Showing papers by "Ming Chuan University published in 2020"

A Content Analysis of Computational Thinking Research: An International Publication Trends and Research Typology

Abstract: This paper aims to provide a comprehensive analysis of publication patterns on computational thinking (CT) over two recent periods (period I: 2006–2012; period II: 2013–2018). Based on keyword analysis, a total of 3798 (period I) and 7175 (period II) keywords were found. Derived from the content analysis, a research typology of two-period keywords was consolidated and framed according to its attributes, including background settings, domain-specific factors, and learning outcomes. Main findings show as follows: (1) Regarding the research background, students from secondary and higher education are the main participants; and computer science, mathematics, and engineering are the major subjects. (2) As the domain-specific factors, game and peer collaboration were found to be the main pedagogies, while web-based and face-to-face learning environments were almost equally referred to in CT research settings. However, compared with traditional command-based tools, Scratch, Lego, and Python were identified as the emerging visual-based programming languages. (3) Finally, the keywords related to learning outcomes were classified based on the Bloom’s framework of three learning domains. First, knowledge and mental understanding are the main goals in the cognitive domain; motivation and attitude are the main tasks in the affective domain; and social and communication skills are the central outcomes in the training of psychomotor ability. Further discussions and research directions are provided.

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

Abstract: The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.

Impacts of service robots on service quality

Abstract: With rapid advances in technologies, especially in artificial intelligence, smart sensors, big data analytics, and robotics, the service industry began introducing robots to perform a variety of functions. While the main purpose of deploying robots has been productivity improvement, the current COVID-19 pandemic has brought more urgent purpose, providing contactless service for social distancing. This study explores the service quality provided by robots based on real data in a hotel setting. A sample of 201 guests provided their expected service quality by robots and the actual performance experience after the service. We analyzed this relationship using importance performance analysis (IPA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The results revealed that customers’ top priorities for robots’ service quality are assurance and reliability, while tangible and empathy were not as important. Customers were not satisfied with robots’ responsiveness, but this construct was found to be a low priority.

How livestreaming increases product sales: role of trust transfer and elaboration likelihood model

Abstract: With the ever-increasing popularity of livestreaming commerce, understanding how livestreaming contributes to online consumption becomes crucial to social commerce. However, studies pertain to live...

Study on an Indoor Positioning System Using Earth’s Magnetic Field

Abstract: With the popularity of smart mobile devices, users can rely on global positioning system (GPS) technology when they are outdoors to determine their geographic location and to use other navigation services. However, GPS cannot reliably obtain satellite signals indoors, GPS is not suitable for indoor positioning applications. Wi-Fi and Bluetooth are the mainstream technologies used for indoor positioning today. But these radio technologies sometimes encounter problems, such as human-shadowing effects, multiple-path delays, and radio-wave interference, which can cause serious errors in the accuracy of indoor positioning results. In addition, using wireless network signals require the setup of certain infrastructure equipment. On the other hand, there are no such problems when using positioning technology based on the Earth’s magnetic field. This paper analyzes how the environment can influence magnetic field measurements from magnetometers in mobile devices and verifies that this system can be used to enable indoor positioning. This paper also proposes the use of a weighted magnetic field component and the k-nearest neighbors ( $k$ -NN) algorithm for enhanced precision in indoor positioning. Finally, the research results show that a positioning accuracy of 91.7% and an average positioning error distance of 0.76 m can be achieved with these methods. The results of experiments show that the system performance is significantly feasible.

Automated Machine Learning: The New Wave of Machine Learning

Abstract: With the explosion in the use of machine learning in various domains, the need for an efficient pipeline for the development of machine learning models has never been more critical. However, the task of forming and training models largely remains traditional with a dependency on domain experts and time-consuming data manipulation operations, which impedes the development of machine learning models in both academia as well as industry. This demand advocates the new research era concerned with fitting machine learning models fully automatically i.e., AutoML. Automated Machine Learning(AutoML) is an end-to-end process that aims at automating this model development pipeline without any external assistance. First, we provide an insights of AutoML. Second, we delve into the individual segments in the AutoML pipeline and cover their approaches in brief. We also provide a case study on the industrial use and impact of AutoML with a focus on practical applicability in a business context. At last, we conclude with the open research issues, and future research directions.

Determinants of Health Literacy and Its Associations With Health-Related Behaviors, Depression Among the Older People With and Without Suspected COVID-19 Symptoms: A Multi-Institutional Study.

