• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.135-151
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• 2023

Objective : This study was conducted to confirm the effect of the oral motor facilitation technique (OMFT) on oral motor function in stroke patients. Methods : This study was conducted on 72 stroke patients with dysphagia were included. Thirty-six patients were randomly assigned to the experimental and control groups were randomly classified into 36 patients each using a random table, and a two-group pre-post test was designed. The experimental group underwent OMFT, and the control group underwent traditional dysphagia therapy for 30 min, once a day, 5 times a week for 4 weeks, for a total of 20 sessions. The Comprehensive Orofacial Function Scale (COFFS) was used to evaluate oral motor function. Repeated-measures analysis of variance (ANOVA) was performed to confirm the effect of the period, and an independent t-test was performed to analyze the difference in change between the two groups. Results : Total COFFS scores improved in both groups. The experimental group showed significant changes in mandibular and lip movements, cheek blows, and tongue movements. In addition, there were significant differences depending on the intervention period in terms of masticatory distribution, food spillage, swallowing of solid and liquid foods, and voice changes. There were significant differences in the mandibular opening and closing categories between the two groups. Conclusion : OMFT is effective in improving oral motor function in stroke patients with dysphagia and can be used as basic evidence in clinical practice.

• The Journal of Art Theory & Practice
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• no.8
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• pp.59-87
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• 2009

This thesis started from the attempt to make it clear that 80's American artist Keith Haring(1958-1990) had conducted social intervention of criticism, resistance, and participation through his works, and so pursued public value. Haring of graffiti fame left popular and familiar cartoon style pictures on the street wall, the billboards, the posters and so on. Popular and playful works was explained as his unique characteristics, but Haring's creative way at the field has more value than just being grasped as artist's personal characteristics. Haring's work pieces became everyday art by joining with people's life, and are working as a social speaking place. So I think that these Haring's art works possess characteristics of 'the public sphere'. 'The Public Sphere' means that is independent and free from the government or partisan economic forces, so that is not connected with the interested relations, and that is the sphere of rational argumentation without 'disguise' or 'fabrication', and that is the sphere where general public can participate in and is inspected by them. The public sphere between the sphere of public authority such a nation and a market and the private sphere of free individual, it is mutually connected with them and works as the space forming public opinion. Private individuals communicate with this public sphere and perform a role of direct and indirect check, balance, and social criticism way off from power. Openness that should include the voice of not only leading power but also the socially weak such as citizens, women, homosexuals, minority races, and so on, and alienated class, is an index of the public characteristics. The public sphere is not working just with speech and mass media. Many artists as well as Haring open their mouth and act through an art at the center of society, and create another public sphere by an art. I understood that the real participatory and practical characteristics on the Haring's work is a phenomenon and current of a part of the art world including Haring. Such current started from 1960s is the in-depth effort to be connected with the life more closely, to communicate with people, and to improve problems of life. And it has pursued public value on the different way from the nation or public power. Artists have intervened in the society with strategic and positive ways in order to raise pushed-out value and sinked rights as the public agenda, and labored to accept the value of variety and difference at the society. The aspect of such social intervention is the notable features, findable on the Haring's works and process. Haring's works include art historical meanings and are expressed with familiar and plastic language, so they were able to communicate with various classes. And he secured various customers at the field and the street. This communicative and public approach factor raised the possibility much for his works to work as the public sphere. Haring presented critical and resistant speech toward society with his works based on this factor. He asserted his position and justice of gender identity as a sexual minority. And his such work continued to movement for alienated class and social week over his own rights. His speech and message on the wall painting, poster, T-shirts, billboard of the subway, and so on worked as a spectacle and pressed concern with social issues and consciousness shift. And he's been trying to protect and care people who is injured by HIV and drug and to realize social justice through social week protection. Haring's works planned to meet many people as much as possible performed its role of intervening in society through criticism, resistance, speech, and participation, and controlling and checking social issues. These things considered, Haring's works show his consciousness about public attributes of art, and obviously include public value seeking. And also we can find the meaning of such his work as that an art is working as the public sphere and shows the possibility to discuss and practice public issues.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Journal of Oral Medicine and Pain
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• v.31 no.1
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• pp.59-67
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• 2006

