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Improvement of Deep Learning Models to Predict the Knowledge Level of Learners based on the EdNet Data
Seulgi Choi, Youngpyo Kim, Sojung Hwang, Heeyoul Choi
http://doi.org/10.5626/JOK.2021.48.12.1335
As online education increases, the field of AI in Education (AIEd), where artificial intelligence is used for education, is being actively studied. Knowledge Tracing (KT), which predicts a student"s knowledge level based on each student"s learning record, is a basic task in the AIEd field. However, there is a lack of utilization of the dataset and research on the KT model architecture. In this paper, we propose to use a total of 11 features, after trying various features related to the problems, and present a new model based on the self-attention mechanism with new query, key, and values, Self-Attentive Knowledge Tracking Extended (SANTE). In experiments, we confirm that the proposed method with the selected features outperforms the previous KT models in terms of the AUC value.
Survey of EEG Neurofeedback methods for Attention Improvement
Hyunji Kim, Daeun Gwon, Kyungho Won, Sung Chan Jun, Minkyu Ahn
http://doi.org/10.5626/JOK.2021.48.10.1105
Neurofeedback is a method through which a user self-regulates the brain state using the feedback of his/her own brain signal. This can be used to restore or improve brain functions. In this study, we reviewed 108 articles on electroencephalogram (EEG) neurofeedback for attention improvement and surveyed the important parameters. As a result, we found that most studies were conducted with patient subjects and mostly brain signals were recorded from central areas on the scalp by using wet and wire EEG systems. Sensory-motor-rhythm or the ratio between theta and low beta rhythms were used as attention index, and this information was provided to users through auditory or visual stimuli. In addition, Continuous Performance Test or Go/NoGo test was employed for behavior evaluation. Based on these results, we suggest the following directions for the further advancement of the practical neurofeedback system; the future work should target non-patient subjects and utilize wireless/dry EEG devices and virtual/augmented reality for increasing user convenience and building more immersive application. Lastly, a standard or guide for developing usable neurofeedback applications should be established.
Analyzing the Impact of Sequential Context Learning on the Transformer Based Korean Text Summarization Model
Subin Kim, Yongjun Kim, Junseong Bang
http://doi.org/10.5626/JOK.2021.48.10.1097
Text summarization reduces the sequence length while maintaining the meaning of the entire article body, solving the problem of overloading information and helping readers consume information quickly. To this end, research on a Transformer-based English text summarization model has been actively conducted. Recently, an abstract text summary model reflecting the characteristics of English with a fixed word order by adding a Recurrent Neural Network (RNN)-based encoder was proposed. In this paper, we study the effect of sequential context learning on the text abstract summary model by using an RNN-based encoder for Korean, which has more free word order than English. Transformer-based model and a model that added RNN-based encoder to existing Transformer model are trained to compare the performance of headline generation and article body summary for the Korean articles collected directly. Experiments show that the model performs better when the RNN-based encoder is added, and that sequential contextual information learning is required for Korean abstractive text summarization.
Denoising Multivariate Time Series Modeling for Multi-step Time Series Prediction
Jungsoo Hong, Jinuk Park, Jieun Lee, Kyeonghun Kim, Seung-Kyun Hong, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.8.892
The research field of time series forecasting predicts the future time point using seasonality in time series. In the industrial environment, since decision-making through continuous perspective prediction of the future is important, multi-step time series forecasting is necessary. However, multi-step prediction is highly unstable because of its dependency on predicted value of previous time prediction result. Therefore, the traditional time series forecasting makes a statistical prediction for the single time point. To address this limitation, we propose a novel encoder-decoder based neural network named ‘DTSNet’ which predicts multi-step time points for multivariate time series. To stabilize multi-step prediction, we exploit positional encoding to enhance representation for time point and propose a novel denoising training method. Moreover, we propose dual attention to resolve long-term dependencies and modeling complex patterns in time series, and we adopt multi-head strategy at linear projection layer for variable-specific modeling. To verify the performance improvement of our approach, we compare and analyze it with baseline models, and we demonstrate the proposed methods through comparison tests, such as, component ablation study and denoising degree experiment.
EFA-DTI: Prediction of Drug-Target Interactions Using Edge Feature Attention
Erkhembayar Jadamba, Sooheon Kim, Hyeonsu Lee, Hwajong Kim
http://doi.org/10.5626/JOK.2021.48.7.825
Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.
