Designing for Autonomy, Competence and Relatedness in Robot-Assisted Language Learning

The current number of immigrants has risen quickly in recent years due to globalization. People move to another country for economic, educational, emotional, and other reasons. As a result, immigrants need to learn the host language to integrate into their new living environment. However, the process of learning the host language for adult immigrants faces many challenges. Among those challenges, maintaining intrinsic motivation is critical for a long-term language study process and the well-being of adult immigrants. Self-Determination Theory (SDT) is a popular theoretical framework that explains human motivation, especially intrinsic motivation, through a psychological approach to understand its nature. According to SDT, humans are intrinsically motivated through the satisfaction of the three basic needs of Autonomy, Competence, and Relatedness. Many researchers have applied the theory to different topics and directions, including language learning. On the other hand, social robots have been used extensively in the language learning context due to their physical embodiments and the application of artificial intelligence in robotics. Furthermore, research has proven that social robots can create a relaxed and engaging learning environment, thus motivating language learners. The thesis designs and implements a RALL application called SAMQ using QTrobot, a humanoid social robot capable of producing body gestures, displaying different facial expressions, and multilingual communication. The study aims to investigate SAMQ’s ability to evoke intrinsic motivations of adult immigrants in learning the Finnish language. While previous research focuses on English as the second language (L2) and targets children, this thesis’s L2 is Finnish, and the learners are adult immigrants. The thesis conducts semi-structured interviews during the Pre-study phase (N=6) to gather real insights from adult immigrants living in Finland, to understand demotivating factors in their language learning experience and the unsatisfied aspects of the three basic needs. The qualitative findings from the Pre-study contribute to the design and implementation of two versions of SAMQ, aiming at evoking intrinsic motivations through satisfying unmet needs. The first version is a Quiz-only program that tests several assumptions regarding human-robot interaction (HRI). The final version of SAMQ is a more comprehensive language learning application that supports two modes of study: Learning and Quizzes. It consists of multiple modifications that address all adult immigrants’ basic needs while additionally promoting intrinsic motivation through media. The final Evaluation of SAMQ (N=6) includes a questionnaire and a semi-structured interview. The quantitative results of the questionnaire validated the ability of using social robots to evoke adult learners’ intrinsic motivation in the RALL context. The qualitative findings from the research high-light the importance of social robots’ physical embodiments in eliciting intrinsic motivation for adult learners through satisfying Relatedness. In addition, the use of voice modality creates a genuine HRI for adult learners, fulfilling both Autonomy and Competence, resulting in an engaging and smooth learning experience. Besides that, the use of adult learners’ L1 plays a crucial role in facilitating a relaxed and familiar learning environment, supplying both Competence and Relatedness. Moreover, multimedia learning materials make the learning experience more vivid and attractive. Ultimately, the result shows that accessibility and flexibility are essential attributes for adult learners to maintain their motivation for long-term language study through the satisfaction of Autonomy. Finally, the thesis proposes a design guideline for the RALL context. It consists of five design implications for evoking intrinsic motivation in adult learners through satisfying the three basic psychological needs of Autonomy, Competence, and Relatedness. The design guideline acts as a proposal for future design and implementation of RALL programs for adults and contributes to developing the human-robot interaction field

Investigate the Structural Response of Ultra High Performance Concrete Column under the High Explosion

Most of the structures that are damaged by an explosion are not initially designed to resist this kind of load. In the overall structure of any building, columns play an important role to prevent the collapse of frame structure under blast impact. Hence, the main concept in the blast resistance design of the structure is to improve the blast load capacity of the column. In this study, dynamic analysis and numerical model of Ultra High Performance Concrete (UHPC) column under high explosive load, is presented. Based on the Johnson Holmquist 2 damage model and the subroutine in the ABAQUS platform, a total of twenty UHPC model of the column were calculated. The objective of the article is to investigate the structural response of the UHPC column and locate the most vulnerable scenarios to propose necessary recommendations for the UHPC column in the blast loading resistance design. The input parameters, including the effect of various shapes of cross-section, scaled distance, steel reinforcement ratio, and cross-section area, are analyzed to clarify the dynamic behavior of the UHPC column subjected to blast loading. Details of the numerical data, and the discussion on the important obtained results, are also provided in this paper

Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data

When floods hit populated areas, quick detection of flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain-specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric-features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1 = 96:12) and outperform conventional flood mapping methods

Personal attitude or experience? Which factors influence residents??? acceptance of mixed-income communities?

