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Graph Structure Learning-Based Neural Network for ETF Price Movement Prediction
Hyeonsoo Jo, Jin-gee Kim, Taehun Kim, Kijung Shin
http://doi.org/10.5626/JOK.2024.51.5.473
Exchange-Traded Funds (ETFs) are index funds that mirror particular market indices, usually associated with their low risk and expense ratio to individual investors. Various methods have emerged for accurately predicting ETF price movements, and recently, AI-based technologies have been developed. One representative method involves using time-series-based neural networks to predict the price movement of ETFs. This approach effectively incorporates past price information of ETFs, allowing the prediction of their movement. However, it has a limitation as it only utilizes historical information of individual ETFs and does not account for the relationships and interactions between different ETFs. To address this issue, we propose a model that can capture relationships between ETFs. The proposed model uses graph structure learning to infer a graph representing relationships between ETFs. Based on this, a graph neural network predicts the ETF price movement. The proposed model demonstrates superior performance compared to time-series-based deep-learning models that only use individual ETF information.
Cross-Project Defect Prediction for Ansible Projects
Sungu Lee, Sunjae Kwon, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2024.51.3.229
Infrastructure-as-Code (IaC) refers to the activities of automating overall management through code, such as creating and deploying infrastructure. Infrastructure-as-Code is used by many companies due to its efficiency, and many within-project defect prediction techniques have been proposed targeting Ansible, one of the IaC tools. Recently, a study on the applicability of Ansible"s cross-project defect prediction has been proposed. Therefore, Ansible’s cross-project defect prediction technique was used in this study, and its effectiveness was analyzed. Experimental results showed that the performance of the F1-based cross-project defect prediction was measured to be 0.3 to 0.5, and that it could be used as an alternative to the internal project defect prediction technique. It is therefore anticipated that this will be put to use in support of Ansible’s software quality assurance activities.
A Hybrid Deep Learning Model for Real-Time Forecasting Fire Temperature in Underground Utility Tunnel Based on Residual CNN-LSTM
http://doi.org/10.5626/JOK.2024.51.2.131
Underground utility tunnels (UUTs) play major roles in sustaining the life of citizens and industries with regard to carrying electricity, telecommunication, water supply pipes. Fire is one of the most commonly common disasters in underground facilities, which can be prevented through proper management. This paper proposes a hybrid deep learning model named Residual CNN-LSTM to predict fire temperatures. Scenarios of underground facility fire outbreaks were created and fire temperature data was collected using FDS software. In the experiment, we analyzed the appropriate depth of residual learning of the proposed model and compared the performance to other predictive models. The results showed that RMSE, MAE and MAPE of Residual CNN-LSTM are each 0.061529, 0.053851, 6.007076 respectively, making Residual CNN-LSTM far superior to other models in terms of its predictive performance.
Applying Deep Neural Networks and Random Forests to Predict the Pathogenicity of Single Nucleotide Variants in Hereditary Cancer-associated Genes
Da-Bin Lee, Seonhwa Kim, Moonjong Kang, Changbum Hong, Kyu-Baek Hwang
http://doi.org/10.5626/JOK.2023.50.9.746
The recent proliferation of genetic testing has made it possible to explore an individual"s genetic variants and use pathogenicity information to diagnose and prevent genetic diseases. However, the number of identified variants with pathogenicity information is quite small. A method for predicting the pathogenicity of variants by machine learning was proposed to address this problem. In this study, we apply and compare deep neural networks with random forests and logistic regression, which have been widely used in previous studies, to predict variant pathogenicity. The experimental data consisted of 1,068 single-nucleotide variants in genes associated with hereditary cancers. Experiments on 100 random data-sets generated for hyperparameter selection showed that random forests performed best in terms of area under the precision-recall curve. On 15 holdout gene data-sets, deep neural networks performed best on average, but the difference in performance from the second-best random forest was not significant. Logistic regression was also statistically significantly worse than that of either model. In conclusion, we found that deep neural networks and random forests were generally better than logistic regression at predicting the pathogenicity of single-nucleotide variants associated with hereditary cancer.
Time-Series Data Augmentation Based on Adversarial Training
http://doi.org/10.5626/JOK.2023.50.8.671
Recently, time series data are being generated in various industries with advancement of the Internet of Things (IoT). Accordingly, demands for time series forecasting in various industries are increasing. With acquisition of a large amount of time-series data, studies on traditional statistical method based time-series forecasting and deep learning-based forecasting methods have become active and the need for data augmentation techniques has emerged. In this paper, we proposed a novel data augmentation method for time series forecasting based on adversarial training. Unlike conventional adversarial training, the proposed method could fix the hyperparameter about the number of adversarial training iterations and utilize blockwise clipping of perturbations. We carried out various experiments to verify the performance of the proposed method. As a result, we were able to confirm that the proposed method had consistent performance improvement effect on various datasets. In addition, unlike conventional adversarial training, the necessity of blockwise clipping and the hyperparameter value fixing proposed in this paper were also verified through comparative experiments.
