• 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 Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.79-104
• /
• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.89-115
• /
• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Cartoon and Animation Studies
• /
• s.48
• /
• pp.85-116
• /
• 2017

The purpose of this study is to analyze conditions that robots depicted in cinematographic works like animations or movies sympathize with and form an attachment with the nuclear person and organize characterizing strategies for emotional sympathetic robots. Along with the development of technology, the areas of artificial intelligence and robots are no longer considered to belong to science fiction but as realistic issues. Therefore, this author assumes that the expressive characteristics of emotional sympathetic robots created by cinematographic works should be used as meaningful factors in expressively embodying human-friendly service robots to be distributed widely afterwards, that is, in establishing the features of characters. To lay the grounds for it, this research has begun. As the subjects of analysis, this researcher has chosen robot characters whose emotional intimacy with the main person is clearly observed among those found in movies and animations produced after the 1920 when robot's contemporary concept was declared. Also, to understand robots' appearance and behavioral tendency, this study (1) has classified robots' external impressions into five types (human-like, cartoon, tool-like, artificial bring, pet or creature) and (2) has classified behavioral tendencies considered to be the outer embodiment of personality by using DiSC, the tool to diagnose behavioral patterns. Meanwhile, it has been observed that robots equipped with high emotional intimacy are all strongly independent about their duties and indicate great emotional acceptance. Therefore, 'influence' and 'Steadiness' types show great emotional acceptance, the influencing type tends to be highly independent, and the 'Conscientiousness' type tends to indicate less emotional acceptance and independency in general. Yet, according to the analysis on external impressions, appearance factors hardly have any significant relationship with emotional sympathy. It implies that regarding the conditions of robots equipped with great emotional sympathy, emotional sympathy grounded on communication exerts more crucial effects than first impression similarly to the process of forming interpersonal relationship in reality. Lastly, to study the characters of robots, it is absolutely needed to have consilient competence embracing different areas widely. This author also has felt that only with design factors or personality factors, it is hard to estimate robot characters and also analyze a vast amount of information demanded in sympathy with humans entirely. However, this researcher will end this thesis as the foundation for it expecting that the general artistic value of animations can be used preciously afterwards in developing robots that have to be studied interdisciplinarily.

• The Journal of the Korea Contents Association
• /
• v.20 no.8
• /
• pp.350-358
• /
• 2020

The objective of this empirical study is to examine the mediating and moderating effects of perceived risks on the relationship between knowledge, feelings and acceptance intention towards AI. Subjects in their teens to forties were surveyed and the final sample comprised 1,969 subjects. Data were analyzed using Mediation using Multiple Regression and Moderated Multiple Regression. Results showed that people's knowledge and feelings towards AI affected their acceptance intention of AI. Results also showed that the perceived risks of AI partially mediated and moderated the relationship between feelings and acceptance intention towards AI and moderated but not mediated the relationship between knowledge and acceptance intention towards AI. Overall, these results suggest that people's perceived risks of AI are associated more strongly with their feelings towards AI than their knowledge towards AI. Implications and directions for future research were discussed in relation to increasing general population's acceptance intention towards AI.

• Journal of the Korea Convergence Society
• /
• v.9 no.1
• /
• pp.51-58
• /
• 2018

The late 20th century saw the industrial period end only to transform into the digital era where people have begun to pay attention to craft because it a field that respects emotion as the essential value, an alternative to overcome the side effect that people have created. Today a new world - where the virtual and the real co-exist through artificial intelligence (AI) - has suddenly approached us and the future of craft is faced with a new situation as it needs to present a new creative solution as a tool that is necessary for human way of life - a tool that has been a necessity throughout history and the evolution of life. As a result for a continued development, craft attempts to establish a new paradigm through current trends represented by our modern society, which is the emergence of creative development through convergence. This study presents creative experiments attempted through the convergence of craft with other heterogeneous tendencies connected to the field. The objective of the study is to enable makers to acquire a more creative way of thinking at the same time as inspiring them and suggesting new creative possibilities in order to develop their work through creative convergence. In Chapter 2, the study investigates on the current status of craft in general, and compares it with what is taking place in Korea; in Chapter 3 the significance of convergence in craft and the process of creating is addressed through case studies. Lastly in Chapter 4, with the basis on analytical case studies, the attribute and the potential of convergence in the field of craft is observed. By analyzing different phenomena presented through attempts to converge in contemporary craft, it has been possible to view the future of the 21st century craft through assessments on what is active and what is as yet hidden potential.