There exist patients complaining speech problem due to dysesthesia or anesthesia following dental surgical procedure accompanied by local anesthesia in clinical setting. However, it is not clear whether sensory problems in orofacial region may have an influence on motor speech abilities. The purpose of this study was to investigate whether transitory sensory impairment of mandibular nerve by local anesthesia may influence on the motor speech abilities and thus to evaluate possibility of distorted motor speech abilities due to dysesthesia of mandibular nerve. The subjects in this study consisted of 7 men and 3 women, whose right inferior alveolar nerve, lingual nerve and long buccal nerve was anesthetized by 1.8 mL lidocaine containing 1:100,000 epinephrine. All the subjects were instructed to self estimate degree of anesthesia on the affected region and speech discomfort with VAS before anesthesia, 30 seconds, 30, 60, 90, 120 and 150 minutes after anesthesia. In order to evaluate speech problems objectively, the words and sentences suggested to be read for testing speech speed, diadochokinetic rate, intonation, tremor and articulation were recorded according to the time and evaluated using a Computerized Speech $Lab^{(R)}$. Articulation was evaluated by a speech language clinician. The results of this study indicated that subjective discomfort of speech and depth of anesthesia was increased with time until 60 minutes after anesthesia and then decreased. Degree of subjective speech discomfort was correlated with depth of anesthesia self estimated by each subject. On the while, there was no significant difference in objective assessment item including speech speed, diadochokinetic rate, intonation and tremor. There was no change in articulation related with anesthesia. Based on the results of this study, it is not thought that sensory impairment of unilateral mandibular nerve deteriorates motor speech abilities in spite of individual's complaint of speech discomfort.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.9-18
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• 2019

The purpose of this study is to determine whether the mean fundamental frequency (F0) changes depending on language, task, or language proficiency for Korean-English bilinguals. A total of forty-eight Korean-English speakers (28 balanced bilinguals and 20 Korean dominant bilinguals) participated in the study. Participants were asked to read aloud two types of tasks in English and Korean. For statistical analyses, the language ${\times}$ task two-way repeated ANOVAs were conducted within the balanced bilingual group first, and then group ${\times}$ language two-way mixed ANOVAs. The results showed that the females in both bilingual groups changed their mean F0 depending on the language they used and the tasks (p<.05), whereas no significant results were found in the males in either group under any conditions. The mean fundamental frequency in the Korean reading task was significantly higher than that in the English reading task for females in both balanced and Korean dominant bilingual groups. Thus, changes in mean F0 depending on language and context may reflect gender-specific characteristics, and females seem to be more sensitive to the socio-cultural standards that are imposed on them.

• Journal of Popular Narrative
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• v.25 no.1
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• pp.349-392
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• 2019

In the 1960s of Korea, the normalization of diplomatic relations between Korea and Japan led to a sense of a vigorous anxiety and fear that "Japan will once again come to the Korean peninsula". As a reaction to this, the discourse on the criticism of 'Japanese Style' strongly emerged. If the prior discourse of criticism was to express the national antipathy toward the colonial remnants that had not yet been disposed of, the critical discourse of the 1960s was the wariness of the newly created 'Japanese Style' in popular culture, and to grasp it as a symptomatic phenomenon that 'evil-minded Japan' was revealed. Thus, this new logic of criticism of the 'Japanese Style' had a qualitative difference from the existing ones. It was accompanied by a willingness to inspect and censor the 'masses' that grew up as consumers of transnational 'mass culture' that flowed and chained in the geopolitical order under the Cold War system. Therefore, the topology of 'popular things=Japanese things=consuming things' reveals the paradox of moral demands that existed within Korean society in the 1960s. This was to solidify the divisive circulation structure that caused them to avoid direct contact with the other called 'Japan', but at the same time, get as close to it as ever. It is a repetitive obsession that pushes the other to another side through the moral segregation that strictly draws a line of demarcation between oneself and the other, but on the other hand is attracted to the object and pulls it back to its side. This paper intends to listen to the different voices that have arisen in the repetitive obsession to understand the significance of the dissonance that has been repeated in the contemporary era. This will be an examination of the paradoxical object of Japan that has been repeatedly asked to build the internal control principle of Korean society, or to hide the oppressive and violent side of the power, and that can neither be accepted nor destroyed completely as part of oneself.

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