Abstract: Purpose: We examined factors associated with health literacy among elders with and without suspected COVID-19 symptoms (S-COVID-19-S). Methods: A cross-sectional study was conducted at outpatient departments of nine hospitals and health centers 14 February-2 March 2020. Self-administered questionnaires were used to assess patient characteristics, health literacy, clinical information, health-related behaviors, and depression. A sample of 928 participants aged 60-85 years were analyzed. Results: The proportion of people with S-COVID-19-S and depression were 48.3 and 13.4%, respectively. The determinants of health literacy in groups with and without S-COVID-19-S were age, gender, education, ability to pay for medication, and social status. In people with S-COVID-19-S, one-score increment of health literacy was associated with 8% higher healthy eating likelihood (odds ratio, OR, 1.08; 95% confidence interval, 95%CI, 1.04, 1.13; p < 0.001), 4% higher physical activity likelihood (OR, 1.04; 95%CI, 1.01, 1.08, p = 0.023), and 9% lower depression likelihood (OR, 0.90; 95%CI, 0.87, 0.94; p < 0.001). These associations were not found in people without S-COVID-19-S. Conclusions: The older people with higher health literacy were less likely to have depression and had healthier behaviors in the group with S-COVD-19-S. Potential health literacy interventions are suggested to promote healthy behaviors and improve mental health outcomes to lessen the pandemic's damage in this age group.

Enabling Intelligent Environment by the Design of Emotionally Aware Virtual Assistant: A Case of Smart Campus

Abstract: With the advent of the 5G and Artificial Intelligence of Things (AIoT) era, related technologies such as the Internet of Things, big data analysis, cloud applications, and artificial intelligence have brought broad prospects to many application fields, such as smart homes, autonomous vehicles, smart cities, healthcare, and smart campus. At present, most university campus app is presented in the form of static web pages or app menus. This study mainly developed a Deep Neural Network (DNN) based emotionally aware campus virtual assistant. The main contributions of this research are: (1) This study introduces the Chinese Word Embedding to the robot dialogue system, effectively improving dialogue tolerance and semantic interpretation. (2) The traditional method of emotion identification must first tokenize the Chinese sentence, analyze the clauses and part of speech, and capture the emotional keywords before being interpreted by the expert system. Different from the traditional method, this study classifies the input directly through the convolutional neural network after the input sentence is converted into a spectrogram by Fourier Transform. (3) This study is presented in App mode, which is easier to use and economical. (4) This system provides a simple voice response interface, without the need for users to find information in complex web pages or app menus.

The interplay among ecological footprint, real income, energy consumption, and trade openness in 13 Asian countries

Abstract: This article investigates the effects of real income, trade openness, and energy consumption on the ecological footprint using a panel data of 13 Asian countries over the 1973–2014 period. The empirical findings suggest that the panel variance-ratio test confirms the existence of a long-run equilibrium relationship among ecological footprint real income, trade openness, and energy consumption. Results from panel pooled mean group estimates confirm that the long-run elasticity of real income, trade openness, and energy consumption is 0.16, −0.07, and 0.51, respectively. The real income and energy consumption have a positive impact on the ecological footprint. There are three bidirectional causal relationships that were found between ecological footprint and real income; between energy consumption and ecological footprint; and between trade openness and ecological footprint. In addition, three unidirectional causalities can be found: a unidirectional causality running from real income to trade openness; from real income to energy consumption; and from trade openness to energy consumption. Those causal relationships show that economic indicators are highly related to ecological footprint. The findings recommend that various governments should fund more in renewable energy and efficiency upgrade and continue sustaining their growth without hurting the environment.

Study on emotion recognition and companion Chatbot using deep neural network

Abstract: With the development of technology, the importance of the research on speech emotion recognition and semantic analysis has increased. The research is primarily applied in companion robot, technology products and medical purpose. In this research, a communication system with speech emotion recognition is proposed. The system pre-process speech with sound data enhancing method in speech emotion recognition and transform the sound into spectrogram by MFCC (Mel Frequency Cepstral Coefficient). Then, GoogLeNet of CNN (Convolutional Neural Network) is applied to recognize the five emotions, which are peace, happy, sad, angry and fear, and the top accuracy of recognition is 79.81%. When applying semantic analysis, the training texts are divided into two categories, positive and negative, and the chatting conversations are conducted in the framework Seq2Seq of RNN (Recurrent Neural Network). The systematic framework of this research has two parts, the client and the server. The former one is developed on Android system to be used in Application, and the latter one is established by Ubuntu Linux system and combined with the web server. With the bi-terminal framework system, the users can record voice in APP one his/her cellphone and upload the voice file to the server. Then, the voice undergoes speech emotion recognition by CNN and semantic analysis by RNN to function as a chatting machine that can respond positively or negatively based on the detected emotion and show the results on APP of the user’s cell phone. The main contributions of this research are: 1) This study introduces the Chinese word vector to the robot dialogue system, effectively improving dialogue tolerance and semantic interpretation, 2) The traditional method of emotion identification must first tokenize the Chinese words, analyze the clauses and part of speech, and capture the emotional keywords before being interpreted by the expert system. Different from the traditional method, this study classifies the input directly through the convolutional neural network after the input sentence is converted into a spectrogram by MFCC, and 3) in addition to implementing the companion robot, the user’s emotional index can be collected for analysis by the back-end care organization. In addition, compared with other commercial humanoid companion robots, this study is presented in an App, which is easier to use and economical.