• Journal of Service Research and Studies
• /
• v.9 no.2
• /
• pp.31-40
• /
• 2019

In this paper, we have studied the improvement of operational control for the enhancement of business continuity of information system becoming more important with the development of information technology such as big data, Iot, and artificial intelligence. The operational management and audit guidance of the current information system, which is coming in the fourth industrial age, where various services, data and industries are converged, is based on the existing general information system pattern and needs to be improved. The provision of services at fixed times is linked to the survival of enterprises and countries and serves as a key element. Therefore, it is necessary to study the application of optimized check items of the operation audits to minimize the service interruption damage of the information system and to provide the stable service in terms of business continuity management. To accomplish this, the check items presented in the operational control of the information system were derived by combining the PDCA step contents and 8 resource requirements provided in ISO 22301. From the point of view of increasing the business continuity according to the derivation criteria of the inspection items, the operational inspection check items were derived by exemplifying the improved check items and review items of the information system operation audit and the products to be checked during the operational audit. The check items were divided into management audit improvement check items for service continuity management, and operational audit improvement check items for performance and availability management. The average score of the IT professionals' survey on the suitability of the proposed checklist was 4.63, which was concluded to be appropriate.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.13-27
• /
• 2012

Integer programming-based local search(IPbLS) is a kind of local search based on simple hill-climbing search and adopts integer programming for neighbor generation unlike general local search. According to an existing research [1], IPbLS is known as an effective method for the multidimensional knapsack problem(MKP) which has received wide attention in operations research and artificial intelligence area. However, the existing research has a shortcoming that it verified the superiority of IPbLS targeting only largest-scale problems among MKP test problems in the OR-Library. In this paper, I verify the superiority of IPbLS more objectively by applying it to other problems. In addition, unlike the existing IPbLS that combines simple hill-climbing search and integer programming, I propose methods combining other local search algorithms like hill-climbing search, tabu search, simulated annealing with integer programming. Through the experimental results, I confirmed that IPbLS shows comparable or better performance than the best known heuristic search also for mid or small-scale MKP test problems.

• Management & Information Systems Review
• /
• v.38 no.1
• /
• pp.83-101
• /
• 2019

Blockchain is considered as an innovative technology along with Artificial Intelligence, Big Data, and Internet of Things. However, since the inception of the genesis of blockchain technology, the cryptocurrency Bitcoin, the technology is not utilized widely, not let alone disruptive applications. Most of the blockchain research deals with the cryptocurrency, general descriptions of the technology such as trend, outlook of the technology, explanation of component technology, and so on. There are no killer applications like Facebook or Google, of course. Reflecting on the slow adoption by businesses, we wanted know about the current status of the research on blockchain in Korea. The main purpose of this paper is to help business practitioners to identify the application of blockchain to enhance the competitiveness of their organization. To do that, we first use the framework by Iansiti et al (2017) and categorize the blockchain related articles published in Korea according to the framework. This is to provide a benchmark or cases of other organizations' adoption of blockchain technology. Second, based on the value proposition of blockchain applications, we suggest evolutionary paths for adopting them. Third, from the demand pull perspective of technology adoption for innovation, we propose applicable areas where blockchain applications can be introduced. Fourth, we use the value chain model to find out the appropriate domains of blockchain applications in the corporate value chains. And the five competitive forces models is adopted to find ways of lowering the power of forces by incorporating blockchain technology.