• Speech Sciences
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• v.9 no.4
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• pp.237-252
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• 2002

MIT has been applied for nonfluent aphasic patients on the basis of lateralization of brain hemisphere. However, its applications for different languages have some inquiry for aphasic patients because of prosodic and rhythmic differences. The purpose of this study is to develop the Korean Melodic Intonation Therapy program using personal computer and its clinical effects for nonfluent aphasic patients. The algorithm was composed to voice analog signal, PCM, AMDF, Short-time autocorrelation function and center clipping. The main menu contains pitch, waveform, sound intensity and speech files on window. Aphasic patients' intonation patterns overlay on selected kMIT patterns. Three aphasic patients with or without kMIT training participated in this study. Four affirmative sentences and two interrogative sentences were uttered on CSL by stimulus of ST. VOT, VD, Hold and TD were measured on Spectrogram. In addition, articulation disorders and intonation patterns were evaluated objectively on spectrogram. The results indicated that nonfluent aphasic patients with kMIT training group showed some clinical effects of speech intelligibility based on VOT, TD values, articulation evaluation and prosodic pattern changes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4958-4970
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• 2019

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

• Proceedings of the IEEK Conference
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• summer
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• pp.103-109
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• 2004

In NLP (Natural Language Processing) tasks, the highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. To disambiguate it, formerly, they based on human-devised rules. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn't cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. So, recently, many NLP tasks have changed from rule-based approaches into corpus-based approaches with large annotated corpora. Corpus-based NLP tasks for such popular languages as English, French, etc. have been well studied with satisfactory achievements. In contrast, corpus-based NLP tasks for Vietnamese are at a deadlock due to absence of annotated training data. Furthermore, hand-annotation of even reasonably well-determined features such as part-of-speech (POS) tags has proved to be labor intensive and costly. In this paper, we present our building an annotated English-Vietnamese parallel aligned corpus named EVC to train for Vietnamese-related NLP tasks such as Word Segmentation, POS-tagger, Word Order transfer, Word Sense Disambiguation, English-to-Vietnamese Machine Translation, etc.

• The Journal of Korean Physical Therapy
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• v.19 no.4
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• pp.25-32
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• 2007

Purpose: The purpose of this study was to evaluate the effect of computerized neurocognitive function program on cognitive function about memory and attention with stroke. Methods: 24subjects with stroke were recruited. Twelve of subjects received conventional therapy including physical therapy, occupational therapy and language therapy. Another subjects received additional computer assisted cognitive training using Computer-aided Cognitive rehabilitation training system(COMCOG, MaxMedica Inc., 2004). All patients were assessed their cognitive function of memory and attention using Computerized Neurocognitive Function Test(CNT, MaxMedica Inc., 2004) before treatment and 6 weeks after treatment. Results: Before the treatment, two groups showed no difference in cognitive function(p>0.05). After 6 weeks, two groups showed significantly difference in digit span (forward, backward), verbal learning(A5, $A1{\sim}A5$), auditory CPT(n), visual CPT(n)(p<0.05). After treatment, the experimental group showed a significant improvement of digit span(forward, backward), verbal learning(A5, $A1{\sim}A5$), visual span (forward, backward), auditory CPT(n, sec), visual CPT(n, sec), and trail-making (A, B)(p<0.05). Conclusion: Computerized neurocognitive function program would be improved cognitive function of memory and attention in patients with stoke.

• Psychiatry investigation
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• v.15 no.12
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• pp.1144-1153
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• 2018

Objective One of the areas of social cognition is Theory of Mind (ToM) is defined as the capacity to interpret, infer and explain mental states underlying the behavior of others. When social cognition studies on neurodevelopmental disorders are examined, it can be seen that this skill has not been studied sufficiently in children with Specific Learning Disorder (SLD). Methods In this study, social cognition skills in children diagnosed with attention deficit hyperactivity disorder (ADHD), SLD or Autism Spectrum Disorder (ASD) evaluated before puberty and compared with controls. To evaluate the ToM skills, the first and second-order false belief tasks, the Hinting Task, the Faux Pas Test and the Reading the Mind in the Eyes Task were used. Results We found that children with neurodevelopmental disorders as ADHD, ASD, and SLD had ToM deficits independent of intelligence and language development. There was a significant correlation between social cognition deficits and problems experienced in many areas such as social communication and interaction, attention, behavior, and learning. Conclusion Social cognition is an important area of impairment in SLD and there is a strong relationship between clinical symptoms and impaired functionality.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.276-280
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• 2021

