• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.243-252
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• 2024

Among the Foley sound generation models that have recently begun to be studied, a sound generation technique using the Vector Quantized-Variational AutoEncoder (VQ-VAE) structure and generation model such as Pixelsnail are one of the important research subjects. On the other hand, in the field of deep learning-based acoustic signal compression, residual vector quantization technology is reported to be more suitable than the conventional VQ-VAE structure. Therefore, in this paper, we aim to study whether residual vector quantization technology can be effectively applied to the Foley sound generation. In order to tackle the problem, this paper applies the residual vector quantization technique to the conventional VQ-VAE-based Foley sound generation model, and in particular, derives a model that is compatible with the existing models such as Pixelsnail and does not increase computational resource consumption. In order to evaluate the model, an experiment was conducted using DCASE2023 Task7 data. The results show that the proposed model enhances about 0.3 of the Fréchet audio distance. Unfortunately, the performance enhancement was limited, which is believed to be due to the decrease in the resolution of time-frequency domains in order to do not increase consumption of the computational resources.

• Journal of Life Science
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• v.26 no.4
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• pp.419-430
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• 2016

Powdery mildew disease caused by Podosphaera xanthii is a major concern for Cucumis melo production worldwide. Knowledge on genetic behavior of the related genes and their modulating phytohormones often offer the most efficient approach to develop resistance against different diseases. Mildew Resistance Locus O (MLO) genes encode proteins with seven transmembrane domains that have significant function in plant resistance to powdery mildew fungus. We collected 14 MLO genes from ‘Melonomics’ database. Multiple sequence analysis of MLO proteins revealed the existence of both evolutionary conserved cysteine and proline residues. Moreover, natural genetic variation in conserved amino acids and their replacement by other amino acids are also observed. Real-time quantitative PCR expression analysis was conducted for the leaf samples of P. xanthii infected and phyto-hormones (methyl jasmonate and salicylic acid) treated plants in melon ‘SCNU1154’ line. Upon P. xanthii infection using 7 different races, the melon line showed variable disease reactions with respect to spread of infection symptoms and disease severity. Three out of 14 CmMLO genes were up-regulated and 7 were down-regulated in leaf samples in response to all races. The up- or down-regulation of the other 4 CmMLO genes was race-specific. The expression of 14 CmMLO genes under methyl jasmonate and salicylic acid application was also variable. Eleven CmMLO genes were up-regulated under salicylic acid treatment, and 7 were up-regulated under methyl jasmonate treatments in C. melo L. Taken together, these stress-responsive CmMLO genes might be useful resources for the development of powdery mildew disease resistant C. melo L.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.1
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• pp.50-61
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• 2017

The purpose of this study is to analysis eye tracking for high school students' learning styles in the process of solving in the behavioral domains of the College Scholastic Ability Test on Earth Science I. The subjects of this study were 50 students from two classes out of 4 classes in E high school in Chungcheong province. Among them, we conducted experiments by randomly sampling 2 students of each type of learning based on the criteria that they had not encountered the problem of Earth Science I from the past two years. The findings indicate that the item correctness rate of divergers, assimilators, convergers, and accommodators were higher in the knowledge domain, application domain, knowledge-understanding domain, and understanding domain. This confirms that there is a difference among the four learning styles in the level of achievement according to the behavioral areas of the assessment questions. The latter finding was that the high eye-share of AOI 2 appeared higher than AOI 1, 3, 4 in the course of solving the problems. This is because the four types of learners pay more careful attention to the AOI 2 area, which is the cue-or-information area of problem solving, that is, the Table, Figure, and Graph area. Therefore, in order to secure the fairness and objectivity of the selection, it is necessary that an equal number of questions of each behavioral domain be selected on the Earth Science I Test of the College Scholastic Ability Test in general. Besides, it seems to be necessary that the knowledge, understanding, application, and the behavior area of the inquiry be highly correlated with the AOI 2 area in development of test questions.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Korean Elementary Science Education
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• v.43 no.3
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• pp.433-445
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• 2024