Utility-Aware Charging Scheduling for Multiple Mobile Chargers in Large-Scale Wireless Rechargeable Sensor Networks

Abstract: Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN) However, relatively low charging utility exists in existing solutions In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs Charging MCs and server MCs are employed to jointly achieve our goal by three aspects First, a path merging scheme is designed to save the traveling paths of MCs Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility

Mycena genomes resolve the evolution of fungal bioluminescence.

Abstract: Mushroom-forming fungi in the order Agaricales represent an independent origin of bioluminescence in the tree of life; yet the diversity, evolutionary history, and timing of the origin of fungal luciferases remain elusive. We sequenced the genomes and transcriptomes of five bonnet mushroom species (Mycena spp.), a diverse lineage comprising the majority of bioluminescent fungi. Two species with haploid genome assemblies ∼150 Mb are among the largest in Agaricales, and we found that a variety of repeats between Mycena species were differentially mediated by DNA methylation. We show that bioluminescence evolved in the last common ancestor of mycenoid and the marasmioid clade of Agaricales and was maintained through at least 160 million years of evolution. Analyses of synteny across genomes of bioluminescent species resolved how the luciferase cluster was derived by duplication and translocation, frequently rearranged and lost in most Mycena species, but conserved in the Armillaria lineage. Luciferase cluster members were coexpressed across developmental stages, with the highest expression in fruiting body caps and stipes, suggesting fruiting-related adaptive functions. Our results contribute to understanding a de novo origin of bioluminescence and the corresponding gene cluster in a diverse group of enigmatic fungal species.

Restaurant Employee Service Sabotage and Customer Deviant Behaviors: The Moderating Role of Corporate Reputation:

Abstract: This article provides a timely exploration of the relationship between hospitality employee service sabotage and customer deviant behaviors in Taiwan The authors also examine the mediating role of

Co-competition, learning, and business strategy for new service development

Abstract: Co-competition and business strategy determine tourism organizations’ survival and growth. The current study highlights the following recent findings for the travel agency industry: (a) a co-compet...

Enhancing the robustness of image watermarking against cropping attacks with dual watermarks

Abstract: In this study, a QR-based digital watermarking scheme that can use color images is proposed. The main purpose of this method is to enhance robustness against cropping attacks. To achieve this aim, each bit of the robust watermark has four copies, which are hidden in different image blocks. The embedding rule is designed based on the sinusoidal function, and the wavelength of the sinusoidal function controls the trade-off between imperceptibility and robustness. The four copies of the watermark bit may be inconsistent if the watermarked image undergoes cropping attacks. Therefore, after the four copies of the watermark bit are extracted, the actual value of the watermark bit is judged based on the result of the tampering detection. Experimental results indicate that improving robustness with the help of tampering detection results effectively assists in watermark extraction. In addition, the method is superior to other methods in terms of invisibility, robustness, and embedding payload.

Dining at luxury restaurants when traveling abroad: incorporating destination attitude into a luxury consumption value model

Abstract: As luxury products have become increasingly accessible to middle-class consumers, many gastronomic tourists might be interested in dining at luxury restaurants when traveling. The purpose of this s...

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques.

Abstract: Background: Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective: This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods: We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results: We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China—and a specific city within China through references to the “Wuhan pneumonia”—potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions: Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

How different advertising formats and calls to action on videos affect advertising recognition and consequent behaviours

Abstract: With theoretical foundations grounded on the Persuasion Knowledge Model, we conducted an online experiment to examine how the formats and calls to action (CTA) of Facebook’s native advertising impa...

