Exploring the culture of an online brand community: A study of a Korean Apple MacBook user community

Master'sMASTER OF ART

BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.Comment: 5 pages, 2 figures, KDD 2023 Machine Learning in Finance worksho

DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs

Mobile robots often rely on pre-existing maps for effective path planning and navigation. However, when these maps are unavailable, particularly in unfamiliar environments, a different approach become essential. This paper introduces DynaCon, a novel system designed to provide mobile robots with contextual awareness and dynamic adaptability during navigation, eliminating the reliance of traditional maps. DynaCon integrates real-time feedback with an object server, prompt engineering, and navigation modules. By harnessing the capabilities of Large Language Models (LLMs), DynaCon not only understands patterns within given numeric series but also excels at categorizing objects into matched spaces. This facilitates dynamic path planner imbued with contextual awareness. We validated the effectiveness of DynaCon through an experiment where a robot successfully navigated to its goal using reasoning. Source code and experiment videos for this work can be found at: https://sites.google.com/view/dynacon.Comment: Submitted to ICRA 202

QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation

With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference sentences. Despite its high utility in the real world, there remain several limitations concerning manual QE data creation: inevitably incurred non-trivial costs due to the need for translation experts, and issues with data scaling and language expansion. To tackle these limitations, we present QUAK, a Korean-English synthetic QE dataset generated in a fully automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and QUAK-H, produced through three strategies that are relatively free from language constraints. Since each strategy requires no human effort, which facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M for QUAK-M. As an experiment, we quantitatively analyze word-level QE results in various ways while performing statistical analysis. Moreover, we show that datasets scaled in an efficient way also contribute to performance improvements by observing meaningful performance gains in QUAK-M, P when adding data up to 1.58M

Molecular population genetics and phylogeographic studies of Ligia exotica and Ligia cinerascens in East Asia

IntroductionSea slater, in the genus Ligia, is widespread in rocky shore habitats, and the taxon is easily isolated due to its limited dispersal capacity. Therefore, most Ligia species exhibit an allopatric distribution, but Ligia exotica and L. cinerascens exhibit an overlapping distribution distribution in East Asia. Previous studies on both species have confirmed the existence of highly divergent lineages based on 16S rRNA.MethodsIn the present study, 282 Ligia individuals were collected at ten, three, and three sites in South Korea, Japan, and Vietnam, respectively, and 41 haplotypes were observed based on 16S rRNA.Results and discussionThe results of phylogeny, phylogenetic network, and TCS network, Principal Coordinates Analysis, and four Molecular Species Delimitation Analyses revealed that six genetic lineages including L. cinerascens, Lineages N and S of L. exotica, Ligia sp. 1, sp.2 and sp.3 were present. The three genetic lineages, including L. cinerascens, Lineage N of L. exotica, and Lineage S of L. exotica, were also identified in the phylogeny based on a nuclear gene of the sodium–potassium ATPase α-subunit (Nak). Phylogeographic analysis revealed that L. cinerascens and Lineage N of L. exotica were distributed overlappingly in South Korea, Japan, and the northern region of China. Generally, the two lineages of L. exotica were distributed allopatrically, which was more evident along the coastline of mainland China than that of Japan. The results of time-calibrated phylogeny suggested that the speciation events of Ligia species might be associated with Japanese mainland formation from Oligocene to Miocene (approximately 30-5 million years ago, Mya). The results of the present study provide insights that could facilitate the understanding of the evolutionary history of Ligia, tracking of geological processes, and evolutionary effects of palaeogeographical events at the population level

Osteochondrodysplasia in three Scottish Fold cats

This report explains typical radiographic features of Scottish Fold osteochondrodysplasia. Three Scottish Fold cats suffering from lameness were referred to the Veterinary Medical Teaching Hospital, Seoul National University, Korea. Based on the breed predisposition, history, clinical signs, physical examination, and radiographic findings, Scottish Fold osteochondrodysplasia was confirmed in three cases. Radiographic changes mainly included exostosis and secondary arthritis around affected joint lesions, and defective conformation in the phalanges and caudal vertebrae. The oral chondroprotective agents such as glucosamine and chondroitin sulfate make the patients alleviate their pain without adverse effects

A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models

A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models

Verification of a Dataset for Korean Machine Reading Comprehension with Numerical Discrete Reasoning over Paragraphs

Numerical reasoning in machine reading comprehension (MRC) has demonstrated significant performance improvements in the past few years. However, due to the process being restricted to specific languages, low-resource languages are not considered, and MRC studies on such languages are limited. In addition, the methods that rely on existing information extracted within the span of a paragraph have limitations in responding to questions requiring actual reasoning. To overcome these shortcomings, this study establishes a dataset for learning Korean Question and Answering (QA) models that not only answer within the span of passages but also perform numerical reasoning on passages and questions. Its efficacy was verified by training the model. We recruited eight annotators to tag the ground truth label, and they annotated datasets with 920, 115, and 115 passages in the train, dev, and test, respectively. A simple yet sophisticated automatic inter-annotation tool was created by effectively reducing the possibility of inaccuracy and error entailed by humans in the data construction process. This tool used common KoBERT and KoELECTRA. We defined four general conditions, and six conditions humans must inspect and fine-tune the pre-trained language models with numerically aware architecture. The KoELECTRA and NumNet+ with KoELECTRA were fine-tuned, and experiments in identical hyperparameter settings showed that compared with other models, the performance of NumNet+ with KoELECTRA was higher by more than 1.3 points. Our research contributes to the Korean MRC research and suggests potential and insight into MRC models capable of numerical reasoning