This study aims to compare and analyze the educational contents of the material area in the elementary science curriculums of North and South Korea. The research subjects are materials and motion and energy (partial) areas of the revised science curriculum of South Korea in 2022 and materials around us and science in daily life (partial) areas of the nature and education program of North Korea in 2013. This study compared the elements of the educational content of the material domain between North and South Korea according to the grade. Furthermore, the reflection of the material domain goals of North and South Korea at the international level was analyzed using the evaluation framework of the Trends in International Mathematics and Science Study (TIMSS) 2023 for the material content domains for fourth-grade elementary schools. Four teachers who majored in elementary science education and one expert in science education participated in the analysis. The results are as follows. First, in terms of the properties of matter, the content covered in the curriculum of North and South Korea differed in application period by grade and in the scope and level of content. Second, regarding material change, North Korea did not cover acids and bases but included methods for speeding up dissolution. Third, North Korea reflected the goal of the TIMSS 2023 properties of materials more highly than South Korea. Fourth, similar to the results for the analysis on the properties of materials, North Korea reflected the goal of the TIMSS 2023 for changes of materials more highly than did South Korea. In conclusion, the elements and timing of application of the material contents differed between North and South Korea, and the degree of reflection of goals at the international level was found to be higher for North Korea. In the future, this study hopes that cooperation and research on the development of integrated science and curriculum will occur along with the revitalization of educational exchange between North and South Korea from the perspective of the preparation for unification beyond the ideological conflict between them.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Music and Human Behavior
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• v.4 no.2
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• pp.84-105
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• 2007

Cerebral palsy is a collection of motor disorders resulting from damage to the central nervous system that arise in multiple handicaps including cognitive disorders, speech disorders, epilepsy, perception disorders, and emotion disorders. Today spastic cerebral palsy has become more prevalent because intensive care for newborns has resulted in higher survival rates for very small premature babies. Since the children grow the fastest in order for a development during one year after birth, the therapeutic intervention is provided as early as possible to the children with cerebral palsy. After seven year old, there is no effect of intervention. So, the necessity of early intervention to spastic cerebral palsied infants is increasing. The purpose of this study is to develop the music therapy activity program using the techniques of neurological music therapy(NMT), the therapeutic application of music to dysfunctions due to neurologic disease of the human nervous system, for rudimentary movement phase of spastic cerebral palsied infant. This music therapy activity program was developed on the basis of the major developmental tasks of the rudimentary movement phase, the period that children can acquire the most basic movement function at the 0 to 2. Then the developmental characteristics of spastic cerebral palsy were applied to this music therapy activity program. This music therapy activity program was classified to three domains, those are stability, locomotion, and manipulation. This study has been consisted of three steps, those are the development of the activities, the evaluation of the activities by th panels, and the adjustment and complement of the activities. Reviewing literatures and interviews were done for the development of the activities, and the evaluation the activities was done by seven music therapists. In the evaluation steps, the questionnaire was used for estimating the content validity and application efficiency. The adjustment and complement of the activities were evaluated by the panels who were participating in the music therapy for cerebral palsied children in the clinical setting, and the results of the adjustment and complement were confirmed by the panels. The evaluation was presented in a mean value with the comment of the panels. In conclusion, the music therapy activity program for the spastic cerebral palsied infants using the techniques of NMT was developed on the basis of the major developmental tasks of the rudimentary movement phase. The program is comprised of 38 activities, those are 14 activities for developing the stability, 10 activities for developing the locomotion, and 14 activities for developing the manipulation. The programed activities would bring out the answers in the affirmative for the conformance with infants' development phase, the harmony between the objective and the activity, the conformance with the cerebral palsied infants, the properness of the music and the instruments, and the utility in the clinic field. This results mean that this developed music activity program is appropriate to help spastic cerebral palsied infants progress their movement development by stages.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.553-564
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• 2012

Tangible user interfaces have been developed in the area of Human-Computer Interaction for the last decades, however, the applied domains recently have been extended into the product design and interactive art. Tangible User Interfaces are the combination of digital information and physical objects or environments, thus they provide tangible and intuitive interaction as input and output devices, often combined with Augmented Reality. The research developed a design guideline for tangible user interfaces based on key properties of tangible user interfaces defined previously in five representative research: Tangible Interaction, Intuitiveness and Convenience, Expressive Representation, Context-aware and Spatial Interaction, and Social Interaction. Using the guideline emphasizing user interaction, this research evaluated installation in a science museum in terms of the applied characteristics of tangible user interfaces. The selected 15 installations which were evaluated are to educate visitors for science by emphasizing manipulation and experience of interfaces in those installations. According to the input devices, they are categorized into four Types. TUI properties in Type 3 installation, which uses body motions for interaction, shows the highest score, where items for context-aware and spatial interaction were highly rated. The context-aware and spatial interaction have been recently emphasized as extended properties of tangible user interfaces. The major type of installation in the science museum is equipped with buttons and joysticks for physical manipulation, thus multimodal interfaces utilizing visual, aural, tactile senses etc need to be developed to provide more innovative interaction. Further, more installation need to be reconfigurable for embodied interaction between users and the interactive space. The proposed design guideline can specify the characteristics of tangible user interfaces, thus this research can be a basis for the development and application of installation involving more TUI properties in future.