Although many researchers and policy makers have argued that social mixing could contribute to sustainable communities, most people still prefer to live in a homogeneous rather than a diverse community. Considering the large gap between the political need for social mixing and people's preference, it is essential to understand residents' perceptions and preferences regarding socially-mixed neighborhoods in order to promote sustainable community development. This study explorers residents' willingness to accept living in mixed-income communities in Korea, with attention to various levels of income mix. This study conducted an online survey of 2,000 respondents living in seven metropolitan cities in Korea, including Seoul. The study aimed to investigate residents' comfortability and willingness to move into different mixed-income communities. The results showed that residents with higher openness to diversity are more likely to accept mixed-income communities, but frequent interaction with low-income people reduces higher-income people's willingness to accept mixed-income communities. As both personal attitudes and experience are important determinants of individuals' social mix preference, a more systematic community development strategy is required to achieve successful social mixing

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)

INFLUENCING FACTORS TO LOGISTICS CENTRE FORMATION – A STUDY OF VIETNAM-BASED LOGISTICS SECTOR

Purpose of the study: The paper tries to model dynamic interactions of factors that contribute to the logistics center building. Conducting the desk review and expert consultation, the causality of the factors is systemized in a form of Causal Loop Diagram using the System Dynamics approach. Methodology: System Dynamics (SD) is an approach for studying interlinked behaviors within a system and reflects the interactions of feedback loops. Compared to other approaches, SD demonstrates the real world by using factors and stocks for components and feedback loops for inter-relationships among them. SD model qualitatively illustrates the causal relationship among factors that influence the building of the logistics center. Main Findings: A combination of four different sub-systems, using a questionnaire survey conducted with logistics service users and providers to sort out the high scored factors. Besides, the survey also helps to study the practical conditions and characteristics in showing the demand, the trend, and the development of logistics centers in Vietnam. Applications of this study: Logistics centers (LCs) can be considered as a depot for vehicles where drivers and managers of vehicles are supposed to maintain, repair vehicles, and adjust vehicle operation schedules. Novelty/Originality of this study: As defined by the scope of the project, the SD model provides a qualitative demonstration of the interaction among factors. The built model gives a systematic insight into how factors link to each other

Key factors influencing on Vietnamese construction performance

Together with the economic development, Vietnamese construction industry also develops very fast and occupies a large portion in economics. However, there are still many problems such as wastes, loses, low quality or low productivity relating to construction activities need to be improved. This paper aims at finding out the factors that influence on the performance of construction sites. A survey was carried out and found out 25 key factors that influence on the performance of construction sites. These factors are divided into six groups such as management, human resources, technology, finance, material/equipment and design after factor analysis. By ranking the importance of factors, this paper helps the contractors to focus on the most important factors to upgrade their competency

Cider Production from King Mandarin (Citrus nobilis Lour.) and Its Antioxidant Activity

With the necessity of diversifying alcoholic beverages, cider has become a kind of drink that can fulfill this demand. This is because the cider will be diversified depending on the kinds of fruit that are chosen to be used for the cider fermentation. Therefore, this study aims to investigate the effects of dilution ratio, Brix, pH, and yeast concentration on the production of cider from king mandarin (Citrus nobilis Lour.), and to evaluate the analytical characteristics and antioxidant activity of the product. After the investigation, it can be claimed that the dilution of the juice causes the ethanol content to decrease, whereas the increase of Brix, pH, and yeast concentration makes the ethanol content increase. However, the proportional increase in the ethanol content with Brix, pH, and yeast concentration has its limitations. Specifically, when the Brix and the yeast concentrations were, respectively, higher than 16°Brix and 0.04%, the ethanol content tended to maintain the same. This is also the same when the pH was lower than 4.5. In addition, by using the DPPH and ABTS●+ methods, the antioxidant activity of cider is estimated to be lower than the one of the juice before fermentation, which is smaller than 3.78 times for the DPPH method and 3.76 times for the ABTS●+ method