• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.1-19
• /
• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.165-186
• /
• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.48 no.5
• /
• pp.42-58
• /
• 2020

The purpose of this study is to classify types of urban flower landscape. As a result of the study, first, through literature and case review, it was found that the four elements of place element, form element, natural element, artificial element, should be included in the sentence and key expression for defining the concept of flower landscape. In contemplating these four elements, a newly reconstructed concept of flower landscape was presented. This is expected to be the basis for the flower landscape integration theory. Second, flower landscape was defined as a genre and a unit of urban landscape. In addition, in order to build a system of flower landscape as a specialized area, after considering the concept, characteristics, and functions of a large category of urban landscape, its hierarchical categories with flower landscape were newly arranged. Thus, the flower landscape as an urban landscape was suggested. Third, in order to provide rational selection materials to consumers through type classification, related theories were investigated by expanding not only to the flower field, but also to the urban planning and urban ecology fields. 41 elements for the type classification were extracted, and 4 core elements were derived through the clustering process. Based on the 4 elements as the classification criteria, through the opinion verification from the FGI with experts, 9 types of middle-classification and 30 types of small-classification were derived. As a follow-up research suggestion, if a valid type is additionally established through a monitoring in the type application process, and more specified application types are developed and organized by expanding second-level classification hierarchy to the third-level hierarchy, this will lead to great studies improving the system of the types.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.5
• /
• pp.191-205
• /
• 2020

This study examined the possibility of utilizing next-generation technology, such as Virtual Reality or AI robots, in the long-term care facilities for the elderly. For the study, the Focus Group Interview was conducted in three different groups of 14 participants (care workers, social workers, and directors of long-term care facilities for the elderly). The analysis revealed a total of three topics, eight categories, and 26 sub-categories. The main results of the study showed that the use of next-generation technology could assist the psychological and emotional stability, provide curiosity and interest, and relieve the desire for physical activity for the elderly. In addition, for long-term care services staff, it could provide useful services for the elderly with physical constraints, facilitate effective management of the elderly roaming around, and enhance emotional support services. Finally, it could also help directors of long-term care facilities promote their services, educate staff, and keep up with current trends. Participants expressed concerns about the introduction of new technologies, but they generally expected that the application of next-generation technology would be positive for the elderly as well as for care workers and directors of long-term care facilities. Therefore, the use of next-generation technology in long-term care facilities for the elderly will also help develop gerontechnology.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.225-230
• /
• 2020

This paper highlights the influence and the importance of the syntactic-grammatical knowledge on "the reading machine", appeared in Jackendoff (1999). Due to the lack of the detailed testing and implementation in his research, this paper tests an extensive data array using a component of Google Translate, currently available freely and most widely on the internet. Although outdated, Jackendoff's paper, "Why can't Computers use English?", argues that syntactic-grammatical knowledge plays a key role in the outputs of computers and computer-based reading machines. The current research has implemented some testings of his thought-provoking examples, in order to find out whether Google Translate can handle the same problems after two decades or so. As a result, it is argued that in the field of NLP, I-language in the sense of Chomsky (1986, 1995 etc) is real and the syntactic, grammatical, and categorial knowledge is essential in the faculty of language. Therefore, it is reassured in this paper that when it comes to human language, even the most advanced "machine" is still no match for human faculty of language, the syntactic-grammatical knowledge.

• Journal of Practical Engineering Education
• /
• v.14 no.3
• /
• pp.505-512
• /
• 2022

The purpose of this study is to synthesize effects of the living lab-based education through meta-analysis. Seven primary studies reporting the effect of living lab-based education were carefully selected for data analysis. Research questions are as follows. First, what is the overall effect size of the living lab-based education? The overall effect size refers to the effect on the cognitive and affective domains. Second, what is the effect size of the living lab-based education according to categorical variables? Categorical variables are outcome characteristics, study characteristics, and design characteristics. Results are summarized as follows. First, the overall effect size of living lab-based education was 0.347. Second, the effect size according to the cognitive domain was 1.244 for information process, 0.593 for communication, 0.261 for problem solving, and 0.26 for creativity. Third, the effect size according to subject area was shown in the order of electrical and electronic engineering 1.146, technology and home economics 0.489, artificial intelligence 0.379, and practical arts 0.168. Fourth, the effect size according to school level was 1.058 for high school, 0.312 for middle school, and 0.217 for elementary school. Fifth, the effect size by grade level was 0.295 when two or more grades were integrated and 0.294 for a single grade.

• Cartoon and Animation Studies
• /
• s.50
• /
• pp.215-237
• /
• 2018

I analyze orange that is consistently used, even though not consciously, in the films whose function and meaning are clear. In detail, there are examples of color in films, psychological phenomena of colors expressed in posters and opening titles, color characteristics of clothes and costumes, and semiotic analysis of color names in film titles. (1) Fact and Truth; civilization and criticism. The film tries to tell the truth than the fact. It represents facts as it is, but it presupposes truth. This is a unique characteristic of media films. The truth of the fact is not important. The film tells the truth believing and wanting to show off. The film, which has inherent characteristics of the gap between fact and truth, represents nature and civilization. It carries nature as it is and criticizes the harm of civilization. Orange is nature and civilization. Realistic films such as Hong Sang Soo and Kim Ki Duk, fall into this category. For example, there are A Taxi Driver(2017) and I Can Speak(2017). (2) Virtual History; fake images and memories. In Hollywood SF genres like The Matrix(1999), orange was dealt with virtual reality. However, in Korean films they are replaced by historical dramas. The representation of history becomes a virtual reality. Films such as The Fortress(2017), Masquerade(2012), and Roaring Currents(2014) deal with virtual history. In these films, orange is a fake image and memory. (3) Light=color; Aura. The color and light of orange is aura. At sunrise and sunset, the orange of the incandescent light is almost similar to that of the artificial light. Orange of tungsten makes the real characters surrealistic and mysterious. For example, there are The City of Madness(2016), The Man from Nowhere(2010), and Coinlocker Girl(2014). (4) Fantasy; communication with other worlds. Orange is a sweet fantasy. In our daily life, we go to a supermarket, share a chat with friends in a coffee shop, and spend time in front of a television. Orange makes our life free and dreams. It is the communication between the former being and the other world. This can be found in the sexual fantasy scenes of all genres. For example, there are Sunny(2011), Welcome To Dongmakgol(2005), and 200 Pounds Beauty(2006).

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.391-404
• /
• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1215-1225
• /
• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.

• KIPS Transactions on Software and Data Engineering
• /
• v.13 no.1
• /
• pp.35-49
• /
• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.