• Therapeutic Science for Rehabilitation
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• v.7 no.3
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• pp.47-58
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• 2018

Objective : This study was conducted to investigate the effects of iPad applications on improvement of attention and short-term memory in mild dementia. Methods : A single-case experimental study using A-B-A design was conducted. A total of 20 sessions, including 5 each for baseline phase A and A' and 10 for the intervention phase, were provided to the subject. Interventions were only provided during the intervention phase and were iOS-based iPad applications named "Memorado-Moving Balls" and "Circles." "Fit Brains-Matching Pairs" and "Fit-Brains-Spot the Difference" were used for each session to evaluate attention and short-term memory. MMSE-K, K-TMT-e A and B, and DST assessment tools were used pre- and post-intervention to assess attention and memory. Result : Fit Brains scores indicated improvement in both attention and memory during the intervention phase. K-TMT-e A showed 3 increased correct points and 3 reduced error points, and B showed 7 increased correct points and 2 reduced error points in post-tests, but the DST and MMSE-K showed no meaningful change. Conclusion : This single-case study identified improvements in attention and short-term memory in a person with mild dementia using iPad applications. Further studies regarding different applications and larger samples with long-term designs are necessary.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.220-225
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• 2020

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck feature-based method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Molecules and Cells
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• v.27 no.1
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• pp.5-14
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• 2009

The availability of effective vaccines has had the most profound positive effect on improving the quality of public health by preventing infectious diseases. Despite many successful vaccines, there are still old and new emerging pathogens against which there is no vaccine available. A better understanding of how vaccines work for providing protection will help to improve current vaccines as well as to develop effective vaccines against pathogens for which we do not have a proper means to control. Recent studies have focused on innate immunity as the first line of host defense and its role in inducing adaptive immunity; such studies have been an intense area of research, which will reveal the immunological mechanisms how vaccines work for protection. Toll-like receptors (TLRs), a family of receptors for pathogen-associated molecular patterns on cells of the innate immune system, play a critical role in detecting and responding to microbial infections. Importantly, the innate immune system modulates the quantity and quality of long-term T and B cell memory and protective immune responses to pathogens. Limited studies suggest that vaccines which mimic natural infection and/or the structure of pathogens seem to be effective in inducing long-term protective immunity. A better understanding of the similarities and differences of the molecular and cellular events in host responses to vaccination and pathogen infection would enable the rationale for design of novel preventive measures against many challenging pathogens.

• Journal of KIISE
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• v.44 no.11
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• pp.1125-1129
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• 2017

LSTM(Long Short-term Memory) is a kind of RNN(Recurrent Neural Network) in which a next-state is updated by remembering the previous states. The information of calling a sequence in a malware can be defined as system call function that is called at each time. In this paper, we use calling sequences of system calls in malware codes as input for malware classification to utilize the feature remembering previous states via LSTM. We run an experiment to show that our method can classify malware and measure accuracy by changing the length of system call sequences.

• International Journal of Contents
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• v.15 no.4
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• pp.8-15
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• 2019

This paper presents a solution of the 'Quick, Draw! Doodle Recognition Challenge' hosted by Google. Doodles are drawings comprised of concrete representational meaning or abstract lines creatively expressed by individuals. In this challenge, a doodle is presented as a sequence of sketches. From the view of at the sketch level, to learn the pattern of strokes representing a doodle, we propose a sequential model stacked with multiple convolution layers and Long Short-Term Memory (LSTM) cells following the attention mechanism [15]. From the view at the image level, we use multiple models pre-trained on ImageNet to recognize the doodle. Finally, an ensemble and a post-processing method using the minimum cost flow algorithm are introduced to combine multiple models in achieving better results. In this challenge, our solutions garnered 11th place among 1,316 teams. Our performance was 0.95037 MAP@3, only 0.4% lower than the winner. It demonstrates that our method is very competitive. The source code for this competition is published at: https://github.com/ngxbac/Kaggle-QuickDraw.

• ETRI Journal
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• v.42 no.5
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• pp.761-772
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• 2020

This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced-alignment step using a native AM and non-native AM, and (c) a linear regression-based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un-segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

• Journal of Fashion Business
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• v.16 no.2
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• pp.124-137
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• 2012

This study aims to identify the meaning and formation cause of a style, and the essential elements of style formation, through psychobiological research as well as an analysis of the designs of Iris Van Herpen, a fashion designer, who in just 6 years has developed into a world-renowned new designer. As a result, it has been found that the psychobiological causes to form a style stem from the action of 'long-term memory', which is consolidated by 'selective attention', 'perceptional subjectivity', the principle of the 'neuron's connection specificity and invariance', and the principle of a 'neuronal signal's unilateral flow'. With such action, Herpen could develop her own original composition techniques. The formative shapes created by such composition techniques are characterized by enumeration, superposition, and hanging. The study has also found that the essential elements for a designer to be able to form his/her own style include 'aesthetic originality' in which the designer views the property of a thing from his/her inherent perspective, and finds the uniqueness from the thing that only he/she can express, 'technical differences' that are creative and original, and 'formative specificity' that is summarized into one property through an impressive shape.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.320-320
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• 2019

유역의 수문 자료를 정확하게 분석하는 것은 수리 구조물을 효율적으로 운영하기 위한 중요한 요소이다. 인공신경망(Artificial Neural Networks, ANNs) 모형은 입 출력 자료의 비선형적인 관계를 해석할 수 있는 모형으로 강우-유출 해석 등 수문 분야에 다양하게 적용되어 왔다. 이후 기존의 인공신경망 모형을 연속적인(sequential) 자료의 분석에 더 적합하도록 개선한 회귀신경망(Recurrent Neural Networks, RNNs) 모형과 회귀신경망 모형의 '장기 의존성 문제'를 개선한 장단기메모리(Long Short-Term Memory Networks, 이하 LSTM)가 차례로 제안되었다. LSTM은 최근에 주목받는 딥 러닝(Deep learning) 기법의 하나로 수문 자료와 같은 시계열 자료의 분석에 뛰어난 성능을 보일 것으로 예상되며, 수문 분야에서 이에 대한 적용성 평가가 요구되고 있다. 본 연구에서는 인공신경망 모형과 LSTM 모형으로 유출량을 모의하여 두 모형의 성능을 비교하고 향후 LSTM 모형의 활용 가능성을 검토하고자 하였다. 나주 수위관측소의 수위 자료와 인접한 기상관측소의 강우량 자료로 모형의 입 출력 자료를 구성하여 강우 사상에 대한 시간별 유출량을 모의하였다. 연구 결과, 1시간 후의 유출량에 대해서는 두 모형 모두 뛰어난 모의 능력을 보였으나, 선행 시간이 길어질수록 LSTM의 정확성은 유지되는 반면 인공신경망 모형의 정확성은 점차 떨어지는 것으로 나타났다. 앞으로의 연구에서 유역 내 다양한 수리 구조물에 의한 유 출입량을 추가로 고려한다면 LSTM 모형의 활용성을 보다 더 확장할 수 있을 것이다.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.