Earticle

Volunteering has important insight for tourism, especially in the context of special events and festivals. In particular, local festival relies heavily upon volunteers because a large number of individuals are often necessary for creating and delivering various services. Therefore, understand the link between motivation and satisfaction will help festival managers to make appropriate decision in the selection and recruitment of volunteers. The purpose of this study is to examine the impact of motivations on individual’s volunteer satisfaction. Data were gathered from 183 individuals that volunteered for the 2018 Gokseong Festival and finally 154 samples were used for the empirical analysis. According to the results, motivation factor analysis showed that intrinsic motivation is a main volunteer motivation to participate in the festival. This research concludes with the proposed conceptual framework that discusses the categories of motivational factors and also volunteers’ satisfaction. The theoretical and practical implications of these findings are discussed in more detail.

In the field of excavation, it is important to recognize and analyze the factors that cause the ground collapse in order to predict and cope with the ground subsidence. However, it is difficult for field engineers to predict ground collapse due to insufficient knowledge of ground subsidence influence factors. Although there are many cases and studies related to the ground subsidence, there is no manual to help practitioners. In this study, we present the criteria for describing and quantifying the influential factors to help the practitioners understand the existing ground collapse cases and classification of the ground subsidence factors revealed through the research. This study aims to improve the understanding of the factors affecting the ground collapse and to provide a GSRp for the ground subsidence risk assessment which can be applied quickly in the field.

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

Securing field screens through LTE broadcasting is a very valuable technology in news production because of its locality and concurrency. TV has the advantage of being able to deliver news faster than newspapers and make live coverage on the scene of events and events. To maximize this, not only middle-end broadcasters but also existing broadcasting stations are taking an active role in broadcasting using LTE. However LTE broadcasting has yet to secure any conflicting values of broadcasting stability and securing high-definition. The key to solving this difficulty is the transmission rate of Bit Rate. This paper compares the Bit Rate transmission method, which is variable and fixed, to provide more on-site screens through LTE, and presents the Bit Rate adjustment method as a method of screen stabilization through experiment. Consequently, the most stable broadcast screen can be obtained when transmitting and connecting it to broadcasting in a variable 5M method.

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell’s gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell’s gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain’s feature and at the same time adding the new domain’s feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input,

As environmental pollution has become worse green technologies to replace or reduce consumption of fossil fuel get spotlight from government, industry and academia globally. It is reported that 40% of carbon dioxide emission is caused by electricity power generation. And 37% of end user electricity power is used by residential costumer in US. Smart Grid is considered as one of promising technology to alleviate severe environmental problem. In residential environment, Home Energy Management System (HEMS) can play a key role for green smart home. The HEMS can give several benefits like as lowering electric utility bill, improvement of efficiency of electric power consumption and integration of generator using renewable energy resources. However just limited functions of HEMS can be used for residential customer in real life because of lack of smart function in home appliances and optimal managing software for HEMS. This study provides comprehensive analysis for Home Energy Management System for residential customer. Simple HEMS system with real products on the market are explained and limitation of current HEMS are also discussed.

This study proposes a Bayesian stochastic frontier model that is well-suited to productivity/efficiency analysis particularly using panel data. A unique feature of our proposal is that both production frontier and efficiency are estimable for each individual firm and their linkage to various firm characteristics enriches our understanding of the source of productivity/efficiency. Empirical application of the proposed analysis to Human Capital Corporate Panel data enables identification and quantification of the effects of Human Resource factors on firm efficiency in tandem with those of firm types on production frontier. A comprehensive description of the Markov Chain Monte Carlo estimation procedure is forwarded to facilitate the use of our proposed stochastic frontier analysis.

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

The main purpose of this study is to understand how cognitive factors influence the way people interact with information/information systems, by conducting comprehensive and in-depth literature reviews and a theoretical synthesis of related research. Adaptive systems have been built around an individual user’s characteristics, such as interests, preferences, knowledge and goals. Individual differences in the ability to use new information and communication technology have been an important issue in all fields. Performance differences in utilizing new information and communication technology are sufficiently predictable that we can begin to coordinate them. Therefore, it is necessary to understand cognitive mechanisms to explain differences between individuals as well as the levels of performance. The theoretical synthesis from this study can be applied to design intelligent (i.e., human friendly) systems in our everyday lives. Further research should explore optimization design for user, by integrating user’s individual traits (such as emotion and intent) and system modules to improve the interactions of human-system in data-driven environments.

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

The growth of the older population is expected to further increase social problems associated with population aging, such as isolation, poverty, and depression. The emerging issues associated with the older population are also expected to provide further momentum on studies about the dwelling environment as factors that ensure the health of older people as well as improve their quality of life. Therefore, approaches for explaining the issues of the older age group should be diversified using a variety of factors and appropriate analytic tools. Studies on measuring depression have principally focused on assessing an objective self-report questionnaire, usually in a highly structured, textual form which may not reflect the cognitive impairment of older adults. The aim of this study was to define and measure dwelling depression among older adults in Korea. There are two specific hypotheses in this study as follows: (a) there will be statistically significant relationships with dwelling dissatisfaction and depression, and (b) dwelling depression tools containing text and images will be, respectively, assessment tools that have a good construct with content validity and reliability. In the first experiment, to define and measure dwelling depression, 301 people over 65 years old living in single and two-person households were surveyed using a text-based dwelling depression questionnaires from September 1–30, 2017. In the second experiment, to examine whether the projective image questionnaire could serve as a suitable replacement for the text-based questionnaires, the same participants were surveyed from January 22 to February 2, 2018. The results show that depression has a close correlation with dwelling dissatisfaction. In addition, the geriatric dwelling depression index (GDDI) based on the projective image was refined. Additionally, the projective image questionnaire has a close correlation with the text-based questionnaire. Finally, through ROC curve analysis, it was found that the projective image questionnaire can accurately predict a depression group. To this end, this preliminary study examined the validity of the projective image questionnaire in older adults to make this instrument feasible for older populations and to contribute to a profound understanding of geriatric depression due to the living environment. We hope they will provide a basis for further research on psychological diagnoses using projective images.