Investigating the effects of ubiquitous self-organized learning and learners-as-designers to improve students’ learning performance, academic motivation, and engagement in a cloud course

Abstract: In the past decade, the developments of the Internet and educational technologies have facilitated innovative approaches to modern education. In addition, computers and related software are used in all professional fields of the workplace; therefore, students should acquire related essential abilities before they enter the workforce. Teachers should devote attention to designing and implementing appropriate online teaching methods and guiding their students to adopt suitable learning strategies to develop related abilities and improve their learning effectiveness. Thus, in this study, two innovative teaching methods, namely self-organized learning (SOL) and learners-as-designers (LaD), were integrated with educational technology and ubiquitous learning (u-learning) to develop students’ computing skills, academic motivation, and engagement in a blended course. A quasi-experiment was conducted to examine the effects of ubiquitous SOL and LaD. The experiment used a 2 (SOL vs. non-SOL) × 2 (LaD vs. non-LaD) factorial pretest–posttest design. First-year students from four classes who were taking a one-semester university course titled “Applied Information Technology: Data Processing” were the participants in the empirical study. The results revealed that students who received the ubiquitous LaD intervention exhibited significantly improved computing skills compared with those of students who did not receive the intervention. However, the ubiquitous SOL intervention did not enhance students’ computing skills, academic motivation, or engagement. The study results may be used as references for online educators when designing an online, cloud, or ubiquitous course for their students.

CEO Tenure and R&D investment: founders vs. agents and the Role of independent directors

Abstract: R&D investment is necessary for firms to pursue innovation. However, few studies have simultaneously investigated the different tenure effects of founder CEOs and agent CEOs on R&D investment. Furt...

Being excellent teams: managing innovative climate, politics, and team performance

Abstract: This research develops a team-level model based on transactive memory theory to analyze how teams achieve excellent performance by managing innovative climate and politics. In the model, team perfo...

Flying on the clouds: how mobile applications enhance impulsive buying of low cost carriers

Abstract: The goal of this research is to determine how design credibility, functional benefit, hedonic shopping value, and visual appeal affect impulse buying; the model and hypotheses were tested with structural equation modeling. Design credibility encourages passengers to engage in usage of low cost carriers’ apps, and the functional benefits play a role of inducing positive emotions in the usage process of apps. Moreover, low cost carriers’ apps ambient conditions evoke passengers' emotion and in turn create hedonic shopping value; the overall benefit of the low cost carriers’ app will help passengers to increase impulsive purchasing behavior.

Understanding the Learning Challenges of English-Medium Instruction Learners and Ways to Facilitate Their Learning: A Case Study of Taiwan Psychology Students’ Perspectives

Abstract: Entender los desafios de aprendizaje de los estudiantes de asignaturas en ingles y formas de facilitar su aprendizaje: un estudio de caso de las perspectivas de estudiantes de psicologia de TaiwanCompreender os desafios da aprendizagem dos alunos de disciplinas em ingles e maneiras de facilitar seu aprendizado: um estudo de caso das perspectivas de estudantes de psicologia de TaiwanEnglish-medium instruction (EMI) has become a global education phenomenon in recent decades, especially in higher education. However, its implementation is still facing criticism. On the one hand, learners are welcoming the envisaged benefits of preparing them with professional content knowledge and English language proficiency to compete in the new global economy; on the other hand, they are reluctant to take the courses because of the challenges and difficulties EMI creates. Meanwhile, EMI lecturers are concerned about students’ inability to survive, or better still thrive through EMI learning. EMI lecturers are experts in their professional domain but are not trained or prepared to teach EMI courses yet. This qualitative study examines the perception of first-year undergraduate students taking the EMI foundation psychology course in Taiwan, focusing on their perspectives of factors facilitating or hindering their EMI learning, and their suggestions for improving the situation. The results not only point out what kinds of teaching practices contribute to challenges for EMI learners in their learning process but also the underlying reasons and the pedagogy practice that students suggest can be used to improve the situation. Insights provided by this study can be used to assist EMI lecturers in examining their pedagogical practice and expand their knowledgeability about pedagogy. They can also be used as a way forward for assisting in the development of EMI teacher training and professional development, and the implementation of EMI.To reference this article (APA) / Para citar este articulo (APA) / Para citar este artigo (APA)Hua, T-L. (2019). Understanding the Learning Challenges of English-Medium Instruction Learners and Ways to Facilitate Their Learning: A Case Study of Taiwan Psychology Students’ Perspectives. Latin American Journal of Content & Language Integrated Learning, 12(2), 321-340. https://doi.org/10.5294/laclil.2019.12.2.6Received: 22/09/2019Approved: 20/02/2020Published: 11/05/2020