• Information Systems Review
• /
• v.25 no.1
• /
• pp.75-91
• /
• 2023

Thanks to information technologies, sharing economy services offer a new way of consumption. Carsharing appeared as a novel type of service that transformed the conventional way of personal transportation, from owning a vehicle to using an on-demand service. Allowing users to use a vehicle without owning a car, carsharing provides various social benefits such as the reduction of resource allocation inefficiencies and the alleviation of transportation problems. To strengthen such positive aspects of carsharing service, it is essential to understand an individual's service usage pattern and reveal factors that affect users' reuse behavior. This study investigates the factors that have an influence on carsharing reuse of users applying RFMC (Recency, Frequency, Monetary, and Clumpiness) model, the popular model for understanding the reuse likelihood of customers. Using data from a leading carsharing service provider in South Korea, we empirically analyze the effect of RFMC on carsharing reuse behavior. The findings show that recency and monetary values are negatively related to reuse while frequency is positively related to carsharing service reuse. Moreover, the impact of recency and monetary value are more salient whereas the impact of frequency is smaller among users with higher clumpiness. Based on these findings, this study elaborates on theoretical and practical implications.

• Knowledge Management Research
• /
• v.25 no.1
• /
• pp.21-41
• /
• 2024

The global Industry has been shaped by start-ups that originated with knowledge-based innovative strategies or technologies in the 21st century. Specifically, laboratory start-ups that rely on research papers or patents for new technology development are recognized for their high survival rate and the creation of employment opportunities. Our study concentrated on 'I-Corps', which also introduced in Korea, standing for innovation corps is a laboratory startup program launched in 2011 by the NSF(National Research Foundation) to commercialize R&D results and foster entrepreneurship as part of the policy to build a start-up system at the national innovation level. In this study, we proposed and empirically tested a research model focusing on teams participating in the I-Corps program to determine how startup team diversity, among the team characteristics of laboratory startups, affected patent performance. As a result of the analysis, among the proposed variables, age diversity, educational background diversity, and value diversity had a significant impact on patent performance. The results of this study are expected to further strengthen the theoretical and practical foundations of researchers or practitioners of the I-Corps program, as well as related areas involving technology & laboratory startups, intellectual property and knowledge management fields in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.1-25
• /
• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Service Research and Studies
• /
• v.13 no.3
• /
• pp.172-189
• /
• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.37 no.6
• /
• pp.708-713
• /
• 2008

The effects of administration alone or mixed of Angelica keiskei and turmeric extract on blood lipids were evaluated in hypercholesterolemic diet or P-407-induced hyperlipidemic rats. In the study 1, female Sprague-Dawley rats weighing 160-180 g were divided into each groups and fed high cholesterol diets for 8 weeks. Experimental groups were administered with following diets: basal diet (Normal), high cholesterol diets (1% cholesterol). We did the oral administration for evaluation in experimental groups: C (vehicle), A (angelica extract), T (turmeric extract), AT (angelica extract, turmeric extract/ 1:1 complex). The concentrations of serum total cholesterol, and LDL-cholesterol were decreased by 6.8%, 9.8% in A group, by 22.1%, 28.8% in C group and by 28.2%, 35.6% in AT group, compared to the C group, respectively. HDL-cholesterol levels were not different among the experimental groups. In the study 2, we induced the hypertriglyceridemia in rats by intraperitoneal injection of P-407 (0.5 g/kg) once per three days. From the next day after P-407 injection beginning, we did the oral administration as the study 1. Angelica keiskei extract, turmeric extract and complex extract decreased serum triglyceride by 17.2%, 19.7% and 48.3%, respectively. These results suggested that Angelica keiskei and turmeric extract complex might have synergistic effect in lowering total cholesterol, LDL-cholesterol and triglyceride in hyperlipidemic rats.

• Journal of the Korean Institute of Gas
• /
• v.21 no.4
• /
• pp.47-61
• /
• 2017

The preconditions of the usefulness of an operator safety training model in large plants are the versatility and accuracy of operational procedures, obtained by detailed analysis of the various types of risks associated with the operation, and the systematic representation of knowledge. In this study, we consider the artificial intelligence planning method for the generation of operation procedures; classify them into general actions, actions and technical terms of the operator; and take into account the sharing and reuse of knowledge, defining a knowledge expression ontology. In order to expand and extend the general operations of the operation, we apply a Hierarchical Task Network (HTN). Actual boiler plant case studies are classified according to operating conditions, states and operating objectives between the units, and general emergency shutdown procedures are created to confirm the applicability of the proposed method. These results based on systematic knowledge representation can be easily applied to general plant operation procedures and operator safety training scenarios and will be used for automatic generation of safety training scenarios.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.11
• /
• pp.305-313
• /
• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• Journal of Korean Society of Environmental Engineers
• /
• v.27 no.9
• /
• pp.1006-1015
• /
• 2005

We investigated the effect of hydroxyl radicals on the photolysis of triclosan (TCS), which is a potent broad-spectrum antimicrobial agent. TCS degradation during the initial reaction time of 5 min followed a pseudo-first order kinetic model ai all light intensities at a wavelength of 365 nm and at the low light intensities at a wavelength of 254 nm. The photodegradation rate significantly increased with decreasing wavelength and increasing the UV intensities. The activity of hydroxyl radicals was suppressed when methanol was used as the solvent instead of water. An increase in the photon effect was observed when the UV intensity was higher than $5.77{\times}10^{-5}$ einstein $L^{-1}min^{-1}$ at 254 nm, and lower than $1.56{\times}10^{-4}$ einstein $L^{-1}min^{-1}$ at 365 nm. The quantum yield efficiency for the photolysis of TCS was higher at 365 nm than at 254 nm among the above mentioned UV intensities. Dibenzodichloro-p-dioxin (DCDD) and dibenzo-p-dioxin were detected as intermediates at both UV intensities of $1.37{\times}10^{-4}$ and $1.56{\times}10^{-4}$ einstein $L^{-1}min^{-1}$ at 365 nm. Dichlorophenol and phenol were also detected in all cases. Based on our findings, we presented a possible mechanism of TCS photolysis.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.77-87
• /
• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.199-208
• /
• 2021

In this paper, the effect of artificial intelligence convergence education program that provides STEAM education using machine learning platform on elementary school students' STEAM literacy and learning flow was analyzed. A homogeneous group of 44 elementary school 6th graders was divided into an experimental group and a control group. The control group received 10 lessons of general subject convergence class, and the experimental group received 10 lessons of STEAM-based artificial intelligence convergence education using Machine learning for Kids. To develop the artificial intelligence convergence education program, the goals, achievement standards, and content elements of the 2015 revised curriculum to select subjects and class contents is analyzed. As a result of the STEAM literacy test and the learning flow test, there was a significant difference between the experimental group and the control group. In particular, it can be confirmed that the coding environment in which the artificial intelligence function is expanded has a positive effect on learners' learning flow and STEAM literacy. Among the sub-elements of convergence talent literacy, significant differences were found in the areas of personal competence such as convergence and creativity. Among the sub-elements of learning flow, significant differences were found in the areas such as harmony of challenge and ability, clear goals, focus on tasks, and self-purposed experiences. If further expanded research is conducted in the future, it will be a basic research for more effective education for the future.