Vol. 50, No. 5,
May 2023
Digital Library
Dynamic Group Management to Improve the Scalability of PBFT
Jinsung Cho, Gwangyong Kim, Geunmo Kim, Bongjae Kim, Min Choi
http://doi.org/10.5626/JOK.2023.50.5.369
A consensus algorithm that affects the reliability and performance of a blockchain is used for identical decision-making of nodes participating in the consensus. PBFT(Practical Byzantine Falut Tolerance) is a voting-based consensus algorithm with an O(n2) time complexity. The scalability of PBFT is generally poor. This paper proposes a scheme for grouping the nodes participating in a PBFT-based blockchain network and dynamically managing each group by layering it. In addition, we create a mathematical model for estimating the expected time required for consensus of the proposed scheme. Afterwards, we propose a dynamic consensus algorithm for dynamically adjusting the structures of groups and layers based on the model for estimating the expected time of a consensus. As a result of the experiment, the proposed scheme improves the performance of the consensus time by about 97% on average compared to the group-based PBFT without hierarchical structures.
Type-specific Multi-Head Shared-Encoder Model for Commonsense Machine Reading Comprehension
http://doi.org/10.5626/JOK.2023.50.5.376
Machine reading comprehension (MRC) is a task introduced to a machine that can understand natural languages by solving various tasks based on given context. To evaluate natural language understanding of machine, a machine must make commonsense inference under full comprehension of a given context. To enhance model obtaining such abilities, we proposed a multi-task learning scheme and a model for commonsense MRC. Contributions of this study are as follows: 1) a method of task-specific dataset configuration is proposed; 2) a type-specific multi-head shared-encoder model with multi-task learning scheme including batch sampling and loss scaling is developed; and 3) when the method is evaluated on CosmosQA dataset (commonsense MRC), the accuracy was improved by 2.38% compared to the performance at baseline with fine-tuning.
Multidimensional Subset-based Systems for Bias Elimination Within Binary Classification Datasets
KyeongSu Byun, Goo Kim, Joonho Kwon
http://doi.org/10.5626/JOK.2023.50.5.383
As artificial intelligence technology develops, artificial intelligence-related fairness issues are drawing attention. As a result, many related studies have been conducted on this issue, but most of the research has focused on developing models and training methods. Research on removing bias existing in data used for learning, which is a fundamental cause, is still insufficient. Therefore, in this paper, we designed and implemented a system that divides the biases existing within the data into label biases and subgroup biases and removes the biases to generate datasets with improved fairness. The proposed system consists of two steps: (1) subset generation and (2) bias removal. First, the subset generator divides the existing data into subsets on formed by a combination of values in an datasets. Subsequently, the subset is divided into dominant and weak groups based on the fairness indicator values obtained by validating the existing datasets based on the validation datasets. Next, the bias remover reduces the bias shown in the subset by repeating the process of sequentially extracting and verifying the dominant group of each subset to reduce the difference from the weak group. Afterwards, the biased subsets are merged and a fair data set is returned. The fairness indicators used for the verification use the F1 score and the equalized odd. Comprehensive experiments with real-world Census incoming data, COMPAS data, and bank marketing data as verification data demonstrated that our proposed system outperformed the existing technique by yielding a better fairness improvement rate and providing more accuracy in most machine learning algorithms.
Analysis of Lunar Landing Strategy for Chang’e-3 Utilizing Image Process Result
http://doi.org/10.5626/JOK.2023.50.5.395
China’s first lunar lander, Chang’e-3, successfully landed on the moon in 2013. This research tracks movement of a lunar crater in the real-time lunar landing video of Chang’e-3 and combines altitude & time information shown in the video to extract 4-dimensional behavior just before the lunar landing. For this purpose, one crater edge in the lunar surface image at an altitude of 129m is selected as a feature point. It is continuously traced by computational image process while descending to an altitude of 50 m, which is the most decisive section in the successful moon soft landing. This research restores realistic details of landing behavior including hovering and obstacle avoidance maneuvers of Chang’e-3 and analyzes the success strategy of lunar landing.
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.
CommonAI: Quantitative and Qualitative Analysis for Automatic-generation of Commonsense Reasoning Conversations Suitable for AI
Hyeon Gyu Shin, Hyun Jo You, Young Sook Song
http://doi.org/10.5626/JOK.2023.50.5.407
Human-like common sense reasoning is now considered an essential component for improving the quality of natural language generation for chatbots and conversational agents, However, there is no clear consensus at present about to what extent AI systems require common sense. We discussed common sense requirements for AI chatbots based on quantitative and qualitative analysis of results from two experimental surveys to show differences between gender and age groups and variations according to conversation topics. The contribution of this paper is to refine preferences for chatbot conversations that provide useful information and show an appropriate level of empathy.
LncRNA-Disease Association Prediction Model Applying Distance-based Data Labeling
Jaein Kim, Seung-Won Yoon, In-Woo Hwang, Kyu-Chul Lee
http://doi.org/10.5626/JOK.2023.50.5.420
lncRNAs are noncoding RNAs of 200 or more nucleotides. For a long time, non-coding RNA has been considered unimportant because it cannot directly produce proteins, but recent studies have reported that non-coding RNA plays a role in regulating protein expression. Abnormal expression of lncRNAs causes various diseases and predicting the associations between lncRNAs and diseases would help diagnose diseases in the early stages or prevent diseases. However, research that predicts the correlation of biological data is time-consuming and costly if it is conducted as a direct experiment. Therefore, it is important to overcome these challenges using computational methods. Therefore, in this study, we propose a lncRNA-disease association prediction model based on Long Short-Term Memory (LSTM). In addition, since negative samples were randomly generated in previous studies, there is uncertainty in the data. So this study also proposes a distance-based data labeling method that solves this uncertainty. Our model achieved the highest AUC (0.97) through the data labeling method and classification model presented in this study.
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.
A Deep Learning Approach for Target-oriented Communication Resource Allocation in Holographic MIMO
Apurba Adhikary, Md. Shirajum Munir, Avi Deb Raha, Min Seok Kim, Jong Won Choe, Choong Seon Hong
http://doi.org/10.5626/JOK.2023.50.5.441
In this paper, we propose a single-cell massive multiple-input multiple-output (mMIMO) system assisted with holography that performs target-oriented communication resource allocation for heterogeneous users. This paper proposes a technique that can minimize the number of active grids from holographic grid arrays (HGA) for confirming the requirement of lower power toward beamforming to serve target-oriented users. Therefore, we formulated a problem by maximizing the signal-to-interference-noise ratio (SINR), which, in turn, maximizes the efficient resource allocation for the users by generating effective beamforming and controlling the sum-power rule. Additionally, our holography-assisted mMIMO system is capable of serving heterogeneous user equipment simultaneously with a lower power budget. To devise the artificial intelligence (AI)-based solution, we developed a sequential neural network model for grid activation decisions with minimized power constraint. Finally, the simulation and performance evaluation results show that power was allocated efficiently, and effective beams were formed for serving the users with a lower RMSE score of 0.01.
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