The article presents the concept of ICT - competence, which is considered as the most important characteristic of professional competence, which includes a combination of the following components: motivational-value (orientation of the individual to the development of his ITC-competence in future professional activities); technological (complex of skills and abilities of ICT activities); cognitive (a system of knowledge of modern technologies of future professional activity); it is determined that the pedagogical support of the formation of ICT competence of future specialists is the individualization of the process training, due to their personal and professional needs and the specifics of a regional university, providing the necessary conditions for the implementation of this process.

• Cross-Cultural Studies
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• v.36
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• pp.241-270
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• 2014

The aim of this article is to review the traditional methodologies of teaching translation that concentrate on text-typologies and, as an alternative, to propose an eclectic multi-componential approach that involves a set of interdisciplinary skills with a view to improving the trainee translators' competences and skills. To this end, three approaches, namely a minimalist approach, a pre-transferring adjustment approach and a revision vs. editing approach are proposed to shift the focus of attention from teacher-centred approaches towards student-centred approaches. It has been shown that translator training programmes need to focus on improving the trainee translators' competences and skills, such as training them how to produce and select among the different versions they produce by themselves with justified confidence as quickly as they can (minimalist approach), adjust the original text semantically, syntactically and/or textually in a way that the source text supplely accommodates itself in the linguistic system of the target language (pre-transferring adjustment), and revise and edit others' translations. As the validity of the approach proposed relies partially on instructors' competences and skills in teaching translation, universities, particularly in the Arab world, need to invest in recruiting expert practitioners instead of depending mainly on bilingual teachers to teach translation.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.97-104
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• 2021

The authors provide characteristics of the open educational platforms, classification and quantitative analysis regarding the availability of IT courses, teaching language, thematic directions on the following platforms: Coursera, EdX, Udemy, MIT Open Course Ware, OpenLearn, Intuit, Prometheus, UoPeople, Open Learning Initiative, Open University of Maidan (OUM). The quantitative analysis results are structured and visualized by tables and diagrams. The authors propose to use open educational resources (teaching, learning or research materials that are in the public domain or released with an intellectual property license that allows free use, adaptation, and distribution) for organization of independent work; for organization of distance or correspondence training; for professional development of teachers; for possibility and expediency of author's methods dissemination in the development of their own courses and promoting them on open platforms. Post-project activities are considered in comparing the courses content of one thematic direction, as well as studying the experience of their attending on different platforms.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2022

This thesis explores the principle of a performer's organic action by means of his/her bodily responses on stage. This research has been developed to define the nature of a performer's central task in order to constitute empirical understanding of acting and the purpose of training in addressing the question of what sort of qualitative bodily training is necessary to be in a state of the full body involvement. This study investigates to articulate a performer's fundamental task at the most rudimentary level by utilizing those theatre artists' concepts with practical assumptions. In particular, the key terms, happen and change signifies the quality of a performer's body that has to fit into the given environment in which the performer's body can be subordinated through the moment on stage. Here, we argue that a performer's essential task parallel to make the following moment to happen and change by means of progressing a set of the next moment. In this manner, we also argue that a moment of displaying the performer's conscious effort, forceful and externalizing the visible elements under the use of erroneous language leads his/her body not to function on stage, a state of disengagement from his/her body. Finally, we provide a way to facilitate a performer's organic action by focused on his/her lived experience to create the functional moment which is opposite to the predominance of a representation, maintaining the performer's intellectual sense.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.269-271
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• 2022

In recent years, RNN networks with LSTM blocks have been used extensively in machine learning tasks that process sequential data. These networks have proven to be particularly good at sequential language processing tasks by being more able to accurately predict the next most likely word in a given sequence than traditional neural networks. This study trained an RNN / LSTM neural network on three different translations of 150 biblical Psalms - in both English and Korean. The resulting model is then fed an input word and a length number from which it automatically generates a new Psalm of the desired length based on the patterns it recognized while training. The results of training the network on both English text and Korean text are compared and discussed.