Analysis of Adversarial Learning-Based Deep Domain Adaptation for Cross-Version Defect Prediction
Jiwon Choi, Jaewook Lee, Duksan Ryu, Suntae Kim
http://doi.org/10.5626/JOK.2023.50.6.460
Software defect prediction is a helpful technique for effective testing resource allocation. Software cross-version defect prediction reflects the environment in which the software is developed in a continuous version, with software modules added or deleted through a version update process. Repetition of this process can cause differences in data distribution between versions, which can negatively affect defect prediction performance. Deep domain adaptation(DeepDA) techniques are methods used to reduce distribution difference between sources and target data in the field of computer vision. This paper aims to reduce difference in data distribution between versions using various DeepDA techniques and to identify techniques with the best defect prediction performance. We compared performance between deep domain adaptation techniques (i.e., Domain-Adversarial Neural Network (DANN), Adversarial Discriminator Domain Apaptation (ADDA), and Wasserstein Distance Guided Representation Learning (WDGRL)) and identified performance differences according to the pair of source data. We also checked performance difference according to the ratio of target data used in the learning process and performance difference in terms of hyperparameter setting of the DANN model. Experimental results showed that DANN was more suitable for cross-version defect prediction environments. The DANN model performed the best when using all previous versions of data except the target version as a source. In particular, it showed the best performance when setting the number of hidden layers of the DANN model to 3. In addition, when applying the DeepDA technique, the more target data used in the learning process, the better the performance. This study suggests that various DeepDA techniques can be used to predict software cross-version defects in the future.
Document-level Machine Translation Data Augmentation Using a Cluster Algorithm and NSP
http://doi.org/10.5626/JOK.2023.50.5.401
In recent years, research on document level machine translation has been actively conducted to understand the context of the entire document and perform natural translation. Similar to the sentence-level machine translation model, a large amount of training data is required for training of the document-level machine translation model, but there is great difficulty in building a large amount of document-level parallel corpus. Therefore, in this paper, we propose a data augmentation technique effective for document-level machine translation in order to improve the lack of parallel corpus per document. As a result of the experiment, by applying the data augmentation technique using the cluster algorithm and NSP to the sentence unit parallel corpus without context, the performance of the document-level machine translation is improved by S-BLEU 3.0 and D-BLEU 2.7 compared to that before application of the data augmentation technique.
Early Anomaly Detection of LNG-Carrier Main Engine System based on Multivariate Time-Series Boundary Forecasting and Confidence Evaluation Technique
Donghyun Kim, Taigon Kim, Minji An, Yunju Baek
http://doi.org/10.5626/JOK.2023.50.5.429
Recently, a variety of studies have been conducted to detect abnormal operation of ships and their causes and in the marine and shipbuilding industries. This study proposed a method for early anomaly detection of the main engine system using a multivariate time series sensor data extracted from LNG carriers built at a shipyard. For early anomaly detection, the process of predicting the future value through the sensor data at present is necessary, and in this process, the prediction residual, which is the difference between the actual future value and the predicted value, is generated. Since the generated residual has a significant effect on the early anomaly detection results, a compensating process is necessary. We propose novel loss functions that can learn the upper or lower prediction boundary of a time-series forecasting model. The time-series forecasting model trained with the proposed loss function improves the performance of the early anomaly detection algorithm by compensating the prediction residual. In addition, the real-time confidence of the predicted value is evaluated through the newly proposed confidence model by utilizing the similarity between time-series forecasting residual and confidence residual. With the early anomaly detection algorithm proposed in this study, the prediction model, which learns the upper boundary, outputs the upper limit of the predicted value that can be output by the baseline prediction model learned with the MSE loss function and can predict abnormal behavior that threshold-based anomaly discriminator could not predict because the future prediction of the baseline model is lower than the actual future value. Based on the results of this study, the performance of the proposed method was improved to 0.9532 compared to 0.4001 of the baseline model in Recall. This means that robust early anomaly detection is possible in various operating styles of the actual ship operations.
Integrating Domain Knowledge with Graph Convolution based on a Semantic Network for Elderly Depression Prediction
Seok-Jun Bu, Kyoung-Won Park, Sung-Bae Cho
http://doi.org/10.5626/JOK.2023.50.3.243
Depression in the elderly is a global problem that causes 300 million patients and 800,000 suicides every year, so it is critical to detect early daily activity patterns closely related to mobility. Although a graph-convolution neural network based on sensing logs has been promising, it is required to represent high-level behaviors extracted from complex sensing information sequences. In this paper, a semantic network that structuralizes the daily activity patterns of the elderly was constructed using additional domain knowledge, and a graph convolution model was proposed for complementary uses of low-level sensing log graphs. Cross-validation with 800 hours of data from 69 senior citizens provided by DNX, Inc. revealed improved prediction performance for the suggested strategy compared to the most recent deep learning model. In particular, the inference of a semantic network was justified by a graph convolution model by showing a performance improvement of 28.86% compared with the conventional model.
A Study of Metric and Framework Improving Fairness-utility Trade-off in Link Prediction
Heeyoon Yang, YongHoon Kang, Gahyung Kim, Jiyoung Lim, SuHyun Yoon, Ho Seung Kim, Jee-Hyong Lee
http://doi.org/10.5626/JOK.2023.50.2.179
The advance in artificial intelligence (AI) technology has shown remarkable improvements over the last decade. However, sometimes, AI makes biased predictions based on real-world big data that intrinsically contain discriminative social factors. This problem often arises in friend recommendations in Social Network Services (SNS). In the case of social network datasets, Graph Neural Network (GNN) is utilized for training these datasets, but it has a high tendency to connect similar nodes (Homophily effect). Furthermore, it is more likely to make a biased prediction based on socially sensitive attributes, such as, gender or religion, making it ethically more problematic. To overcome these problems, various fairness-aware AI models and fairness metrics have been proposed. However, most of the studies used different metrics to evaluate fairness and did not consider the trade-off relationship that existed between accuracy and fairness. Thus, we propose a novel fairness metric called Fairβ-metri which takes both accuracy and prediction into consideration, and a framework called FairU that shows outstanding performance in the proposed metric.
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