AttDRP: Attention Mechanism-based Model for Anti-Cancer Drug Response Prediction
Jonghwan Choi, Sangmin Seo, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.6.713
Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs. We hope that our proposed method would contribute to the development of precision medicine for effective chemotherapy. Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs.
An Explainable Knowledge Completion Model Using Explanation Segments
Min-Ho Lee, Wan-Gon Lee, Batselem Jagvaral, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.6.680
Recently, a large number of studies that used deep learning have been conducted to predict new links in incomplete knowledge graphs. However, link prediction using deep learning has a major limitation as the inferred results cannot be explained. We propose a high-utility knowledge graph prediction model that yields explainable inference paths supporting the inference results. We define paths to the object from the knowledge graph using a path ranking algorithm and define them as the explanation segments. Then, the generated explanation segments are embedded using a Convolutional neural network (CNN) and a Bidirectional Long short-term memory (BiLSTM). The link prediction model is then trained by applying an attention mechanism, based on the calculation of the semantic similarity between the embedded explanation segments and inferred candidate predicates to be inferred. The explanation segment suitable for link prediction explanation is selected based on the measured attention scores. To evaluate the performance of the proposed method, a link prediction comparison experiment and an accuracy verification experiment are performed to measure the proportion of the explanation segments suitable to explain the link prediction results. We used the benchmark datasets NELL-995, FB15K-237, and countries for the experiment, and accuracy verification experiments showed the accuracies of 89%, 44%, and 97%, respectively. Compared with the existing method, the NELL-995, FB15K-237 data exhibited 35%p and 21%p higher performance on average.
Research on Social Information Processing using the Augmented Reality Device: Comparison with Real-World Human Interaction
Jaehwan You, Jiwoong Heo, Enjoo Kim, Kwanguk Kim
http://doi.org/10.5626/JOK.2021.48.3.308
Research on augmented reality (AR) has focused on the development of technologies and its task performances. However, the effect of AR technologies on social interaction is yet to be rigorously examined. Social interaction is one of the key components of human learning and cognitive developments. In this study, we compared initiating and responding joint attention with the social information processing task, and each participant conducted both AR and real-world conditions. Thirty-three participants were enrolled in the current study, and dependent measures included accuracies of target, non-target, and novel pictures, and total head-movements. The results suggested that there were no significant differences in information processing of target and novel pictures, but we found that accuracies of non-target pictures and total head-movements were significantly different between AR and real-world conditions. These results suggested that AR devices can be used for social information processing tasks, but they need improvements, which are discussed in the current study.
Self-revising Transformer with Multi-view for Image Captioning
Jieun Lee, Jinuk Park, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.3.340
Image captioning is a task of automatically describing a scene by identifying an object element from a given image. In prior research, information has mainly been captured from the image using a single feature extractor, and captions have then been generated by a recurrent neural network-based decoder. However, multi-view image information is not available with this method because of the use of a single feature extractor, and the use of a recurrent neural network-based decoder causes a long-term dependency problem. To address these issues, the proposed model employs a multi-view encoder using a couple of feature extractors that provide processed image information from various view. In addition, to supplement the limits of the recurrent neural network, we propose a self-revising transformer that increases the completeness of sentences by revising the generated sentences by focusing additional multi-head attention in the transformer-based decoder layer. To present the proposed model, we verify its superiority through quantitative and qualitative evaluations with various comparative experiments using MSCOCO datasets.
Joint Model of Morphological Analysis and Named Entity Recognition Using Shared Layer
Hongjin Kim, Seongsik Park, Harksoo Kim
http://doi.org/10.5626/JOK.2021.48.2.167
Named entity recognition is a natural language processing technology that finds words with unique meanings such as human names, place names, organization names, dates, and time in sentences and attaches them. Morphological analysis in Korean is generally divided into morphological analysis and part-of-speech tagging. In general, named entity recognition and morphological analysis studies conducted in independently. However, in this architecture, the error of morphological analysis propagates to named entity recognition. To alleviate the error propagation problem, we propose an integrated model using Label Attention Network (LAN). As a result of the experiment, our model shows better performance than the single model of named entity recognition and morphological analysis. Our model also demonstrates better performance than previous integration models.
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