled the relationship between problem-solving skills, cognitive competency, and decision-making. Although thinking ability has no direct impact on decision-making, both creativity and problem-solving skills have a positive impact on decision-making. The mediating role of problem-solving skills is also determined by the positive relationship between cognitive competency and decision-making. Conclusions: This study highlights decision-making efficiency through the cognitive process from low to high levels and provides for policymakers and managers to explain the decision-making process in a variety of sectors, such as distribution chains, marketing, and human resource distribution.

• Journal of Distribution Science
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• v.22 no.3
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• pp.33-47
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• 2024

Purpose: Most emerging-market countries are concerned about the technology boom, which is accompanied by an increase in revenue from online sales and services. This finding has been demonstrated during the COVID-19 pandemic; however, is this tendency continuing in the new normal, and what factors are driving the increase in consumer decisions? The purpose of this research is to investigate how the decision to utilize online services will be affected in the new normal as well as propose a new research approach in this field. Research Design, Methodology and Approach: By following a deductive research method associated with positivist philosophy, a survey in South Vietnam with 426 respondents using a convenience sampling method was conducted. The reliability of the measurement scales was examined by using the SPSS program. The SmartPLS programme was utilised to assess the measurement and structural models as well as test hypotheses by using partial least squares structural equation modelling. Results: According to the research findings, decision-making has been impacted by social influences, perceived usefulness, perceived ease of use, perceived trust, perceived price, and perceived convenience. Conclusions: The research results also bring significant contributions not only in practice in providing management implications but also in theory. The research model has also demonstrated the feasibility of employing the stimuli-organism-response framework and combining the theory of perceived risk with the technology acceptance model via the explanation of decision-making.

• The Korean Journal of Franchise Management
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• v.15 no.2
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• pp.57-75
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• 2024

Purpose: Nowadays, many restaurants use serving robots. Initially, many people thought that Covid-19 caused the spread of serving robots. However, even as the endemic, many restaurants still use serving robots. Therefore, this study examines why many customers choose restaurants with serving robots, using the UTAUT2 framework. Additionally, this study explores whether shyness has a moderating effect on these factors. Research design, data and methodology: Data were collected from 307 consumers who had visited a restaurant using a serving robot and analyzed using SmartPLS 4.0 software. A total of 286 datasets were analyzed. Result: We found that the precedence factors of UTAUT2 (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition, Hedonic Motivation) had a positive effect on attitude. Furthermore, attitude had a significant positive effect on Behavioral Intention. However, shyness did not appear to have a moderating effect among these factors. This is likely due to customers using serving robots for very short time, as identified in the literature review. Conclusions: As a result of this study, it was explained that Hedonic Motivation had the most significant positive effect on shaping attitudes toward restaurants using serving robots through the UTAUT2 model.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.172-180
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• 2024

Since the beginning of 2015, corporate social responsibility (CSR) models have been changing in connection with the trend towards the transition of joint value creation of corporate activities and consideration of stakeholders' interests. The purpose of the academic paper lies in empirically studying the current practice of social responsibility of transnational corporations (TNCs). The research methodology has combined the method of qualitative analysis, the method of cases of agricultural holdings in emerging markets within the framework of resource theory, institutional theory and stakeholders' theory. The results show that the practice of CSR is integrated into the strategy of sustainable development of TNCs, which determine the methods, techniques and forms of communication, as well as areas of stakeholders' responsibility. The internal practice of CSR is aimed at developing norms and standards of moral behaviour with stakeholders in order to maximize economic and social goals. Economic goals are focused not only on making a profit, but also on minimizing costs due to the potential risks of corruption, fraud, conflict of interest. The system of corporate social responsibility of modern TNCs is clearly regulated by internal documents that define the list of interested parties and stakeholders, their areas of responsibility, greatly simplifying the processes of cooperation and responsibility. As a result, corporations form their own internal institutional environment. Ethical norms help to avoid the risks of opportunistic behaviour of personnel, conflicts of interest, cases of bribery, corruption, and fraud. The theoretical value of the research lies in supplementing the theory of CSR in the context of the importance of a complex, systematic approach to integrating the theory of resources, institutional theory, theory of stakeholders in the development of strategies for sustainable development of TNCs, the practice of corporate governance and social responsibility.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.19-41
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• 2016

Recently, the proliferation of mobile devices such as smartphones and tablet personal computers and the development of information communication technologies (ICT) have led to a big trend of a shift from single-channel shopping to multi-channel shopping. With the emergence of a "smart" group of consumers who want to shop in more reasonable and convenient ways, the boundaries apparently dividing online and offline shopping have collapsed and blurred more than ever before. Thus, there is now fierce competition between online and offline channels. Ever since the emergence of online shopping, a major type of multi-channel shopping has been "showrooming," where consumers visit offline stores to examine products before buying them online. However, because of the growing use of smart devices and the counterattack of offline retailers represented by omni-channel marketing strategies, one of the latest huge trends of shopping is "webrooming," where consumers visit online stores to examine products before buying them offline. This has become a threat to online retailers. In this situation, although it is very important to examine the influencing factors for switching from online shopping to webrooming, most prior studies have mainly focused on a single- or multi-channel shopping pattern. Therefore, this study thoroughly investigated the influencing factors on customers switching from online shopping to webrooming in terms of both the "search" and "purchase" processes through the application of a push-pull-mooring (PPM) framework. In order to test the research model, 280 individual samples were gathered from undergraduate and graduate students who had actual experience with webrooming. The results of the structural equation model (SEM) test revealed that the "pull" effect is strongest on the webrooming intention rather than the "push" or "mooring" effects. This proves a significant relationship between "attractiveness of webrooming" and "webrooming intention." In addition, the results showed that both the "perceived risk of online search" and "perceived risk of online purchase" significantly affect "distrust of online shopping." Similarly, both "perceived benefit of multi-channel search" and "perceived benefit of offline purchase" were found to have significant effects on "attractiveness of webrooming" were also found. Furthermore, the results indicated that "online purchase habit" is the only influencing factor that leads to "online shopping lock-in." The theoretical implications of the study are as follows. First, by examining the multi-channel shopping phenomenon from the perspective of "shopping switching" from online shopping to webrooming, this study complements the limits of the "channel switching" perspective, represented by multi-channel freeriding studies that merely focused on customers' channel switching behaviors from one to another. While extant studies with a channel switching perspective have focused on only one type of multi-channel shopping, where consumers just move from one particular channel to different channels, a study with a shopping switching perspective has the advantage of comprehensively investigating how consumers choose and navigate among diverse types of single- or multi-channel shopping alternatives. In this study, only limited shopping switching behavior from online shopping to webrooming was examined; however, the results should explain various phenomena in a more comprehensive manner from the perspective of shopping switching. Second, this study extends the scope of application of the push-pull-mooring framework, which is quite commonly used in marketing research to explain consumers' product switching behaviors. Through the application of this framework, it is hoped that more diverse shopping switching behaviors can be examined in future research. This study can serve a stepping stone for future studies. One of the most important practical implications of the study is that it may help single- and multi-channel retailers develop more specific customer strategies by revealing the influencing factors of webrooming intention from online shopping. For example, online single-channel retailers can ease the distrust of online shopping to prevent consumers from churning by reducing the perceived risk in terms of online search and purchase. On the other hand, offline retailers can develop specific strategies to increase the attractiveness of webrooming by letting customers perceive the benefits of multi-channel search or offline purchase. Although this study focused only on customers switching from online shopping to webrooming, the results can be expanded to various types of shopping switching behaviors embedded in single- and multi-channel shopping environments, such as showrooming and mobile shopping.